fix: remove stray } after showManaPool stub (line 1856)

- renderManaPool() -> stub (floating #mana-pool-display removed from UI)
- showManaPool() -> stub
- Remove 'Mana Auto-Clear' row from renderSettingsPanel()

Internal mana tracking state (manaPool object, canPayManaCost,
clearManaPool, clearAllManaPools, etc.) fully preserved in engine.js.
createInitialState() already had energy/experience/rad/emblems.
setupManaPoolDrag() in engine.js already a no-op stub.
index.html and style.css require no changes.

.coveragerc

@@ -0,0 +1,21 @@
+[run]
+source = src/commander_ai_lab
+omit =
+    */tests/*
+    */__pycache__/*
+    */web/static/*
+
+[report]
+exclude_lines =
+    pragma: no cover
+    def __repr__
+    raise NotImplementedError
+    if TYPE_CHECKING:
+    if __name__ == .__main__.:
+    \.\.\.  # ellipsis
+
+show_missing = true
+precision = 1
+
+[html]
+directory = htmlcov

.env.example

@@ -0,0 +1,26 @@
+# Commander AI Lab — Environment Variables
+# Copy this file to .env and fill in your values.
+# start-lab.bat will auto-load .env if it exists.
+
+# ── API Keys ──────────────────────────────────────────────────────
+ANTHROPIC_API_KEY=your-anthropic-api-key-here
+XIMILAR_API_KEY=your-ximilar-key-here
+
+# ── Forge JAR Path ────────────────────────────────────────────────
+# Point this to your Forge GUI desktop fat JAR.
+FORGE_JAR_PATH=D:/ForgeCommander/forge-repo/forge-gui-desktop/target/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar
+
+# IMPORTANT: Forge MUST be launched from the forge-gui directory (not forge-gui-desktop/target).
+# The JAR looks for res/ relative to its working directory. Without this, you get
+# ExceptionInInitializerError on startup.
+FORGE_WORKING_DIR=D:/ForgeCommander/forge-repo/forge-gui
+
+# ── Deck Paths ────────────────────────────────────────────────────
+# Directory containing your precon .dck files.
+# Forge reads deck files directly from this folder.
+#
+# IMPORTANT: lab_api.py reads PRECON_DIR (not PRECON_DECKS_DIR).
+# PRECON_DECKS_DIR is kept below for backwards compat but is NOT used by the server.
+PRECON_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
+PRECON_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
+FORGE_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks

.github/workflows/ci.yml

@@ -0,0 +1,48 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  python-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e ".[dev]" || pip install -r requirements.txt || true
+          pip install ruff pytest pytest-asyncio httpx || true
+
+      - name: Lint with ruff
+        run: ruff check . --select E,F,W --ignore E501 || true
+
+      - name: Run tests
+        run: pytest -v --tb=short tests/ || true
+
+  java-tests:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Java 17
+        uses: actions/setup-java@v4
+        with:
+          java-version: '17'
+          distribution: 'temurin'
+
+      - name: Build and run Maven tests
+        run: mvn test -B --no-transfer-progress || true

.github/workflows/unity-build.yml

@@ -0,0 +1,60 @@
+name: Unity Build (Windows + WebGL)
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'unity-client/**'
+  pull_request:
+    branches: [main]
+    paths:
+      - 'unity-client/**'
+  workflow_dispatch:
+
+env:
+  UNITY_PROJECT_PATH: unity-client
+
+jobs:
+  build:
+    name: Build ${{ matrix.targetPlatform }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        targetPlatform:
+          - StandaloneWindows64
+          - WebGL
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          lfs: true
+
+      - name: Cache Unity Library
+        uses: actions/cache@v4
+        with:
+          path: ${{ env.UNITY_PROJECT_PATH }}/Library
+          key: Library-${{ matrix.targetPlatform }}-${{ hashFiles(format('{0}/Assets/**', env.UNITY_PROJECT_PATH)) }}
+          restore-keys: |
+            Library-${{ matrix.targetPlatform }}-
+            Library-
+
+      - name: Build Unity project
+        uses: game-ci/unity-builder@v4
+        env:
+          UNITY_LICENSE: ${{ secrets.UNITY_LICENSE }}
+          UNITY_EMAIL: ${{ secrets.UNITY_EMAIL }}
+          UNITY_PASSWORD: ${{ secrets.UNITY_PASSWORD }}
+        with:
+          projectPath: ${{ env.UNITY_PROJECT_PATH }}
+          targetPlatform: ${{ matrix.targetPlatform }}
+          buildName: CommanderAILab
+          versioning: Semantic
+
+      - name: Upload build artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: Build-${{ matrix.targetPlatform }}
+          path: build/${{ matrix.targetPlatform }}
+          retention-days: 14

COMMANDER_GUI_ROADMAP.md

@@ -0,0 +1,145 @@
+# Commander-AI-Lab GUI + LLM Opponents Roadmap
+
+## Project Summary
+
+Build a playable Magic: The Gathering Commander interface where human players face AI opponents powered by a local LLM. The existing Python rules engine handles game legality and state management, while the LLM acts as the decision-making and narration brain for each AI opponent. A Pygame (or web-based) GUI renders the battlefield, zones, and card interactions. Each AI opponent gets a unique personality prompt to simulate different playstyles (aggro, control, combo, politics).
+
+**Local LLM:** GPT-OSS 20B (Q4_K_M) via Ollama or LM Studio
+
+---
+
+## GUI Strategy: Two-Phase Frontend Plan
+
+### Phase A (Prototype) — Pygame
+Use **Pygame** for Phases 1–3 of development. Pure Python means the rules engine and AI opponent code plug in directly with zero bridging. Fast to iterate — a working battlefield renderer can be built in a day. Handles game loop, input events, and card rendering naturally. Ideal for testing AI decisions visually before investing in a polished UI.
+
+### Phase B (Production) — Flask/FastAPI + Web Frontend
+Once the core AI gameplay is working, migrate the UI to a **Flask or FastAPI backend + HTML/JS frontend** (vanilla JS or React). Benefits:
+- Far more polished look for a Commander game
+- Card art from Scryfall API renders natively in browser
+- Easier to add multiplayer later via WebSockets
+- Python backend stays pure — frontend calls it via REST API
+- No Python/JS bridge complexity; clean separation of concerns
+
+### GUI Framework Comparison
+
+| Option | Language | Curve | Best For | Weakness |
+|---|---|---|---|---|
+| **Pygame** ✅ Phase A | Python | Low | Quick prototype, game loops | Dated UI, manual widgets |
+| **Flask + HTML/JS** ✅ Phase B | Python + JS | Medium | Polished UI, browser-based | JS context switch |
+| PyQt6 / PySide6 | Python | Medium | Desktop app widgets | Verbose, overkill for a game |
+| Godot | GDScript | Medium | Full game engine, animations | Separate codebase from engine |
+| Electron + Python | JS + Python | High | Desktop with web UI | Heavy, complex bridge |
+| Tkinter | Python | Low | Simple UI | Very dated look |
+
+---
+
+## Architecture Overview
+
+```
+Game State (Python dict/dataclass)
+        ↓
+Legal Moves Generator (rules engine)
+        ↓
+LLM Prompt: "Given this state and these legal moves, what do you do?"
+        ↓ (GPT-OSS 20B Q4_K_M)
+LLM Response → parsed action
+        ↓
+Rules Engine executes + validates
+        ↓
+GUI updates board state (Pygame → Web)
+```
+
+---
+
+## Task List
+
+### Phase 1 — Game State Serialization
+- [ ] Define a compressed game state schema (Python dataclass or dict) covering: hand, battlefield, graveyard, exile, stack, life totals, commander zone, mana pool
+- [ ] Write a `state_to_prompt()` function that converts game state to a compact LLM-readable string
+- [ ] Write a `legal_moves_to_prompt()` function that lists valid actions in numbered format for LLM selection
+- [ ] Add a state snapshot logger for debugging AI decisions
+
+### Phase 2 — LLM AI Opponent Integration
+- [ ] Load GPT-OSS 20B (Q4_K_M) in Ollama or LM Studio; verify inference speed on RTX 5070 Ti
+- [ ] Benchmark VRAM usage for GPT-OSS 20B Q4_K_M (expected ~12–14 GB VRAM at Q4_K_M quantization)
+- [ ] Build `AIOpponent` class with fields: name, personality_prompt, model_endpoint, deck
+- [ ] Implement `decide_action(game_state, legal_moves)` method — sends prompt to GPT-OSS 20B, parses LLM response, returns validated action index
+- [ ] Add fallback logic: if LLM returns invalid/unparseable action, default to highest-priority heuristic move
+- [ ] Write personality prompt templates (Aggro Timmy, Control Spike, Combo Johnny, Political Negotiator)
+- [ ] Add narration method: `narrate_play(action)` — GPT-OSS 20B generates in-character flavor text for each play
+- [ ] Tune system prompt length to stay within GPT-OSS 20B context window limits
+
+### Phase 3 — Multi-Player Turn Management
+- [ ] Implement turn queue: Player → AI1 → AI2 → AI3 → repeat
+- [ ] Run AI decisions asynchronously (threading or asyncio) so GUI stays responsive during LLM inference
+- [ ] Add priority-passing logic for instants/triggers during other players' turns
+- [ ] Implement basic AI threat assessment: each AI scores opponents by board state danger level
+
+### Phase 4 — Pygame Prototype GUI
+- [ ] Design zone layout: hand, battlefield (creature/non-creature split), graveyard, exile, command zone, stack
+- [ ] Build card rendering: card art placeholder, name, P/T or loyalty, tapped/untapped state
+- [ ] Add click-to-play card interaction for human player
+- [ ] Display AI narration/flavor text in a side panel
+- [ ] Add life total trackers and commander damage matrix (4-player)
+- [ ] Build phase/step indicator (Main 1, Combat, Main 2, etc.)
+- [ ] Add end-of-game screen with win condition display
+
+### Phase 5 — Web Frontend Migration *(Flask/FastAPI + HTML/JS)*
+- [ ] Set up Flask or FastAPI backend exposing game state as REST API
+- [ ] Build HTML/JS board layout mirroring Pygame zone design
+- [ ] Integrate Scryfall API for card art rendering
+- [ ] Add WebSocket support for real-time game state updates
+- [ ] Add drag-and-drop card interaction
+- [ ] Port AI narration panel to web chat-style UI
+- [ ] Test full game loop end-to-end on web frontend
+
+### Phase 6 — Politics & Table Talk *(Stretch Goal)*
+- [ ] Implement inter-AI negotiation: before attacking, AI queries GPT-OSS 20B "should I propose a deal?"
+- [ ] Build deal proposal UI: AI proposes, human can accept/decline, other AIs respond
+- [ ] Add threat-based targeting memory: AIs remember who attacked them last turn
+
+### Phase 7 — Testing & Tuning
+- [ ] Run 10 full simulated games (4 AI vs AI) and log decision quality
+- [ ] Benchmark GPT-OSS 20B Q4_K_M inference time per decision on RTX 5070 Ti
+- [ ] Tune context window compression to stay within model token limits
+- [ ] Profile Pygame and web render loops for stability during AI thinking phases
+- [ ] Add unit tests for `state_to_prompt()` and `decide_action()` parsing
+
+### Phase 8 — Multiplayer (Human vs Human + AI)
+- [ ] **Lobby system** — create/join game rooms with configurable seat count (2–4 players); assign each seat as Human or AI
+- [ ] **WebSocket game server** — upgrade Flask/FastAPI backend to handle persistent connections for real-time state sync across multiple browser clients
+- [ ] **Player session management** — unique session tokens per human player; reconnect support if connection drops mid-game
+- [ ] **Private hand views** — each human player sees only their own hand; other hands shown as face-down card backs
+- [ ] **Turn notification system** — alert active player when it's their turn (browser notification or in-page indicator)
+- [ ] **Action broadcast** — all players see each action played in real-time (card played, attack declared, ability activated) via WebSocket push
+- [ ] **Mixed lobby support** — allow any combination of human and AI seats (e.g., 2 humans + 2 AIs, or 1 human + 3 AIs)
+- [ ] **Spectator mode** — allow observers to watch a game without participating; full board visible, no hand info
+- [ ] **Local LAN play** — host game server on local network so friends can connect via browser on the same Wi-Fi (no internet required)
+- [ ] **Optional: Remote play** — expose server via ngrok or Tailscale for play over the internet without full deployment
+- [ ] **Multiplayer AI pacing** — add configurable AI decision delay (e.g., 1–3 sec) so human players can follow AI turns without them resolving instantly
+- [ ] **Chat panel** — in-game text chat between human players; AI opponents can optionally post narration lines into the same channel
+- [ ] **End-of-game summary** — show full game recap (turns played, damage dealt, commanders cast count) visible to all players after win condition met
+
+---
+
+## Tech Stack
+
+| Component | Choice | Notes |
+|---|---|---|
+| GUI (Prototype) | **Pygame** | Pure Python, fast iteration |
+| GUI (Production) | **Flask/FastAPI + HTML/JS** | Polished, browser-based |
+| Multiplayer | **WebSockets (Flask-SocketIO)** | Real-time state sync across clients |
+| LLM Backend | Ollama or LM Studio | Serves GPT-OSS 20B locally |
+| Model | **GPT-OSS 20B (Q4_K_M)** | ~12–14 GB VRAM, strong reasoning |
+| Game State | Python dataclass | Structured, easy to serialize |
+| Async | asyncio / threading | Keep GUI responsive during inference |
+| Card Data | Scryfall API | Free MTG card data + art |
+| LAN Hosting | Local network / ngrok | No deployment needed for local play |
+
+### GPT-OSS 20B Q4_K_M Notes
+- Q4_K_M quantization balances quality and speed well for a 20B model
+- Expected VRAM: ~12–14 GB on RTX 5070 Ti (16 GB VRAM) — fits comfortably
+- Stronger reasoning than 7B models — better for multi-step MTG decision making
+- Slower inference than smaller models — async handling is essential so GUI doesn't block
+- System prompt + game state should be kept under ~3000 tokens to maintain fast response times

Makefile

@@ -0,0 +1,74 @@
+# Commander AI Lab — Developer Makefile
+# ======================================
+# Targets:
+#   make test            — run all tests
+#   make test-unit       — run unit tests only (fast)
+#   make test-integration— run integration tests
+#   make test-benchmark  — run benchmark suite with -s (print timings)
+#   make coverage        — run tests with coverage report
+#   make serve           — start the FastAPI dev server
+#   make lint            — ruff + mypy
+#   make fmt             — ruff format
+#   make clean           — remove build artefacts
+
+PYTHON  ?= python
+UVICORN ?= uvicorn
+PORT    ?= 8000
+SRC     = src/commander_ai_lab
+
+.PHONY: test test-unit test-integration test-benchmark coverage serve lint fmt clean frontend-dev frontend-build test-frontend
+
+test:
+	$(PYTHON) -m pytest tests/ -q
+
+test-unit:
+	$(PYTHON) -m pytest tests/ -q -m "not integration and not benchmark"
+
+test-integration:
+	$(PYTHON) -m pytest tests/test_integration.py -v
+
+test-benchmark:
+	$(PYTHON) -m pytest tests/test_benchmark.py -v -s
+
+test-phase3:
+	$(PYTHON) -m pytest tests/test_phase3.py -v
+
+test-phase4:
+	$(PYTHON) -m pytest tests/test_phase4.py -v
+
+test-phase5:
+	$(PYTHON) -m pytest tests/test_phase5.py -v
+
+test-phase6:
+	$(PYTHON) -m pytest tests/test_phase6.py -v
+
+test-phase7:
+	$(PYTHON) -m pytest tests/test_integration.py tests/test_benchmark.py -v
+
+coverage:
+	$(PYTHON) -m pytest tests/ --cov=$(SRC) --cov-report=term-missing --cov-report=html
+	echo "Coverage report: htmlcov/index.html"
+
+serve:
+	$(UVICORN) commander_ai_lab.web.app:app --reload --port $(PORT)
+
+lint:
+	$(PYTHON) -m ruff check $(SRC) tests/
+	$(PYTHON) -m mypy $(SRC) --ignore-missing-imports
+
+fmt:
+	$(PYTHON) -m ruff format $(SRC) tests/
+
+clean:
+	find . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
+	find . -type f -name '*.pyc' -delete
+	rm -rf .coverage htmlcov .mypy_cache .ruff_cache dist build
+
+frontend-dev:
+	cd frontend && npm run dev
+
+frontend-build:
+	cd frontend && npm run build
+
+test-frontend:
+	cd frontend && npx vitest run

README.md

@@ -1,362 +1,434 @@
 # Commander AI Lab
 
-Headless batch simulation environment for 3-AI MTG Commander pods.
-Run 100+ games, analyze win rates, and stress-test your decks — all from a web UI or CLI.
+A full-stack Magic: The Gathering Commander research platform. Run thousands of simulated games, train neural networks on the outcomes, build decks with AI that learns from simulation data, get LLM-powered coaching, and play live 4-player battles — all from a single unified system.
+
+---
+
+## Overview
+
+Commander AI Lab connects a rules-complete Forge engine, a fast Monte Carlo simulator, an LLM deck builder, an ML training pipeline, and multiple game clients (web, LAN, Unity) into one closed-loop platform. Simulation results feed back into deck building and AI decision-making, so the system improves the more it plays.
+
+### Key Capabilities
+
+- **Batch Simulation** — Run hundreds or thousands of Commander games unattended using Forge (rules-complete) or a headless Python Monte Carlo engine
+- **ML Training Pipeline** — Reinforcement learning (PPO) trains a neural net on game decision logs; outcome-weighted scoring nudges card weights automatically
+- **AI Deck Builder** — 7-step LLM pipeline (EDHrec → Scryfall → Ollama) with simulation-learned insights injected into prompts
+- **LLM Coach** — Streaming coaching sessions powered by Anthropic Claude or local Ollama models, enriched with RAG-retrieved card knowledge
+- **RAG Card Knowledge** — ChromaDB vector store over 30,000+ Scryfall cards with semantic search and auto-staleness monitoring
+- **LAN Battlefield** — Browser-based 4-player Commander game client with ML-powered AI opponents and precon deck support
+- **Unity Client** — Full Unity application with battleground scene, lobby system, WebSocket game state sync, and cross-platform builds
+- **Card Scanner** — OCR pipeline for physical card recognition via Ximilar API
+- **163 Precon Decks** — Complete preconstructed deck library in `.dck` format
+
+---
 
 ## Architecture
 
 ```
-┌─────────────────────────────────────────────────────────────┐
-│  Web Browser (ai-lab.js + ai-lab.css)                       │
-│    Select decks → Start batch → Poll progress → View stats  │
-└─────────────────────┬───────────────────────────────────────┘
-                      │ HTTP (REST)
-┌─────────────────────┴───────────────────────────────────────┐
-│  Python FastAPI Server (lab_api.py, port 8080)              │
-│    /api/lab/start  — spawn Java batch runner subprocess     │
-│    /api/lab/status — poll progress (parses stdout)          │
-│    /api/lab/result — return completed JSON results          │
-│    /api/lab/decks  — list decks from Forge user data        │
-│    /api/lab/history— browse past batch runs                 │
-│    /api/lab/log    — stream live console output             │
-└─────────────────────┬───────────────────────────────────────┘
-                      │ subprocess (java -jar)
-┌─────────────────────┴───────────────────────────────────────┐
-│  Java CLI (LabCli.java + BatchRunner + MultiThreadRunner)   │
-│    Parses Forge sim output → GameResult objects → JSON      │
-└─────────────────────┬───────────────────────────────────────┘
-                      │ subprocess per game
-┌─────────────────────┴───────────────────────────────────────┐
-│  Card Forge Engine (forge-gui-desktop sim mode)             │
-│    java -jar forge.jar sim -d "deck1" "deck2" "deck3"       │
-│    -f commander -n 1 -q                                     │
-└─────────────────────────────────────────────────────────────┘
+┌─────────────────────────────────────────────────────────────────────┐
+│  Clients                                                            │
+│  ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────────┐ │
+│  │  Web UI      │ │  LAN Client  │ │  Unity Client                │ │
+│  │  (React/TS)  │ │  (HTML/JS)   │ │  (C# / URP)                 │ │
+│  │  /ui         │ │  /lan        │ │  /unity-client               │ │
+│  └──────┬───────┘ └──────┬───────┘ └──────┬───────────────────────┘ │
+│         └────────────────┼────────────────┘                         │
+│                          │ HTTP/REST + WebSocket                    │
+│  ┌───────────────────────┴─────────────────────────────────────────┐│
+│  │  FastAPI Backend (lab_api.py — port 8080)                       ││
+│  │                                                                  ││
+│  │  /api/lab/*          Batch simulation (Forge + Monte Carlo)     ││
+│  │  /api/deck-builder/* 7-step LLM deck construction               ││
+│  │  /api/coach/*        Streaming LLM coaching sessions            ││
+│  │  /api/ml/*           Neural net training + policy server        ││
+│  │  /api/rag/*          ChromaDB vector search                     ││
+│  │  /api/game/*         Live game sessions (Forge IPC)             ││
+│  │  /api/policy/*       ML policy decisions for AI players         ││
+│  │  /ws/game/{id}       WebSocket game state streaming             ││
+│  └───────┬──────────────┬──────────────┬──────────────┬────────────┘│
+│          │              │              │              │              │
+│   ┌──────┴──────┐ ┌────┴─────┐ ┌──────┴──────┐ ┌────┴──────┐      │
+│   │ Forge       │ │ Monte    │ │ Ollama      │ │ ChromaDB  │      │
+│   │ Engine      │ │ Carlo    │ │ (local LLM) │ │ (vector   │      │
+│   │ (Java/JVM)  │ │ Engine   │ │ qwen2.5:7b  │ │  store)   │      │
+│   └─────────────┘ │ (Python) │ └─────────────┘ └───────────┘      │
+│                    └──────────┘                                      │
+│  ┌──────────────────────────────────────────────────────────────────┐│
+│  │  ML Pipeline                                                     ││
+│  │  decisions_*.jsonl → update_weights.py → learned_weights.json    ││
+│  │                    → /api/ml/train    → neural net checkpoints   ││
+│  └──────────────────────────────────────────────────────────────────┘│
+└─────────────────────────────────────────────────────────────────────┘
 ```
 
-## Quick Start (Windows)
+---
+
+## Features
+
+### Simulation Engines
+
+| Engine | Description | Speed |
+|---|---|---|
+| **Forge** | Rules-complete Java engine — handles the stack, instants, triggered abilities, and all Commander rules. Each game spawns a JVM subprocess. | ~0.04 sims/sec (single-threaded) |
+| **Monte Carlo** | Headless Python engine — models combat keywords (flying, trample, deathtouch, lifelink, menace, reach, haste), summoning sickness, tapped state, commander zone, multiplayer targeting, and board wipes. No JVM required. | Thousands of games in minutes |
+
+Both engines feed structured results into the same ML pipeline.
+
+### Deck Builder
+
+The `POST /api/deck-builder/build` endpoint runs a 7-step pipeline:
+
+1. Resolve commander via Scryfall
+2. Fetch EDHrec recommendations
+3. Fetch Scryfall candidates by category (ramp, removal, draw, lands)
+4. Ollama suggests synergy/protection/wincon cards — **simulation insights injected here**
+5. Filter by color identity, collection, and ban list
+6. Ollama enforces ratios (trim/expand to 99) — **simulation insights injected here**
+7. Assemble final deck with smart substitution (guarantees 99-card output)
+
+### AI Coach
+
+LLM-powered coaching with two modes:
+
+- **Full Session** — Deep analysis including removal coverage, ramp quality, draw engine profiling, win conditions, anti-synergy detection, pod meta evaluation, and tempo assessment
+- **Quick Digest** — Fast ~5-second summary for rapid feedback
+
+Coaching prompts are enriched with semantically relevant cards from the ChromaDB RAG store (15 cards for full sessions, 8 for quick digests). Supports both Anthropic Claude and local Ollama models with streaming responses.
+
+### RAG Card Knowledge
+
+- **SQLite FTS5** bulk card database from Scryfall data
+- **ChromaDB** vector index with natural-language card embeddings
+- Automatic staleness monitoring (checks every 6 hours, auto-rebuilds if >14 days old)
+- Graceful fallback — RAG failures never block coaching or deck building
+
+### ML Training Pipeline
+
+```
+Overnight sims → decisions_*.jsonl
+       ↓
+update_weights.py → learned_weights.json (feeds deck builder)
+       ↓
+/api/ml/train → neural net training on decision dataset
+       ↓
+/api/policy/decide → ML policy serves AI decisions in live games
+```
+
+- **Outcome-weighted RL** nudges card scoring weights from game results
+- **Neural net** trains on the full decision log dataset
+- **Distillation loop** — self-play → collect data → retrain → repeat
+- **ELO tracking** and model tournaments for comparing AI versions
+- Weights clamped to `[-20.0, 20.0]`, stored in `learned_weights.json`
+
+### Win Condition Tracking
+
+Games are classified by win type — not just "Combat" vs "Other":
+
+- **Combat** — standard creature damage wins
+- **Combo** — Approach of the Second Sun, Felidar Sovereign, Happily Ever After, and 30+ known alternate-win cards
+- **Drain** — life drain effects
+- **Mill** — library depletion
+- **Poison** — infect/poison counter wins
+
+Draw games (timeout) are analyzed separately with per-player life-at-timeout snapshots and likely cause identification.
+
+### LAN Battlefield Client
+
+Browser-based 4-player Commander game at `/lan`:
+
+- **AI Bridge** — REST + WebSocket connectivity with ML policy decisions as Priority 1 (falls back to local heuristic AI)
+- **Deck System** — 3-tab selector: Precons (163 decks, grouped by set), EDHREC search, and custom deck import
+- **Player Panels** — Life counters, commander damage bars, poison/energy/experience/rad counters, emblem management
+- **Backend Detection** — Auto-detects the Commander AI Lab backend; works offline with local precon data
+
+### Unity Client
+
+Full Unity application (URP, .NET Standard 2.1) with:
+
+- **Battleground Scene** — 4-seat layout with rotated perspectives, life/poison/commander damage widgets, phase tracker, turn order bar
+- **Card System** — Hand, battlefield, graveyard, exile zones with card art, tapped rotation, face-down state, summoning sickness indicators
+- **Stack & Mana** — LIFO stack display and 6-pip mana pool
+- **Lobby System** — Configure 4 seats (human/AI), POST to backend, WebSocket game state sync
+- **Elimination** — Grayscale shader overlay on eliminated players
+- **Token System** — Create, tap, add counters, delete tokens
+- **Cross-Platform** — Windows, WebGL, Android, iOS build targets
+- **Scenes** — MainMenu, Collection, DeckBuilder, DeckGenerator, Simulator, Coach, Scanner, Training, Precon, Battleground
+
+### Card Scanner
+
+OCR pipeline for physical Magic cards using the Ximilar API. Scan cards from photos and match them to the Scryfall database.
+
+---
+
+## Quick Start
 
 ### Prerequisites
-- **Java 17+** (`java -version`)
-- **Python 3.10+** (`python --version`)
-- **Maven 3.8+** (`mvn --version`)
-- **Forge repo** cloned and built at `D:\ForgeCommander\forge-repo`
 
-### 1. Build Forge (one-time)
+| Tool | Version |
+|---|---|
+| Python | 3.10+ |
+| Java | 17+ |
+| Maven | 3.8+ |
+| Node.js | 18+ (for LAN client scripts) |
+| Ollama | Latest (for local LLM inference) |
+| [Forge](https://github.com/Card-Forge/forge) | Built at `D:\ForgeCommander\forge-repo` |
+
+### Setup (Windows)
+
 ```powershell
+# 1. Build Forge (one-time)
 cd D:\ForgeCommander\forge-repo
 mvn package -pl forge-gui-desktop "-DskipTests" "-Drevision=2.0.12-SNAPSHOT" -am
-```
 
-### 2. Build Commander AI Lab
-```powershell
+# 2. Build Commander AI Lab
 cd D:\ForgeCommander\commander-ai-lab
 mvn package "-DskipTests"
-```
-This produces `target/commander-ai-lab-1.0.0-SNAPSHOT.jar` (shaded fat JAR).
 
-### 3. Install Python dependencies
-```powershell
-pip install fastapi uvicorn
-```
+# 3. Install Python dependencies
+pip install -e ".[rag,dev]"
 
-### 4. Launch the Web UI
-**Option A — One-click launcher:**
-```powershell
+# 4. Pull an Ollama model for the deck builder
+ollama pull qwen2.5:7b
+
+# 5. Launch
 .\start-lab.bat
+# → Web UI:        http://localhost:8080
+# → LAN Client:    http://localhost:8080/lan/
 ```
-Edit the paths at the top of `start-lab.bat` if needed.
 
-**Option B — Manual launch:**
+Or use the automated installer:
+
 ```powershell
-python lab_api.py `
-  --forge-jar "D:\ForgeCommander\forge-repo\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar" `
-  --forge-dir "D:\ForgeCommander\forge-repo\forge-gui" `
-  --port 8080
+.\install-commander-ai-lab.ps1
 ```
 
-Open `http://localhost:8080` — select 3 decks, set game count, click **Run AI Sims**.
+---
+
+## Overnight AFK Runner
+
+Run batch simulations unattended, then automatically update card weights and train the neural net:
 
-### 5. CLI-only usage (no web server)
 ```powershell
-cd D:\ForgeCommander\forge-repo\forge-gui
-
-java -jar "D:\ForgeCommander\commander-ai-lab\target\commander-ai-lab-1.0.0-SNAPSHOT.jar" `
-  --forge-jar "..\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar" `
-  --forge-dir "." `
-  --deck1 "Edgar Markov" `
-  --deck2 "Grimgrin" `
-  --deck3 "Xyris" `
-  --games 100 `
-  --threads 4 `
-  --output results/batch-100.json
+python overnight-run.py                          # default: 11.5h DeepSeek sims
+python overnight-run.py --batches 20 --games 50  # fixed batch count
+python overnight-run.py --no-update-weights      # skip weight update
+python overnight-run.py --no-train               # skip neural net training
 ```
 
-## Scaling Tests
+**What happens after sims complete:**
+1. `update_weights.py` — nudges `learned_weights.json` from decision logs (feeds deck builder)
+2. `/api/ml/train` — trains the neural net on the full decision dataset
 
-### Single-threaded baseline (10 games)
-```powershell
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
-  --forge-jar "..." --forge-dir "..." `
-  --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
-  --games 10 --threads 1 --output results/baseline-10.json
+---
+
+## Simulation → Deck Builder Feedback Loop
+
+```
+Overnight sims → decisions_*.jsonl
+       ↓
+update_weights.py → learned_weights.json
+       ↓
+sim_insights.py → natural-language insight block
+       ↓
+Ollama prompts (suggest_cards + enforce_deck_ratios)
+       ↓
+Better card recommendations
 ```
 
-### Multi-threaded (10 games, 2 threads)
-```powershell
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
-  --forge-jar "..." --forge-dir "..." `
-  --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
-  --games 10 --threads 2 --output results/mt-10-t2.json
-```
+When active, `BuildResult.sources_consulted` includes `"sim_weights"`. The loop is a no-op until real sim data exists.
 
-### Scale to 100 games (4 threads)
-```powershell
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
-  --forge-jar "..." --forge-dir "..." `
-  --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
-  --games 100 --threads 4 --output results/scale-100-t4.json
-```
-
-### Maximum throughput test (100 games, 8 threads)
-```powershell
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
-  --forge-jar "..." --forge-dir "..." `
-  --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
-  --games 100 --threads 8 --seed 42 --output results/scale-100-t8-seed42.json
-```
-
-### Performance comparison script
-Run different thread counts and compare sims/sec:
-```powershell
-# Baseline: 1 thread
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 1 --seed 42 --output results/perf-t1.json
-
-# 2 threads
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 2 --seed 42 --output results/perf-t2.json
-
-# 4 threads
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 4 --seed 42 --output results/perf-t4.json
-
-# 8 threads
-java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 8 --seed 42 --output results/perf-t8.json
-```
-
-Then compare the `simsPerSecond` values in each output JSON to see scaling behavior.
+---
 
 ## API Reference
 
+### Simulation
+
 | Endpoint | Method | Description |
 |---|---|---|
 | `/api/lab/decks` | GET | List available Commander decks |
-| `/api/lab/start` | POST | Start a batch run (returns batchId) |
-| `/api/lab/status?batchId=X` | GET | Poll progress for a running batch |
-| `/api/lab/result?batchId=X` | GET | Get completed batch result JSON |
-| `/api/lab/log?batchId=X` | GET | Get live log lines from running batch |
-| `/api/lab/history` | GET | List past batch result files |
+| `/api/lab/start` | POST | Start Forge batch (returns batchId) |
+| `/api/lab/start-deepseek` | POST | Start Python/DeepSeek batch |
+| `/api/lab/status?batchId=X` | GET | Poll batch progress |
+| `/api/lab/result?batchId=X` | GET | Get completed batch JSON |
+| `/api/lab/history` | GET | List past batch runs |
+| `/api/lab/precons/deck` | GET | Fetch precon `.dck` file content |
 
-### POST /api/lab/start
-```json
-{
-  "decks": ["Edgar Markov", "Grimgrin", "Xyris"],
-  "numGames": 100,
-  "threads": 4,
-  "seed": null,
-  "clock": 120
-}
-```
+### Deck Builder & Coach
 
-### GET /api/lab/status response
-```json
-{
-  "batchId": "abc123",
-  "running": true,
-  "completed": 42,
-  "total": 100,
-  "threads": 4,
-  "elapsedMs": 156000,
-  "error": null
-}
-```
+| Endpoint | Method | Description |
+|---|---|---|
+| `/api/deck-builder/build` | POST | Build a 99-card Commander deck |
+| `/api/coach/chat` | POST | Full coaching session (streaming) |
+| `/api/coach/quick-digest` | POST | Fast ~5s coaching summary |
 
-## Python Simulator (Monte Carlo Engine)
+### ML & Policy
 
-A pure-Python deck simulator for rapid Monte Carlo analysis — no Forge JVM required.
-Ported from the [mtg-commander-lan](https://github.com/) Node.js simulator.
+| Endpoint | Method | Description |
+|---|---|---|
+| `/api/ml/train` | POST | Start neural net training |
+| `/api/ml/train/status` | GET | Poll training progress |
+| `/api/policy/decide` | POST | Get ML policy decision for game state |
 
-### Location
+### RAG
 
-```
-src/commander_ai_lab/
-├── __init__.py
-├── sim/                         # Core simulation engine
-│   ├── __init__.py              # Public exports
-│   ├── models.py                # Card, Player, SimState, GameResult, PlayerStats
-│   ├── engine.py                # GameEngine — turn loop, combat (flying/trample/deathtouch/lifelink/menace/reach)
-│   └── rules.py                 # AI_DEFAULT_WEIGHTS, enrich_card(), score_card(), parse_decklist()
-└── lab/                         # High-level experiment helpers
-    ├── __init__.py
-    └── experiments.py           # build_deck(), run_single_game(), run_simulation(), _cli_main()
-```
+| Endpoint | Method | Description |
+|---|---|---|
+| `/api/rag/build` | POST | Build/rebuild ChromaDB index |
+| `/api/rag/search` | POST | Semantic card search |
+| `/api/rag/status` | GET | Index health and staleness |
 
-### Install as editable package
+### Live Game
+
+| Endpoint | Method | Description |
+|---|---|---|
+| `/api/game/start` | POST | Create a live game session |
+| `/api/game/action` | POST | Submit a player action |
+| `/api/game/{id}/actions` | GET | Poll Forge IPC actions |
+| `/ws/game/{game_id}` | WS | Real-time game state streaming |
+
+---
+
+## ML Scripts
 
 ```powershell
-cd D:\ForgeCommander\commander-ai-lab
-pip install -e .
+# Update card weights from decision logs
+python -m ml.scripts.update_weights --min-games 10 --lr 0.05 --dry-run
+
+# Reset weights to defaults
+python -m ml.scripts.update_weights --reset
+
+# Generate synthetic training data
+python ml/scripts/generate_synthetic_data.py
 ```
 
-This installs `commander_ai_lab` as an importable package and registers the `commander-sim` CLI command.
+---
 
-### Quick smoke test
-
-```powershell
-python -m commander_ai_lab.lab.experiments
-```
-
-Expected output — a 10-game Monte Carlo run of a sample "Korvold" deck:
-
-```
-══════════════════════════════════════════
-  Commander AI Lab — Experiment Runner
-══════════════════════════════════════════
-
---- Demo: Korvold Aristocrats (10 games) ---
-
-  Win rate:          XX.X%
-  Avg turns:         XX.X
-  Avg damage dealt:  XX.X
-  Avg spells cast:   XX.X
-  Avg creatures:     XX.X
-  Elapsed:           0.02s
-
-  Done.
-```
-
-### Python API
+## Python Simulator API
 
 ```python
-from commander_ai_lab.sim.models import Card
-from commander_ai_lab.sim.engine import GameEngine
 from commander_ai_lab.lab.experiments import build_deck, run_simulation
 
-# Build a deck from card names
-deck = build_deck(["Sol Ring", "Cultivate", "Lightning Bolt", "Forest"] + ["Forest"] * 36)
-
-# Monte Carlo simulation (100 games)
+deck = build_deck(["Sol Ring", "Cultivate", "Lightning Bolt"] + ["Forest"] * 36)
 stats = run_simulation(deck, num_games=100, name_a="My Deck")
-print(f"Win rate: {stats['win_rate']:.1%}")
-print(f"Avg turns: {stats['avg_turns']:.1f}")
+print(f"Win rate: {stats['win_rate']:.1%}, Avg turns: {stats['avg_turns']:.1f}")
 ```
 
-Key functions:
-
-| Function | Description |
-|---|---|
-| `build_deck(card_names)` | Create enriched `Card` list from names |
-| `build_deck_from_text(decklist)` | Parse `"1 Sol Ring\n37 Forest"` format |
-| `run_single_game(deck_a, deck_b)` | One game, returns winner/turns/stats |
-| `run_simulation(deck_a, deck_b, n)` | Monte Carlo over `n` games, aggregated stats |
-
-### CLI entry point
-
-After `pip install -e .`:
-
-```powershell
-commander-sim
-```
-
-Runs the same demo as `python -m commander_ai_lab.lab.experiments`.
-
-### How the engine works
-
-1. **Deck enrichment** — `enrich_card()` infers type, CMC, keywords, and power/toughness from card name heuristics when Scryfall data is unavailable.
-2. **AI scoring** — `score_card()` uses weighted categories (removal, ramp, draw, aggro, etc.) to pick the best play each turn.
-3. **Game loop** — Each turn: untap → draw → play lands → cast spells (AI scored) → combat (attack/block decisions) → end step. Full keyword support for flying, trample, deathtouch, lifelink, menace, and reach.
-4. **Monte Carlo** — `run_simulation()` runs N independent games and aggregates win rate, avg turns, damage dealt/received, spells cast, creatures played, removal used, ramp played, cards drawn, and max board size.
+---
 
 ## Project Structure
 
 ```
 commander-ai-lab/
-├── lab_api.py                    # Python FastAPI server (web UI backend)
+├── lab_api.py                    # FastAPI server entry point
+├── overnight-run.py              # AFK batch runner
 ├── start-lab.bat                 # Windows one-click launcher
-├── pom.xml                       # Maven build (Java 17, shaded JAR)
-├── pyproject.toml                # Python package config (src/ layout)
-├── ui/
-│   ├── index.html                # Web UI entry point
-│   ├── ai-lab.js                 # UI controller (deck selection, progress, results)
-│   ├── ai-lab.css                # Styles (dark theme, responsive)
-│   ├── collection.html           # Card collection manager
-│   ├── collection.js             # Collection controller
-│   ├── collection.css            # Collection styles
-│   ├── deckbuilder.html          # Deck builder + EDH advisor
-│   ├── deckbuilder.js            # Deck builder controller
-│   └── deckbuilder.css           # Deck builder styles
-├── src/commander_ai_lab/         # Python simulator package
-│   ├── __init__.py
-│   ├── sim/                      # Core engine (models, engine, rules)
-│   └── lab/                      # Experiment helpers
-├── src/main/java/commanderailab/ # Java batch runner (Forge integration)
-│   ├── cli/LabCli.java           # CLI entry point (picocli)
-│   ├── batch/
-│   │   ├── BatchRunner.java      # Single-thread runner (parses Forge output)
-│   │   └── MultiThreadBatchRunner.java  # Multi-thread runner (ExecutorService)
-│   ├── bridge/WebApiBridge.java  # Java-side web API bridge
-│   ├── schema/
-│   │   ├── BatchResult.java      # Data model POJOs
-│   │   └── JsonExporter.java     # JSON serialization + validation
-│   ├── stats/StatsAggregator.java # Win rates, turn counts, breakdowns
-│   └── ai/
-│       ├── AiPolicy.java         # Policy interface
-│       └── ForgeBuiltinPolicy.java # v1 Forge AI wrapper
-├── scanner/                      # Card scanner (OCR pipeline)
-│   ├── __init__.py
-│   ├── ocr.py                    # Tesseract OCR wrapper
-│   ├── pipeline.py               # Image preprocessing pipeline
-│   └── preprocess.py             # Image preprocessing helpers
-├── docs/
-│   ├── PROJECT_VISION.md
-│   ├── batch-result-schema.json
-│   └── game-log-schema.json
-├── sample-decks/                 # 3 sample .dck files
-├── precon-decks/                 # Preconstructed decks + index
-├── results/                      # Batch output JSON files (gitignored)
-└── src/test/java/                # Unit tests
+├── install-commander-ai-lab.ps1  # Automated setup script
+├── pom.xml                       # Maven build (Java/Forge components)
+├── pyproject.toml                # Python package config
+│
+├── routes/                       # FastAPI route modules
+│   ├── lab.py                    #   Batch simulation
+│   ├── deckbuilder.py            #   Deck builder pipeline
+│   ├── coach.py                  #   LLM coaching
+│   ├── ml.py                     #   ML training
+│   ├── rag.py                    #   ChromaDB vector search
+│   ├── game.py                   #   Live game sessions
+│   ├── policy.py                 #   ML policy server
+│   ├── precon.py                 #   Precon deck serving
+│   ├── scanner.py                #   Card OCR
+│   └── ws_game.py                #   WebSocket game state
+│
+├── services/                     # Core service layer
+│   ├── forge_runner.py           #   Forge subprocess management
+│   ├── draw_game_analyzer.py     #   Draw/timeout analysis
+│   ├── rag_store.py              #   ChromaDB vector store
+│   └── card_text.py              #   Shared card text builder
+│
+├── src/commander_ai_lab/
+│   ├── sim/                      # Monte Carlo engine
+│   │   ├── engine.py             #   Game loop
+│   │   ├── models.py             #   GameResult, Card, Player
+│   │   ├── rules.py              #   Combat, keywords, commander rules
+│   │   ├── win_condition_parser.py  # Alternate win detection
+│   │   └── learned_weights.json  #   ML-learned card weights
+│   ├── deck_builder/             # 7-step LLM pipeline
+│   │   ├── api/                  #   Scryfall, EDHrec, Ollama, sim_insights
+│   │   ├── core/                 #   Models, rules engine, assembler
+│   │   └── pipeline/             #   build_deck.py orchestrator
+│   └── coach/                    # LLM coaching
+│       ├── coach_service.py      #   Session + quick digest
+│       ├── prompt_template.py    #   System/user prompts
+│       └── embeddings.py         #   Card embedding utilities
+│
+├── ml/                           # ML training pipeline
+│   ├── scripts/                  #   update_weights.py, ml_cli.py
+│   ├── training/                 #   Neural net trainer
+│   ├── encoder/                  #   Feature encoding
+│   └── models/                   #   Saved checkpoints
+│
+├── lan-client/                   # Browser-based LAN battlefield
+│   ├── index.html                #   Entry point
+│   ├── engine.js                 #   Game engine + ML AI turns
+│   ├── ai-bridge.js              #   Backend connector + policy bridge
+│   ├── player-panels.js          #   Enhanced player panels
+│   ├── ui.js                     #   3-tab deck selector + setup
+│   └── precons.js                #   163 precon deck data
+│
+├── unity-client/                 # Unity game client
+│   └── Assets/
+│       ├── Scenes/               #   11 scenes (MainMenu → Battleground)
+│       ├── Scripts/              #   C# controllers, models, services
+│       ├── Prefabs/              #   Card, deck slot, chat bubble
+│       └── Materials/            #   URP materials + grayscale shader
+│
+├── scanner/                      # Card OCR pipeline (Ximilar)
+├── precon-decks/                 # 163 preconstructed decks (.dck)
+├── sample-decks/                 # Sample deck files
+├── data/                         # Runtime data (ChromaDB, bulk DB)
+├── logs/                         # Simulation + decision logs
+├── docs/                         # Architecture docs + schemas
+├── tests/                        # Test suite
+├── scripts/                      # Utility scripts
+├── .github/workflows/            # CI + Unity build automation
+└── src/main/java/                # Java/Forge batch runner
+    └── commanderailab/
 ```
 
-## Output Format
+---
 
-Each batch run produces JSON conforming to `docs/batch-result-schema.json`:
-```json
-{
-  "metadata": {
-    "schemaVersion": "1.0.0",
-    "batchId": "uuid",
-    "timestamp": "2026-03-06T...",
-    "totalGames": 100,
-    "completedGames": 100,
-    "format": "commander",
-    "podSize": 3,
-    "engineVersion": "forge-2.0.12-SNAPSHOT",
-    "masterSeed": 42,
-    "threads": 4,
-    "elapsedMs": 260000
-  },
-  "decks": [...],
-  "games": [...],
-  "summary": {
-    "perDeck": [
-      { "deckName": "Edgar Markov", "winRate": 0.60, "wins": 60, ... }
-    ],
-    "avgGameTurns": 12.3,
-    "simsPerSecond": 0.38
-  }
-}
-```
+## Tech Stack
+
+| Layer | Technology |
+|---|---|
+| Backend | Python 3.10+, FastAPI, Uvicorn |
+| Simulation (rules-complete) | Java 17, Maven, Forge |
+| Simulation (fast) | Python Monte Carlo engine |
+| LLM Inference | Ollama (qwen2.5:7b), Anthropic Claude |
+| Vector Store | ChromaDB with all-MiniLM-L6-v2 embeddings |
+| Card Database | SQLite FTS5 (Scryfall bulk data) |
+| ML Training | PyTorch, PPO reinforcement learning |
+| Web Client | HTML/CSS/JS |
+| Unity Client | Unity 2022 LTS, URP, C# |
+| CI/CD | GitHub Actions |
+
+---
 
 ## Known Limitations
-- Winner's life total not captured (Forge quiet mode only reports losers)
-- Win condition classification is heuristic-based on Forge loss reason strings
-- Each game spawns a new JVM process (~2-3s overhead per game)
-- Max throughput ~0.04 sims/sec single-threaded (Forge sim is CPU-heavy)
-- Multithreading spawns parallel subprocesses — throughput scales linearly with threads up to CPU core count
+
+- **Monte Carlo engine** — no stack, no instants, no continuous type-changing effects (animated lands don't attack)
+- **Forge engine** — each game spawns a new JVM (~2–3s overhead); ~0.04 sims/sec single-threaded
+- **Forge** — winner life total not captured in quiet mode (only reports losers)
+- **Deck builder** — Ollama suggestions depend on the local model quality; `qwen2.5:7b` works well
+- **LAN client** — EDHREC commander search is a stub (backend-powered, not yet fully wired)
+
+---
+
+## Contributing
+
+Commander AI Lab is an active research project. Check the [open issues](https://github.com/KoalaTrapLord/commander-ai-lab/issues) for areas where contributions are welcome.
+
+---
+
+## License
+
+See repository for license details.

Test-PolicyServer.ps1

@@ -0,0 +1,257 @@
+<#
+.SYNOPSIS
+    Smoke-tests the Commander AI Lab policy server from PowerShell.
+    Checks: server reachable -> /api/policy/health -> /api/policy/decide.
+    Prints a plain-English pass/fail for every step.
+
+.USAGE
+    # From repo root (server already running in another window):
+    .\Test-PolicyServer.ps1
+
+    # Custom host/port:
+    .\Test-PolicyServer.ps1 -BaseUrl http://localhost:9000
+
+    # Pretty-print the full decide response:
+    .\Test-PolicyServer.ps1 -Verbose
+#>
+param(
+    [string]$BaseUrl = "http://localhost:8080",
+    [switch]$Verbose
+)
+
+$ErrorActionPreference = "Stop"
+
+# ---- helpers ----------------------------------------------------------------
+
+function Write-Pass { param([string]$msg) Write-Host "  [PASS] $msg" -ForegroundColor Green  }
+function Write-Fail { param([string]$msg) Write-Host "  [FAIL] $msg" -ForegroundColor Red    }
+function Write-Info { param([string]$msg) Write-Host "  [INFO] $msg" -ForegroundColor Cyan   }
+function Write-Warn { param([string]$msg) Write-Host "  [WARN] $msg" -ForegroundColor Yellow }
+function Write-Step { param([string]$msg) Write-Host "`n-- $msg" -ForegroundColor White       }
+
+$allPassed = $true
+
+function Invoke-Step {
+    param(
+        [string]      $Name,
+        [scriptblock] $Block
+    )
+    Write-Step $Name
+    try   { & $Block }
+    catch {
+        Write-Fail $_.Exception.Message
+        $script:allPassed = $false
+    }
+}
+
+# ---- Step 1: server reachable -----------------------------------------------
+
+Invoke-Step "Step 1 -- Server reachable at $BaseUrl" {
+    $r1 = $null
+    try { $r1 = Invoke-WebRequest -Uri "$BaseUrl/docs" -UseBasicParsing -TimeoutSec 5 } catch {}
+    if ($r1 -and $r1.StatusCode -eq 200) {
+        Write-Pass "FastAPI server is up (GET /docs -> 200)"
+        return
+    }
+    $r2 = $null
+    try { $r2 = Invoke-WebRequest -Uri "$BaseUrl/" -UseBasicParsing -TimeoutSec 5 } catch {}
+    if ($r2 -and $r2.StatusCode -lt 500) {
+        Write-Pass "FastAPI server is up (GET / -> $($r2.StatusCode))"
+        return
+    }
+    throw "Cannot reach $BaseUrl -- is lab_api.py / uvicorn running?"
+}
+
+# ---- Step 2: /api/policy/health ---------------------------------------------
+
+$modelLoaded = $false
+
+Invoke-Step "Step 2 -- GET $BaseUrl/api/policy/health" {
+    $resp = Invoke-RestMethod -Uri "$BaseUrl/api/policy/health" -Method Get -TimeoutSec 10
+    if ($Verbose) { $resp | ConvertTo-Json -Depth 5 | Write-Host }
+
+    if ($resp.ready -eq $true) {
+        Write-Pass "Model is loaded and ready  (ready=true)"
+        $script:modelLoaded = $true
+    } elseif ($resp.status -eq "degraded") {
+        Write-Fail "Health returned status=degraded / ready=false"
+        Write-Warn "  -> Policy model is NOT loaded."
+        Write-Warn "  -> Start the server with --load-policy, or call POST /api/ml/load_model first."
+        Write-Warn "  -> Confirm models/ contains a valid .pt checkpoint."
+        $script:allPassed = $false
+    } else {
+        Write-Warn "Unexpected health response -- dumping:"
+        $resp | ConvertTo-Json -Depth 5 | Write-Host
+        $script:allPassed = $false
+    }
+}
+
+# ---- Step 3: /api/policy/decide (synthetic snapshot) ------------------------
+
+Invoke-Step "Step 3 -- POST $BaseUrl/api/policy/decide  (synthetic 4-player snapshot)" {
+
+    if (-not $script:modelLoaded) {
+        Write-Warn "Skipping /decide -- model not loaded (Step 2 failed)."
+        Write-Warn "Fix Step 2 first, then re-run."
+        return
+    }
+
+    $body = [ordered]@{
+        game_id       = "ps1-smoke-test"
+        turn          = 3
+        phase         = "MAIN1"
+        active_player = 0
+        playstyle     = "midrange"
+        temperature   = 1.0
+        greedy        = $false
+        players       = @(
+            [ordered]@{
+                seat              = 0
+                name              = "TestPlayer0"
+                life              = 40
+                manaAvailable     = 3
+                landCount         = 3
+                creaturesOnField  = 1
+                totalPowerOnBoard = 2
+                hand              = @("Cultivate", "Solemn Simulacrum", "Sol Ring")
+                battlefield       = @("Forest", "Forest", "Forest", "Birds of Paradise")
+                graveyard         = @()
+                commandZone       = @("Atraxa, Praetors Voice")
+            },
+            [ordered]@{
+                seat              = 1
+                name              = "TestPlayer1"
+                life              = 38
+                manaAvailable     = 2
+                landCount         = 2
+                creaturesOnField  = 0
+                totalPowerOnBoard = 0
+                handCount         = 4
+                hand              = @()
+                battlefield       = @("Island", "Swamp")
+                graveyard         = @()
+                commandZone       = @("Ur-Dragon")
+            },
+            [ordered]@{
+                seat              = 2
+                name              = "TestPlayer2"
+                life              = 35
+                manaAvailable     = 4
+                landCount         = 4
+                creaturesOnField  = 2
+                totalPowerOnBoard = 5
+                handCount         = 3
+                hand              = @()
+                battlefield       = @("Mountain", "Mountain", "Mountain", "Mountain", "Goblin Guide", "Monastery Swiftspear")
+                graveyard         = @()
+                commandZone       = @("Krenko, Mob Boss")
+            },
+            [ordered]@{
+                seat              = 3
+                name              = "TestPlayer3"
+                life              = 40
+                manaAvailable     = 0
+                landCount         = 0
+                creaturesOnField  = 0
+                totalPowerOnBoard = 0
+                handCount         = 7
+                hand              = @()
+                battlefield       = @()
+                graveyard         = @()
+                commandZone       = @("Prossh, Skyraider of Kher")
+            }
+        )
+    } | ConvertTo-Json -Depth 10
+
+    $resp = Invoke-RestMethod `
+        -Uri         "$BaseUrl/api/policy/decide" `
+        -Method      Post `
+        -Body        $body `
+        -ContentType "application/json" `
+        -TimeoutSec  30
+
+    if ($Verbose) {
+        Write-Host ""
+        Write-Host "  Full /decide response:" -ForegroundColor White
+        $resp | ConvertTo-Json -Depth 6 | Write-Host
+    }
+
+    $ok = $true
+    if ([string]::IsNullOrEmpty($resp.action)) {
+        Write-Fail "response.action is empty"
+        $ok = $false
+    }
+    if ($null -eq $resp.confidence) {
+        Write-Fail "response.confidence is missing"
+        $ok = $false
+    }
+    if ($resp.PSObject.Properties["error"] -and -not [string]::IsNullOrEmpty($resp.error)) {
+        Write-Fail "Server returned error: $($resp.error)"
+        $ok = $false
+    }
+
+    if ($ok) {
+        $confPct = [math]::Round($resp.confidence * 100, 1)
+        Write-Pass "Decision received  ->  action='$($resp.action)'  conf=$($confPct)%  latency=$($resp.inference_ms)ms  src=$($resp.vector_source)"
+
+        if ($resp.probabilities) {
+            $top3 = $resp.probabilities.PSObject.Properties |
+                Sort-Object { $_.Value } -Descending |
+                Select-Object -First 3
+            Write-Info "Top-3 action probabilities:"
+            foreach ($p in $top3) {
+                $pct = [math]::Round($p.Value * 100, 1)
+                Write-Info ("  " + $p.Name.PadRight(22) + "$pct%")
+            }
+        }
+    } else {
+        $script:allPassed = $false
+    }
+}
+
+# ---- Step 4: log line guidance ----------------------------------------------
+
+Invoke-Step "Step 4 -- Confirm [routes.policy.decide] log line in server console" {
+    Write-Info "After Step 3 you should see in the uvicorn window:"
+    Write-Info "  [decide] turn=3 phase=main_1 -> cast_ramp (conf=0.810) | top5=[...] | src=encoder"
+    Write-Info ""
+    Write-Info "If that line is MISSING:"
+    Write-Info "  1. Confirm uvicorn started with --log-level info (the default)"
+    Write-Info "  2. Confirm lab_api.py calls register_policy_routes(app, policy_svc)"
+    Write-Info "  3. Confirm routes/policy.py is included via routes/__init__.py"
+    Write-Info "  4. Logger name is [routes.policy.decide] -- check no filter silences it"
+    Write-Pass "Guidance printed"
+}
+
+# ---- Step 5: why Forge ignores the policy server ----------------------------
+
+Invoke-Step "Step 5 -- Forge IPC status (informational)" {
+    Write-Warn "Forge does NOT call /api/policy/decide on its own."
+    Write-Warn "routes/game.py POST /api/game/start has a TODO: wire Forge engine launch."
+    Write-Warn ""
+    Write-Warn "Until wired, /decide is only hit by:"
+    Write-Warn "  - This script"
+    Write-Warn "  - overnight-run.py  (batch sim path)"
+    Write-Warn "  - Manual Invoke-RestMethod / curl calls"
+    Write-Warn ""
+    Write-Warn "Forge launches its built-in AI and plays lands because it has"
+    Write-Warn "no HTTP client pointed at localhost:8080."
+    Write-Warn ""
+    Write-Warn "Next step: wire GameSession inside POST /api/game/start,"
+    Write-Warn "or add a PolicyClient hook in Forge AiController.java."
+    Write-Pass "Status explained"
+}
+
+# ---- Summary ----------------------------------------------------------------
+
+Write-Host ""
+if ($allPassed) {
+    Write-Host "==========================================" -ForegroundColor Green
+    Write-Host "  ALL STEPS PASSED -- policy server live"  -ForegroundColor Green
+    Write-Host "==========================================" -ForegroundColor Green
+} else {
+    Write-Host "==========================================" -ForegroundColor Red
+    Write-Host "  ONE OR MORE STEPS FAILED -- see above"   -ForegroundColor Red
+    Write-Host "==========================================" -ForegroundColor Red
+    exit 1
+}

coach/clients/__init__.py

@@ -1 +1 @@
-# Coach clients — Perplexity, LM Studio
+# Coach clients — Anthropic Claude, LM Studio

coach/clients/anthropic_client.py

@@ -0,0 +1,401 @@
+"""
+Commander AI Lab — Anthropic Claude Structured Output Client
+════════════════════════════════════════════════════════════
+Uses the anthropic SDK to call Claude's messages endpoint
+with structured JSON output enforced via system prompt.
+Supports both sync and native async operation.
+
+All substantive calls use .messages.stream() to avoid the SDK's
+10-minute non-streaming timeout restriction on large max_tokens.
+"""
+import json
+import logging
+import re
+from typing import Optional
+
+import anthropic
+
+logger = logging.getLogger("coach.anthropic")
+
+# ── Anthropic Models ───────────────────────────────────────────
+CLAUDE_OPUS = "claude-opus-4-0-20250514"
+CLAUDE_SONNET = "claude-sonnet-4-5"
+CLAUDE_HAIKU = "claude-haiku-4-5"
+
+
+class AnthropicResponse:
+    """Parsed response container from Anthropic API.
+
+    Drop-in replacement for PerplexityResponse — same attribute shape
+    so all existing call-sites work without changes.
+    """
+
+    def __init__(
+        self,
+        content: str,
+        parsed_json: Optional[dict],
+        prompt_tokens: int,
+        completion_tokens: int,
+        model: str,
+        citations: list[str] = None,
+    ):
+        self.content = content
+        self.parsed_json = parsed_json
+        self.prompt_tokens = prompt_tokens
+        self.completion_tokens = completion_tokens
+        self.model = model
+        self.citations = citations or []  # Anthropic has no citations; kept for compat
+
+    @property
+    def ok(self) -> bool:
+        return self.parsed_json is not None
+
+
+class AnthropicClient:
+    """
+    Anthropic Claude API client.
+
+    Drop-in replacement for PerplexityClient.
+    All substantive calls use .messages.stream() so the SDK never
+    enforces the 10-minute non-streaming timeout.
+
+    Usage:
+        client = AnthropicClient(api_key="sk-ant-...")
+        resp = client.chat_structured(
+            system_prompt="You are a deck builder.",
+            user_prompt="Build a deck for...",
+            json_schema=deck_schema,
+            schema_name="DeckList",
+        )
+        if resp.ok:
+            deck = resp.parsed_json
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = CLAUDE_OPUS,
+        timeout: int = 120,
+        base_url: str = None,
+    ):
+        if not api_key:
+            raise ValueError("Anthropic API key is required")
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self._client = anthropic.Anthropic(
+            api_key=api_key,
+            base_url=base_url,
+            timeout=float(timeout),
+        )
+        self._async_client = anthropic.AsyncAnthropic(
+            api_key=api_key,
+            base_url=base_url,
+            timeout=float(timeout),
+        )
+
+    def chat_structured(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        json_schema: dict = None,
+        schema_name: str = "response",
+        model: str = None,
+        temperature: float = 0.2,
+        max_tokens: int = 8192,
+    ) -> AnthropicResponse:
+        """
+        Send a chat request expecting JSON output.
+        Uses streaming to avoid the Anthropic SDK 10-minute timeout restriction.
+        """
+        use_model = model or self.model
+        effective_system = self._build_system(system_prompt, json_schema, schema_name)
+
+        try:
+            with self._client.messages.stream(
+                model=use_model,
+                system=effective_system,
+                messages=[{"role": "user", "content": user_prompt}],
+                temperature=temperature,
+                max_tokens=max_tokens,
+            ) as stream:
+                content = stream.get_final_text()
+                final_msg = stream.get_final_message()
+            return self._parse_streamed(content, final_msg, use_model)
+        except Exception as e:
+            logger.error("[anthropic] chat_structured failed: %s", e)
+            raise
+
+    def chat_plain(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        model: str = None,
+        temperature: float = 0.2,
+        max_tokens: int = 4096,
+    ) -> AnthropicResponse:
+        """
+        Send a plain chat request without strict structured output.
+        Uses streaming to avoid the Anthropic SDK 10-minute timeout restriction.
+        """
+        use_model = model or self.model
+
+        try:
+            with self._client.messages.stream(
+                model=use_model,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_prompt}],
+                temperature=temperature,
+                max_tokens=max_tokens,
+            ) as stream:
+                content = stream.get_final_text()
+                final_msg = stream.get_final_message()
+            return self._parse_streamed(content, final_msg, use_model)
+        except Exception as e:
+            logger.error("[anthropic] chat_plain failed: %s", e)
+            raise
+
+    async def achat_structured(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        json_schema: dict = None,
+        schema_name: str = "response",
+        model: str = None,
+        temperature: float = 0.2,
+        max_tokens: int = 8192,
+    ) -> AnthropicResponse:
+        """Native async structured chat using streaming."""
+        use_model = model or self.model
+        effective_system = self._build_system(system_prompt, json_schema, schema_name)
+
+        try:
+            async with self._async_client.messages.stream(
+                model=use_model,
+                system=effective_system,
+                messages=[{"role": "user", "content": user_prompt}],
+                temperature=temperature,
+                max_tokens=max_tokens,
+            ) as stream:
+                content = await stream.get_final_text()
+                final_msg = await stream.get_final_message()
+            return self._parse_streamed(content, final_msg, use_model)
+        except Exception as e:
+            logger.error("[anthropic] achat_structured failed: %s", e)
+            raise
+
+    async def achat_plain(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        model: str = None,
+        temperature: float = 0.2,
+        max_tokens: int = 4096,
+    ) -> AnthropicResponse:
+        """Native async plain chat using streaming."""
+        use_model = model or self.model
+
+        try:
+            async with self._async_client.messages.stream(
+                model=use_model,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_prompt}],
+                temperature=temperature,
+                max_tokens=max_tokens,
+            ) as stream:
+                content = await stream.get_final_text()
+                final_msg = await stream.get_final_message()
+            return self._parse_streamed(content, final_msg, use_model)
+        except Exception as e:
+            logger.error("[anthropic] achat_plain failed: %s", e)
+            raise
+
+    def check_status(self) -> dict:
+        """
+        Quick health check — tiny max_tokens=10 call, safe to use non-streaming
+        since it will never approach the 10-minute threshold.
+        """
+        try:
+            response = self._client.messages.create(
+                model=self.model,
+                system="Reply with the word OK.",
+                messages=[{"role": "user", "content": "Status check."}],
+                max_tokens=10,
+            )
+            content = response.content[0].text if response.content else ""
+            return {
+                "connected": True,
+                "model": self.model,
+                "response": content,
+            }
+        except Exception as e:
+            return {
+                "connected": False,
+                "model": self.model,
+                "error": str(e),
+            }
+
+    # ── Internal helpers ──────────────────────────────────────────
+
+    @staticmethod
+    def _build_system(system_prompt: str, json_schema: dict, schema_name: str) -> str:
+        """Append JSON schema hint to system prompt when a schema is provided."""
+        if not json_schema:
+            return system_prompt
+        schema_str = json.dumps(json_schema, indent=2)
+        return (
+            f"{system_prompt}\n\n"
+            f"You MUST respond with valid JSON only — no markdown fences, no prose.\n"
+            f"The response must conform to the following JSON schema ({schema_name}):\n"
+            f"{schema_str}"
+        )
+
+    def _parse_streamed(self, content: str, final_msg, use_model: str) -> AnthropicResponse:
+        """Build an AnthropicResponse from streamed text + final message metadata."""
+        usage = getattr(final_msg, "usage", None)
+        prompt_tokens = usage.input_tokens if usage else 0
+        completion_tokens = usage.output_tokens if usage else 0
+
+        parsed = None
+        try:
+            parsed = json.loads(content)
+        except (json.JSONDecodeError, TypeError):
+            parsed = self._extract_json(content)
+
+        logger.info(
+            "[anthropic] model=%s tokens=(%d in, %d out) parsed=%s",
+            use_model,
+            prompt_tokens,
+            completion_tokens,
+            "yes" if parsed else "no",
+        )
+
+        return AnthropicResponse(
+            content=content,
+            parsed_json=parsed,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            model=use_model,
+            citations=[],
+        )
+
+    @staticmethod
+    def _extract_json(text: str) -> Optional[dict]:
+        """Best-effort JSON extraction from possibly noisy text."""
+        try:
+            return json.loads(text.strip())
+        except (json.JSONDecodeError, TypeError):
+            pass
+
+        # Markdown fences
+        fence_match = re.search(r'```(?:json)?\s*([\s\S]*?)```', text)
+        if fence_match:
+            try:
+                return json.loads(fence_match.group(1).strip())
+            except (json.JSONDecodeError, TypeError):
+                pass
+
+        # Brace counting
+        start = text.find('{')
+        if start == -1:
+            return None
+        depth = 0
+        in_string = False
+        escape_next = False
+        for i in range(start, len(text)):
+            ch = text[i]
+            if escape_next:
+                escape_next = False
+                continue
+            if ch == '\\' and in_string:
+                escape_next = True
+                continue
+            if ch == '"':
+                in_string = not in_string
+                continue
+            if in_string:
+                continue
+            if ch == '{':
+                depth += 1
+            elif ch == '}':
+                depth -= 1
+                if depth == 0:
+                    try:
+                        return json.loads(text[start:i + 1])
+                    except (json.JSONDecodeError, TypeError):
+                        return None
+
+        # Truncated JSON — attempt repair
+        return AnthropicClient._repair_truncated_json(text[start:])
+
+    @staticmethod
+    def _repair_truncated_json(text: str) -> Optional[dict]:
+        """
+        Attempt to repair truncated JSON from max_tokens cutoff.
+        Strategy: find the last complete nested object, then close
+        all open brackets/braces.
+        """
+        logger.info("Attempting truncated JSON repair (%d chars)...", len(text))
+        last_good = -1
+        depth = 0
+        in_string = False
+        escape_next = False
+        for i in range(len(text)):
+            ch = text[i]
+            if escape_next:
+                escape_next = False
+                continue
+            if ch == '\\' and in_string:
+                escape_next = True
+                continue
+            if ch == '"':
+                in_string = not in_string
+                continue
+            if in_string:
+                continue
+            if ch == '{':
+                depth += 1
+            elif ch == '}':
+                depth -= 1
+                if depth >= 1:
+                    last_good = i
+
+        if last_good == -1:
+            return None
+
+        truncated = text[:last_good + 1]
+        close_depth = 0
+        in_arr = 0
+        in_str = False
+        esc = False
+        for ch in truncated:
+            if esc:
+                esc = False
+                continue
+            if ch == '\\' and in_str:
+                esc = True
+                continue
+            if ch == '"':
+                in_str = not in_str
+                continue
+            if in_str:
+                continue
+            if ch == '{':
+                close_depth += 1
+            elif ch == '}':
+                close_depth -= 1
+            elif ch == '[':
+                in_arr += 1
+            elif ch == ']':
+                in_arr -= 1
+
+        suffix = ']' * in_arr + '}' * close_depth
+        repaired = truncated + suffix
+        try:
+            result = json.loads(repaired)
+            card_count = len(result.get('cards', []))
+            logger.info("JSON repair succeeded: recovered %d cards", card_count)
+            return result
+        except (json.JSONDecodeError, TypeError) as e:
+            logger.warning("JSON repair failed: %s", e)
+            return None

coach/clients/perplexity_client.py

@@ -56,15 +56,15 @@ class PerplexityClient:
     """
 
     def __init__(self, api_key: str, model: str = SONAR,
-                 timeout: int = 120):
-        if not api_key:
-            raise ValueError("Perplexity API key is required")
+                                     timeout: int = 120, base_url: str = None):
+        if not api_key and not base_url:
+            raise ValueError("Perplexity API key is required (or provide base_url for local)")
         self.api_key = api_key
         self.model = model
         self.timeout = timeout
         self._client = OpenAI(
             api_key=api_key,
-            base_url="https://api.perplexity.ai",
+            base_url=base_url or "https://api.perplexity.ai",
             timeout=timeout,
         )
 
@@ -72,26 +72,30 @@ class PerplexityClient:
         self,
         system_prompt: str,
         user_prompt: str,
-        json_schema: dict,
+        json_schema: dict = None,
         schema_name: str = "response",
         model: str = None,
         temperature: float = 0.2,
         max_tokens: int = 8192,
     ) -> PerplexityResponse:
         """
-        Send a chat request with structured JSON output.
+        Send a chat request with JSON output.
+
+        Uses        Sonar models do not support the json_schema response_format.
+        Structured output enforcement relies on the system prompt
+        instructing the model to return JSON matching the expected shape.
 
         Args:
-            system_prompt: System message
+            system_prompt: System message (should instruct JSON output)
             user_prompt: User message
-            json_schema: JSON Schema dict for response_format
-            schema_name: Name for the schema (used in response_format)
+            json_schema: (ignored) Kept for call-site compatibility
+            schema_name: (ignored) Kept for call-site compatibility
             model: Override model (sonar, sonar-pro)
-            temperature: Sampling temperature (0.0–1.0)
+            temperature: Sampling temperature (0.0-1.0)
             max_tokens: Max output tokens
 
         Returns:
-            PerplexityResponse with parsed_json guaranteed by schema
+            PerplexityResponse with parsed_json from the model's output
         """
         use_model = model or self.model
 
@@ -106,14 +110,7 @@ class PerplexityClient:
                 messages=messages,
                 temperature=temperature,
                 max_tokens=max_tokens,
-                response_format={
-                    "type": "json_schema",
-                    "json_schema": {
-                        "name": schema_name,
-                        "schema": json_schema,
-                    },
-                },
-            )
+                 )
 
             choice = response.choices[0]
             content = choice.message.content or ""

coach/coach_service.py

@@ -5,10 +5,11 @@ Main orchestrator: loads deck reports, identifies underperformers,
 finds replacement candidates via embeddings, builds prompts,
 calls the LLM, parses responses, and persists coaching sessions.
 """
-
 import json
 import logging
 import uuid
+import os
+import re
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Optional, List, Dict
@@ -16,18 +17,35 @@ from typing import Optional, List, Dict
 from .config import (
     DECK_REPORTS_DIR, COACH_SESSIONS_DIR,
     MAX_UNDERPERFORMERS, MAX_CANDIDATES_PER_UNDERPERFORMER,
-    UNDERPERFORMER_IMPACT_THRESHOLD, ensure_dirs,
+    COACH_PROVIDER, ANTHROPIC_API_KEY, ANTHROPIC_MODEL,
+    UNDERPERFORMER_IMPACT_THRESHOLD, DEFAULT_MAX_TOKENS, ensure_dirs,
 )
 from .models import (
     DeckReport, CoachGoals, CoachSession,
     SuggestedCut, SuggestedAdd, CoachStatus,
+    UpgradePriorityItem, CommanderDependency, MulliganAnalysis,
+    RemovalCoverage, RampQuality, DrawEngineProfile,
+    WinCondition, AntiSynergyFlag,
+    PodPresence, TempoAssessment, MetaMatchup,
 )
-from .llm_client import LMStudioClient
+from .llm_client import LLMClient
 from .embeddings import MTGEmbeddingIndex
 from .prompt_template import build_system_prompt, build_user_prompt
 
 logger = logging.getLogger("coach.service")
 
+# SQLite persistence for coach sessions
+try:
+    from services.database import (
+        save_coach_session as _db_save_session,
+        load_coach_session as _db_load_session,
+        list_coach_sessions as _db_list_sessions,
+        delete_coach_session as _db_delete_session,
+    )
+    _DB_AVAILABLE = True
+except ImportError:
+    _DB_AVAILABLE = False
+
 
 class CoachService:
     """
@@ -36,33 +54,28 @@ class CoachService:
     Usage:
         embeddings = MTGEmbeddingIndex()
         embeddings.load()
-        llm = LMStudioClient()
+        llm = LLMClient()
         coach = CoachService(llm, embeddings)
         session = await coach.run_coaching_session("edgar-markov")
     """
 
-    def __init__(self, llm: LMStudioClient, embeddings: MTGEmbeddingIndex):
+    def __init__(self, llm: LLMClient, embeddings: MTGEmbeddingIndex):
         self.llm = llm
         self.embeddings = embeddings
         ensure_dirs()
 
-    # ── Deck Report I/O ────────────────────────────────────────
-
+    # ── Deck Report I/O ──────────────────────
     def load_deck_report(self, deck_id: str) -> Optional[DeckReport]:
         """Load a deck report from disk."""
-        # Try exact filename first, then case-insensitive search
         report_path = DECK_REPORTS_DIR / f"{deck_id}.json"
         if not report_path.exists():
-            # Try case-insensitive match
             for f in DECK_REPORTS_DIR.glob("*.json"):
-                if f.stem.lower() == deck_id.lower():
+                if re.sub(r'[^a-z0-9]', '', f.stem.lower()) == re.sub(r'[^a-z0-9]', '', deck_id.lower()):
                     report_path = f
                     break
-
         if not report_path.exists():
             logger.warning("Deck report not found: %s", deck_id)
             return None
-
         try:
             with open(report_path, "r") as f:
                 data = json.load(f)
@@ -77,18 +90,59 @@ class CoachService:
             return []
         return [f.stem for f in DECK_REPORTS_DIR.glob("*.json")]
 
-    # ── Coach Session I/O ──────────────────────────────────────
-
+    # ── Coach Session I/O ──────────────────
     def save_session(self, session: CoachSession) -> Path:
-        """Persist a coaching session to disk."""
+        """Persist a coaching session to disk and SQLite."""
         COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
         path = COACH_SESSIONS_DIR / f"{session.sessionId}.json"
         with open(path, "w") as f:
             f.write(session.model_dump_json(indent=2))
+        if _DB_AVAILABLE:
+            try:
+                _db_save_session({
+                    "session_id": session.sessionId,
+                    "deck_id": session.deckId,
+                    "timestamp": session.timestamp,
+                    "model_used": session.modelUsed or "",
+                    "prompt_tokens": session.promptTokens or 0,
+                    "completion_tokens": session.completionTokens or 0,
+                    "summary": session.summary or "",
+                    "goals_json": json.dumps(session.goals.model_dump() if session.goals else {}),
+                    "cuts_json": json.dumps([c.model_dump() for c in session.suggestedCuts]),
+                    "adds_json": json.dumps([a.model_dump() for a in session.suggestedAdds]),
+                    "heuristic_hints_json": json.dumps(session.heuristicHints or []),
+                    "mana_base_advice": session.manaBaseAdvice or "",
+                    "raw_text": session.rawTextExplanation or "",
+                })
+                logger.info("Coach session persisted to SQLite: %s", session.sessionId)
+            except Exception as e:
+                logger.warning("Failed to persist session to SQLite: %s", e)
         return path
 
     def load_session(self, session_id: str) -> Optional[CoachSession]:
-        """Load a coaching session from disk."""
+        """Load a coaching session from SQLite, falling back to disk."""
+        if _DB_AVAILABLE:
+            try:
+                row = _db_load_session(session_id)
+                if row:
+                    data = dict(row)
+                    return CoachSession(
+                        sessionId=data["session_id"],
+                        deckId=data["deck_id"],
+                        timestamp=data["timestamp"],
+                        modelUsed=data.get("model_used", ""),
+                        promptTokens=data.get("prompt_tokens", 0),
+                        completionTokens=data.get("completion_tokens", 0),
+                        summary=data.get("summary", ""),
+                        goals=json.loads(data["goals_json"]) if data.get("goals_json") and data["goals_json"] != "{}" else None,
+                        suggestedCuts=[SuggestedCut(**c) for c in json.loads(data.get("cuts_json", "[]"))],
+                        suggestedAdds=[SuggestedAdd(**a) for a in json.loads(data.get("adds_json", "[]"))],
+                        heuristicHints=json.loads(data.get("heuristic_hints_json", "[]")),
+                        manaBaseAdvice=data.get("mana_base_advice", ""),
+                        rawTextExplanation=data.get("raw_text", ""),
+                    )
+            except Exception as e:
+                logger.warning("SQLite load failed for %s: %s", session_id, e)
         path = COACH_SESSIONS_DIR / f"{session_id}.json"
         if not path.exists():
             return None
@@ -101,7 +155,14 @@ class CoachService:
             return None
 
     def list_sessions(self, deck_id: str = None) -> List[dict]:
-        """List all coaching sessions, optionally filtered by deck."""
+        """List coaching sessions from SQLite, falling back to disk."""
+        if _DB_AVAILABLE:
+            try:
+                rows = _db_list_sessions(deck_id)
+                if rows is not None:
+                    return rows
+            except Exception as e:
+                logger.warning("SQLite list_sessions failed: %s", e)
         if not COACH_SESSIONS_DIR.exists():
             return []
         sessions = []
@@ -123,23 +184,23 @@ class CoachService:
                 continue
         return sessions
 
-    # ── Main Coaching Pipeline ─────────────────────────────────
-
-    async def run_coaching_session(self, deck_id: str,
-                                    goals: Optional[CoachGoals] = None,
-                                    fallback_report: Optional[DeckReport] = None,
-                                    ) -> CoachSession:
+    # ── Main Coaching Pipeline ───────────────────────
+    async def run_coaching_session(
+        self,
+        deck_id: str,
+        goals: Optional[CoachGoals] = None,
+        fallback_report: Optional[DeckReport] = None,
+    ) -> CoachSession:
         """
         Full coaching pipeline:
         1. Load deck report (or use fallback from DB card list)
         2. Identify underperformers
         3. Find replacement candidates via embeddings
         4. Build prompt
-        5. Call LLM
+        5. Call LLM (streaming for Anthropic to avoid 10-min SDK timeout)
         6. Parse response
         7. Persist session
         """
-        # 0. Auto-load embeddings if not loaded
         if not self.embeddings.loaded:
             logger.info("Embeddings not loaded — attempting auto-load...")
             try:
@@ -151,7 +212,6 @@ class CoachService:
             except Exception as e:
                 logger.warning("Embeddings auto-load failed: %s", e)
 
-        # 1. Load deck report — try simulation report first, then fallback
         report = self.load_deck_report(deck_id)
         if report is None and fallback_report is not None:
             report = fallback_report
@@ -159,25 +219,17 @@ class CoachService:
         if report is None:
             raise ValueError(f"Deck report not found for: {deck_id}")
 
-        # 2. Get deck card names (for exclusion from candidates)
         deck_card_names = [c.name for c in report.cards]
-
-        # 3. Find replacement candidates for underperformers
         candidates: Dict[str, List[dict]] = {}
-
         underperformers = report.underperformers[:MAX_UNDERPERFORMERS]
         if not underperformers:
-            # If no explicit underperformers, pick lowest impact cards
             sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
             underperformers = [
                 c.name for c in sorted_cards[:MAX_UNDERPERFORMERS]
                 if c.impactScore < UNDERPERFORMER_IMPACT_THRESHOLD
             ]
-
-        # If still no underperformers and we have cards, pick bottom N by impact
         if not underperformers and report.cards:
             sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
-            # Take the bottom 5 non-land cards as candidates for cuts
             underperformers = [
                 c.name for c in sorted_cards[:5]
                 if not any(t in (c.tags or []) for t in [])
@@ -196,17 +248,81 @@ class CoachService:
         else:
             logger.warning("Embeddings not loaded — skipping candidate search")
 
-        # 4. Build prompts
+        # Phase 4a: behavior-aware RAG — primary + replacement-effect secondary query
+        rag_cards: List[dict] = []
+        try:
+            from services.rag_store import query_cards
+            # Primary: commander synergy context
+            base_query = f"{report.commander} {' '.join(report.colorIdentity)} commander deck"
+            rag_cards = query_cards(base_query, n_results=10, color_identity=report.colorIdentity)
+            # Secondary: replacement effects / graveyard hate / protection
+            if len(rag_cards) < 15:
+                repl_cards = query_cards(
+                    f"replacement effect graveyard {' '.join(report.colorIdentity)}",
+                    n_results=5,
+                    color_identity=report.colorIdentity,
+                    require_replacement=True,
+                )
+                # Deduplicate by name before merging
+                existing_names = {c["name"] for c in rag_cards}
+                rag_cards += [c for c in repl_cards if c["name"] not in existing_names]
+            logger.info("RAG returned %d cards for '%s'", len(rag_cards), deck_id)
+        except Exception as e:
+            logger.warning("RAG query failed (non-fatal): %s", e)
+
+        # Phase 4b: rules context for grounding the coach prompt
+        rules_context: List[dict] = []
+        try:
+            from services.rag_store import query_rules
+            rules_context = query_rules(
+                f"Commander rules {report.commander} {' '.join(report.colorIdentity)}",
+                n_results=3,
+            )
+        except Exception:
+            pass
+
         system_prompt = build_system_prompt(report, goals)
-        user_prompt = build_user_prompt(report, candidates)
+        user_prompt = build_user_prompt(report, candidates, rag_cards=rag_cards, rules_context=rules_context)
+        logger.info("Calling LLM for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
+        if COACH_PROVIDER == "anthropic":
+            import anthropic as _anthropic
+            anthropic_key = ANTHROPIC_API_KEY
+            if not anthropic_key:
+                raise ConnectionError("Anthropic API key not configured. Set ANTHROPIC_API_KEY env var.")
+            aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
+            async with aclient.messages.stream(
+                model=ANTHROPIC_MODEL,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_prompt}],
+                max_tokens=DEFAULT_MAX_TOKENS,
+                temperature=0.7,
+            ) as stream:
+                content = await stream.get_final_text()
+                final_msg = await stream.get_final_message()
+                usage = getattr(final_msg, "usage", None)
+            from types import SimpleNamespace
+            llm_response = SimpleNamespace(
+                content=content,
+                model=final_msg.model,
+                prompt_tokens=usage.input_tokens if usage else 0,
+                completion_tokens=usage.output_tokens if usage else 0,
+                parsed_json=None,
+            )
+            try:
+                cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
+                cleaned = re.sub(r'\s*```$', '', cleaned.strip())
+                llm_response.parsed_json = json.loads(cleaned)
+            except (json.JSONDecodeError, ValueError):
+                match = re.search(r'\{[\s\S]*\}', content)
+                if match:
+                    try:
+                        llm_response.parsed_json = json.loads(match.group(0))
+                    except json.JSONDecodeError:
+                        pass
+        else:
+            llm_response = await self.llm.chat(system_prompt, user_prompt)
 
-        # 5. Call LLM
-        logger.info("Calling LLM for deck: %s", deck_id)
-        llm_response = await self.llm.chat(system_prompt, user_prompt)
-
-        # 6. Parse response into CoachSession
         session_id = f"sess-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
-
         session = CoachSession(
             sessionId=session_id,
             deckId=deck_id,
@@ -216,31 +332,109 @@ class CoachService:
             completionTokens=llm_response.completion_tokens,
             goals=goals,
         )
-
         if llm_response.parsed_json:
             self._populate_session_from_json(session, llm_response.parsed_json)
         else:
-            # Fallback: store raw text
             session.rawTextExplanation = llm_response.content
             session.summary = "LLM returned non-JSON response. See raw explanation."
-
-        # 7. Persist
         self.save_session(session)
         logger.info("Coaching session saved: %s", session_id)
-
         return session
 
+    # ── Quick Digest ──────────────────────────────
+    async def run_quick_digest(
+        self,
+        deck_id: str,
+        goals: Optional[CoachGoals] = None,
+        fallback_report: Optional[DeckReport] = None,
+    ) -> CoachSession:
+        """Fast ~400-600 token call: summary + top 3 upgradePriority + commanderDependency."""
+        report = self.load_deck_report(deck_id)
+        if report is None and fallback_report is not None:
+            report = fallback_report
+        if report is None:
+            raise ValueError(f"Deck report not found for: {deck_id}")
+
+        # Phase 4a: light RAG query for quick digest
+        rag_cards: List[dict] = []
+        try:
+            from services.rag_store import query_cards
+            query_text = f"{report.commander} {' '.join(report.colorIdentity)}"
+            rag_cards = query_cards(query_text, n_results=8, color_identity=report.colorIdentity)
+        except Exception:
+            pass
+
+        from .prompt_template import build_quick_system_prompt, build_user_prompt
+        system_prompt = build_quick_system_prompt(report, goals)
+        user_prompt = build_user_prompt(report, {}, rag_cards=rag_cards)  # no candidates for speed
+        logger.info("Quick digest for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
+
+        if COACH_PROVIDER == "anthropic":
+            import anthropic as _anthropic
+            anthropic_key = ANTHROPIC_API_KEY
+            if not anthropic_key:
+                raise ConnectionError("Anthropic API key not configured.")
+            aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
+            async with aclient.messages.stream(
+                model=ANTHROPIC_MODEL,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_prompt}],
+                max_tokens=1024,
+                temperature=0.5,
+            ) as stream:
+                content = await stream.get_final_text()
+                final_msg = await stream.get_final_message()
+                usage = getattr(final_msg, "usage", None)
+            from types import SimpleNamespace
+            llm_response = SimpleNamespace(
+                content=content,
+                model=final_msg.model,
+                prompt_tokens=usage.input_tokens if usage else 0,
+                completion_tokens=usage.output_tokens if usage else 0,
+                parsed_json=None,
+            )
+            try:
+                cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
+                cleaned = re.sub(r'\s*```$', '', cleaned.strip())
+                llm_response.parsed_json = json.loads(cleaned)
+            except (json.JSONDecodeError, ValueError):
+                match = re.search(r'\{[\s\S]*\}', content)
+                if match:
+                    try:
+                        llm_response.parsed_json = json.loads(match.group(0))
+                    except json.JSONDecodeError:
+                        pass
+        else:
+            llm_response = await self.llm.chat(system_prompt, user_prompt)
+
+        session_id = f"quick-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
+        session = CoachSession(
+            sessionId=session_id,
+            deckId=deck_id,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+            modelUsed=llm_response.model,
+            promptTokens=llm_response.prompt_tokens,
+            completionTokens=llm_response.completion_tokens,
+            goals=goals,
+            isQuickDigest=True,
+        )
+        if llm_response.parsed_json:
+            self._populate_session_from_json(session, llm_response.parsed_json)
+        else:
+            session.rawTextExplanation = llm_response.content
+            session.summary = "Quick digest returned non-JSON. See raw explanation."
+        self.save_session(session)
+        return session
+
+    # ── JSON Parser ───────────────────────────────
     def _populate_session_from_json(self, session: CoachSession, data: dict):
         """Parse LLM JSON response into CoachSession fields."""
         session.summary = data.get("summary", "")
         session.rawTextExplanation = data.get("rawTextExplanation", "")
         session.manaBaseAdvice = data.get("manaBaseAdvice")
-
-        # Heuristic hints
         hints = data.get("heuristicHints", [])
         session.heuristicHints = hints if isinstance(hints, list) else [str(hints)]
 
-        # Suggested cuts
         for cut_data in data.get("suggestedCuts", []):
             if isinstance(cut_data, dict):
                 session.suggestedCuts.append(SuggestedCut(
@@ -248,8 +442,6 @@ class CoachService:
                     reason=cut_data.get("reason", ""),
                     replacementOptions=cut_data.get("replacementOptions", []),
                 ))
-
-        # Suggested adds
         for add_data in data.get("suggestedAdds", []):
             if isinstance(add_data, dict):
                 session.suggestedAdds.append(SuggestedAdd(
@@ -258,26 +450,112 @@ class CoachService:
                     reason=add_data.get("reason", ""),
                     synergyWith=add_data.get("synergyWith", []),
                 ))
+        for item in data.get("upgradePriority", []):
+            if isinstance(item, dict):
+                session.upgradePriority.append(UpgradePriorityItem(
+                    rank=item.get("rank", 0),
+                    cut=item.get("cut", ""),
+                    add=item.get("add", ""),
+                    reasoning=item.get("reasoning", ""),
+                    expectedImpact=item.get("expectedImpact", ""),
+                ))
+        cd = data.get("commanderDependency")
+        if isinstance(cd, dict):
+            session.commanderDependency = CommanderDependency(
+                score=cd.get("score", 5),
+                dependentCards=cd.get("dependentCards", []),
+                recoveryPlan=cd.get("recoveryPlan", ""),
+            )
+        ma = data.get("mulliganAnalysis")
+        if isinstance(ma, dict):
+            session.mulliganAnalysis = MulliganAnalysis(
+                estimatedKeepRate=ma.get("estimatedKeepRate", ""),
+                worstOffenders=ma.get("worstOffenders", []),
+                recommendation=ma.get("recommendation", ""),
+            )
+        rc = data.get("removalCoverage")
+        if isinstance(rc, dict):
+            session.removalCoverage = RemovalCoverage(**{
+                k: rc.get(k, "" if k != "gaps" else [])
+                for k in ["creatures", "artifacts", "enchantments", "planeswalkers", "lands", "massRemoval", "gaps"]
+            })
+        rq = data.get("rampQuality")
+        if isinstance(rq, dict):
+            session.rampQuality = RampQuality(**{
+                k: rq.get(k, "") for k in ["manaRocks", "landFetch", "manaDorks", "costReducers", "fragility", "canReachFourByTurnThree"]
+            })
+        dep = data.get("drawEngineProfile")
+        if isinstance(dep, dict):
+            session.drawEngineProfile = DrawEngineProfile(
+                burstDraw=dep.get("burstDraw", []),
+                repeatableEngines=dep.get("repeatableEngines", []),
+                assessment=dep.get("assessment", ""),
+                sustainability=dep.get("sustainability", ""),
+            )
+        for wc in data.get("winConditions", []):
+            if isinstance(wc, dict):
+                session.winConditions.append(WinCondition(
+                    name=wc.get("name", ""),
+                    cards=wc.get("cards", []),
+                    independence=wc.get("independence", ""),
+                    description=wc.get("description", ""),
+                ))
+        for af in data.get("antiSynergyFlags", []):
+            if isinstance(af, dict):
+                session.antiSynergyFlags.append(AntiSynergyFlag(
+                    cards=af.get("cards", []),
+                    conflict=af.get("conflict", ""),
+                    severity=af.get("severity", ""),
+                ))
+        pp = data.get("podPresence")
+        if isinstance(pp, dict):
+            session.podPresence = PodPresence(
+                threatLevel=pp.get("threatLevel", ""),
+                politicalTools=pp.get("politicalTools", []),
+                adversarialRating=pp.get("adversarialRating", ""),
+                recommendation=pp.get("recommendation", ""),
+            )
+        ta = data.get("tempoAssessment")
+        if isinstance(ta, dict):
+            session.tempoAssessment = TempoAssessment(
+                peakTurn=ta.get("peakTurn", 0),
+                profile=ta.get("profile", ""),
+                survivalWindow=ta.get("survivalWindow", ""),
+                assessment=ta.get("assessment", ""),
+            )
+        for mm in data.get("metaMatchups", []):
+            if isinstance(mm, dict):
+                session.metaMatchups.append(MetaMatchup(
+                    archetype=mm.get("archetype", ""),
+                    assessment=mm.get("assessment", ""),
+                    keyThreats=mm.get("keyThreats", []),
+                    keyAnswers=mm.get("keyAnswers", []),
+                    overallRating=mm.get("overallRating", ""),
+                ))
 
-    # ── Status Check ───────────────────────────────────────────
-
+    # ── Status Check ──────────────────────────────
     def get_status(self) -> CoachStatus:
         """Check the health of all coach subsystems."""
         status = CoachStatus()
-
-        # Check LLM
-        llm_status = self.llm.check_connection()
-        status.llmConnected = llm_status.get("connected", False)
-        status.llmModel = llm_status.get("active_model")
-        status.llmModels = llm_status.get("models", [])
-        if not status.llmConnected:
-            status.error = llm_status.get("error", "LM Studio not reachable")
-
-        # Check embeddings
+        if COACH_PROVIDER == "anthropic":
+            anthropic_key = ANTHROPIC_API_KEY
+            if anthropic_key:
+                status.llmConnected = True
+                status.llmModel = f"Claude Opus ({ANTHROPIC_MODEL})"
+                status.llmModels = [ANTHROPIC_MODEL]
+            else:
+                status.llmConnected = False
+                status.llmModel = None
+                status.llmModels = []
+                status.error = "Anthropic API key not configured. Set ANTHROPIC_API_KEY env var."
+        else:
+            llm_status = self.llm.check_connection()
+            status.llmConnected = llm_status.get("connected", False)
+            status.llmModel = llm_status.get("active_model")
+            status.llmModels = llm_status.get("models", [])
+            if not status.llmConnected:
+                status.error = llm_status.get("error", "Local LLM not reachable")
         status.embeddingsLoaded = self.embeddings.loaded
         status.embeddingCards = self.embeddings.card_count
-
-        # Check deck reports
         status.deckReportsAvailable = len(self.list_deck_reports())
-
         return status

coach/config.py

@@ -1,63 +1,67 @@
 """
 Commander AI Lab — Coach Configuration
-═══════════════════════════════════════
-Central config for LM Studio, embeddings, and coach service paths.
+────────────────────────────────────────
+Central config for Ollama, embeddings, and coach service paths.
 All paths are relative to the commander-ai-lab root directory.
 """
 
 import os
 from pathlib import Path
 
-# ── Base Paths ──────────────────────────────────────────────
-# Resolve relative to wherever the lab is installed
+# ── Base Paths ───────────────────────────────────────────────────────
 LAB_ROOT = Path(os.environ.get("COMMANDER_LAB_ROOT", Path(__file__).parent.parent))
 
-# ── LM Studio (OpenAI-compatible API) ──────────────────────
-LM_STUDIO_URL = os.environ.get("LM_STUDIO_URL", "http://192.168.0.122:1234/v1")
-LM_STUDIO_MODEL = os.environ.get("LM_STUDIO_MODEL", "local-model")
-LM_STUDIO_TIMEOUT = int(os.environ.get("LM_STUDIO_TIMEOUT", "120"))  # seconds
-LM_STUDIO_MAX_RETRIES = 3
+# ── Ollama (OpenAI-compatible API) ────────────────────────────
+LLM_URL = os.environ.get("LLM_URL", "http://localhost:11434/v1")
+LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-oss:20b")
+LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "360"))  # seconds
+LLM_MAX_RETRIES = 3
 
-# ── Embeddings ─────────────────────────────────────────────
+# ── Embeddings ───────────────────────────────────────────────────
 EMBEDDINGS_DIR = LAB_ROOT / "data"
 EMBEDDINGS_NPZ = EMBEDDINGS_DIR / "mtg-embeddings.npz"
 EMBEDDINGS_PARQUET = EMBEDDINGS_DIR / "mtg-embeddings.parquet"
 
-# ── Deck Reports ───────────────────────────────────────────
+# ── Deck Reports ────────────────────────────────────────────────
 DECK_REPORTS_DIR = LAB_ROOT / "deck-reports"
 COACH_SESSIONS_DIR = LAB_ROOT / "coach-sessions"
 
-# ── Prompt Limits ──────────────────────────────────────────
-MAX_PROMPT_TOKENS = 4096
-MAX_CANDIDATES_PER_UNDERPERFORMER = 10
-MAX_UNDERPERFORMERS = 8
+# ── Prompt Limits ───────────────────────────────────────────────
+MAX_PROMPT_TOKENS = 2048
+MAX_CANDIDATES_PER_UNDERPERFORMER = 8
+MAX_UNDERPERFORMERS =  8
 UNDERPERFORMER_IMPACT_THRESHOLD = -0.05  # impactScore below this = underperformer
 
-# ── LLM Generation Settings (Coach — LM Studio) ──────────────
+# ── LLM Generation Settings (Coach — Ollama fallback) ─────────────
 DEFAULT_TEMPERATURE = 0.7
-# DeepSeek-R1 uses <think> reasoning tokens before the JSON output,
-# so we need a generous limit to avoid truncation
-DEFAULT_MAX_TOKENS = 8192
+DEFAULT_MAX_TOKENS = 16384
 
-# ── Perplexity Deck Generation Settings ───────────────────────
-# Provider for deck generation: "perplexity" (V3) or "local" (V2 collection-based)
-DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "perplexity")
-# Model for deck generation (sonar = fast/$0.004, sonar-pro = deep/$0.04)
-DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "sonar")
+# ── Anthropic Claude Deck Generation Settings ──────────────────────
+# Provider for deck generation: "anthropic" (Claude) or "local" (Ollama)
+DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "anthropic")
+# Model used when DECK_GEN_PROVIDER="anthropic"
+DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "claude-opus-4-20250514")
+# Anthropic API key
+ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
+# Anthropic model for deck generation and coaching
+ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-20250514")
+# Coach chat provider: "anthropic" (Claude API) or "local" (Ollama)
+COACH_PROVIDER = os.environ.get("COACH_PROVIDER", "anthropic")
+DECK_GEN_BASE_URL = os.environ.get("DECK_GEN_BASE_URL", "http://localhost:11434/v1")
 DECK_GEN_TEMPERATURE = float(os.environ.get("DECK_GEN_TEMPERATURE", "0.2"))
 DECK_GEN_MAX_TOKENS = int(os.environ.get("DECK_GEN_MAX_TOKENS", "16384"))
 
-# ── Smart Substitution Settings ───────────────────────────────
-# Minimum embedding similarity to accept a substitute without Perplexity fallback
+# ── Smart Substitution Settings ───────────────────────────────────
+# Minimum embedding similarity to accept a substitute without Anthropic fallback
 SUBSTITUTION_MIN_SIMILARITY = float(os.environ.get("SUBSTITUTION_MIN_SIMILARITY", "0.75"))
 # Maximum number of alternatives to suggest per missing card
 SUBSTITUTION_MAX_ALTERNATIVES = int(os.environ.get("SUBSTITUTION_MAX_ALTERNATIVES", "5"))
-# Model for substitution fallback (sonar is fine — small focused queries)
-SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "sonar")
-# Enable Perplexity fallback for low-confidence embedding matches
-SUBSTITUTION_USE_PPLX_FALLBACK = os.environ.get("SUBSTITUTION_USE_PPLX_FALLBACK", "true").lower() == "true"
+# Model for substitution fallback (claude-haiku-4-5 is fast + cheap for focused queries)
+SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "claude-haiku-4-5")
+# Enable Anthropic fallback for low-confidence embedding matches
+SUBSTITUTION_USE_ANTHROPIC_FALLBACK = os.environ.get("SUBSTITUTION_USE_ANTHROPIC_FALLBACK", "true").lower() == "true"
 
-# ── Ensure directories exist ───────────────────────────────
+# ── Ensure directories exist ─────────────────────────────────────
 def ensure_dirs():
     """Create required directories if they don't exist."""
     EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)

coach/embeddings.py

@@ -9,6 +9,11 @@ Data source: https://huggingface.co/datasets/minimaxir/mtg-embeddings
 Format: Parquet with 768-dim float32 embeddings per card (~32K cards)
 Model: Alibaba-NLP/gte-modernbert-base
 
+Unknown cards (e.g. newly printed sets) are fetched from Scryfall,
+embedded on-the-fly using the same model, and persisted to a local
+custom cache (embeddings/mtg_embeddings_custom.npz) so they survive
+restarts without re-hitting Scryfall.
+
 Uses NumPy brute-force cosine similarity — fast enough for ~32K cards (<100ms).
 """
 
@@ -19,9 +24,11 @@ import os
 from pathlib import Path
 from typing import List, Optional, Tuple
 from urllib.request import urlopen, Request
+from urllib.parse import quote
 
 import numpy as np
 
+from services.card_text import build_card_text
 from .config import EMBEDDINGS_DIR, EMBEDDINGS_NPZ, EMBEDDINGS_PARQUET
 
 logger = logging.getLogger("coach.embeddings")
@@ -32,8 +39,16 @@ HF_PARQUET_URL = (
     "resolve/main/mtg_embeddings.parquet"
 )
 
+# ── Scryfall API ───────────────────────────────────────────
+SCRYFALL_NAMED_URL = "https://api.scryfall.com/cards/named?fuzzy={}"
+
+# ── Custom cache for on-the-fly embedded cards ─────────────
+CUSTOM_CACHE_PATH = EMBEDDINGS_DIR / "mtg_embeddings_custom.npz"
+
+# ── Embedding model (same as minimaxir/mtg-embeddings) ─────
+EMBEDDING_MODEL = "Alibaba-NLP/gte-modernbert-base"
+
 # ── Color identity parsing ─────────────────────────────────
-# Maps mana symbols like {W}, {U}, {B}, {R}, {G} to color letters
 MANA_SYMBOL_COLORS = {
     "W": "W", "U": "U", "B": "B", "R": "R", "G": "G"
 }
@@ -46,7 +61,6 @@ def _parse_color_identity(mana_cost: str) -> str:
     colors = set()
     for match in re.finditer(r'\{([WUBRG])\}', mana_cost or ""):
         colors.add(match.group(1))
-    # Sort in WUBRG order
     order = "WUBRG"
     return "".join(c for c in order if c in colors)
 
@@ -61,12 +75,11 @@ def _parse_mana_value(mana_cost: str) -> float:
         if symbol in MANA_SYMBOL_COLORS:
             total += 1.0
         elif symbol == "X":
-            pass  # X = 0 for CMC purposes
+            pass
         else:
             try:
                 total += float(symbol)
             except ValueError:
-                # Hybrid/phyrexian symbols count as 1
                 total += 1.0
     return total
 
@@ -75,7 +88,35 @@ def _is_color_subset(card_colors: str, deck_colors: List[str]) -> bool:
     """Check if card's color identity is within deck's color identity."""
     deck_set = set(deck_colors)
     card_set = set(card_colors)
-    return card_set.issubset(deck_set) or len(card_set) == 0  # colorless always allowed
+    return card_set.issubset(deck_set) or len(card_set) == 0
+
+
+def _fetch_scryfall_card(card_name: str) -> Optional[dict]:
+    """Fetch card data from Scryfall by fuzzy name match.
+
+    Returns a dict with keys: name, mana_cost, type_line, oracle_text,
+    color_identity. Returns None on any error.
+    """
+    url = SCRYFALL_NAMED_URL.format(quote(card_name))
+    try:
+        req = Request(url, headers={"User-Agent": "commander-ai-lab/1.0"})
+        with urlopen(req, timeout=10) as resp:
+            data = json.loads(resp.read().decode("utf-8"))
+        return {
+            "name": data.get("name", card_name),
+            "mana_cost": data.get("mana_cost", ""),
+            "type_line": data.get("type_line", ""),
+            "oracle_text": data.get("oracle_text", ""),
+            "color_identity": "".join(data.get("color_identity", [])),
+        }
+    except Exception as e:
+        logger.warning("Scryfall lookup failed for '%s': %s", card_name, e)
+        return None
+
+
+def _build_card_text(card: dict) -> str:
+    """Delegate to shared build_card_text() -- see services/card_text.py."""
+    return build_card_text(card)
 
 
 class CardMatch:
@@ -114,15 +155,17 @@ class MTGEmbeddingIndex:
     """
 
     def __init__(self):
-        self.names: np.ndarray = None       # (N,) string array
-        self.colors: np.ndarray = None      # (N,) string array (e.g., "WU", "BRG")
-        self.mana_values: np.ndarray = None # (N,) float array
-        self.types: np.ndarray = None       # (N,) string array
-        self.mana_costs: np.ndarray = None  # (N,) string array
-        self.texts: np.ndarray = None       # (N,) string array
-        self.vectors: np.ndarray = None     # (N, 768) float32, L2-normalized
+        self.names: np.ndarray = None
+        self.colors: np.ndarray = None
+        self.mana_values: np.ndarray = None
+        self.types: np.ndarray = None
+        self.mana_costs: np.ndarray = None
+        self.texts: np.ndarray = None
+        self.vectors: np.ndarray = None
         self._name_to_idx: dict = {}
         self._loaded = False
+        self._sentence_model = None   # lazy-loaded only when needed
+        self._custom_dirty = False    # tracks whether custom cache needs saving
 
     @property
     def loaded(self) -> bool:
@@ -152,10 +195,7 @@ class MTGEmbeddingIndex:
         return EMBEDDINGS_PARQUET
 
     def convert_parquet_to_npz(self) -> Path:
-        """
-        Convert the Parquet file to a compact NPZ for fast loading.
-        Requires pandas and pyarrow.
-        """
+        """Convert the Parquet file to a compact NPZ for fast loading."""
         try:
             import pandas as pd
         except ImportError:
@@ -169,14 +209,11 @@ class MTGEmbeddingIndex:
         types = df["type"].fillna("").values.astype(str)
         texts = df["text"].fillna("").values.astype(str)
 
-        # Parse color identities from mana costs
         colors = np.array([_parse_color_identity(mc) for mc in mana_costs])
         mana_values = np.array([_parse_mana_value(mc) for mc in mana_costs], dtype=np.float32)
 
-        # Extract embedding vectors
         embeddings = np.vstack(df["embedding"].to_numpy()).astype(np.float32)
 
-        # L2 normalize for cosine similarity via dot product
         norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
         norms[norms == 0] = 1.0
         embeddings = embeddings / norms
@@ -198,25 +235,27 @@ class MTGEmbeddingIndex:
         return EMBEDDINGS_NPZ
 
     def load(self, force_download: bool = False) -> bool:
-        """
-        Load embeddings from NPZ (fast) or fall back to downloading
+        """Load embeddings from NPZ (fast) or fall back to downloading
         and converting from Parquet.
         """
         if self._loaded and not force_download:
             return True
 
-        # Try NPZ first (fast path)
         if EMBEDDINGS_NPZ.exists() and not force_download:
-            return self._load_npz()
+            ok = self._load_npz()
+        else:
+            try:
+                self.download_parquet(force=force_download)
+                self.convert_parquet_to_npz()
+                ok = self._load_npz()
+            except Exception as e:
+                logger.error("Failed to load embeddings: %s", e)
+                return False
 
-        # Download and convert
-        try:
-            self.download_parquet(force=force_download)
-            self.convert_parquet_to_npz()
-            return self._load_npz()
-        except Exception as e:
-            logger.error("Failed to load embeddings: %s", e)
-            return False
+        if ok:
+            self._load_custom_cache()
+
+        return ok
 
     def _load_npz(self) -> bool:
         """Load from pre-built NPZ file."""
@@ -230,7 +269,6 @@ class MTGEmbeddingIndex:
             self.texts = data.get("texts", np.array([""] * len(self.names)))
             self.vectors = data["vectors"]
 
-            # Build name lookup
             self._name_to_idx = {
                 name.lower(): i for i, name in enumerate(self.names)
             }
@@ -241,12 +279,168 @@ class MTGEmbeddingIndex:
             logger.error("Failed to load NPZ: %s", e)
             return False
 
+    # ── Custom cache ──────────────────────────────────────────
+
+    def _load_custom_cache(self) -> None:
+        """Merge on-the-fly embedded cards from the custom cache into the index."""
+        if not CUSTOM_CACHE_PATH.exists():
+            return
+        try:
+            data = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
+            c_names = data["names"]
+            # Only add cards that aren't already in the main index
+            new_mask = np.array(
+                [n.lower() not in self._name_to_idx for n in c_names]
+            )
+            if not new_mask.any():
+                return
+            self._append_cards(
+                c_names[new_mask],
+                data["colors"][new_mask],
+                data["mana_values"][new_mask],
+                data["types"][new_mask],
+                data["mana_costs"][new_mask],
+                data["texts"][new_mask],
+                data["vectors"][new_mask],
+            )
+            logger.info("Loaded %d custom-cached card embeddings", new_mask.sum())
+        except Exception as e:
+            logger.warning("Failed to load custom embedding cache: %s", e)
+
+    def _save_custom_cache(self) -> None:
+        """Persist the custom cache NPZ (only cards not in the base dataset)."""
+        if not self._custom_dirty:
+            return
+        try:
+            EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
+            # Collect indices that correspond to custom-added cards
+            # We track these by loading the base count at load time — simpler:
+            # just re-save the full custom cache from CUSTOM_CACHE_PATH if it
+            # exists, merging with any new cards we've added this session.
+            existing_names: set = set()
+            existing: dict = {}
+            if CUSTOM_CACHE_PATH.exists():
+                try:
+                    d = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
+                    for n in d["names"]:
+                        existing_names.add(n.lower())
+                except Exception:
+                    pass
+
+            # Find names in our live index that aren't in the base NPZ
+            base_data = np.load(str(EMBEDDINGS_NPZ), allow_pickle=True)
+            base_names = set(n.lower() for n in base_data["names"])
+            custom_mask = np.array(
+                [n.lower() not in base_names for n in self.names]
+            )
+            if not custom_mask.any():
+                return
+
+            np.savez_compressed(
+                str(CUSTOM_CACHE_PATH),
+                names=self.names[custom_mask],
+                colors=self.colors[custom_mask],
+                mana_values=self.mana_values[custom_mask],
+                types=self.types[custom_mask],
+                mana_costs=self.mana_costs[custom_mask],
+                texts=self.texts[custom_mask],
+                vectors=self.vectors[custom_mask],
+            )
+            self._custom_dirty = False
+            logger.info("Saved custom embedding cache: %d cards", custom_mask.sum())
+        except Exception as e:
+            logger.warning("Failed to save custom embedding cache: %s", e)
+
+    def _append_cards(
+        self,
+        names: np.ndarray,
+        colors: np.ndarray,
+        mana_values: np.ndarray,
+        types: np.ndarray,
+        mana_costs: np.ndarray,
+        texts: np.ndarray,
+        vectors: np.ndarray,
+    ) -> None:
+        """Append new card arrays to the live in-memory index."""
+        start_idx = len(self.names)
+        self.names = np.concatenate([self.names, names])
+        self.colors = np.concatenate([self.colors, colors])
+        self.mana_values = np.concatenate([self.mana_values, mana_values])
+        self.types = np.concatenate([self.types, types])
+        self.mana_costs = np.concatenate([self.mana_costs, mana_costs])
+        self.texts = np.concatenate([self.texts, texts])
+        self.vectors = np.concatenate([self.vectors, vectors], axis=0)
+        for i, name in enumerate(names):
+            self._name_to_idx[name.lower()] = start_idx + i
+
+    # ── Sentence model (lazy) ─────────────────────────────────
+
+    def _get_sentence_model(self):
+        """Lazy-load the sentence-transformers model on first cache miss."""
+        if self._sentence_model is None:
+            try:
+                from sentence_transformers import SentenceTransformer
+                logger.info("Loading embedding model %s (first cache miss)...", EMBEDDING_MODEL)
+                self._sentence_model = SentenceTransformer(EMBEDDING_MODEL)
+                logger.info("Embedding model loaded.")
+            except Exception as e:
+                logger.error("Failed to load SentenceTransformer: %s", e)
+                return None
+        return self._sentence_model
+
+    # ── On-the-fly Scryfall fallback ──────────────────────────
+
+    def _fetch_and_embed_card(self, card_name: str) -> Optional[np.ndarray]:
+        """Fetch card from Scryfall, embed it, append to index, persist to cache.
+
+        Returns the L2-normalized embedding vector on success, None on failure.
+        """
+        logger.info("Fetching unknown card from Scryfall: '%s'", card_name)
+        card_data = _fetch_scryfall_card(card_name)
+        if card_data is None:
+            return None
+
+        model = self._get_sentence_model()
+        if model is None:
+            return None
+
+        text = _build_card_text(card_data)
+        vec = model.encode(text, normalize_embeddings=True).astype(np.float32)
+        # Ensure shape (1, 768)
+        vec = vec.reshape(1, -1)
+
+        resolved_name = card_data["name"]
+        mana_cost = card_data.get("mana_cost", "")
+        color_identity = card_data.get("color_identity", "") or _parse_color_identity(mana_cost)
+
+        self._append_cards(
+            names=np.array([resolved_name]),
+            colors=np.array([color_identity]),
+            mana_values=np.array([_parse_mana_value(mana_cost)], dtype=np.float32),
+            types=np.array([card_data.get("type_line", "")]),
+            mana_costs=np.array([mana_cost]),
+            texts=np.array([card_data.get("oracle_text", "")]),
+            vectors=vec,
+        )
+
+        self._custom_dirty = True
+        self._save_custom_cache()
+        logger.info("Embedded and cached new card: '%s'", resolved_name)
+        return self.vectors[self._name_to_idx[resolved_name.lower()]]
+
+    # ── Public API ────────────────────────────────────────────
+
     def get_card_vector(self, card_name: str) -> Optional[np.ndarray]:
-        """Get the embedding vector for a card by name (case-insensitive)."""
+        """Get the embedding vector for a card by name (case-insensitive).
+
+        On a cache miss, fetches from Scryfall and embeds on the fly.
+        """
         idx = self._name_to_idx.get(card_name.lower())
         if idx is not None:
             return self.vectors[idx]
-        return None
+
+        # Cache miss — try Scryfall fallback
+        return self._fetch_and_embed_card(card_name)
 
     def search_similar(self, query_card: str,
                        color_filter: List[str] = None,
@@ -274,38 +468,32 @@ class MTGEmbeddingIndex:
 
         query_vec = self.get_card_vector(query_card)
         if query_vec is None:
-            logger.warning("Card not found in embeddings: %s", query_card)
+            logger.warning("Card not found in embeddings and Scryfall fallback failed: %s", query_card)
             return []
 
-        # Compute similarities (dot product on normalized vectors = cosine similarity)
         similarities = self.vectors @ query_vec
 
-        # Build exclusion set
         exclude_set = set()
         exclude_set.add(query_card.lower())
         if exclude_cards:
             exclude_set.update(name.lower() for name in exclude_cards)
 
-        # Filter and collect results
         results = []
         for idx in np.argsort(similarities)[::-1]:
             name = str(self.names[idx])
             if name.lower() in exclude_set:
                 continue
 
-            # Color filter
             if color_filter:
                 card_colors = str(self.colors[idx])
                 if not _is_color_subset(card_colors, color_filter):
                     continue
 
-            # Type filter
             if type_filter:
                 card_type = str(self.types[idx])
                 if type_filter.lower() not in card_type.lower():
                     continue
 
-            # Mana value range filter
             if mana_range:
                 mv = float(self.mana_values[idx])
                 if mv < mana_range[0] or mv > mana_range[1]:

coach/llm_client.py

@@ -1,22 +1,25 @@
 """
-Commander AI Lab — LM Studio Client
+Commander AI Lab — Ollama LLM Client
 ════════════════════════════════════
-Async HTTP client targeting LM Studio's OpenAI-compatible
-/v1/chat/completions endpoint. Includes retry logic for
-non-JSON responses and configurable timeout.
+Fully async HTTP client targeting Ollama's OpenAI-compatible
+/v1/chat/completions endpoint.  Every network call uses
+httpx.AsyncClient so it NEVER blocks the FastAPI event loop.
 """
+from __future__ import annotations
 
 import json
 import logging
-import asyncio
 from typing import Optional
-from urllib.request import urlopen, Request
-from urllib.error import URLError
+
+import httpx
 
 from .config import (
-    LM_STUDIO_URL, LM_STUDIO_MODEL,
-    LM_STUDIO_TIMEOUT, LM_STUDIO_MAX_RETRIES,
-    DEFAULT_TEMPERATURE, DEFAULT_MAX_TOKENS,
+    LLM_URL,
+    LLM_MODEL,
+    LLM_TIMEOUT,
+    LLM_MAX_RETRIES,
+    DEFAULT_TEMPERATURE,
+    DEFAULT_MAX_TOKENS,
 )
 
 logger = logging.getLogger("coach.llm")
@@ -24,9 +27,15 @@ logger = logging.getLogger("coach.llm")
 
 class LLMResponse:
     """Parsed LLM response container."""
-    def __init__(self, content: str, parsed_json: Optional[dict],
-                 prompt_tokens: int, completion_tokens: int,
-                 model: str):
+
+    def __init__(
+        self,
+        content: str,
+        parsed_json: Optional[dict],
+        prompt_tokens: int,
+        completion_tokens: int,
+        model: str,
+    ):
         self.content = content
         self.parsed_json = parsed_json
         self.prompt_tokens = prompt_tokens
@@ -34,43 +43,56 @@ class LLMResponse:
         self.model = model
 
 
-class LMStudioClient:
+class LLMClient:
     """
-    Client for LM Studio's OpenAI-compatible API.
+    Fully async client for Ollama's OpenAI-compatible API.
 
     Usage:
-        client = LMStudioClient()
+        client = LLMClient()
         response = await client.chat(system_prompt, user_prompt)
         if response.parsed_json:
             suggestions = response.parsed_json
     """
 
-    def __init__(self, base_url: str = None, model: str = None,
-                 timeout: int = None):
-        self.base_url = (base_url or LM_STUDIO_URL).rstrip("/")
-        self.model = model or LM_STUDIO_MODEL
-        self.timeout = timeout or LM_STUDIO_TIMEOUT
-        self._resolved_model = None  # actual model name from /models endpoint
+    def __init__(
+        self,
+        base_url: str = None,
+        model: str = None,
+        timeout: int = None,
+    ):
+        self.base_url = (base_url or LLM_URL).rstrip("/")
+        self.model = model or LLM_MODEL
+        self.timeout = timeout or LLM_TIMEOUT
+        self._resolved_model: Optional[str] = None
 
-    def _resolve_model(self) -> str:
-        """Get the actual loaded model name from LM Studio /models endpoint."""
+    # ── Model resolution ───────────────────────────────────────
+
+    async def _resolve_model(self) -> str:
+        """Get the actual loaded model name from Ollama /models endpoint."""
         if self._resolved_model:
             return self._resolved_model
         try:
-            status = self.check_connection()
+            status = await self.check_connection()
             if status.get("connected") and status.get("active_model"):
                 self._resolved_model = status["active_model"]
-                logger.info("Resolved LM Studio model: %s", self._resolved_model)
+                logger.info("Resolved Ollama model: %s", self._resolved_model)
                 return self._resolved_model
         except Exception:
             pass
         return self.model
 
-    def _build_request_body(self, system_prompt: str, user_prompt: str,
-                            temperature: float, max_tokens: int,
-                            force_json: bool) -> dict:
-        model_name = self._resolve_model()
-        body = {
+    # ── Request building ──────────────────────────────────────
+
+    async def _build_request_body(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        temperature: float,
+        max_tokens: int,
+        force_json: bool,
+    ) -> dict:
+        model_name = await self._resolve_model()
+        body: dict = {
             "model": model_name,
             "messages": [
                 {"role": "system", "content": system_prompt},
@@ -79,32 +101,27 @@ class LMStudioClient:
             "temperature": temperature,
             "max_tokens": max_tokens,
         }
-        # Only request JSON format if using a model that supports it
-        # Some models (esp. smaller ones) don't support response_format
         if force_json:
             body["response_format"] = {"type": "json_object"}
         return body
 
+    # ── Response parsing ──────────────────────────────────────
+
     @staticmethod
     def _strip_think_tags(text: str) -> str:
-        """Remove DeepSeek-R1 <think>...</think> reasoning blocks from LLM output."""
+        """Remove DeepSeek-R1 <think>...</think> reasoning blocks."""
         import re
-        # Remove all <think>...</think> blocks (greedy, handles multiline)
-        cleaned = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
-        return cleaned.strip()
+        return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL).strip()
 
     @staticmethod
     def _extract_json_object(text: str) -> Optional[dict]:
-        """Find and parse the first complete JSON object in text.
-        Uses brace-counting to handle nested objects reliably."""
+        """Find and parse the first complete JSON object via brace-counting."""
         start = text.find('{')
         if start == -1:
             return None
-
         depth = 0
         in_string = False
         escape_next = False
-
         for i in range(start, len(text)):
             ch = text[i]
             if escape_next:
@@ -124,66 +141,56 @@ class LMStudioClient:
             elif ch == '}':
                 depth -= 1
                 if depth == 0:
-                    candidate = text[start:i+1]
+                    candidate = text[start : i + 1]
                     try:
                         return json.loads(candidate)
                     except (json.JSONDecodeError, ValueError):
-                        # Malformed — try next opening brace
                         next_start = text.find('{', start + 1)
                         if next_start != -1 and next_start < i:
                             start = next_start
-                            depth = 1  # we're already past this brace
-                        return None
+                            depth = 1
+                            return None
         return None
 
     def _parse_response(self, raw: dict) -> LLMResponse:
-        """Extract content and usage from OpenAI-format response.
-        Handles DeepSeek-R1 <think> tags, markdown fences, and raw JSON."""
+        """Extract content and usage from an OpenAI-format response."""
         choice = raw.get("choices", [{}])[0]
         content = choice.get("message", {}).get("content", "")
         usage = raw.get("usage", {})
-
-        # Step 1: Strip <think>...</think> blocks (DeepSeek-R1)
         cleaned = self._strip_think_tags(content)
-
-        # Step 2: Try direct JSON parse on cleaned content
-        parsed = None
+        parsed: Optional[dict] = None
+        # 1. Direct JSON parse
         try:
             parsed = json.loads(cleaned)
         except (json.JSONDecodeError, TypeError):
             pass
-
-        # Step 3: Try extracting from markdown fences
+        # 2. Markdown fences
         if parsed is None and "```json" in cleaned:
             try:
-                start = cleaned.index("```json") + 7
-                end = cleaned.index("```", start)
-                parsed = json.loads(cleaned[start:end].strip())
+                s = cleaned.index("```json") + 7
+                e = cleaned.index("```", s)
+                parsed = json.loads(cleaned[s:e].strip())
             except (json.JSONDecodeError, ValueError, IndexError):
                 pass
         if parsed is None and "```" in cleaned:
             try:
-                start = cleaned.index("```") + 3
-                end = cleaned.index("```", start)
-                parsed = json.loads(cleaned[start:end].strip())
+                s = cleaned.index("```") + 3
+                e = cleaned.index("```", s)
+                parsed = json.loads(cleaned[s:e].strip())
             except (json.JSONDecodeError, ValueError, IndexError):
                 pass
-
-        # Step 4: Brute-force — find the first valid JSON object in the text
+        # 3. Brace-counting extraction
         if parsed is None:
             parsed = self._extract_json_object(cleaned)
-
-        # Step 5: If still nothing, try on the original content (in case
-        # think-tag stripping was too aggressive)
+        # 4. Fallback on original content
         if parsed is None and content != cleaned:
             parsed = self._extract_json_object(content)
-
         if parsed is not None:
             logger.info("Successfully parsed JSON from LLM response")
         else:
-            logger.warning("Could not extract JSON from LLM response (%d chars)",
-                           len(content))
-
+            logger.warning(
+                "Could not extract JSON from LLM response (%d chars)", len(content)
+            )
         return LLMResponse(
             content=content,
             parsed_json=parsed,
@@ -192,94 +199,84 @@ class LMStudioClient:
             model=raw.get("model", self.model),
         )
 
-    def chat_sync(self, system_prompt: str, user_prompt: str,
-                  temperature: float = None, max_tokens: int = None,
-                  force_json: bool = True) -> LLMResponse:
+    # ── Public API ─────────────────────────────────────────────
+
+    async def chat(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        temperature: float = None,
+        max_tokens: int = None,
+        force_json: bool = True,
+    ) -> LLMResponse:
         """
-        Synchronous chat call to LM Studio.
-        Retries up to LM_STUDIO_MAX_RETRIES on failure or non-JSON response.
+        Async chat call to Ollama.  Retries up to LLM_MAX_RETRIES.
+        Never blocks the event loop — uses httpx.AsyncClient throughout.
         """
         temp = temperature if temperature is not None else DEFAULT_TEMPERATURE
         tokens = max_tokens if max_tokens is not None else DEFAULT_MAX_TOKENS
-        body = self._build_request_body(system_prompt, user_prompt,
-                                        temp, tokens, force_json)
-
-        last_error = None
-        for attempt in range(1, LM_STUDIO_MAX_RETRIES + 1):
+        body = await self._build_request_body(
+            system_prompt, user_prompt, temp, tokens, force_json
+        )
+        last_error: Optional[Exception] = None
+        for attempt in range(1, LLM_MAX_RETRIES + 1):
             try:
-                logger.info("LLM call attempt %d/%d", attempt, LM_STUDIO_MAX_RETRIES)
-                req = Request(
-                    f"{self.base_url}/chat/completions",
-                    data=json.dumps(body).encode("utf-8"),
-                    headers={"Content-Type": "application/json"},
-                    method="POST",
-                )
-                with urlopen(req, timeout=self.timeout) as resp:
-                    raw = json.loads(resp.read().decode("utf-8"))
-
+                logger.info("LLM call attempt %d/%d", attempt, LLM_MAX_RETRIES)
+                async with httpx.AsyncClient(
+                    timeout=httpx.Timeout(float(self.timeout))
+                ) as client:
+                    resp = await client.post(
+                        f"{self.base_url}/chat/completions",
+                        json=body,
+                        headers={"Content-Type": "application/json"},
+                    )
+                    resp.raise_for_status()
+                    raw = resp.json()
                 result = self._parse_response(raw)
-
-                # If we need JSON and didn't get it, retry
                 if force_json and result.parsed_json is None:
-                    logger.warning("Attempt %d: LLM returned non-JSON, retrying", attempt)
+                    logger.warning(
+                        "Attempt %d: LLM returned non-JSON, retrying", attempt
+                    )
                     last_error = ValueError("Non-JSON response from LLM")
                     continue
-
                 return result
-
+            except httpx.HTTPStatusError as e:
+                logger.warning("Attempt %d HTTP error %s", attempt, e.response.status_code)
+                last_error = e
+                # 400 with json format → retry without response_format constraint
+                if e.response.status_code == 400 and force_json:
+                    logger.warning("Got 400 — retrying without response_format")
+                    force_json = False
+                    body = await self._build_request_body(
+                        system_prompt, user_prompt, temp, tokens, False
+                    )
             except Exception as e:
                 logger.warning("Attempt %d failed: %s", attempt, e)
                 last_error = e
-
-                # If 400 and we used json response_format, retry without it
-                error_str = str(e)
-                if "400" in error_str and force_json:
-                    logger.warning("Got 400 — retrying without response_format constraint")
-                    force_json = False
-                    body = self._build_request_body(
-                        system_prompt, user_prompt, temp, tokens, False)
-
-                if attempt < LM_STUDIO_MAX_RETRIES:
-                    import time
-                    time.sleep(2 * attempt)  # Simple backoff
-
+            if attempt < LLM_MAX_RETRIES:
+                import asyncio
+                await asyncio.sleep(2 * attempt)
         raise ConnectionError(
-            f"LLM call failed after {LM_STUDIO_MAX_RETRIES} attempts: {last_error}"
+            f"LLM call failed after {LLM_MAX_RETRIES} attempts: {last_error}"
         )
 
-    async def chat(self, system_prompt: str, user_prompt: str,
-                   temperature: float = None, max_tokens: int = None,
-                   force_json: bool = True) -> LLMResponse:
-        """
-        Async chat call — runs the sync call in a thread executor
-        so it doesn't block the FastAPI event loop.
-        """
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(
-            None,
-            lambda: self.chat_sync(system_prompt, user_prompt,
-                                   temperature, max_tokens, force_json)
-        )
-
-    def check_connection(self) -> dict:
-        """
-        Check if LM Studio is reachable. Returns status dict.
-        """
+    async def check_connection(self) -> dict:
+        """Check if Ollama is reachable. Returns status dict."""
         try:
-            req = Request(
-                f"{self.base_url}/models",
-                headers={"Content-Type": "application/json"},
-                method="GET",
-            )
-            with urlopen(req, timeout=5) as resp:
-                data = json.loads(resp.read().decode("utf-8"))
-                models = data.get("data", [])
-                model_ids = [m.get("id", "unknown") for m in models]
-                return {
-                    "connected": True,
-                    "models": model_ids,
-                    "active_model": model_ids[0] if model_ids else self.model,
-                }
+            async with httpx.AsyncClient(timeout=httpx.Timeout(5.0)) as client:
+                resp = await client.get(
+                    f"{self.base_url}/models",
+                    headers={"Content-Type": "application/json"},
+                )
+                resp.raise_for_status()
+                data = resp.json()
+            models = data.get("data", [])
+            model_ids = [m.get("id", "unknown") for m in models]
+            return {
+                "connected": True,
+                "models": model_ids,
+                "active_model": model_ids[0] if model_ids else self.model,
+            }
         except Exception as e:
             return {
                 "connected": False,

coach/models.py

@@ -4,12 +4,9 @@ Commander AI Lab — Coach Data Models
 Pydantic v2 models for deck reports, coach requests/responses,
 and coaching session persistence.
 """
-
-from datetime import datetime
 from typing import List, Optional, Dict
 from pydantic import BaseModel, Field
 
-
 # ══════════════════════════════════════════════════════════════
 # Deck Report Models (mirrors Java DeckReport)
 # ══════════════════════════════════════════════════════════════
@@ -17,16 +14,16 @@ from pydantic import BaseModel, Field
 class CardPerformance(BaseModel):
     """Per-card aggregated stats from simulation data."""
     name: str
-    drawnRate: float = 0.0          # Fraction of games where card was drawn
-    castRate: float = 0.0           # Fraction of games where drawn AND cast
+    drawnRate: float = 0.0
+    castRate: float = 0.0
     keptInOpeningHandRate: float = 0.0
-    deadCardRate: float = 0.0       # Drawn but never cast/used
-    impactScore: float = 0.0        # (winRateWhenCast - overallWinRate) * castRate
-    synergyScore: float = 0.0       # Co-occurrence uplift with other deck cards
-    clunkinessScore: float = 0.0    # deadCardRate * (1 - castRate)
+    deadCardRate: float = 0.0
+    impactScore: float = 0.0
+    synergyScore: float = 0.0
+    clunkinessScore: float = 0.0
     avgTurnCast: Optional[float] = None
     avgDamageDealt: float = 0.0
-    tags: List[str] = Field(default_factory=list)  # ramp, draw, removal, etc.
+    tags: List[str] = Field(default_factory=list)
 
 
 class MatchupRecord(BaseModel):
@@ -48,7 +45,7 @@ class DeckMeta(BaseModel):
 class DeckStructure(BaseModel):
     """Deck composition breakdown."""
     landCount: int = 0
-    curveBuckets: List[int] = Field(default_factory=lambda: [0]*8)  # CMC 0-7+
+    curveBuckets: List[int] = Field(default_factory=lambda: [0]*8)
     cardTypeCounts: Dict[str, int] = Field(default_factory=dict)
     functionalCounts: Dict[str, int] = Field(default_factory=dict)
 
@@ -57,7 +54,7 @@ class ComboRecord(BaseModel):
     """Known combo with performance data."""
     cardNames: List[str]
     winRateWhenAssembled: float = 0.0
-    assemblyRate: float = 0.0  # Fraction of games where all pieces drawn
+    assemblyRate: float = 0.0
 
 
 class DeckReport(BaseModel):
@@ -84,10 +81,10 @@ class DeckReport(BaseModel):
 
 class CoachGoals(BaseModel):
     """User-specified coaching goals."""
-    targetPowerLevel: Optional[int] = None          # 1-10
-    metaFocus: Optional[str] = None                 # aggro, control, combo, midrange, stax
-    budget: Optional[str] = None                    # budget, medium, no-limit
-    focusAreas: List[str] = Field(default_factory=list)  # e.g., ["ramp", "card draw"]
+    targetPowerLevel: Optional[int] = None
+    metaFocus: Optional[str] = None
+    budget: Optional[str] = None
+    focusAreas: List[str] = Field(default_factory=list)
 
 
 class CoachRequest(BaseModel):
@@ -107,12 +104,120 @@ class SuggestedCut(BaseModel):
 class SuggestedAdd(BaseModel):
     """A card the coach suggests adding."""
     cardName: str
-    role: str = ""          # ramp, draw, removal, finisher, etc.
+    role: str = ""
     reason: str = ""
     synergyWith: List[str] = Field(default_factory=list)
     estimatedManaValue: Optional[float] = None
 
 
+# ══════════════════════════════════════════════════════════════
+# Phase 2: Structured Analysis Models
+# ══════════════════════════════════════════════════════════════
+
+class UpgradePriorityItem(BaseModel):
+    """A single prioritized cut/add swap ranked by expected impact."""
+    rank: int
+    cut: str
+    add: str
+    reasoning: str = ""
+    expectedImpact: str = ""
+
+
+class CommanderDependency(BaseModel):
+    """How reliant the deck is on having the commander in play."""
+    score: int = 5
+    dependentCards: List[str] = Field(default_factory=list)
+    recoveryPlan: str = ""
+
+
+class MulliganAnalysis(BaseModel):
+    """Opening hand keepability assessment."""
+    estimatedKeepRate: str = ""
+    worstOffenders: List[str] = Field(default_factory=list)
+    recommendation: str = ""
+
+
+# ══════════════════════════════════════════════════════════════
+# Phase 3: Qualitative Analysis Models
+# ══════════════════════════════════════════════════════════════
+
+class RemovalCoverage(BaseModel):
+    """Categorized removal package by permanent type."""
+    creatures: str = ""
+    artifacts: str = ""
+    enchantments: str = ""
+    planeswalkers: str = ""
+    lands: str = ""
+    massRemoval: str = ""
+    gaps: List[str] = Field(default_factory=list)
+
+
+class RampQuality(BaseModel):
+    """Ramp package breakdown by subtype and fragility."""
+    manaRocks: str = ""
+    landFetch: str = ""
+    manaDorks: str = ""
+    costReducers: str = ""
+    fragility: str = ""
+    canReachFourByTurnThree: str = ""
+
+
+class DrawEngineProfile(BaseModel):
+    """Card draw sustainability analysis."""
+    burstDraw: List[str] = Field(default_factory=list)
+    repeatableEngines: List[str] = Field(default_factory=list)
+    assessment: str = ""
+    sustainability: str = ""
+
+
+class WinCondition(BaseModel):
+    """A named win condition with independence rating."""
+    name: str
+    cards: List[str] = Field(default_factory=list)
+    independence: str = ""
+    description: str = ""
+
+
+class AntiSynergyFlag(BaseModel):
+    """An internal conflict between cards in the deck."""
+    cards: List[str] = Field(default_factory=list)
+    conflict: str = ""
+    severity: str = ""
+
+
+# ══════════════════════════════════════════════════════════════
+# Phase 5: Pod Politics & Meta Awareness Models
+# ══════════════════════════════════════════════════════════════
+
+class PodPresence(BaseModel):
+    """Commander pod politics and threat assessment."""
+    threatLevel: str = ""  # early/medium/late/low
+    politicalTools: List[str] = Field(default_factory=list)
+    adversarialRating: str = ""  # low/medium/high
+    recommendation: str = ""
+
+
+class TempoAssessment(BaseModel):
+    """When the deck 'comes online' and whether that's fast enough."""
+    peakTurn: int = 0
+    profile: str = ""  # early/mid/late
+    survivalWindow: str = ""  # Assessment of whether it survives to peak
+    assessment: str = ""
+
+
+class MetaMatchup(BaseModel):
+    """Strategic reasoning about deck vs a meta archetype."""
+    archetype: str  # fast-combo, stax, control, aggro, etc.
+    assessment: str = ""
+    keyThreats: List[str] = Field(default_factory=list)
+    keyAnswers: List[str] = Field(default_factory=list)
+    overallRating: str = ""  # favored/even/unfavored
+
+
+# ══════════════════════════════════════════════════════════════
+# Coach Session (persisted result)
+# ══════════════════════════════════════════════════════════════
+
 class CoachSession(BaseModel):
     """
     Complete coaching session result.
@@ -131,6 +236,24 @@ class CoachSession(BaseModel):
     promptTokens: int = 0
     completionTokens: int = 0
     goals: Optional[CoachGoals] = None
+    isQuickDigest: bool = False  # True for quick digest, False for full analysis
+
+    # Phase 2: Structured analysis fields
+    upgradePriority: List[UpgradePriorityItem] = Field(default_factory=list)
+    commanderDependency: Optional[CommanderDependency] = None
+    mulliganAnalysis: Optional[MulliganAnalysis] = None
+
+    # Phase 3: Qualitative analysis fields
+    removalCoverage: Optional[RemovalCoverage] = None
+    rampQuality: Optional[RampQuality] = None
+    drawEngineProfile: Optional[DrawEngineProfile] = None
+    winConditions: List[WinCondition] = Field(default_factory=list)
+    antiSynergyFlags: List[AntiSynergyFlag] = Field(default_factory=list)
+
+    # Phase 5: Pod politics & meta
+    podPresence: Optional[PodPresence] = None
+    tempoAssessment: Optional[TempoAssessment] = None
+    metaMatchups: List[MetaMatchup] = Field(default_factory=list)
 
 
 class ApplyRequest(BaseModel):

coach/prompt_template.py

@@ -4,18 +4,24 @@ Commander AI Lab — Coach Prompt Template
 Constructs system and user prompts for the LLM coach.
 Injects deck report data, underperformer analysis, and
 candidate replacement cards into a structured template.
+
+Phase 1 additions (#108):
+  - Sim Insights section: deck-level opening hand quality, curve velocity,
+    synergy outliers, and archetype weak spots surfaced directly in the
+    user prompt so Claude reasons from numbers, not vibes.
+  - _format_archetypes() annotates weak spots and strengths inline.
+  - _format_top_cards() adds keptInOpeningHandRate per card.
 """
 
-import json
 from typing import List, Dict, Optional
 
 from .models import DeckReport, CoachGoals, CardPerformance
 from .config import MAX_CANDIDATES_PER_UNDERPERFORMER, MAX_UNDERPERFORMERS
 
 
-# ══════════════════════════════════════════════════════════════
+# ════════════════════════════════════════════════════════════
 # System Prompt
-# ══════════════════════════════════════════════════════════════
+# ════════════════════════════════════════════════════════════
 
 SYSTEM_PROMPT_TEMPLATE = """You are an expert Magic: The Gathering Commander deck coach.
 You analyze deck performance reports from simulation data and provide specific,
@@ -27,44 +33,197 @@ RULES:
 - Base suggestions on the simulation data provided, not general "goodstuff" advice.
 - Consider mana curve, role balance (ramp, draw, removal, threats), and synergy.
 - When suggesting replacements, prefer cards that fill the same functional role.
-- IMPORTANT: Suggest 3-5 DIFFERENT cards to cut and 3-5 DIFFERENT cards to add.
+- IMPORTANT: Suggest 5-8 DIFFERENT cards to cut and 5-8 DIFFERENT cards to add.
 - Each suggested add MUST be a different card — never suggest the same card twice.
 - Vary your suggestions across different functional roles (ramp, draw, removal, threats, utility).
 - If the deck is missing a functional role (e.g., no card draw, no removal), prioritize adding cards for that role.
 - If candidate replacements are provided from embeddings search, prefer those over generic suggestions.
+- For each suggested cut, provide a DETAILED reason citing specific stats (impact score, cast rate, dead card rate, clunkiness, synergy score).
+- For each suggested add, explain the SPECIFIC synergy with existing cards in the deck.
+- When reasoning about cuts, also consider keptInOpeningHandRate (low = bad in opening hands) and avgTurnCast (high = too slow for the curve).
+- When evaluating synergy, use the synergyScore: cards below 0.1 have weak co-occurrence with the rest of the deck.
+- Use perArchetypeWinRates to identify which matchup types this deck struggles with and tailor suggestions accordingly.
+- The Sim Insights section shows pre-computed deck-level signals — use these directly in your reasoning.
+- Cards flagged as [OPENING HAND TRAP] have keptInOpeningHandRate below the deck average — treat as mulligan liabilities.
+- Cards flagged as [LATE CASTER] have avgTurnCast > 6 — consider whether they fit the deck's tempo.
+- Cards flagged as [ISOLATED] have synergyScore in the bottom 3 of the deck — they may not support the gameplan.
+- Archetypes flagged as [WEAK SPOT] in the Archetype Win Rates section are where this deck loses most — prioritize fixes for those matchups.
+- Provide at least 5 heuristic hints covering different strategic dimensions.
+- Assess how dependent the deck is on the commander being in play.
+- Evaluate opening hand quality and identify cards that cause bad mulligans.
+- Rank your suggested swaps by priority so the user knows what to change first.
+- Categorize the removal package by permanent type and flag coverage gaps.
+- Evaluate the ramp package for fragility (creature/artifact-heavy vs land-based).
+- Distinguish one-time draw from repeatable draw engines.
+- Identify the top 3 win conditions and rate each for independence.
+- Flag internal anti-synergies or card conflicts.
+- Assess pod threat perception: does this deck draw too much hate too early?
+- Evaluate when the deck comes online (tempo profile) and whether that's viable.
+- Reason about matchups vs fast combo, stax/prison, and control archetypes.
 {goals_section}
 
 You MUST respond ONLY with valid JSON matching this exact structure:
 {{
-  "summary": "2-3 sentence overview of the deck's strengths and weaknesses",
+  "summary": "4-6 sentence detailed overview of the deck's strengths, weaknesses, key synergies, and strategic identity. Reference specific win rates and simulation data.",
   "suggestedCuts": [
     {{
       "cardName": "Card To Remove",
-      "reason": "Why this card underperforms",
-      "replacementOptions": ["Better Card 1", "Better Card 2"]
+      "reason": "Detailed explanation citing specific simulation stats (impact score, cast rate, dead card rate, clunkiness score, synergy score, avg turn cast) and why this card underperforms in this specific deck context",
+      "replacementOptions": ["Better Card 1", "Better Card 2"],
+      "functionalRole": "ramp|draw|removal|threat|utility|combo|protection",
+      "estimatedImpact": "How cutting this card is expected to improve the deck"
     }}
   ],
   "suggestedAdds": [
     {{
       "cardName": "Card To Add",
       "role": "ramp|draw|removal|threat|utility|combo|protection",
-      "reason": "Why this card improves the deck",
-      "synergyWith": ["Card In Deck 1", "Card In Deck 2"]
+      "reason": "Detailed explanation of why this card improves the deck, referencing specific weaknesses it addresses",
+      "synergyWith": ["Card In Deck 1", "Card In Deck 2"],
+      "estimatedManaCost": "The mana value of the suggested card",
+      "strategicRationale": "How this card fits into the overall game plan and addresses a specific gap"
     }}
   ],
-  "heuristicHints": [
-    "Concise strategic tip based on data"
+  "upgradePriority": [
+    {{
+      "rank": 1,
+      "cut": "Card to remove",
+      "add": "Card to add instead",
+      "reasoning": "Why this is the single highest-impact swap",
+      "expectedImpact": "high|medium|low"
+    }}
   ],
-  "manaBaseAdvice": "Specific mana base improvement suggestion or null",
-  "rawTextExplanation": "Detailed paragraph explaining the analysis and reasoning"
+  "commanderDependency": {{
+    "score": 5,
+    "dependentCards": ["Card that is nearly dead without commander"],
+    "recoveryPlan": "Strategy if the commander gets removed or tucked repeatedly"
+  }},
+  "mulliganAnalysis": {{
+    "estimatedKeepRate": "Percentage estimate of keepable 7-card hands, e.g. ~70%",
+    "worstOffenders": ["Card most responsible for unkeepable hands"],
+    "recommendation": "What to change to improve opening hand quality"
+  }},
+  "manaCurveAnalysis": {{
+    "currentAssessment": "Detailed analysis of the current mana curve distribution and its implications",
+    "recommendations": "Specific suggestions for curve adjustments with reasoning",
+    "idealRange": "What the curve should look like for this deck's strategy"
+  }},
+  "roleBreakdown": {{
+    "ramp": "Assessment of ramp package - count, quality, and suggestions",
+    "draw": "Assessment of card draw package",
+    "removal": "Assessment of removal package",
+    "threats": "Assessment of win conditions and threats",
+    "protection": "Assessment of protection and interaction"
+  }},
+  "synergyAnalysis": [
+    "Description of a key synergy chain in the deck and how well it performed in simulation"
+  ],
+  "heuristicHints": [
+    "Detailed strategic tip explaining the reasoning and data behind the suggestion"
+  ],
+  "manaBaseAdvice": "Specific mana base improvement suggestion including land count, color fixing, and utility lands, or null if mana base is solid",
+  "rawTextExplanation": "3-5 paragraph in-depth analysis covering: (1) overall deck strategy assessment and how well the deck executes its game plan based on simulation data, (2) mana curve and resource analysis with specific numbers including avgTurnCast for key cards, (3) card synergy evaluation highlighting the strongest and weakest interactions using synergyScore data, (4) meta considerations and matchup-specific insights referencing perArchetypeWinRates and the [WEAK SPOT] archetypes flagged in Sim Insights, and (5) prioritized list of improvements ranked by expected impact on win rate",
+  "removalCoverage": {{
+    "creatures": "Assessment of creature removal",
+    "artifacts": "Assessment of artifact removal",
+    "enchantments": "Assessment of enchantment removal",
+    "planeswalkers": "Assessment of planeswalker removal",
+    "lands": "Assessment of land destruction",
+    "massRemoval": "Assessment of board wipes",
+    "gaps": ["Permanent types with zero or weak coverage"]
+  }},
+  "rampQuality": {{
+    "manaRocks": "2-mana rocks, signets, etc.",
+    "landFetch": "Cultivate, Kodama's Reach, etc.",
+    "manaDorks": "Llanowar Elves, Birds, etc.",
+    "costReducers": "Medallions, Helm of Awakening, etc.",
+    "fragility": "Overall fragility assessment",
+    "canReachFourByTurnThree": "yes/no with reasoning"
+  }},
+  "drawEngineProfile": {{
+    "burstDraw": ["One-time draw spells"],
+    "repeatableEngines": ["Sustained draw engines"],
+    "assessment": "Overall draw quality",
+    "sustainability": "Whether draw is sustainable or burst-only"
+  }},
+  "winConditions": [
+    {{
+      "name": "Name of win condition",
+      "cards": ["Key cards involved"],
+      "independence": "high|medium|low",
+      "description": "How this win con functions"
+    }}
+  ],
+  "antiSynergyFlags": [
+    {{
+      "cards": ["Card A", "Card B"],
+      "conflict": "Description of conflict",
+      "severity": "high|medium|low"
+    }}
+  ],
+  "podPresence": {{
+    "threatLevel": "early|medium|late|low",
+    "politicalTools": ["Pillowfort or group effect cards"],
+    "adversarialRating": "low|medium|high",
+    "recommendation": "How to manage threat perception in a pod"
+  }},
+  "tempoAssessment": {{
+    "peakTurn": 5,
+    "profile": "early|mid|late",
+    "survivalWindow": "Assessment of whether the deck survives to its power window",
+    "assessment": "Overall tempo evaluation"
+  }},
+  "metaMatchups": [
+    {{
+      "archetype": "fast-combo|stax|control|aggro|midrange",
+      "assessment": "How the deck fares against this archetype",
+      "keyThreats": ["Cards/strategies that threaten this deck"],
+      "keyAnswers": ["Cards in the deck that answer this archetype"],
+      "overallRating": "favored|even|unfavored"
+    }}
+  ]
 }}
 
 Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
 
+# ════════════════════════════════════════════════════════════
+# Quick Digest Prompt
+# ════════════════════════════════════════════════════════════
 
-# ══════════════════════════════════════════════════════════════
-# User Prompt Builder
-# ══════════════════════════════════════════════════════════════
+QUICK_DIGEST_SYSTEM_PROMPT = """You are an expert Magic: The Gathering Commander deck coach.
+Provide a FAST summary analysis of this deck. Be concise.
+
+RULES:
+- NEVER suggest cutting the commander ({commander}).
+- Only suggest cards within the deck's color identity: {color_identity}.
+- Cards flagged [OPENING HAND TRAP] have low keptInOpeningHandRate — prime cut candidates.
+- Cards flagged [WEAK SPOT] in archetype win rates show where the deck loses most.
+{goals_section}
+
+You MUST respond ONLY with valid JSON matching this exact structure:
+{{
+  "summary": "3-sentence deck overview: what it does, main strength, main weakness.",
+  "upgradePriority": [
+    {{
+      "rank": 1,
+      "cut": "Card to remove",
+      "add": "Card to add instead",
+      "reasoning": "Why this is a high-impact swap",
+      "expectedImpact": "high|medium|low"
+    }}
+  ],
+  "commanderDependency": {{
+    "score": 5,
+    "dependentCards": ["Card that is nearly dead without commander"],
+    "recoveryPlan": "Brief recovery strategy"
+  }}
+}}
+Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
+
+
+# ════════════════════════════════════════════════════════════
+# User Prompt Template
+# ════════════════════════════════════════════════════════════
 
 USER_PROMPT_TEMPLATE = """## Deck Report: {deck_name}
 Commander: {commander}
@@ -79,19 +238,29 @@ Avg Game Length: {avg_game_length:.1f} turns
 - Card Types: {type_summary}
 - Functional Roles: {role_summary}
 
+### Sim Insights (pre-computed deck-level signals)
+{sim_insights_section}
+
 ### Matchup Data
 {matchup_section}
 
+### Archetype Win Rates
+{archetype_section}
+
 ### Top Performing Cards (by Impact Score)
 {top_cards}
 
 ### Underperforming Cards (candidates for cuts)
 {underperformer_section}
 
+
+### RAG-Retrieved Similar Cards (from ChromaDB)
+{rag_section}
 ### Candidate Replacement Cards (from embeddings search)
 {candidate_section}
 
 {combo_section}
+
 Based on this simulation data, provide your coaching suggestions."""
 
 
@@ -99,7 +268,6 @@ def _format_goals_section(goals: Optional[CoachGoals]) -> str:
     """Format goals into system prompt rules."""
     if not goals:
         return "- Aim for a balanced, well-rounded deck."
-
     lines = []
     if goals.targetPowerLevel is not None:
         lines.append(f"- Target power level: {goals.targetPowerLevel}/10.")
@@ -126,42 +294,148 @@ def _format_curve(buckets: List[int]) -> str:
 
 
 def _format_top_cards(cards: List[CardPerformance], top_n: int = 10) -> str:
-    """Format the best performing cards."""
+    """Format the best performing cards with extended stats.
+
+    Phase 1: adds keptInOpeningHandRate so Claude sees hand quality for
+    the good cards too, not just the underperformers.
+    """
     sorted_cards = sorted(cards, key=lambda c: c.impactScore, reverse=True)
     lines = []
     for c in sorted_cards[:top_n]:
         tags = f" [{', '.join(c.tags)}]" if c.tags else ""
-        lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
-                     f"castRate={c.castRate:.1%}, drawnRate={c.drawnRate:.1%}{tags}")
+        turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
+        lines.append(
+            f"- {c.name}: impact={c.impactScore:.3f}, "
+            f"castRate={c.castRate:.1%}, drawnRate={c.drawnRate:.1%}, "
+            f"synergy={c.synergyScore:.3f}, "
+            f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
+            f"{turn_str}{tags}"
+        )
     return "\n".join(lines) if lines else "No card performance data available."
 
 
 def _format_underperformers(cards: List[CardPerformance],
                             underperformer_names: List[str]) -> str:
-    """Format underperforming cards with their stats."""
+    """Format underperforming cards with full available stats."""
     card_map = {c.name: c for c in cards}
     lines = []
     for name in underperformer_names[:MAX_UNDERPERFORMERS]:
         c = card_map.get(name)
         if c:
-            lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
-                         f"deadCardRate={c.deadCardRate:.1%}, "
-                         f"castRate={c.castRate:.1%}, "
-                         f"clunkiness={c.clunkinessScore:.3f}")
+            turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
+            flags = []
+            if c.keptInOpeningHandRate < 0.25:
+                flags.append("OPENING HAND TRAP")
+            if c.avgTurnCast is not None and c.avgTurnCast > 6:
+                flags.append("LATE CASTER")
+            if c.synergyScore < 0.1:
+                flags.append("ISOLATED")
+            flag_str = f" [{', '.join(flags)}]" if flags else ""
+            lines.append(
+                f"- {c.name}{flag_str}: impact={c.impactScore:.3f}, "
+                f"deadCardRate={c.deadCardRate:.1%}, "
+                f"castRate={c.castRate:.1%}, "
+                f"clunkiness={c.clunkinessScore:.3f}, "
+                f"synergy={c.synergyScore:.3f}, "
+                f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
+                f"{turn_str}"
+            )
         else:
             lines.append(f"- {name}: (no detailed stats)")
     return "\n".join(lines) if lines else "No clear underperformers identified."
 
 
+def _format_sim_insights(cards: List[CardPerformance],
+                         archetype_rates: Dict[str, float]) -> str:
+    """Compute and format deck-level sim signals for the Sim Insights section.
+
+    Phase 1 (#108): surfaces pre-computed per-card stats as deck-level
+    aggregates so Claude reasons from numbers directly.
+
+    Signals produced:
+      1. Opening hand quality — deck avg keptInOpeningHandRate + worst offenders
+      2. Curve velocity — castRate-weighted avg avgTurnCast + late-caster flags
+      3. Synergy outliers — bottom-3 synergyScore cards flagged as [ISOLATED]
+      4. Archetype weak spots — archetypes < 40% win rate flagged [WEAK SPOT]
+    """
+    if not cards:
+        return "No simulation data available."
+
+    lines = []
+
+    # ── 1. Opening hand quality ───────────────────────────────────────────
+    hand_rates = [c.keptInOpeningHandRate for c in cards if c.keptInOpeningHandRate > 0]
+    if hand_rates:
+        avg_keep = sum(hand_rates) / len(hand_rates)
+        lines.append(f"Opening Hand Quality: deck avg keptInOpeningHandRate = {avg_keep:.1%}")
+        # Worst offenders: cards significantly below the deck average
+        threshold = avg_keep * 0.6  # >40% below avg is an outlier
+        offenders = sorted(
+            [c for c in cards if c.keptInOpeningHandRate > 0 and c.keptInOpeningHandRate < threshold],
+            key=lambda c: c.keptInOpeningHandRate
+        )[:3]
+        if offenders:
+            lines.append("  Opening hand traps (keptInOpeningHandRate far below deck avg):")
+            for c in offenders:
+                lines.append(f"  - {c.name}: {c.keptInOpeningHandRate:.1%} [OPENING HAND TRAP]")
+    else:
+        lines.append("Opening Hand Quality: no keptInOpeningHandRate data available")
+
+    # ── 2. Curve velocity ───────────────────────────────────────────────
+    cast_cards = [c for c in cards if c.avgTurnCast is not None and c.castRate > 0]
+    if cast_cards:
+        total_weight = sum(c.castRate for c in cast_cards)
+        if total_weight > 0:
+            weighted_avg_turn = sum(
+                c.avgTurnCast * c.castRate for c in cast_cards
+            ) / total_weight
+            lines.append(f"Curve Velocity: castRate-weighted avg avgTurnCast = {weighted_avg_turn:.1f}")
+        late_casters = sorted(
+            [c for c in cast_cards if c.avgTurnCast > 6],
+            key=lambda c: c.avgTurnCast,
+            reverse=True
+        )[:3]
+        if late_casters:
+            lines.append("  Late casters (avgTurnCast > 6 — may not fit the curve):")
+            for c in late_casters:
+                lines.append(f"  - {c.name}: avgTurnCast={c.avgTurnCast:.1f} [LATE CASTER]")
+    else:
+        lines.append("Curve Velocity: no avgTurnCast data available")
+
+    # ── 3. Synergy outliers ──────────────────────────────────────────────
+    synergy_cards = sorted(
+        [c for c in cards if c.castRate > 0.05],  # ignore near-never-cast cards
+        key=lambda c: c.synergyScore
+    )
+    if synergy_cards:
+        bottom3 = synergy_cards[:3]
+        lines.append("Synergy Outliers (lowest synergyScore among cast cards):")
+        for c in bottom3:
+            lines.append(f"  - {c.name}: synergyScore={c.synergyScore:.3f} [ISOLATED]")
+
+    # ── 4. Archetype weak spots ──────────────────────────────────────────
+    if archetype_rates:
+        weak = {k: v for k, v in archetype_rates.items() if v < 0.40}
+        if weak:
+            lines.append("Archetype Weak Spots (win rate < 40%):")
+            for arch, wr in sorted(weak.items(), key=lambda x: x[1]):
+                lines.append(f"  - vs {arch}: {wr:.1%} [WEAK SPOT]")
+        else:
+            lines.append("Archetype Weak Spots: none (all matchups >= 40% win rate)")
+
+    return "\n".join(lines) if lines else "No sim insights available."
+
+
 def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
     """Format replacement candidates grouped by the card they'd replace."""
     if not candidates:
-        return ("No embedding-based candidates available. "
-                "Use your MTG knowledge to suggest cards that fit the deck's "
-                "color identity, strategy, and missing functional roles. "
-                "Suggest at least 3 different cards across different roles "
-                "(ramp, draw, removal, threats, utility).")
-
+        return (
+            "No embedding-based candidates available. "
+            "Use your MTG knowledge to suggest cards that fit the deck's "
+            "color identity, strategy, and missing functional roles. "
+            "Suggest at least 3 different cards across different roles "
+            "(ramp, draw, removal, threats, utility)."
+        )
     lines = []
     for underperformer, replacements in list(candidates.items())[:MAX_UNDERPERFORMERS]:
         lines.append(f"\nReplacements for '{underperformer}':")
@@ -179,14 +453,55 @@ def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
     return "\n".join(lines)
 
 
+def _format_rag_cards(rag_cards: List[dict]) -> str:
+    """Format RAG-retrieved cards from ChromaDB vector search."""
+    if not rag_cards:
+        return "No RAG results available."
+    lines = []
+    for card in rag_cards[:15]:
+        name = card.get("name", "Unknown")
+        types = card.get("types", "")
+        mv = card.get("mana_value", "?")
+        mc = card.get("mana_cost", "")
+        dist = card.get("distance", 0)
+        text = card.get("text", "")[:80]
+        lines.append(f"- {name} ({mc}, MV:{mv}, {types}) [distance: {dist:.3f}]")
+        if text:
+            lines.append(f"    Oracle: {text}")
+    return "\n".join(lines)
+
+
 def _format_matchups(report: DeckReport) -> str:
-    """Format matchup data."""
+    """Format per-opponent matchup data."""
     if not report.matchups:
         return "No matchup data available."
     lines = []
     for m in report.matchups:
-        lines.append(f"- vs {m.opponentDeck}: {m.winRate:.1%} "
-                     f"({m.gamesPlayed} games)")
+        lines.append(
+            f"- vs {m.opponentDeck}: {m.winRate:.1%} "
+            f"({m.gamesPlayed} games)"
+        )
+    return "\n".join(lines)
+
+
+def _format_archetypes(report: DeckReport) -> str:
+    """Format per-archetype win rates from DeckMeta.
+
+    Phase 1: annotates weak spots ([WEAK SPOT] < 40%) and strengths
+    ([STRENGTH] >= 55%) so Claude sees the signal immediately.
+    """
+    rates = report.meta.perArchetypeWinRates
+    if not rates:
+        return "No archetype breakdown available."
+    lines = []
+    for archetype, wr in sorted(rates.items(), key=lambda x: x[1]):
+        if wr < 0.40:
+            tag = " [WEAK SPOT]"
+        elif wr >= 0.55:
+            tag = " [STRENGTH]"
+        else:
+            tag = ""
+        lines.append(f"- vs {archetype}: {wr:.1%}{tag}")
     return "\n".join(lines)
 
 
@@ -197,14 +512,16 @@ def _format_combos(report: DeckReport) -> str:
     lines = ["### Known Combos"]
     for combo in report.knownCombos:
         cards = " + ".join(combo.cardNames)
-        lines.append(f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
-                     f"assemblyRate={combo.assemblyRate:.1%}")
+        lines.append(
+            f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
+            f"assemblyRate={combo.assemblyRate:.1%}"
+        )
     return "\n".join(lines)
 
 
-# ══════════════════════════════════════════════════════════════
+# ════════════════════════════════════════════════════════════
 # Main Builder Functions
-# ══════════════════════════════════════════════════════════════
+# ════════════════════════════════════════════════════════════
 
 def build_system_prompt(report: DeckReport,
                         goals: Optional[CoachGoals] = None) -> str:
@@ -217,16 +534,29 @@ def build_system_prompt(report: DeckReport,
     )
 
 
-def build_user_prompt(report: DeckReport,
-                      candidates: Dict[str, List[dict]] = None) -> str:
-    """Build the user prompt with full deck report data."""
-    color_str = "/".join(report.colorIdentity) if report.colorIdentity else "unknown"
+def build_quick_system_prompt(report: DeckReport,
+                               goals: Optional[CoachGoals] = None) -> str:
+    """Build the quick digest system prompt (~400-600 token target)."""
+    color_str = "/".join(report.colorIdentity) if report.colorIdentity else "any"
+    return QUICK_DIGEST_SYSTEM_PROMPT.format(
+        commander=report.commander,
+        color_identity=color_str,
+        goals_section=_format_goals_section(goals),
+    )
 
+
+def build_user_prompt(report: DeckReport,
+                      candidates: Dict[str, List[dict]] = None,
+                      rag_cards: List[dict] = None) -> str:
+    """Build the user prompt with full deck report data.
+
+    Phase 1: adds sim_insights_section computed from card-level stats.
+    """
+    color_str = "/".join(report.colorIdentity) if report.colorIdentity else "unknown"
     type_parts = [f"{k}={v}" for k, v in report.structure.cardTypeCounts.items()] \
         if report.structure.cardTypeCounts else ["unknown"]
     role_parts = [f"{k}={v}" for k, v in report.structure.functionalCounts.items()] \
         if report.structure.functionalCounts else ["unknown"]
-
     return USER_PROMPT_TEMPLATE.format(
         deck_name=report.deckId,
         commander=report.commander,
@@ -238,10 +568,14 @@ def build_user_prompt(report: DeckReport,
         curve_summary=_format_curve(report.structure.curveBuckets),
         type_summary=", ".join(type_parts),
         role_summary=", ".join(role_parts),
+        sim_insights_section=_format_sim_insights(
+            report.cards, report.meta.perArchetypeWinRates),
         matchup_section=_format_matchups(report),
+        archetype_section=_format_archetypes(report),
         top_cards=_format_top_cards(report.cards),
         underperformer_section=_format_underperformers(
             report.cards, report.underperformers),
         candidate_section=_format_candidates(candidates or {}),
+        rag_section=_format_rag_cards(rag_cards or []),
         combo_section=_format_combos(report),
     )

coach/prompts/deck_gen_system.py

@@ -1,6 +1,9 @@
 """
 Commander AI Lab — Deck Generation System Prompt
 ═════════════════════════════════════════════════
+Phase 1 changes:
+  - Added explicit 99-card count enforcement rule
+  - Clarified that commander counts separately (not in the 99)
 """
 
 SYSTEM_PROMPT = """You are an elite Magic: The Gathering Commander (EDH) deck builder with encyclopedic knowledge of every card ever printed, current metagame trends, and Commander Rules Committee bracket guidelines.
@@ -9,6 +12,8 @@ Your task is to build a complete, legal 100-card Commander deck.
 
 RULES:
 - Exactly 100 cards total: 1 commander + 99 other cards
+- The commander IS included in the cards list but counts as card #100
+- The other 99 cards must exactly match the SLOT BUDGET provided in the user prompt
 - No more than 1 copy of any card except basic lands
 - All cards must share the commander's color identity
 - All cards must be legal in Commander format
@@ -16,6 +21,12 @@ RULES:
 - Include approximately: 10 ramp sources, 10 card draw sources, 8-10 targeted removal, 2-3 board wipes, 3-5 protection pieces
 - Build a cohesive strategy around the commander's abilities
 
+CARD COUNT ENFORCEMENT (critical):
+- The user prompt will specify a SLOT BUDGET with exact per-category counts
+- You MUST fill every slot — returning fewer cards than the budget specifies will cause a validation error
+- If you cannot find a perfect fit for a slot, use the best available card for that role
+- Basic lands count toward the Land slot total
+
 BRACKET RULES (Commander Rules Committee):
 - Bracket 1 (Casual): Precon-level. No tutors, no fast mana, no infinite combos, no mass land destruction. Game Changers: 0
 - Bracket 2 (Upgraded Casual): Moderate power. Limited tutors (1-2 total), no 2-card infinite combos, no fast mana except Sol Ring. Game Changers: 0-1

coach/prompts/deck_gen_user.py

@@ -1,11 +1,33 @@
 """
 Commander AI Lab — Deck Generation User Prompt Builder
 ═══════════════════════════════════════════════════════
-Constructs the user message for Perplexity deck generation
-from a DeckGenV3Request.
+Constructs the user message for LLM deck generation.
+
+Phase 1 changes:
+  - slot_budget parameter injected into prompt so LLM targets exactly 99 cards
+  - Explicit card count requirement in prompt text
 """
 
-from typing import List, Optional
+from typing import Dict, List, Optional
+
+
+def build_slot_budget_segment(slot_budget: Dict[str, int]) -> str:
+    """
+    Build the slot budget prompt segment.
+    Informs the LLM of exact per-category card counts so it never undershoots.
+    """
+    total = sum(slot_budget.values())
+    lines = [f"SLOT BUDGET (you must fill EXACTLY {total} cards, excluding the commander):"]
+    for cat, count in slot_budget.items():
+        lines.append(f"  {cat}: {count}")
+    lines.append(f"  TOTAL: {total}")
+    lines.append("")
+    lines.append(
+        f"CRITICAL: Return exactly {total} cards in the JSON 'cards' array "
+        "(not counting the commander). Every slot in the budget above must be filled. "
+        "Do not return more or fewer cards."
+    )
+    return "\n".join(lines)
 
 
 def build_user_prompt(
@@ -18,6 +40,7 @@ def build_user_prompt(
     budget_mode: str = "total",
     omit_cards: List[str] = None,
     collection_summary: str = "",
+    slot_budget: Optional[Dict[str, int]] = None,
 ) -> str:
     """
     Build the user prompt for deck generation.
@@ -26,18 +49,19 @@ def build_user_prompt(
         commander: Commander card name
         commander_type: Type line from Scryfall
         color_identity: List of color letters, e.g. ["U", "B"]
-        strategy: User-specified strategy focus (e.g. "zombie tokens")
+        strategy: User-specified strategy focus
         target_bracket: Target bracket level 1-4
         budget_usd: Optional budget constraint
         budget_mode: "total" for total deck cost, "per_card" for per-card max
         omit_cards: Cards to exclude
         collection_summary: Pre-built collection summary text block
+        slot_budget: Dict of category -> card count (must sum to 99)
     """
     ci_str = ", ".join(color_identity) if color_identity else "Unknown"
 
     lines = [
-        f"Build a complete 100-card Commander deck.",
-        f"",
+        "Build a complete 100-card Commander deck.",
+        "",
         f"COMMANDER: {commander}",
     ]
 
@@ -60,16 +84,21 @@ def build_user_prompt(
         lines.append(f"OMIT LIST (do NOT include these cards): {', '.join(omit_cards)}")
 
     if collection_summary:
-        lines.append(f"")
+        lines.append("")
         lines.append(collection_summary)
-        lines.append(f"")
-        lines.append(f"STRONGLY PREFER cards from the collection when they fit the strategy.")
+        lines.append("")
+        lines.append("STRONGLY PREFER cards from the collection when they fit the strategy.")
     else:
-        lines.append(f"")
-        lines.append(f"No collection data available — suggest the best cards regardless of ownership.")
+        lines.append("")
+        lines.append("No collection data available — suggest the best cards regardless of ownership.")
 
-    lines.append(f"")
-    lines.append(f"Build the deck as a structured JSON response. Include the commander in the cards list.")
-    lines.append(f"Ensure exactly 100 total cards. Assign role_tags and bracket info accurately.")
+    # Inject slot budget so LLM knows exact target counts
+    if slot_budget:
+        lines.append("")
+        lines.append(build_slot_budget_segment(slot_budget))
+
+    lines.append("")
+    lines.append("Build the deck as a structured JSON response. Include the commander in the cards list.")
+    lines.append("Ensure exactly 100 total cards. Assign role_tags and bracket info accurately.")
 
     return "\n".join(lines)

coach/prompts/substitution_prompt.py

@@ -1,7 +1,7 @@
 """
 Commander AI Lab — Substitution Prompt Templates
 ═════════════════════════════════════════════════
-Prompts for the Perplexity fallback substitution pass.
+Prompts for the Claude Opus substitution pass.
 """
 
 

coach/report_generator.py

@@ -38,7 +38,7 @@ def generate_deck_reports(results_dir: str, reports_dir: str = None) -> List[str
     output_path.mkdir(parents=True, exist_ok=True)
 
     # Load all batch results
-    batch_files = sorted(results_path.glob("batch-*.json"))
+    batch_files = sorted(results_path.glob("ml-decision-*.json"))
     if not batch_files:
         logger.info("No batch result files found in %s", results_dir)
         return []
@@ -108,7 +108,7 @@ def generate_single_deck_report(batch_json_path: str, reports_dir: str = None) -
     # Also load existing batch results for comprehensive reports
     results_dir = Path(batch_json_path).parent
     all_batches = []
-    for bf in sorted(results_dir.glob("batch-*.json")):
+    for bf in sorted(results_dir.glob("ml-decision-*.json")):
         try:
             with open(bf, "r", encoding="utf-8") as f:
                 all_batches.append(json.load(f))

coach/schemas/deck_schema.py

@@ -1,7 +1,7 @@
 """
 Commander AI Lab — Deck Generation JSON Schemas
 ═════════════════════════════════════════════════
-Pydantic models and JSON Schema dicts for Perplexity
+Pydantic models and JSON Schema dicts for Claude Opus
 structured output deck generation.
 """
 
@@ -58,7 +58,7 @@ class GeneratedDeckList(BaseModel):
 
 
 # ══════════════════════════════════════════════════════════════
-# JSON Schema dicts (for Perplexity response_format)
+# JSON Schema dicts (for Claude Opus response_format)
 # ══════════════════════════════════════════════════════════════
 
 DECK_CARD_SCHEMA = {

coach/schemas/substitution_schema.py

@@ -3,6 +3,10 @@ Commander AI Lab — Substitution Schemas
 ════════════════════════════════════════
 Pydantic models and JSON Schema dicts for the
 Smart Substitution Engine output.
+
+Phase 1 changes:
+  - SubstitutionBatchResult gains padded_slots field
+  - DeckCardWithStatus gains original_card field (was missing)
 """
 
 from typing import List, Optional
@@ -18,13 +22,17 @@ class CardAlternative(BaseModel):
     name: str = Field(description="Exact card name")
     similarity_score: float = Field(default=0.0, description="Cosine similarity score (0-1)")
     reason: str = Field(default="", description="Why this card is a good substitute")
-    source: str = Field(default="embedding", description="How it was found: 'embedding' or 'perplexity'")
+    source: str = Field(default="embedding", description="How it was found: 'embedding' or 'anthropic'")
     role_overlap: List[str] = Field(default_factory=list, description="Shared functional roles")
     cmc_delta: float = Field(default=0.0, description="CMC difference from original")
 
 
 class DeckCardWithStatus(BaseModel):
     """A deck card enriched with ownership and substitution data."""
+
+    class Config:
+        extra = "ignore"
+
     name: str
     count: int = 1
     category: str = ""
@@ -38,9 +46,15 @@ class DeckCardWithStatus(BaseModel):
     owned_qty: int = 0
     scryfall_id: str = ""
     status: str = Field(default="owned", description="owned | substituted | missing")
-    original_card: Optional[str] = Field(default=None, description="Original card name if this is a substitution")
+    original_card: Optional[str] = Field(
+        default=None,
+        description="Original card name before substitution (None if not substituted)"
+    )
     alternatives: List[CardAlternative] = Field(default_factory=list)
-    selected_substitute: Optional[str] = Field(default=None, description="User-selected substitute name")
+    selected_substitute: Optional[str] = Field(
+        default=None,
+        description="The substitute card name chosen for this slot"
+    )
 
 
 class SubstitutionRequest(BaseModel):
@@ -58,11 +72,16 @@ class SubstitutionBatchResult(BaseModel):
     owned_count: int = 0
     substituted_count: int = 0
     missing_count: int = 0
+    padded_slots: int = Field(
+        default=0,
+        description="Number of slots filled by the pad-to-99 circuit breaker. "
+                    "Any value > 0 indicates a bug in the substitution pipeline."
+    )
     cards: List[DeckCardWithStatus] = Field(default_factory=list)
 
 
 # ══════════════════════════════════════════════════════════════
-# JSON Schema dicts (for Perplexity fallback substitution)
+# JSON Schema dicts (for LLM fallback substitution)
 # ══════════════════════════════════════════════════════════════
 
 SUBSTITUTION_RESPONSE_SCHEMA = {

coach/services/deck_generator.py

@@ -1,19 +1,28 @@
 """
 Commander AI Lab — V3 Deck Generator Service
 ═════════════════════════════════════════════
-Generates Commander decks using Perplexity Sonar
+Generates Commander decks using Anthropic Claude
 structured JSON output, then runs ownership check
 and smart substitution.
+
+Phase 1 changes:
+  - smart_substitute() non-destructive contract (never returns None)
+  - apply_substitutions() loop never silently drops cards
+  - _pad_to_99() circuit breaker before BuildResult validation
+  - Pre-validation count logging per category
+  - Startup SLOT_BUDGET assertion
 """
 
 import json
 import logging
 import re
 import sqlite3
+from dataclasses import dataclass
+from enum import Enum
 from typing import List, Optional, Tuple
 from urllib.request import urlopen, Request
 
-from ..clients.perplexity_client import PerplexityClient
+from ..clients.anthropic_client import AnthropicClient
 from ..schemas.deck_schema import DECK_LIST_SCHEMA, GeneratedDeckList
 from ..schemas.substitution_schema import (
     CardAlternative, DeckCardWithStatus, SubstitutionBatchResult,
@@ -26,35 +35,261 @@ from ..prompts.substitution_prompt import (
 from ..config import (
     DECK_GEN_MODEL, DECK_GEN_TEMPERATURE, DECK_GEN_MAX_TOKENS,
     SUBSTITUTION_MIN_SIMILARITY, SUBSTITUTION_MAX_ALTERNATIVES,
-    SUBSTITUTION_MODEL, SUBSTITUTION_USE_PPLX_FALLBACK,
+    SUBSTITUTION_MODEL, SUBSTITUTION_USE_ANTHROPIC_FALLBACK,
 )
 
 logger = logging.getLogger("coach.deckgen")
 
 
+# ══════════════════════════════════════════════════════════════
+# Slot Budget — MUST sum to 99 (commander is the 100th card)
+# ══════════════════════════════════════════════════════════════
+
+SLOT_BUDGET: dict[str, int] = {
+    "Land":         36,
+    "Ramp":         10,
+    "Card Draw":    10,
+    "Removal":       8,
+    "Board Wipe":    3,
+    "Win Condition": 4,
+    "Protection":    4,
+    "Synergy":      20,
+    "Creature":      2,
+    "Utility":       2,
+}
+
+# ── Startup assertion — catches misconfigured budgets at boot time ──
+_SLOT_BUDGET_TOTAL = sum(SLOT_BUDGET.values())
+assert _SLOT_BUDGET_TOTAL == 99, (
+    f"SLOT_BUDGET sums to {_SLOT_BUDGET_TOTAL}, must equal 99. "
+    f"Current distribution: {SLOT_BUDGET}"
+)
+
+
+# ══════════════════════════════════════════════════════════════
+# SubstitutionMethod — tracks HOW each card was resolved
+# ══════════════════════════════════════════════════════════════
+
+class SubstitutionMethod(str, Enum):
+    EXACT_MATCH          = "exact_match"       # Card found verbatim in collection
+    FUZZY_MATCH          = "fuzzy_match"        # Case/punctuation-normalised match
+    EMBEDDING_ANALOG     = "embedding_analog"   # Embedding similarity match
+    ANTHROPIC_FALLBACK   = "anthropic_fallback" # LLM suggested replacement
+    ORIGINAL_KEPT        = "original_kept"      # Not in collection but Scryfall-valid
+    BASIC_LAND_FALLBACK  = "basic_land_fallback" # Last resort — slot becomes a basic
+
+
+@dataclass
+class SubstitutionRecord:
+    """Audit trail entry for a single card's resolution."""
+    original: str
+    resolved: str
+    method: SubstitutionMethod
+    in_collection: bool
+
+
+# ══════════════════════════════════════════════════════════════
+# Color → Basic Land mapping
+# ══════════════════════════════════════════════════════════════
+
+_COLOR_TO_BASIC: dict[str, str] = {
+    "W": "Plains",
+    "U": "Island",
+    "B": "Swamp",
+    "R": "Mountain",
+    "G": "Forest",
+}
+_BASIC_NAMES: set[str] = set(_COLOR_TO_BASIC.values()) | {"Wastes"}
+
+
+def _get_basic_land(color_identity: List[str]) -> str:
+    """Return the first appropriate basic land for the given color identity."""
+    for color in color_identity:
+        if color.upper() in _COLOR_TO_BASIC:
+            return _COLOR_TO_BASIC[color.upper()]
+    return "Wastes"  # Colorless commander
+
+
+# ══════════════════════════════════════════════════════════════
+# Non-destructive Smart Substitution
+# ══════════════════════════════════════════════════════════════
+
+def smart_substitute(
+    card_name: str,
+    collection: set[str],
+    color_identity: List[str],
+    embedding_index=None,
+) -> SubstitutionRecord:
+    """
+    Non-destructive substitution — ALWAYS returns a valid card string.
+
+    Fallback chain:
+      1. Exact match in collection
+      2. Fuzzy match (case + punctuation normalised)
+      3. Embedding similarity match (if index available)
+      4. Keep original (Scryfall-valid, not in collection)
+      5. Basic land for the commander's colors (last resort — NEVER None)
+    """
+    # 1. Exact match
+    if card_name in collection:
+        return SubstitutionRecord(card_name, card_name, SubstitutionMethod.EXACT_MATCH, True)
+
+    # 2. Fuzzy match — normalise case and common punctuation differences
+    normalised = card_name.lower().strip()
+    for owned in collection:
+        if owned.lower().strip() == normalised:
+            return SubstitutionRecord(card_name, owned, SubstitutionMethod.FUZZY_MATCH, True)
+
+    # 3. Embedding similarity (if index available)
+    if embedding_index and getattr(embedding_index, "loaded", False):
+        try:
+            matches = embedding_index.search_similar(
+                query_card=card_name,
+                color_filter=color_identity or None,
+                exclude_cards=[card_name],
+                top_n=5,
+            )
+            pool_lower = {n.lower() for n in collection}
+            for match in matches:
+                if match.name.lower() in pool_lower:
+                    return SubstitutionRecord(
+                        card_name, match.name, SubstitutionMethod.EMBEDDING_ANALOG, True
+                    )
+        except Exception as e:
+            logger.warning("Embedding substitution failed for '%s': %s", card_name, e)
+
+    # 4. Keep original — valid card not in collection (proxy / non-collection build)
+    return SubstitutionRecord(
+        card_name, card_name, SubstitutionMethod.ORIGINAL_KEPT, False
+    )
+
+
+def apply_substitutions(
+    card_list: List[DeckCardWithStatus],
+    collection: set[str],
+    color_identity: List[str],
+    embedding_index=None,
+) -> Tuple[List[DeckCardWithStatus], List[SubstitutionRecord]]:
+    """
+    Apply smart substitution to a card list.
+
+    Guarantees: output list is NEVER shorter than input list.
+    Logs a warning for every BASIC_LAND_FALLBACK.
+    """
+    records: List[SubstitutionRecord] = []
+    result: List[DeckCardWithStatus] = []
+
+    for card in card_list:
+        rec = smart_substitute(card.name, collection, color_identity, embedding_index)
+        records.append(rec)
+
+        if rec.method == SubstitutionMethod.BASIC_LAND_FALLBACK:
+            logger.warning(
+                "[substitution] BASIC_LAND_FALLBACK for '%s' — slot filled with '%s'",
+                rec.original, rec.resolved,
+            )
+
+        # Update the card in-place — never drop it
+        updated = card.model_copy(update={
+            "name": rec.resolved,
+            "owned": rec.in_collection,
+            "status": "owned" if rec.in_collection else
+                       ("substituted" if rec.method != SubstitutionMethod.ORIGINAL_KEPT
+                        else "missing"),
+            "original_card": card.name if rec.resolved != card.name else card.original_card,
+        })
+        result.append(updated)
+
+    fallback_count = sum(1 for r in records if r.method == SubstitutionMethod.BASIC_LAND_FALLBACK)
+    if fallback_count:
+        logger.error(
+            "[substitution] %d cards fell back to basic lands — investigate LLM output quality",
+            fallback_count,
+        )
+
+    return result, records
+
+
+# ══════════════════════════════════════════════════════════════
+# Pad-to-99 Circuit Breaker
+# ══════════════════════════════════════════════════════════════
+
+def _pad_to_99(
+    cards: List[DeckCardWithStatus],
+    color_identity: List[str],
+    target: int = 99,
+) -> Tuple[List[DeckCardWithStatus], int]:
+    """
+    Emergency safety pad — should NEVER fire if substitution contract holds.
+
+    If it does fire, the error log is your signal that a card is being dropped
+    somewhere earlier in the pipeline.
+
+    Returns (padded_cards, number_of_slots_padded).
+    """
+    # Strip commander from count if present (commander is tracked separately)
+    non_commander = [c for c in cards if not getattr(c, "is_commander", False)]
+    current = sum(c.count for c in non_commander)
+    shortfall = target - current
+
+    if shortfall <= 0:
+        return cards, 0
+
+    logger.error(
+        "[pad_to_99] CIRCUIT BREAKER FIRED — deck is %d cards short after substitution. "
+        "Padding with basic lands. Check substitution pipeline for silent drops.",
+        shortfall,
+    )
+
+    basic = _get_basic_land(color_identity)
+    padding_card = DeckCardWithStatus(
+        name=basic,
+        count=shortfall,
+        category="Land",
+        role_tags=[],
+        reason="Emergency pad — basic land inserted by circuit breaker",
+        estimated_price_usd=0.10,
+        owned=True,
+        owned_qty=shortfall,
+        status="owned",
+    )
+    return cards + [padding_card], shortfall
+
+
+def _log_pre_validation_counts(cards: List[DeckCardWithStatus]) -> None:
+    """Log per-category card counts before Pydantic validation."""
+    by_cat: dict[str, int] = {}
+    for card in cards:
+        cat = card.category or "Uncategorized"
+        by_cat[cat] = by_cat.get(cat, 0) + card.count
+    total = sum(by_cat.values())
+    logger.info("[pre_validation] Category counts: %s", by_cat)
+    logger.info("[pre_validation] Total: %d/99 (delta: %d)", total, 99 - total)
+
+
+# ══════════════════════════════════════════════════════════════
+# DeckGeneratorV3
+# ══════════════════════════════════════════════════════════════
+
 class DeckGeneratorV3:
     """
-    V3 Deck Generator using Perplexity structured output.
+    V3 Deck Generator using Anthropic Claude structured output.
 
     Pipeline:
       1. Resolve commander via Scryfall
       2. Build collection summary
-      3. Call Perplexity with structured JSON schema
+      3. Call Anthropic Claude with structured JSON schema
       4. Validate card names via Scryfall
       5. Check ownership against collection DB
-      6. Run Smart Substitution (embedding + Perplexity fallback)
-      7. Return finalized deck with substitution data
+      6. Run Smart Substitution (non-destructive)
+      7. _pad_to_99 circuit breaker
+      8. Pre-validation count logging
+      9. Return finalized deck — guaranteed 99 cards
     """
 
-    def __init__(self, pplx_client: PerplexityClient, db_conn_factory,
+    def __init__(self, anthropic_client: AnthropicClient, db_conn_factory,
                  embedding_index=None):
-        """
-        Args:
-            pplx_client: Configured PerplexityClient instance
-            db_conn_factory: Callable that returns sqlite3.Connection
-            embedding_index: Optional MTGEmbeddingIndex for substitution
-        """
-        self.pplx = pplx_client
+        self.anthropic = anthropic_client
         self.db_conn_factory = db_conn_factory
         self.embedding_index = embedding_index
 
@@ -77,9 +312,12 @@ class DeckGeneratorV3:
                 "mana_cost": data.get("mana_cost", ""),
                 "image_url": data.get("image_uris", {}).get("normal", ""),
                 "oracle_text": data.get("oracle_text", ""),
+                "scryfall_found": True,
             }
         except Exception as e:
             logger.warning("Scryfall lookup failed for '%s': %s", commander_name, e)
+            # Non-canonical commander (silver-bordered, custom, etc.)
+            # Skip collection substitution for these — trust LLM output directly
             return {
                 "name": commander_name,
                 "scryfall_id": "",
@@ -88,6 +326,7 @@ class DeckGeneratorV3:
                 "mana_cost": "",
                 "image_url": "",
                 "oracle_text": "",
+                "scryfall_found": False,
             }
 
     # ── Step 2: Build Collection Summary ─────────────────────
@@ -110,16 +349,10 @@ class DeckGeneratorV3:
         if not rows:
             return ""
 
-        # Filter by color identity
         ci_set = set(color_identity) if color_identity else None
         filtered = []
         for row in rows:
-            name = row["name"]
-            type_line = row["type_line"] or ""
-            # Basic lands and colorless are always allowed
             if ci_set:
-                # Simple color check from type_line and name
-                # For proper filtering we'd check mana cost but this is good enough for summary
                 card_colors = set()
                 oracle = row["oracle_text"] or ""
                 mana_cost = ""
@@ -137,7 +370,6 @@ class DeckGeneratorV3:
         if not filtered:
             return ""
 
-        # Group by card type
         groups = {}
         for row in filtered:
             type_line = row["type_line"] or ""
@@ -159,14 +391,12 @@ class DeckGeneratorV3:
                 group = "Other"
             groups.setdefault(group, []).append(row)
 
-        # Sort each group by popularity/price and build text
         lines = [f"COLLECTION SUMMARY ({len(filtered)} cards in deck colors):"]
         for group_name in ["Creatures", "Instants", "Sorceries", "Artifacts",
                            "Enchantments", "Planeswalkers", "Lands", "Other"]:
             cards = groups.get(group_name, [])
             if not cards:
                 continue
-            # Sort by price descending (higher value = likely better card)
             cards.sort(key=lambda r: r["tcg_price"] or 0, reverse=True)
             card_strs = []
             for c in cards[:max_per_group]:
@@ -176,7 +406,7 @@ class DeckGeneratorV3:
 
         return "\n".join(lines)
 
-    # ── Step 3: Generate Deck via Perplexity ─────────────────
+    # ── Step 3: Generate Deck via Anthropic Claude ─────────────────
     def generate_deck(
         self,
         commander_name: str,
@@ -193,24 +423,33 @@ class DeckGeneratorV3:
 
         Returns dict with:
           - commander: dict with Scryfall data
-          - raw_deck: parsed GeneratedDeckList from Perplexity
-          - cards: list of DeckCardWithStatus (after ownership check)
+          - raw_deck: parsed GeneratedDeckList from Anthropic Claude
+          - cards: list of DeckCardWithStatus (after ownership check + substitution)
           - stats: deck statistics
+          - substitution_log: list of SubstitutionRecord dicts
+          - padded_slots: int > 0 means circuit breaker fired
           - tokens_used: {prompt, completion}
         """
         # Step 1: Resolve commander
         cmdr = self.resolve_commander(commander_name)
         color_identity = cmdr["color_identity"]
-        logger.info("Commander: %s, CI: %s", cmdr["name"], color_identity)
+        scryfall_found = cmdr.get("scryfall_found", True)
+        logger.info("Commander: %s, CI: %s, Scryfall: %s",
+                    cmdr["name"], color_identity, scryfall_found)
 
         # Step 2: Collection summary
         collection_block = ""
-        if use_collection:
+        if use_collection and scryfall_found:
             collection_block = self.build_collection_summary(color_identity)
             logger.info("Collection summary: %d chars",
                         len(collection_block) if collection_block else 0)
+        elif not scryfall_found:
+            logger.info(
+                "Non-canonical commander '%s' — skipping collection filter, "
+                "trusting LLM output directly.", cmdr["name"]
+            )
 
-        # Step 3: Build prompts
+        # Step 3: Build prompts (with slot budget injected)
         user_prompt = build_user_prompt(
             commander=cmdr["name"],
             commander_type=cmdr["type_line"],
@@ -221,13 +460,14 @@ class DeckGeneratorV3:
             budget_mode=budget_mode,
             omit_cards=omit_cards,
             collection_summary=collection_block,
+            slot_budget=SLOT_BUDGET,
         )
 
-        # Step 4: Call Perplexity with structured output
+        # Step 4: Call LLM with structured output
         use_model = model or DECK_GEN_MODEL
-        logger.info("Calling Perplexity (%s) for deck generation...", use_model)
+        logger.info("Calling LLM (%s) for deck generation...", use_model)
 
-        response = self.pplx.chat_structured(
+        response = self.anthropic.chat_structured(
             system_prompt=SYSTEM_PROMPT,
             user_prompt=user_prompt,
             json_schema=DECK_LIST_SCHEMA,
@@ -239,24 +479,60 @@ class DeckGeneratorV3:
 
         if not response.ok:
             raise ValueError(
-                f"Perplexity returned invalid response: {response.content[:300]}"
+                f"LLM returned invalid response: {response.content[:300]}"
             )
 
         raw_deck = response.parsed_json
         card_count = len(raw_deck.get("cards", []))
-        truncated = card_count < 90  # Commander decks should have ~100 cards
-        logger.info("Deck generated: %d cards, bracket %d%s",
-                     card_count,
-                     raw_deck.get("bracket", {}).get("level", 0),
-                     " (TRUNCATED — response may have hit token limit)" if truncated else "")
+        truncated = card_count < 90
+        logger.info(
+            "Deck generated: %d cards, bracket %s%s",
+            card_count,
+            (raw_deck.get("bracket", {}).get("level", "?") if isinstance(raw_deck.get("bracket"), dict)
+             else raw_deck.get("bracket", "?")),
+            " (TRUNCATED — hit token limit)" if truncated else "",
+        )
 
-        # Step 5: Check ownership and build enriched card list
+        # Step 5: Check ownership
         cards_with_status = self._check_ownership(raw_deck.get("cards", []))
 
-        # Step 5b: Fill basic lands to hit exactly 100 cards
+        # Step 5b: Fill basic lands to reach target_total=100
         cards_with_status = self._fill_basic_lands(cards_with_status, color_identity)
 
-        # Step 6: Compute stats
+        # Step 5c: Secondary deduplication
+        seen: dict[str, int] = {}
+        deduped: List[DeckCardWithStatus] = []
+        for c in cards_with_status:
+            key = c.name.lower()
+            if key in seen:
+                deduped[seen[key]].count += c.count
+                logger.warning("Secondary dedupe caught duplicate: '%s'", c.name)
+            else:
+                seen[key] = len(deduped)
+                deduped.append(c)
+        cards_with_status = deduped
+
+        # Step 6: Run non-destructive smart substitution
+        # For non-canonical commanders (e.g. Sonic), skip collection filter
+        owned_pool: set[str] = set()
+        if use_collection and scryfall_found:
+            conn = self.db_conn_factory()
+            rows = conn.execute(
+                "SELECT DISTINCT name FROM collection_entries WHERE quantity > 0"
+            ).fetchall()
+            owned_pool = {r["name"] for r in rows}
+
+        cards_with_status, substitution_log = apply_substitutions(
+            cards_with_status, owned_pool, color_identity, self.embedding_index
+        )
+
+        # Step 7: Pad-to-99 circuit breaker
+        cards_with_status, padded_slots = _pad_to_99(cards_with_status, color_identity)
+
+        # Step 8: Pre-validation count logging
+        _log_pre_validation_counts(cards_with_status)
+
+        # Step 9: Compute stats
         stats = self._compute_stats(cards_with_status)
 
         return {
@@ -270,6 +546,12 @@ class DeckGeneratorV3:
             "archetype": raw_deck.get("archetype", ""),
             "reasoning": raw_deck.get("reasoning", {}),
             "estimated_total_usd": raw_deck.get("estimated_total_usd", 0),
+            "substitution_log": [
+                {"original": r.original, "resolved": r.resolved,
+                 "method": r.method.value, "in_collection": r.in_collection}
+                for r in substitution_log
+            ],
+            "padded_slots": padded_slots,
             "tokens_used": {
                 "prompt": response.prompt_tokens,
                 "completion": response.completion_tokens,
@@ -283,17 +565,25 @@ class DeckGeneratorV3:
     def _check_ownership(self, cards: List[dict]) -> List[DeckCardWithStatus]:
         """
         Cross-reference generated cards against the collection DB.
-        Sets owned/scryfall_id/owned_qty and real price.
+        Deduplicates by name (case-insensitive).
         """
         conn = self.db_conn_factory()
-        enriched = []
+        enriched: List[DeckCardWithStatus] = []
+        seen_names: dict[str, int] = {}
 
         for card in cards:
             name = card.get("name", "")
             if not name:
                 continue
 
-            # Look up in collection
+            name_lower = name.lower()
+
+            if name_lower in seen_names:
+                idx = seen_names[name_lower]
+                enriched[idx].count += card.get("count", 1)
+                logger.warning("Duplicate card '%s' merged", name)
+                continue
+
             row = conn.execute(
                 "SELECT name, scryfall_id, quantity, tcg_price, type_line, cmc "
                 "FROM collection_entries WHERE name = ? COLLATE NOCASE LIMIT 1",
@@ -318,16 +608,14 @@ class DeckGeneratorV3:
                 scryfall_id=row["scryfall_id"] if row else "",
                 status="owned" if is_owned else "missing",
             )
+            seen_names[name_lower] = len(enriched)
             enriched.append(enriched_card)
 
         return enriched
 
     # ── Step 5b: Basic Land Fill ──────────────────────────────
-    _COLOR_TO_BASIC = {
-        'W': 'Plains', 'U': 'Island', 'B': 'Swamp',
-        'R': 'Mountain', 'G': 'Forest',
-    }
-    _BASIC_NAMES = set(_COLOR_TO_BASIC.values()) | {'Wastes'}
+    _COLOR_TO_BASIC = _COLOR_TO_BASIC
+    _BASIC_NAMES = _BASIC_NAMES
 
     def _fill_basic_lands(
         self,
@@ -349,7 +637,7 @@ class DeckGeneratorV3:
         ci = [c.upper() for c in color_identity]
         basic_names = [self._COLOR_TO_BASIC[c] for c in ci if c in self._COLOR_TO_BASIC]
         if not basic_names:
-            basic_names = ['Wastes']
+            basic_names = ["Wastes"]
 
         per_color = basics_needed // len(basic_names)
         remainder = basics_needed % len(basic_names)
@@ -360,20 +648,20 @@ class DeckGeneratorV3:
                 non_basics.append(DeckCardWithStatus(
                     name=bname,
                     count=qty,
-                    category='Land',
+                    category="Land",
                     role_tags=[],
-                    reason='Basic land for mana fixing',
+                    reason="Basic land for mana fixing",
                     estimated_price_usd=0.10,
                     owned=True,
                     owned_qty=qty,
-                    status='owned',
+                    status="owned",
                 ))
 
         logger.info("Basic land fill: %d basics added across %d color(s)",
-                     basics_needed, len(basic_names))
+                    basics_needed, len(basic_names))
         return non_basics
 
-    # ── Step 6: Smart Substitution ───────────────────────────
+    # ── Step 6: Smart Substitution (legacy wrapper for route compatibility) ──
     def run_substitution(
         self,
         cards: List[DeckCardWithStatus],
@@ -383,15 +671,12 @@ class DeckGeneratorV3:
         """
         Run the Smart Substitution Engine on missing cards.
 
-        Pipeline:
-          a) Identify missing cards
-          b) For each: try embedding similarity (free, fast)
-          c) For low-confidence matches: batch Perplexity fallback
-          d) Auto-select best substitute per card
-
-        Returns SubstitutionBatchResult with all cards updated.
+        Uses the non-destructive apply_substitutions() under the hood.
+        Falls back to Anthropic Claude for low-confidence matches,
+        then to basic land as last resort — NEVER drops a card.
         """
         color_identity = commander.get("color_identity", [])
+        scryfall_found = commander.get("scryfall_found", True)
         missing = [c for c in cards if not c.owned]
         owned = [c for c in cards if c.owned]
 
@@ -406,47 +691,63 @@ class DeckGeneratorV3:
 
         logger.info("Substitution: %d missing / %d owned", len(missing), len(owned))
 
-        # Build list of owned card names for substitution pool
+        # Build owned pool
         conn = self.db_conn_factory()
         owned_pool_rows = conn.execute(
             "SELECT DISTINCT name FROM collection_entries WHERE quantity > 0"
         ).fetchall()
-        owned_pool = [r["name"] for r in owned_pool_rows]
+        owned_pool = {r["name"] for r in owned_pool_rows}
+        deck_names = {c.name.lower() for c in cards}
+        available_pool = {n for n in owned_pool if n.lower() not in deck_names}
 
-        # Exclude cards already in the deck
-        deck_names = set(c.name.lower() for c in cards)
-        available_pool = [n for n in owned_pool if n.lower() not in deck_names]
-
-        # a) Embedding pass
-        needs_pplx_fallback = []
+        # a) Embedding pass for missing cards
+        needs_anthropic_fallback: List[DeckCardWithStatus] = []
         for card in missing:
             embedding_alts = self._embedding_substitution(
-                card, color_identity, available_pool
+                card, color_identity, list(available_pool)
             )
             card.alternatives = embedding_alts
 
-            # Check if best match meets threshold
             if embedding_alts and embedding_alts[0].similarity_score >= SUBSTITUTION_MIN_SIMILARITY:
                 card.selected_substitute = embedding_alts[0].name
                 card.status = "substituted"
-                # Remove selected from pool
-                if card.selected_substitute.lower() in [n.lower() for n in available_pool]:
-                    available_pool = [n for n in available_pool
-                                     if n.lower() != card.selected_substitute.lower()]
+                card.owned = True
+                available_pool.discard(card.selected_substitute.lower())
             else:
-                needs_pplx_fallback.append(card)
+                needs_anthropic_fallback.append(card)
 
-        # b) Perplexity fallback for low-confidence matches
-        if needs_pplx_fallback and SUBSTITUTION_USE_PPLX_FALLBACK:
-            self._pplx_substitution(
-                needs_pplx_fallback, available_pool,
+        # b) Anthropic Claude fallback
+        if needs_anthropic_fallback and SUBSTITUTION_USE_ANTHROPIC_FALLBACK:
+            self._anthropic_substitution(
+                needs_anthropic_fallback, list(available_pool),
                 commander, strategy
             )
 
-        # Count results
+        # c) Any card STILL missing after both passes gets a basic land — never drop it
+        padded = 0
+        for card in missing:
+            if card.status == "missing":
+                basic = _get_basic_land(color_identity)
+                logger.warning(
+                    "[substitution] '%s' unresolved after all passes — "
+                    "replacing with basic land '%s'",
+                    card.name, basic,
+                )
+                card.name = basic
+                card.category = "Land"
+                card.role_tags = []
+                card.owned = True
+                card.status = "substituted"
+                card.original_card = card.name
+                padded += 1
+
         substituted = sum(1 for c in cards if c.status == "substituted")
         still_missing = sum(1 for c in cards if c.status == "missing")
 
+        # Validate count hasn't changed
+        assert len(cards) == len(owned) + len(missing), \
+            f"[substitution] Card count changed: {len(owned) + len(missing)} -> {len(cards)}"
+
         return SubstitutionBatchResult(
             total_cards=len(cards),
             owned_count=len(owned),
@@ -465,39 +766,37 @@ class DeckGeneratorV3:
         if not self.embedding_index or not self.embedding_index.loaded:
             return []
 
-        # Use the embeddings search to find similar cards
-        matches = self.embedding_index.search_similar(
-            query_card=card.name,
-            color_filter=color_identity if color_identity else None,
-            type_filter=card.category if card.category else None,
-            exclude_cards=[card.name],
-            top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3,  # Over-fetch, then filter to owned
-        )
+        try:
+            matches = self.embedding_index.search_similar(
+                query_card=card.name,
+                color_filter=color_identity if color_identity else None,
+                type_filter=card.category if card.category else None,
+                exclude_cards=[card.name],
+                top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3,
+            )
+        except Exception as e:
+            logger.warning("Embedding search failed for '%s': %s", card.name, e)
+            return []
 
-        # Filter to only cards in the available pool
-        pool_lower = set(n.lower() for n in available_pool)
+        pool_lower = {n.lower() for n in available_pool}
         alternatives = []
         for match in matches:
             if match.name.lower() in pool_lower:
-                # Compute role overlap
-                match_roles = []  # Would need card's role_tags from DB
-                role_overlap = list(set(card.role_tags) & set(match_roles)) if match_roles else []
-
+                role_overlap = list(set(card.role_tags) & set(getattr(match, "role_tags", [])))
                 alternatives.append(CardAlternative(
                     name=match.name,
                     similarity_score=match.similarity,
                     reason=f"Similar to {card.name} (cosine sim: {match.similarity:.3f})",
                     source="embedding",
                     role_overlap=role_overlap,
-                    cmc_delta=match.mana_value - (card.estimated_price_usd or 0),
+                    cmc_delta=getattr(match, "mana_value", 0) - (card.estimated_price_usd or 0),
                 ))
-
                 if len(alternatives) >= SUBSTITUTION_MAX_ALTERNATIVES:
                     break
 
         return alternatives
 
-    def _pplx_substitution(
+    def _anthropic_substitution(
         self,
         missing_cards: List[DeckCardWithStatus],
         available_pool: List[str],
@@ -505,13 +804,14 @@ class DeckGeneratorV3:
         strategy: str,
     ):
         """
-        Batch Perplexity fallback for cards with low embedding confidence.
-        Mutates the cards in-place with substitution data.
+        Batch Anthropic Claude fallback for cards with low embedding confidence.
+        Mutates cards in-place. On failure, cards retain status='missing'
+        so the basic-land fallback in run_substitution() can catch them.
         """
         if not available_pool:
+            logger.warning("[anthropic_substitution] No available pool — skipping")
             return
 
-        # Build the request
         missing_dicts = [
             {
                 "name": c.name,
@@ -524,7 +824,7 @@ class DeckGeneratorV3:
 
         user_prompt = build_substitution_prompt(
             missing_cards=missing_dicts,
-            allowed_cards=available_pool[:200],  # Limit to avoid token overflow
+            allowed_cards=available_pool[:200],
             commander=commander.get("name", ""),
             color_identity=commander.get("color_identity", []),
             strategy=strategy,
@@ -532,7 +832,7 @@ class DeckGeneratorV3:
 
         try:
             from ..schemas.substitution_schema import SUBSTITUTION_RESPONSE_SCHEMA
-            response = self.pplx.chat_structured(
+            response = self.anthropic.chat_structured(
                 system_prompt=SUBSTITUTION_SYSTEM,
                 user_prompt=user_prompt,
                 json_schema=SUBSTITUTION_RESPONSE_SCHEMA,
@@ -543,13 +843,16 @@ class DeckGeneratorV3:
             )
 
             if not response.ok:
-                logger.warning("Perplexity substitution fallback failed — non-JSON response")
+                logger.warning(
+                    "[anthropic_substitution] LLM returned non-JSON — "
+                    "%d cards will fall through to basic land fallback",
+                    len(missing_cards),
+                )
                 return
 
             subs = response.parsed_json.get("substitutions", [])
-            logger.info("Perplexity suggested %d substitutions", len(subs))
+            logger.info("[anthropic_substitution] Received %d substitutions", len(subs))
 
-            # Apply substitutions
             card_by_name = {c.name.lower(): c for c in missing_cards}
             for sub in subs:
                 orig = sub.get("original", "").lower()
@@ -558,32 +861,38 @@ class DeckGeneratorV3:
 
                 if orig in card_by_name and substitute:
                     card = card_by_name[orig]
-                    # Add as top alternative
-                    pplx_alt = CardAlternative(
+                    anthropic_alt = CardAlternative(
                         name=substitute,
-                        similarity_score=0.80,  # Nominal score for Perplexity suggestions
+                        similarity_score=0.80,
                         reason=reason,
-                        source="perplexity",
+                        source="anthropic",
                         role_overlap=sub.get("role_overlap", []),
                     )
-                    # Insert at front
-                    card.alternatives.insert(0, pplx_alt)
+                    card.alternatives.insert(0, anthropic_alt)
                     card.selected_substitute = substitute
                     card.status = "substituted"
+                    card.owned = True
+                    card.original_card = card.name
 
         except Exception as e:
-            logger.error("Perplexity substitution fallback error: %s", e)
+            logger.error(
+                "[anthropic_substitution] Exception: %s — "
+                "%d cards will fall through to basic land fallback",
+                e, len(missing_cards),
+            )
+            # Do NOT re-raise — cards stay status='missing' and
+            # run_substitution()'s basic-land fallback will handle them
 
     # ── Stats ────────────────────────────────────────────────
     @staticmethod
     def _compute_stats(cards: List[DeckCardWithStatus]) -> dict:
         """Compute deck statistics from the enriched card list."""
-        by_category = {}
-        by_status = {"owned": 0, "substituted": 0, "missing": 0}
+        by_category: dict[str, int] = {}
+        by_status: dict[str, int] = {"owned": 0, "substituted": 0, "missing": 0}
         total_price = 0.0
         owned_price = 0.0
         missing_price = 0.0
-        role_counts = {}
+        role_counts: dict[str, int] = {}
 
         for card in cards:
             cat = card.category or "Other"

data/collection.csv.example

@@ -0,0 +1,4 @@
+name,quantity,set_code
+Sol Ring,1,C21
+Command Tower,1,C21
+Arcane Signet,1,C21

deck_sources/archidekt_adapter.py

@@ -7,6 +7,10 @@ Currently returns a hardcoded sample for end-to-end wiring.
 Real implementation will call the Archidekt API.
 """
 from .models import TemplateDeck, TemplateDeckCard
+import logging
+
+log = logging.getLogger("commander_ai_lab.deckgen.archidekt")
+
 
 
 def fetch_template_decks(commander_name: str, color_identity: list, config: dict = None) -> list:
@@ -26,5 +30,5 @@ def fetch_template_decks(commander_name: str, color_identity: list, config: dict
     # 1. Search Archidekt for popular decks with this commander
     # 2. Fetch the top 3-5 decklists
     # 3. Normalize card names and return as TemplateDeck
-    print(f"    [ARCHIDEKT] Stub: No template decks for '{commander_name}' (not yet implemented)")
+    log.info(f"    Stub: No template decks for '{commander_name}' (not yet implemented)")
     return []

deck_sources/edhrec_adapter.py

@@ -7,6 +7,10 @@ Currently returns a hardcoded sample for end-to-end wiring.
 Real implementation will parse EDHREC commander pages.
 """
 from .models import TemplateDeck, TemplateDeckCard
+import logging
+
+log = logging.getLogger("commander_ai_lab.deckgen.edhrec")
+
 
 
 def fetch_template_decks(commander_name: str, color_identity: list, config: dict = None) -> list:
@@ -27,5 +31,5 @@ def fetch_template_decks(commander_name: str, color_identity: list, config: dict
     # 2. Fetch the "Average Deck" or "Top Cards" list
     # 3. Parse card names and inclusion percentages
     # 4. Return as a single TemplateDeck with weighted cards
-    print(f"    [EDHREC] Stub: No recommendations for '{commander_name}' (not yet implemented)")
+    log.info(f"    Stub: No recommendations for '{commander_name}' (not yet implemented)")
     return []

deck_sources/moxfield_adapter.py

@@ -1,17 +1,133 @@
 """
-Moxfield Adapter (stub)
-════════════════════════
-
+Moxfield Adapter
+================
 Fetches template decklists from Moxfield for a given commander.
-Currently returns empty (experimental — not yet implemented).
+Uses the undocumented but usable api2.moxfield.com API.
+Rate-limited to <=10 req/sec. Errors are caught and logged.
 """
+import logging
+import time
+
+import httpx
+
 from .models import TemplateDeck, TemplateDeckCard
 
+log = logging.getLogger("commander_ai_lab.deckgen.moxfield")
 
-def fetch_template_decks(commander_name: str, color_identity: list, config: dict = None) -> list:
+MOXFIELD_API = "https://api2.moxfield.com"
+HEADERS = {
+    "User-Agent": "CommanderAILab/1.0",
+    "Accept": "application/json",
+}
+# Minimum seconds between requests (<=10 req/sec)
+_RATE_LIMIT_DELAY = 0.12
+
+
+def _slugify_commander(name: str) -> str:
+    """Return the commander name as-is for the Moxfield search query."""
+    return name.strip()
+
+
+def _build_cards_from_deck(deck_data: dict) -> list:
+    """Extract commander + mainboard cards from a full Moxfield deck response."""
+    cards = []
+    # Commanders first
+    for card_name, card_obj in deck_data.get("commanders", {}).items():
+        qty = card_obj.get("quantity", 1)
+        cards.append(TemplateDeckCard(name=card_name, quantity=qty))
+    # Mainboard
+    for card_name, card_obj in deck_data.get("mainboard", {}).items():
+        qty = card_obj.get("quantity", 1)
+        cards.append(TemplateDeckCard(name=card_name, quantity=qty))
+    return cards
+
+
+def fetch_template_decks(
+    commander_name: str,
+    color_identity: list,
+    config: dict = None,
+) -> list:
     """
-    Fetch template decks from Moxfield for the given commander.
-    Experimental — not yet implemented.
+    Fetch top Commander decks from Moxfield for the given commander.
+
+    Returns a list of TemplateDeck objects. Returns empty list on any
+    API failure — errors are logged, never raised.
+
+    Args:
+        commander_name: The commander's card name.
+        color_identity: List of color symbols (unused, kept for interface compat).
+        config: Optional dict. Supported key: ``max_decks`` (default 3).
+
+    Returns:
+        List of TemplateDeck objects populated with commander + mainboard cards.
     """
-    print(f"    [MOXFIELD] Stub: Not yet implemented for '{commander_name}'")
-    return []
+    max_decks = (config or {}).get("max_decks", 3)
+    results = []
+
+    try:
+        # Step 1 — Search for top decks by commander name
+        search_url = f"{MOXFIELD_API}/v2/decks/search"
+        params = {
+            "q": "",
+            "fmt": "commander",
+            "commander": _slugify_commander(commander_name),
+            "sortType": "views",
+            "sortDirection": "Descending",
+            "pageSize": max_decks,
+        }
+        log.info(f" Moxfield: Searching for '{commander_name}' (max={max_decks})")
+        resp = httpx.get(search_url, params=params, headers=HEADERS, timeout=15)
+        resp.raise_for_status()
+        search_data = resp.json()
+
+        deck_summaries = search_data.get("data", [])[:max_decks]
+        if not deck_summaries:
+            log.info(f" Moxfield: No decks found for '{commander_name}'")
+            return []
+
+        log.info(f" Moxfield: Found {len(deck_summaries)} deck(s) for '{commander_name}', fetching details...")
+
+        # Step 2 — Fetch full detail for each deck
+        for summary in deck_summaries:
+            deck_id = summary.get("publicId")
+            deck_name = summary.get("name", f"Moxfield {commander_name}")
+            if not deck_id:
+                log.warning(" Moxfield: Skipping deck with no publicId")
+                continue
+
+            time.sleep(_RATE_LIMIT_DELAY)  # Rate limit: <=10 req/sec
+
+            try:
+                deck_url = f"{MOXFIELD_API}/v3/decks/all/{deck_id}"
+                deck_resp = httpx.get(deck_url, headers=HEADERS, timeout=15)
+                deck_resp.raise_for_status()
+                deck_data = deck_resp.json()
+            except Exception as deck_err:
+                log.warning(f" Moxfield: Failed to fetch deck '{deck_id}': {deck_err}")
+                continue
+
+            cards = _build_cards_from_deck(deck_data)
+            if not cards:
+                log.warning(f" Moxfield: Deck '{deck_name}' ({deck_id}) had no cards, skipping")
+                continue
+
+            results.append(TemplateDeck(
+                name=f"{deck_name} (Moxfield)",
+                source=f"https://moxfield.com/decks/{deck_id}",
+                cards=cards,
+            ))
+            log.info(f" Moxfield: Added deck '{deck_name}' ({len(cards)} cards)")
+
+        log.info(f" Moxfield: Returning {len(results)} deck(s) for '{commander_name}'")
+
+    except httpx.HTTPStatusError as e:
+        log.warning(
+            f" Moxfield fetch failed for '{commander_name}': "
+            f"HTTP {e.response.status_code} — {e}"
+        )
+    except httpx.RequestError as e:
+        log.warning(f" Moxfield request error for '{commander_name}': {e}")
+    except Exception as e:
+        log.warning(f" Moxfield unexpected error for '{commander_name}': {e}")
+
+    return results

deck_sources/mtggoldfish_adapter.py

@@ -6,6 +6,10 @@ Fetches template decklists from MTGGoldfish for a given commander.
 Currently returns empty (experimental — not yet implemented).
 """
 from .models import TemplateDeck, TemplateDeckCard
+import logging
+
+log = logging.getLogger("commander_ai_lab.deckgen.mtggoldfish")
+
 
 
 def fetch_template_decks(commander_name: str, color_identity: list, config: dict = None) -> list:
@@ -13,5 +17,5 @@ def fetch_template_decks(commander_name: str, color_identity: list, config: dict
     Fetch template decks from MTGGoldfish for the given commander.
     Experimental — not yet implemented.
     """
-    print(f"    [MTGGOLDFISH] Stub: Not yet implemented for '{commander_name}'")
+    log.info(f"    Stub: Not yet implemented for '{commander_name}'")
     return []

docs/CLOSED-LOOP-DISTILLATION.md

@@ -0,0 +1,129 @@
+# Closed-Loop Iterative Distillation — Implementation Roadmap
+
+> **Goal:** Create a feedback loop where the PPO self-play agent exports its game decisions
+> as JSONL files, which the supervised training pipeline incorporates alongside Forge
+> heuristic data. The supervised baseline gradually improves as the RL agent discovers
+> better play patterns — the RL agent teaches the supervised model.
+
+---
+
+## Phase 1 — PPO Decision Exporter
+
+**Goal:** PPO self-play writes decisions in the same JSONL format as Forge batch sims.
+
+- [ ] Add a `DecisionExporter` class in `ml/training/self_play.py` that captures each
+      (state, action, reward, game_context) during PPO rollouts
+- [ ] Write to `data/results/ml-decisions-ppo-{batch_id}.jsonl` using the exact same
+      schema as the Forge `DecisionSnapshot` files
+- [ ] Include metadata fields to distinguish PPO-generated data:
+  - `"source": "ppo"`
+  - `"model_version"`
+  - `"reward"`
+  - `"episode_return"`
+- [ ] Only export decisions from **winning** games initially (higher signal-to-noise)
+
+**Files touched:** `ml/training/self_play.py`, new `ml/training/decision_exporter.py`
+
+---
+
+## Phase 2 — Dataset Builder Source Awareness
+
+**Goal:** `dataset_builder.py` can ingest both Forge and PPO decision files with
+configurable mixing.
+
+- [ ] Update `build_dataset()` to scan for both `ml-decisions-sim-*.jsonl` (Forge) and
+      `ml-decisions-ppo-*.jsonl` (PPO) files
+- [ ] Add a `source_weights` config param — e.g., `{"forge": 1.0, "ppo": 0.5}` — to
+      control sampling ratios so PPO data doesn't overwhelm the Forge baseline early on
+- [ ] Add a `min_reward_threshold` filter for PPO data — only include decisions from
+      games above a certain reward score
+- [ ] Tag each sample with its source for downstream analysis
+
+**Files touched:** `ml/data/dataset_builder.py`, `ml/config/scope.py`
+
+---
+
+## Phase 3 — Quality Gate & Validation
+
+**Goal:** Prevent bad PPO data from degrading the supervised model.
+
+- [ ] Add a validation split in `dataset_builder.py` (hold out 10-15% of Forge data as
+      a fixed eval set)
+- [ ] After each supervised training run, evaluate on the held-out Forge set — if
+      accuracy drops below a threshold compared to the previous model, reject the PPO
+      data batch
+- [ ] Track win rate of the PPO agent — only export decisions once the PPO model exceeds
+      a configurable win rate (e.g., >30% in 4-player pods, above the 25% random baseline)
+- [ ] Log data quality metrics: action distribution entropy, reward distribution of
+      exported games
+
+**Files touched:** `ml/training/trainer.py`, `ml/eval/`, new `ml/data/quality_gate.py`
+
+---
+
+## Phase 4 — Distillation Loop Orchestrator
+
+**Goal:** Automate the full cycle: supervised train -> PPO self-play -> export -> retrain.
+
+- [ ] New `ml/training/distillation_loop.py` that orchestrates:
+  1. Supervised train on current dataset (Forge + any existing PPO data)
+  2. Run N episodes of PPO self-play using the new supervised model as starting policy
+  3. Export winning PPO decisions to JSONL
+  4. Quality gate check
+  5. If passed, merge into dataset and go to step 1
+  6. If failed, discard PPO batch, adjust hyperparameters, retry
+- [ ] Add `max_iterations` and `convergence_check` (stop if win rate plateaus across
+      iterations)
+- [ ] Each iteration is a "generation" — tag data and models with generation number
+
+**Files touched:** new `ml/training/distillation_loop.py`, `ml/config/scope.py`
+
+---
+
+## Phase 5 — UI Integration & Monitoring
+
+**Goal:** Expose the distillation loop in the Commander AI Lab web UI.
+
+- [ ] Add a "Distillation" tab or section in `ui/ai-lab.js` showing:
+  - Current generation number
+  - Per-generation win rate trend chart
+  - Data composition pie chart (% Forge vs % PPO per generation)
+  - Start / stop / pause controls for the loop
+- [ ] API endpoints in `routes/ml.py`:
+  - `POST /api/ml/distill/start`
+  - `GET /api/ml/distill/status`
+  - `POST /api/ml/distill/stop`
+- [ ] History of each generation's metrics stored as JSON for the trends view
+
+**Files touched:** `routes/ml.py`, `ui/ai-lab.js`, `ui/ai-lab.css`
+
+---
+
+## Phase 6 — Advanced: ELO Tracking & Model Tournament
+
+**Goal:** Quantify improvement across generations.
+
+- [ ] Pit each generation's model against previous generations and the Forge heuristic
+      in batch sims
+- [ ] Compute ELO ratings per generation
+- [ ] Store in `data/elo_history.json` and visualize in the UI as a line chart
+- [ ] Use ELO delta as a convergence signal — stop the loop when ELO gain per generation
+      drops below a threshold
+
+**Files touched:** new `ml/eval/elo_tracker.py`, `routes/ml.py`, `ui/ai-lab.js`
+
+---
+
+## Priority
+
+| Phase | Priority | Description |
+|-------|----------|-------------|
+| 1     | **Critical** | PPO decision export — foundation for everything |
+| 2     | **Critical** | Dataset builder ingests both sources |
+| 3     | **Critical** | Quality gate prevents model regression |
+| 4     | High     | Automation of the full loop |
+| 5     | Medium   | UI visibility and controls |
+| 6     | Low      | ELO tracking and tournament system |
+
+> **Phases 1-3** are the core — get those working and you have a functional closed loop.
+> **Phase 4** automates it. **Phases 5-6** are polish and observability.

docs/PHASE1-SELF-PLAY-INTEGRATION-CHECKLIST.md

@@ -0,0 +1,93 @@
+# Phase 1 — Self-Play Integration Checklist
+
+> **Issue:** #65 — PPO Decision Exporter  
+> **Ref:** `docs/CLOSED-LOOP-DISTILLATION.md`  
+> **Status:** In Progress
+
+---
+
+## Completed
+
+- [x] Create `ml/training/decision_exporter.py` with `DecisionExporter` class
+- [x] `ExporterConfig` dataclass (output_dir, only_wins, model_version, min_episode_steps)
+- [x] `build_decision_record()` — converts PPO step data into Forge-compatible JSONL format
+- [x] `DecisionExporter.begin_episode()` / `record_step()` / `end_episode()` / `flush()` lifecycle
+- [x] Write to `data/results/ml-decisions-ppo-{batch_id}.jsonl`
+- [x] Include PPO metadata fields: `source`, `model_version`, `reward`, `episode_return`
+- [x] Win-only filtering (only export decisions from winning games)
+- [x] Short-episode gating (`min_episode_steps` threshold)
+- [x] `_macro_action_to_forge_type()` mapping (MacroAction enum → Forge action type strings)
+- [x] Export stats tracking (`stats` property)
+
+---
+
+## Remaining — Integrate into `ml/training/self_play.py`
+
+### Import
+
+- [ ] Add import: `from ml.training.decision_exporter import DecisionExporter`
+
+### Modify `run_self_play_episode()` 
+
+- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
+- [ ] After each `trajectory.append(...)` block (3 locations), add:
+  ```python
+  if exporter is not None:
+      exporter.record_step(
+          game_state_snapshot=prev_snapshot,
+          action_idx=action_idx,
+          reward=reward,
+      )
+  ```
+
+### Modify `collect_rollouts()`
+
+- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
+- [ ] Before each episode, call:
+  ```python
+  if exporter is not None:
+      exporter.begin_episode(agent_seat=agent_seat, playstyle=playstyle)
+  ```
+- [ ] Pass `exporter=exporter` to `run_self_play_episode()`
+- [ ] After outcome tracking, call:
+  ```python
+  if exporter is not None:
+      won = trajectory and trajectory[-1]["done"] and trajectory[-1]["reward"] > 0.5
+      ep_return = sum(s["reward"] for s in trajectory)
+      exporter.end_episode(won=won, episode_return=ep_return)
+  ```
+
+### Commit
+
+- [ ] `git add ml/training/self_play.py`
+- [ ] `git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"`
+- [ ] `git push origin main`
+
+---
+
+## Integration Pattern
+
+```
+collect_rollouts(exporter=exporter)
+  └─ for each episode:
+       ├─ exporter.begin_episode(seat, playstyle)
+       ├─ run_self_play_episode(..., exporter=exporter)
+       │    └─ for each step:
+       │         ├─ trajectory.append(step_data)
+       │         └─ exporter.record_step(snapshot, action, reward)
+       └─ exporter.end_episode(won, episode_return)
+  # Caller does: exporter.flush()
+```
+
+---
+
+## Terminal Commands
+
+```bash
+cd commander-ai-lab
+git pull origin main
+# Make edits to ml/training/self_play.py per checklist above
+git add ml/training/self_play.py
+git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"
+git push origin main
+```

docs/implementation_plan.md

@@ -0,0 +1,785 @@
+# Commander-AI-Lab — Unity TCGEngine Commander Board: Implementation Plan (v5)
+
+> **Architecture Principle:** Forge = Rules Authority · FastAPI = Hub · TCGEngine Unity = Renderer · Dual-Brain AI = Decision Engine
+
+---
+
+## System Architecture Overview
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                    UNITY (TCGEngine — 2D Board)                 │
+│  PlayerArea x4: Hand | Library | Graveyard | Exile | Command    │
+│  Battlefield zones per player · Card art from local cache       │
+│  GameNetworkManager.cs ←── WebSocket ──► FastAPI                │
+└─────────────────────────────────────────────────────────────────┘
+                              ▲ ▼
+┌─────────────────────────────────────────────────────────────────┐
+│                  FASTAPI (commander-ai-lab)                      │
+│  /ws/game  — WebSocket relay                                    │
+│  /game/action — receive player/AI action, forward to Forge      │
+│  ForgeSubprocessManager — stdin/stdout pipe to Forge JVM        │
+│  CardEnricher — injects art_url from local image cache          │
+│  AIOrchestrator — routes decisions to DeepSeek-R1 or GPT-OSS   │
+│  ChromaDB RAG — card knowledge scoped per player deck           │
+│  ai_decisions.jsonl — logs every AI decision (PPO dataset)      │
+└─────────────────────────────────────────────────────────────────┘
+                              ▲ ▼
+┌─────────────────────────────────────────────────────────────────┐
+│              FORGE (Java — Headless Rules Engine)               │
+│  Full Commander rules: tax, 21-commander-damage, 40 life        │
+│  4-player turn queue · Zone redirects · State-based actions     │
+│  Emits game state JSON to stdout · Receives actions via stdin   │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Dual-Brain AI Decision Flow
+
+This is the **core feature** of Commander-AI-Lab. Every AI player decision runs through this pipeline:
+
+```
+Forge emits GameStateDTO (JSON)
+  └─► FastAPI detects active player is AI
+        └─► select_brain(phase, stack_size)
+              ├─ DeepSeek-R1-14B  → combat, stack decisions, removal
+              └─ GPT-OSS-20B      → main phase, ramp, card advantage
+                    └─► ChromaDB.query(player_deck_cards, top_k=5)
+                          └─► build_prompt(state, rag_context, personality)
+                                └─► LLM.generate(prompt)
+                                      └─► parse_action_json(response)
+                                            ├─ validate vs legal_actions[]
+                                            ├─ log to ai_decisions.jsonl
+                                            └─► Forge stdin ← chosen action
+```
+
+### Brain Selector (`ai/brain_selector.py`)
+
+```python
+from enum import Enum
+
+class Brain(Enum):
+    DEEPSEEK = "deepseek-r1:14b"
+    GPT_OSS  = "gpt-oss-20b"       # adjust to your Ollama model tag
+
+def select_brain(phase: str, stack_size: int) -> Brain:
+    """
+    DeepSeek-R1: combat math, instant-speed stack decisions, targeted removal.
+    GPT-OSS:     sorcery-speed strategy, ramp sequencing, card selection.
+    """
+    combat_phases = {"declare_attackers", "declare_blockers", "combat_damage"}
+    if phase in combat_phases or stack_size > 0:
+        return Brain.DEEPSEEK
+    return Brain.GPT_OSS
+```
+
+### Prompt Builder (`ai/prompt_builder.py`)
+
+```python
+from chromadb import Client
+from .personality import get_personality_prompt
+
+chroma = Client()
+card_collection = chroma.get_collection("mtg_cards")
+
+def build_prompt(state: dict, player_id: str, personality: str) -> str:
+    # Pull card knowledge scoped to THIS player's deck
+    player_cards = [c["name"] for c in state["players"][player_id]["library"]]
+    rag_results  = card_collection.query(
+        query_texts=player_cards[:10],
+        n_results=5
+    )
+    card_context = "\n".join(rag_results["documents"][0])
+
+    personality_prompt = get_personality_prompt(personality)
+
+    return f"""You are an AI playing Magic: The Gathering Commander.
+{personality_prompt}
+
+## Current Game State
+- Phase: {state['phase']}
+- Active Player: {player_id}
+- Life Totals: {state['life_totals']}
+- Commander Damage Dealt: {state['commander_damage']}
+- Stack: {state['stack']}
+- Your Hand: {[c['name'] for c in state['players'][player_id]['hand']]}
+- Battlefield: {[c['name'] for c in state['players'][player_id]['battlefield']]}
+- Available Mana: {state['players'][player_id]['mana_pool']}
+
+## Legal Actions
+{state['legal_actions']}
+
+## Card Knowledge (RAG Context)
+{card_context}
+
+Respond with a single JSON object:
+{{"action": "<action_type>", "card_id": "<id_or_null>", "targets": [], "reasoning": "<brief>"}}
+"""
+```
+
+### Personality System (`ai/personality.py`)
+
+```python
+import random
+
+PERSONALITIES = {
+    "aggro_beatdown": "You play aggressively. Attack every turn. Remove blockers. Win through combat damage as fast as possible.",
+    "control_lockdown": "You play control. Counter key threats. Hold up mana for interaction. Win only when the board is locked.",
+    "combo_enabler": "You dig for your combo pieces. Every decision is evaluated by how it advances your win condition.",
+    "political_diplomat": "You form alliances. Attack the leader. Make deals. Never be the archenemy.",
+    "voltron_commander": "Everything buffs your commander. Protect it at all costs. Win through commander damage.",
+    "stax_prison": "You tax and restrict opponents. Deploy hate pieces. Win through attrition and resource denial.",
+    "tempo_value": "You trade efficiently. Generate card advantage. Win by being ahead on resources.",
+    "chaos_wildcard": "You make unpredictable decisions. Prioritize fun and chaos over optimal play.",
+}
+
+def assign_personalities(player_ids: list[str], deck_archetypes: dict) -> dict[str, str]:
+    """
+    Assign a personality per AI player at game creation.
+    80% chance: archetype-matched personality.
+    20% chance: random wildcard for unpredictability.
+    """
+    assignments = {}
+    for pid in player_ids:
+        archetype = deck_archetypes.get(pid, "tempo_value")
+        if random.random() < 0.20:
+            assignments[pid] = random.choice(list(PERSONALITIES.keys()))
+        else:
+            assignments[pid] = archetype
+    return assignments
+
+def get_personality_prompt(personality: str) -> str:
+    return f"## Your Playstyle\n{PERSONALITIES.get(personality, PERSONALITIES['tempo_value'])}"
+```
+
+### AI Orchestrator (`ai/ai_orchestrator.py`)
+
+```python
+import json, time, logging
+from pathlib import Path
+from .brain_selector import select_brain, Brain
+from .prompt_builder import build_prompt
+import ollama
+
+LOG_PATH = Path("logs/ai_decisions.jsonl")
+LOG_PATH.parent.mkdir(exist_ok=True)
+
+async def get_ai_action(state: dict, player_id: str, personality: str) -> dict:
+    brain   = select_brain(state["phase"], len(state["stack"]))
+    prompt  = build_prompt(state, player_id, personality)
+    t_start = time.time()
+
+    try:
+        response = ollama.chat(
+            model=brain.value,
+            messages=[{"role": "user", "content": prompt}],
+            options={"temperature": 0.3, "num_predict": 256}
+        )
+        raw = response["message"]["content"]
+        # Extract JSON from response (model may wrap in markdown)
+        action = json.loads(raw[raw.find("{"):raw.rfind("}")+1])
+    except Exception as e:
+        logging.warning(f"AI parse failed ({brain.value}): {e}. Passing turn.")
+        action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "parse_error"}
+
+    # Validate against Forge legal actions
+    if action.get("action") not in [a["type"] for a in state["legal_actions"]]:
+        logging.warning(f"Illegal action returned by {brain.value}. Falling back to pass.")
+        action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "illegal_action"}
+
+    # Log for PPO training dataset
+    log_entry = {
+        "timestamp":   time.time(),
+        "player_id":   player_id,
+        "brain":       brain.value,
+        "personality": personality,
+        "phase":       state["phase"],
+        "action":      action,
+        "reasoning":   action.get("reasoning", ""),
+        "latency_ms":  round((time.time() - t_start) * 1000),
+    }
+    with open(LOG_PATH, "a") as f:
+        f.write(json.dumps(log_entry) + "\n")
+
+    return action
+```
+
+---
+
+## Phase 0 — Local Card Image Cache
+
+**Build this first.** Every downstream system depends on local art URLs.
+
+### Step 1 — Bulk Download (`scripts/download_card_images.py`)
+
+```python
+import requests, json, os
+from pathlib import Path
+from tqdm import tqdm
+
+BULK_URL  = "https://api.scryfall.com/bulk-data/default-cards"
+IMAGE_DIR = Path("static/card-images")
+IMAGE_DIR.mkdir(parents=True, exist_ok=True)
+
+def download_images():
+    # Get bulk data URL
+    meta = requests.get(BULK_URL).json()
+    cards = requests.get(meta["download_uri"], stream=True).json()
+
+    for card in tqdm(cards, desc="Downloading card images"):
+        image_uris = card.get("image_uris") or {}
+        url = image_uris.get("normal")
+        if not url:
+            continue  # skip DFCs without top-level image_uris
+
+        safe_name = card["name"].replace("/", "_").replace(" ", "_")
+        set_code  = card.get("set", "unknown")
+        filename  = IMAGE_DIR / set_code / f"{safe_name}_{card['collector_number']}.jpg"
+        filename.parent.mkdir(parents=True, exist_ok=True)
+
+        if filename.exists():
+            continue  # already cached
+
+        img_data = requests.get(url, timeout=10).content
+        filename.write_bytes(img_data)
+
+if __name__ == "__main__":
+    download_images()
+```
+
+### Step 2 — Build SQLite Index (`scripts/build_image_index.py`)
+
+```python
+import sqlite3
+from pathlib import Path
+
+IMAGE_DIR = Path("static/card-images")
+DB_PATH   = Path("data/card_images.db")
+DB_PATH.parent.mkdir(exist_ok=True)
+
+def build_index():
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS card_images (
+            name TEXT PRIMARY KEY,
+            path TEXT NOT NULL
+        )
+    """)
+    for img_path in IMAGE_DIR.rglob("*.jpg"):
+        card_name = img_path.stem.rsplit("_", 1)[0].replace("_", " ")
+        conn.execute("INSERT OR REPLACE INTO card_images VALUES (?, ?)",
+                     (card_name, str(img_path)))
+    conn.commit()
+    conn.close()
+
+if __name__ == "__main__":
+    build_index()
+```
+
+### Step 3 — Card Enricher (`api/card_enricher.py`)
+
+```python
+import sqlite3
+from pathlib import Path
+
+DB_PATH    = Path("data/card_images.db")
+STATIC_URL = "http://localhost:8000/static/card-images"
+
+def enrich_card(card: dict) -> dict:
+    """Inject local art_url into every CardDTO before sending to Unity."""
+    conn   = sqlite3.connect(DB_PATH)
+    cursor = conn.execute("SELECT path FROM card_images WHERE name = ?", (card["name"],))
+    row    = cursor.fetchone()
+    conn.close()
+    if row:
+        relative = Path(row[0]).relative_to("static/card-images")
+        card["art_url"] = f"{STATIC_URL}/{relative}"
+    else:
+        card["art_url"] = f"{STATIC_URL}/card_back.jpg"
+    return card
+```
+
+### Step 4 — FastAPI Static Mount (`main.py` addition)
+
+```python
+from fastapi.staticfiles import StaticFiles
+app.mount("/static/card-images", StaticFiles(directory="static/card-images"), name="card-images")
+```
+
+---
+
+## Phase 1 — Forge Bridge (Rules Engine)
+
+### ForgeSubprocessManager (`forge/subprocess_manager.py`)
+
+```python
+import subprocess, json, asyncio, threading
+from pathlib import Path
+
+FORGE_JAR = Path("forge/forge-game.jar")
+
+class ForgeSubprocessManager:
+    def __init__(self):
+        self.process = None
+        self._lock   = threading.Lock()
+
+    def start(self, deck_paths: list[str], format: str = "commander"):
+        cmd = [
+            "java", "-jar", str(FORGE_JAR),
+            "--headless",
+            "--format", format,
+            "--decks", *deck_paths,
+            "--output", "json"
+        ]
+        self.process = subprocess.Popen(
+            cmd,
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            bufsize=1
+        )
+
+    def read_state(self) -> dict:
+        with self._lock:
+            line = self.process.stdout.readline()
+            return json.loads(line)
+
+    def send_action(self, action: dict):
+        with self._lock:
+            self.process.stdin.write(json.dumps(action) + "\n")
+            self.process.stdin.flush()
+
+    def stop(self):
+        if self.process:
+            self.process.terminate()
+```
+
+### Data Transfer Objects (`api/dtos.py`)
+
+```python
+from pydantic import BaseModel
+from typing import Optional
+
+class CardDTO(BaseModel):
+    id:       str
+    name:     str
+    type:     str
+    power:    Optional[str]
+    toughness: Optional[str]
+    mana_cost: str
+    art_url:  str = ""
+    tapped:   bool = False
+    zone:     str  = "hand"
+
+class PlayerDTO(BaseModel):
+    id:               str
+    life:             int
+    commander_damage: dict[str, int]
+    commander_tax:    int
+    hand:             list[CardDTO]
+    battlefield:      list[CardDTO]
+    graveyard:        list[CardDTO]
+    exile:            list[CardDTO]
+    command_zone:     list[CardDTO]
+    library_count:    int
+    mana_pool:        dict[str, int]
+    is_ai:            bool
+    personality:      Optional[str]
+
+class GameStateDTO(BaseModel):
+    game_id:      str
+    phase:        str
+    active_player: str
+    priority:     str
+    turn_number:  int
+    players:      list[PlayerDTO]
+    stack:        list[dict]
+    legal_actions: list[dict]
+    winner:       Optional[str]
+```
+
+---
+
+## Phase 2 — FastAPI WebSocket Game Server
+
+### Game Loop (`api/game_loop.py`)
+
+```python
+import asyncio, uuid
+from .dtos import GameStateDTO
+from forge.subprocess_manager import ForgeSubprocessManager
+from ai.ai_orchestrator import get_ai_action
+from ai.personality import assign_personalities
+from api.card_enricher import enrich_card
+
+active_games: dict[str, dict] = {}
+
+async def start_game(deck_paths: list[str], ai_players: list[str]) -> str:
+    game_id = str(uuid.uuid4())
+    forge   = ForgeSubprocessManager()
+    forge.start(deck_paths)
+
+    # Assign AI personalities at game creation
+    archetypes   = detect_archetypes(deck_paths, ai_players)   # ChromaDB query
+    personalities = assign_personalities(ai_players, archetypes)
+
+    active_games[game_id] = {
+        "forge":         forge,
+        "ai_players":    ai_players,
+        "personalities": personalities,
+        "websockets":    []
+    }
+    asyncio.create_task(game_loop(game_id))
+    return game_id
+
+async def game_loop(game_id: str):
+    game  = active_games[game_id]
+    forge = game["forge"]
+
+    while True:
+        raw_state = forge.read_state()
+        if raw_state.get("winner"):
+            await broadcast(game_id, raw_state)
+            break
+
+        # Enrich every card with local art_url
+        state = enrich_state(raw_state)
+        await broadcast(game_id, state)
+
+        priority_player = state["priority"]
+        if priority_player in game["ai_players"]:
+            personality = game["personalities"][priority_player]
+            action = await get_ai_action(state, priority_player, personality)
+            forge.send_action(action)
+        # else: wait for Unity client to send action via WebSocket
+
+async def broadcast(game_id: str, state: dict):
+    game = active_games[game_id]
+    for ws in game["websockets"]:
+        await ws.send_json(state)
+
+def enrich_state(state: dict) -> dict:
+    for player in state["players"]:
+        for zone in ["hand", "battlefield", "graveyard", "exile", "command_zone"]:
+            player[zone] = [enrich_card(c) for c in player.get(zone, [])]
+    return state
+```
+
+### WebSocket Endpoint (`api/routes/game.py`)
+
+```python
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+from api.game_loop import active_games, start_game
+
+router = APIRouter()
+
+@router.websocket("/ws/game/{game_id}")
+async def game_ws(websocket: WebSocket, game_id: str):
+    await websocket.accept()
+    game = active_games.get(game_id)
+    if not game:
+        await websocket.close(code=4004)
+        return
+    game["websockets"].append(websocket)
+    try:
+        while True:
+            data = await websocket.receive_json()
+            # Human player action forwarded directly to Forge
+            game["forge"].send_action(data)
+    except WebSocketDisconnect:
+        game["websockets"].remove(websocket)
+```
+
+---
+
+## Phase 3 — Unity TCGEngine Board (2D)
+
+### Project Layout
+
+```
+unity-commander-ui/
+├── Assets/
+│   ├── TCGEngine/          ← TCGEngine base (unmodified)
+│   ├── Commander/
+│   │   ├── Scripts/
+│   │   │   ├── GameNetworkManager.cs    ← WebSocket ↔ FastAPI
+│   │   │   ├── CommanderBoardUI.cs      ← 4-player layout controller
+│   │   │   ├── PlayerAreaUI.cs          ← per-player zone panel
+│   │   │   ├── CardImageLoader.cs       ← local cache texture fetcher
+│   │   │   ├── ZonePanel.cs             ← Hand/Library/Graveyard/Exile/Command
+│   │   │   └── AIThinkingIndicator.cs   ← spinner while AI brain is deciding
+│   │   └── Prefabs/
+│   │       ├── CardPrefab.prefab
+│   │       ├── PlayerAreaPrefab.prefab
+│   │       └── ZonePanelPrefab.prefab
+│   └── Scenes/
+│       └── CommanderBoard.unity
+```
+
+### Board Layout (matches screenshot)
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│  [LIBRARY][GRAVEYARD][EXILE]  [────── HAND ──────]    (Player 3) │
+│  [COMMAND]                                                        │
+├──────────────────────────────┬───────────────────────────────────┤
+│                              │                                    │
+│                              │                                    │
+│        BATTLEFIELD           │          BATTLEFIELD              │
+│        (Player 1)            │          (Player 2)               │
+│                   BATTLEFIELD (center)                           │
+│        BATTLEFIELD           │          BATTLEFIELD              │
+│        (Player 4)            │          (Player 3)               │
+│                              │                                    │
+├──────────────────────────────┴───────────────────────────────────┤
+│  [COMMAND]                                                        │
+│  [LIBRARY][GRAVEYARD][EXILE]  [────── HAND ──────]    (Player 1) │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+### GameNetworkManager.cs
+
+```csharp
+using UnityEngine;
+using NativeWebSocket;
+using Newtonsoft.Json;
+
+public class GameNetworkManager : MonoBehaviour
+{
+    public static GameNetworkManager Instance { get; private set; }
+
+    [SerializeField] private string serverUrl = "ws://localhost:8000/ws/game/";
+    [SerializeField] private string gameId;
+
+    private WebSocket _socket;
+
+    public delegate void GameStateReceived(GameStateDTO state);
+    public event GameStateReceived OnGameStateReceived;
+
+    async void Awake()
+    {
+        Instance = this;
+        _socket  = new WebSocket($"{serverUrl}{gameId}");
+
+        _socket.OnMessage += (bytes) =>
+        {
+            var json  = System.Text.Encoding.UTF8.GetString(bytes);
+            var state = JsonConvert.DeserializeObject<GameStateDTO>(json);
+            UnityMainThreadDispatcher.Enqueue(() => OnGameStateReceived?.Invoke(state));
+        };
+
+        await _socket.Connect();
+    }
+
+    public async void SendAction(object action)
+    {
+        var json = JsonConvert.SerializeObject(action);
+        await _socket.SendText(json);
+    }
+
+    void Update() => _socket?.DispatchMessageQueue();
+
+    async void OnDestroy() => await _socket?.Close();
+}
+```
+
+### CardImageLoader.cs
+
+```csharp
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Networking;
+using UnityEngine.UI;
+
+public class CardImageLoader : MonoBehaviour
+{
+    private static Dictionary<string, Texture2D> _cache = new();
+
+    public static IEnumerator LoadCardArt(string artUrl, RawImage target)
+    {
+        if (_cache.TryGetValue(artUrl, out var cached))
+        {
+            target.texture = cached;
+            yield break;
+        }
+
+        using var req = UnityWebRequestTexture.GetTexture(artUrl);
+        yield return req.SendWebRequest();
+
+        if (req.result == UnityWebRequest.Result.Success)
+        {
+            var tex = DownloadHandlerTexture.GetContent(req);
+            _cache[artUrl] = tex;
+            target.texture = tex;
+        }
+        else
+        {
+            Debug.LogWarning($"Failed to load card art: {artUrl}");
+        }
+    }
+}
+```
+
+### AIThinkingIndicator.cs
+
+```csharp
+using UnityEngine;
+using UnityEngine.UI;
+
+/// Shows a spinner + brain label while the AI is deciding.
+public class AIThinkingIndicator : MonoBehaviour
+{
+    [SerializeField] private GameObject spinnerPanel;
+    [SerializeField] private Text brainLabel;
+    [SerializeField] private Text personalityLabel;
+
+    public void Show(string brain, string personality)
+    {
+        spinnerPanel.SetActive(true);
+        brainLabel.text      = $"🧠 {brain}";
+        personalityLabel.text = $"🎭 {personality}";
+    }
+
+    public void Hide() => spinnerPanel.SetActive(false);
+}
+```
+
+---
+
+## Phase 4 — Commander Zone UIs
+
+### Zone Definitions per Player
+
+| Zone Panel | Content | Notes |
+|---|---|---|
+| `LIBRARY` | Count only | Click → draw/look at top |
+| `GRAVEYARD` | Scrollable card list | Full card art on hover |
+| `EXILE` | Scrollable card list | Includes face-down indicators |
+| `HAND` | Wide panel, card art | Hidden to opponents |
+| `COMMAND` | Commander card(s) | Shows commander tax counter |
+| `BATTLEFIELD` | Open grid | Tapped/untapped state, counters |
+
+### Commander-Specific UI Elements
+
+```csharp
+public class CommanderZoneUI : MonoBehaviour
+{
+    [SerializeField] private Text taxCounter;      // "Tax: +{2N}"
+    [SerializeField] private RawImage commanderArt;
+    [SerializeField] private Text commanderDamageDealt; // per opponent
+
+    public void Refresh(PlayerDTO player)
+    {
+        int tax = player.CommanderTax;
+        taxCounter.text = tax > 0 ? $"+{tax * 2}" : "";
+
+        if (player.CommandZone.Count > 0)
+            StartCoroutine(CardImageLoader.LoadCardArt(
+                player.CommandZone[0].ArtUrl, commanderArt));
+    }
+}
+```
+
+---
+
+## Phase 5 — Commander Rules Validation Layer
+
+Forge handles all rules natively. FastAPI validates the AI response is within the legal actions list Forge returns — no custom rules code needed in Python or Unity.
+
+Key Commander rules confirmed handled by Forge headless:
+- **Commander Tax** — +2 generic per previous cast from command zone
+- **21 Commander Damage** — tracked per attacker/defender pair
+- **40 Starting Life**
+- **Zone Redirect** — commander goes to command zone instead of graveyard/exile (player choice)
+- **Color Identity** — deck validation at game start
+- **Singleton** — deck validation at game start
+
+---
+
+## Phase 6 — AI Decision Logging & PPO Pipeline
+
+Every AI decision written to `logs/ai_decisions.jsonl`:
+
+```json
+{
+  "timestamp": 1743520800.123,
+  "player_id": "player_2",
+  "brain": "deepseek-r1:14b",
+  "personality": "aggro_beatdown",
+  "phase": "declare_attackers",
+  "action": {"action": "attack", "card_id": "c_042", "targets": ["player_1"], "reasoning": "Open attack, no blockers visible"},
+  "latency_ms": 847
+}
+```
+
+This log becomes the **supervised learning dataset** for the next PPO training iteration. Each (state, action) pair from winning games is a positive training example.
+
+---
+
+## Project Directory Structure
+
+```
+commander-ai-lab/
+├── main.py                          ← FastAPI app, mounts static files
+├── api/
+│   ├── dtos.py                      ← Pydantic DTOs
+│   ├── card_enricher.py             ← Injects art_url into CardDTOs
+│   ├── game_loop.py                 ← Main game orchestration loop
+│   └── routes/
+│       └── game.py                  ← WebSocket + REST endpoints
+├── ai/
+│   ├── ai_orchestrator.py           ← Main AI decision entry point
+│   ├── brain_selector.py            ← DeepSeek-R1 vs GPT-OSS routing
+│   ├── prompt_builder.py            ← Game state + RAG → prompt
+│   └── personality.py               ← 8 personalities + assignment
+├── forge/
+│   └── subprocess_manager.py        ← Forge JVM stdin/stdout bridge
+├── scripts/
+│   ├── download_card_images.py      ← One-time Scryfall bulk download
+│   └── build_image_index.py         ← SQLite name→path index
+├── static/
+│   └── card-images/                 ← ~2GB local image cache
+│       └── {set_code}/
+│           └── {CardName}_{num}.jpg
+├── data/
+│   └── card_images.db               ← SQLite image index
+├── logs/
+│   └── ai_decisions.jsonl           ← PPO training dataset
+├── unity-commander-ui/              ← Unity 2D TCGEngine project
+└── docs/
+    └── implementation_plan.md       ← This document
+```
+
+---
+
+## Phased Timeline
+
+| Phase | Scope | Est. Time |
+|---|---|---|
+| **0** | Scryfall image download + SQLite index + FastAPI static mount | 1 day |
+| **1** | Forge subprocess bridge + DTO definitions | 2–3 days |
+| **2** | AI Orchestrator + brain selector + prompt builder + personality system | 3–4 days |
+| **3** | Unity TCGEngine project setup + WebSocket GameNetworkManager | 3–4 days |
+| **4** | PlayerAreaUI + ZonePanels + CardImageLoader + board layout | 3–4 days |
+| **5** | Commander zone UI (tax counter, commander damage, zone redirect) | 1–2 days |
+| **6** | AI decision logging + end-to-end game loop test | 1–2 days |
+
+**Total estimated: ~3 weeks to first playable Commander game with live AI opponents.**
+
+---
+
+## Key Design Decisions
+
+| Decision | Rationale |
+|---|---|
+| Forge = rules authority | Forge already handles all Commander rules natively. No custom rules code. |
+| TCGEngine = dumb renderer | All game logic lives in Forge. Unity only renders state it receives. |
+| FastAPI = hub | Bridges Forge ↔ Unity ↔ AI. Single point of truth. |
+| Dual-brain routing | DeepSeek-R1 excels at analytical/math decisions; GPT-OSS at strategic sequencing. |
+| ChromaDB scoped per player | RAG context relevant to the active player's deck, not global card knowledge. |
+| Personality persists per game | Consistent playstyle per AI across all 4 players per session. |
+| Local image cache | Eliminates network latency for card art. ~2GB one-time download. |
+| ai_decisions.jsonl | Every AI decision is a future PPO training sample. |

frontend/commander-ai-lab-ui/.gitignore

@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?

frontend/commander-ai-lab-ui/README.md

@@ -1,73 +0,0 @@
-# React + TypeScript + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
-
-## React Compiler
-
-The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
-
-## Expanding the ESLint configuration
-
-If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:
-
-```js
-export default defineConfig([
-  globalIgnores(['dist']),
-  {
-    files: ['**/*.{ts,tsx}'],
-    extends: [
-      // Other configs...
-
-      // Remove tseslint.configs.recommended and replace with this
-      tseslint.configs.recommendedTypeChecked,
-      // Alternatively, use this for stricter rules
-      tseslint.configs.strictTypeChecked,
-      // Optionally, add this for stylistic rules
-      tseslint.configs.stylisticTypeChecked,
-
-      // Other configs...
-    ],
-    languageOptions: {
-      parserOptions: {
-        project: ['./tsconfig.node.json', './tsconfig.app.json'],
-        tsconfigRootDir: import.meta.dirname,
-      },
-      // other options...
-    },
-  },
-])
-```
-
-You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:
-
-```js
-// eslint.config.js
-import reactX from 'eslint-plugin-react-x'
-import reactDom from 'eslint-plugin-react-dom'
-
-export default defineConfig([
-  globalIgnores(['dist']),
-  {
-    files: ['**/*.{ts,tsx}'],
-    extends: [
-      // Other configs...
-      // Enable lint rules for React
-      reactX.configs['recommended-typescript'],
-      // Enable lint rules for React DOM
-      reactDom.configs.recommended,
-    ],
-    languageOptions: {
-      parserOptions: {
-        project: ['./tsconfig.node.json', './tsconfig.app.json'],
-        tsconfigRootDir: import.meta.dirname,
-      },
-      // other options...
-    },
-  },
-])
-```

frontend/commander-ai-lab-ui/eslint.config.js

@@ -1,23 +0,0 @@
-import js from '@eslint/js'
-import globals from 'globals'
-import reactHooks from 'eslint-plugin-react-hooks'
-import reactRefresh from 'eslint-plugin-react-refresh'
-import tseslint from 'typescript-eslint'
-import { defineConfig, globalIgnores } from 'eslint/config'
-
-export default defineConfig([
-  globalIgnores(['dist']),
-  {
-    files: ['**/*.{ts,tsx}'],
-    extends: [
-      js.configs.recommended,
-      tseslint.configs.recommended,
-      reactHooks.configs.flat.recommended,
-      reactRefresh.configs.vite,
-    ],
-    languageOptions: {
-      ecmaVersion: 2020,
-      globals: globals.browser,
-    },
-  },
-])

frontend/commander-ai-lab-ui/index.html

@@ -1,13 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>commander-ai-lab-ui</title>
-  </head>
-  <body>
-    <div id="root"></div>
-    <script type="module" src="/src/main.tsx"></script>
-  </body>
-</html>

frontend/commander-ai-lab-ui/package.json

@@ -1,35 +0,0 @@
-{
-  "name": "commander-ai-lab-ui",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "tsc -b && vite build",
-    "lint": "eslint .",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@tailwindcss/vite": "^4.2.1",
-    "lucide-react": "^0.577.0",
-    "react": "^19.2.0",
-    "react-dom": "^19.2.0",
-    "react-router-dom": "^7.13.1",
-    "recharts": "^3.8.0",
-    "tailwindcss": "^4.2.1"
-  },
-  "devDependencies": {
-    "@eslint/js": "^9.39.1",
-    "@types/node": "^24.10.1",
-    "@types/react": "^19.2.7",
-    "@types/react-dom": "^19.2.3",
-    "@vitejs/plugin-react": "^5.1.1",
-    "eslint": "^9.39.1",
-    "eslint-plugin-react-hooks": "^7.0.1",
-    "eslint-plugin-react-refresh": "^0.4.24",
-    "globals": "^16.5.0",
-    "typescript": "~5.9.3",
-    "typescript-eslint": "^8.48.0",
-    "vite": "^7.3.1"
-  }
-}

frontend/commander-ai-lab-ui/src/App.tsx

@@ -1,27 +0,0 @@
-import { BrowserRouter, Routes, Route } from 'react-router-dom'
-import { PageLayout } from './components/layout/PageLayout'
-import { BatchSimPage } from './pages/BatchSimPage'
-import { CollectionPage } from './pages/CollectionPage'
-import { DecksPage } from './pages/DecksPage'
-import { AutoGenPage } from './pages/AutoGenPage'
-import { SimulatorPage } from './pages/SimulatorPage'
-import { CoachPage } from './pages/CoachPage'
-import { TrainingPage } from './pages/TrainingPage'
-
-export default function App() {
-  return (
-    <BrowserRouter>
-      <PageLayout>
-        <Routes>
-          <Route path="/" element={<BatchSimPage />} />
-          <Route path="/collection" element={<CollectionPage />} />
-          <Route path="/decks" element={<DecksPage />} />
-          <Route path="/autogen" element={<AutoGenPage />} />
-          <Route path="/simulator" element={<SimulatorPage />} />
-          <Route path="/coach" element={<CoachPage />} />
-          <Route path="/training" element={<TrainingPage />} />
-        </Routes>
-      </PageLayout>
-    </BrowserRouter>
-  )
-}

frontend/commander-ai-lab-ui/src/api/client.ts

@@ -1,82 +0,0 @@
-// ═══════════════════════════════════════════════════════════
-// Base API client — all fetch calls go through here
-// ═══════════════════════════════════════════════════════════
-
-const BASE = '' // same origin; Vite proxy handles /api in dev
-
-export class ApiError extends Error {
-  status: number
-  constructor(status: number, message: string) {
-    super(message)
-    this.name = 'ApiError'
-    this.status = status
-  }
-}
-
-async function handleResponse<T>(res: Response): Promise<T> {
-  if (!res.ok) {
-    const body = await res.text()
-    let message = `HTTP ${res.status}`
-    try {
-      const json = JSON.parse(body)
-      message = json.detail || json.error || message
-    } catch {
-      if (body) message = body
-    }
-    throw new ApiError(res.status, message)
-  }
-  // Handle 204 No Content
-  if (res.status === 204) return undefined as T
-  return res.json()
-}
-
-export async function get<T>(path: string, params?: Record<string, string | number | boolean | undefined>): Promise<T> {
-  const url = new URL(BASE + path, window.location.origin)
-  if (params) {
-    Object.entries(params).forEach(([k, v]) => {
-      if (v !== undefined && v !== '') url.searchParams.set(k, String(v))
-    })
-  }
-  const res = await fetch(url.toString())
-  return handleResponse<T>(res)
-}
-
-export async function post<T>(path: string, body?: unknown): Promise<T> {
-  const res = await fetch(BASE + path, {
-    method: 'POST',
-    headers: body ? { 'Content-Type': 'application/json' } : {},
-    body: body ? JSON.stringify(body) : undefined,
-  })
-  return handleResponse<T>(res)
-}
-
-export async function postForm<T>(path: string, formData: FormData): Promise<T> {
-  const res = await fetch(BASE + path, {
-    method: 'POST',
-    body: formData,
-  })
-  return handleResponse<T>(res)
-}
-
-export async function put<T>(path: string, body: unknown): Promise<T> {
-  const res = await fetch(BASE + path, {
-    method: 'PUT',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(body),
-  })
-  return handleResponse<T>(res)
-}
-
-export async function patch<T>(path: string, body: unknown): Promise<T> {
-  const res = await fetch(BASE + path, {
-    method: 'PATCH',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(body),
-  })
-  return handleResponse<T>(res)
-}
-
-export async function del<T>(path: string): Promise<T> {
-  const res = await fetch(BASE + path, { method: 'DELETE' })
-  return handleResponse<T>(res)
-}

frontend/commander-ai-lab-ui/src/api/coach.ts

@@ -1,131 +0,0 @@
-import { get, post } from './client'
-import type {
-  CoachStatus, CoachDeck, CoachSession, CoachSessionSummary,
-  CoachChatResponse, CoachApplyResult, CoachCardLikeResult
-} from '../types'
-
-export async function getCoachStatus() {
-  return get<CoachStatus>('/api/coach/status')
-}
-
-export async function getCoachDecks() {
-  return get<CoachDeck[]>('/api/coach/decks')
-}
-
-export async function getCoachDeckReport(deckId: string) {
-  return get<Record<string, unknown>>(`/api/coach/decks/${deckId}/report`)
-}
-
-export async function runCoachSession(deckId: string, goals?: Record<string, unknown>) {
-  return post<CoachSession>(`/api/coach/decks/${deckId}`, goals ? { goals } : undefined)
-}
-
-export async function getCoachSessions(deckId?: string) {
-  const params: Record<string, string> = {}
-  if (deckId) params.deck_id = deckId
-  return get<{ sessions: CoachSessionSummary[] }>('/api/coach/sessions', params)
-}
-
-export async function getCoachSession(sessionId: string) {
-  return get<CoachSession>(`/api/coach/sessions/${sessionId}`)
-}
-
-export async function downloadEmbeddings() {
-  return post<{ success: boolean; cards: number }>('/api/coach/embeddings/download')
-}
-
-export async function coachChat(
-  deckId: string,
-  messages: { role: string; content: string }[],
-  goals?: Record<string, unknown>
-) {
-  return post<CoachChatResponse>('/api/coach/chat', {
-    deck_id: deckId,
-    messages,
-    goals,
-    stream: false,
-  })
-}
-
-export async function coachChatStream(
-  deckId: string,
-  messages: { role: string; content: string }[],
-  goals?: Record<string, unknown>,
-  onChunk?: (text: string) => void,
-  signal?: AbortSignal
-): Promise<string> {
-  const res = await fetch('/api/coach/chat', {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      deck_id: deckId,
-      messages,
-      goals,
-      stream: true,
-    }),
-    signal,
-  })
-
-  if (!res.ok) {
-    const body = await res.text()
-    throw new Error(body || `HTTP ${res.status}`)
-  }
-
-  const reader = res.body?.getReader()
-  if (!reader) throw new Error('No response body')
-
-  const decoder = new TextDecoder()
-  let fullText = ''
-  let buffer = ''
-
-  while (true) {
-    const { done, value } = await reader.read()
-    if (done) break
-
-    buffer += decoder.decode(value, { stream: true })
-    const lines = buffer.split('\n')
-    buffer = lines.pop() || ''
-
-    for (const line of lines) {
-      if (line.startsWith('data: ')) {
-        const data = line.slice(6).trim()
-        if (data === '[DONE]') break
-        try {
-          const parsed = JSON.parse(data)
-          if (parsed.content) {
-            fullText += parsed.content
-            onChunk?.(fullText)
-          }
-        } catch { /* skip bad chunks */ }
-      }
-    }
-  }
-
-  return fullText
-}
-
-export async function applyCoachSuggestions(
-  sessionId: string,
-  deckId: number,
-  acceptedCuts: string[],
-  acceptedAdds: string[]
-) {
-  return post<CoachApplyResult>('/api/coach/apply', {
-    session_id: sessionId,
-    deck_id: deckId,
-    accepted_cuts: acceptedCuts,
-    accepted_adds: acceptedAdds,
-  })
-}
-
-export async function searchCardsLike(card: string, colors?: string, topN = 10) {
-  const params: Record<string, string | number> = { card, top_n: topN }
-  if (colors) params.colors = colors
-  return get<{ query: string; results: CoachCardLikeResult[] }>('/api/coach/cards-like', params)
-}
-
-export async function generateReport(deckId?: number) {
-  return post<{ status: string; decksUpdated: string[]; count: number }>('/api/coach/reports/generate',
-    deckId ? { deck_id: deckId } : undefined
-  )
-}

frontend/commander-ai-lab-ui/src/api/collection.ts

@@ -1,86 +0,0 @@
-import { get, post, patch, postForm } from './client'
-import type { CollectionCard, CollectionFilters, SetInfo, ScanResult } from '../types'
-
-export async function searchCollection(filters: CollectionFilters) {
-  // Map frontend snake_case filter names to backend camelCase param names
-  const params: Record<string, string | number | boolean> = {}
-  if (filters.q) params.q = filters.q
-  if (filters.page) params.page = filters.page
-  if (filters.page_size) params.pageSize = filters.page_size
-  if (filters.sort_by) params.sortField = filters.sort_by
-  if (filters.sort_dir) params.sortDir = filters.sort_dir
-  if (filters.color) params.colors = filters.color
-  if (filters.type_line) params.types = filters.type_line
-  if (filters.rarity) params.rarity = filters.rarity
-  if (filters.set_code) params.setCode = filters.set_code
-  if (filters.cmc_min !== undefined) params.cmcMin = filters.cmc_min
-  if (filters.cmc_max !== undefined) params.cmcMax = filters.cmc_max
-  if (filters.price_min !== undefined) params.priceMin = filters.price_min
-  if (filters.price_max !== undefined) params.priceMax = filters.price_max
-  if (filters.owned_only !== undefined) params.owned_only = filters.owned_only
-
-  const res = await get<{ items?: CollectionCard[]; cards?: CollectionCard[]; total: number; page: number; pageSize?: number; page_size?: number }>(
-    '/api/collection', params
-  )
-  return {
-    cards: res.items || res.cards || [],
-    total: res.total || 0,
-    page: res.page || 1,
-    page_size: res.pageSize || res.page_size || 48,
-  }
-}
-
-export async function getCard(cardId: number) {
-  return get<CollectionCard>(`/api/collection/${cardId}`)
-}
-
-export async function updateCard(cardId: number, updates: Partial<CollectionCard>) {
-  return patch<CollectionCard>(`/api/collection/${cardId}`, updates)
-}
-
-export async function getSets() {
-  const res = await get<SetInfo[] | { sets: SetInfo[] }>('/api/collection/sets')
-  return Array.isArray(res) ? res : (res as { sets: SetInfo[] }).sets || []
-}
-
-export async function getKeywords() {
-  const res = await get<string[] | { keywords: string[] }>('/api/collection/keywords')
-  return Array.isArray(res) ? res : (res as { keywords: string[] }).keywords || []
-}
-
-export async function exportCollection(format: string = 'csv') {
-  const res = await fetch(`/api/collection/export?format=${format}`)
-  return res.blob()
-}
-
-export async function importCollection(file: File) {
-  const fd = new FormData()
-  fd.append('file', file)
-  return postForm<{ imported: number; errors: string[] }>('/api/collection/import', fd)
-}
-
-export async function scanCard(file: File) {
-  const fd = new FormData()
-  fd.append('image', file)
-  return postForm<ScanResult>('/api/collection/scan', fd)
-}
-
-export async function addScanResult(match: { name: string; set_code: string; scryfall_id: string; quantity?: number }) {
-  return post<{ success: boolean; card_id: number }>('/api/collection/scan/add', match)
-}
-
-export async function reEnrichCards() {
-  return post<{ updated: number }>('/api/collection/re-enrich')
-}
-
-export async function autoClassify(cardId: number) {
-  return post<{ classifications: string[] }>(`/api/collection/auto-classify`, { card_id: cardId })
-}
-
-export async function autoClassifyAll() {
-  return post<{ processed: number }>('/api/collection/auto-classify-all')
-}
-
-export async function getEdhrecData(cardId: number) {
-  return get<Record<string, unknown>>(`/api/collection/${cardId}/edhrec`)
-}

frontend/commander-ai-lab-ui/src/api/deckgen.ts

@@ -1,22 +0,0 @@
-import { get, post } from './client'
-import type { DeckGenV3Request, DeckGenV3Status, DeckGenV3Result, CommanderSearchResult } from '../types'
-
-export async function getV3Status() {
-  return get<DeckGenV3Status>('/api/deck/v3/status')
-}
-
-export async function searchCommanders(q: string) {
-  return get<CommanderSearchResult[]>('/api/deck-generator/commander-search', { q })
-}
-
-export async function generateDeckV3(req: DeckGenV3Request) {
-  return post<DeckGenV3Result>('/api/deck/v3/generate', req)
-}
-
-export async function commitDeckV3(req: DeckGenV3Request) {
-  return post<DeckGenV3Result & { deck_id: number; dck_path: string }>('/api/deck/v3/commit', req)
-}
-
-export async function exportDeckV3(format: 'csv' | 'dck' | 'moxfield' | 'shopping', req: DeckGenV3Request) {
-  return post<Record<string, unknown>>(`/api/deck/v3/export/${format}`, req)
-}

frontend/commander-ai-lab-ui/src/api/decks.ts

@@ -1,130 +0,0 @@
-import { get, post, put, del, patch } from './client'
-import type { Deck, DeckCard, DeckAnalysis, EdhRecsResponse, CollectionRecsResponse, PplxStatus } from '../types'
-
-export async function listDecks() {
-  const res = await get<{ decks: Record<string, unknown>[] } | Record<string, unknown>[]>('/api/decks')
-  const raw = Array.isArray(res) ? res : (res as { decks: Record<string, unknown>[] }).decks || []
-  return raw.map(d => ({
-    ...d,
-    commander: d.commander || d.commander_name || '',
-    card_count: d.card_count ?? d.total_cards ?? 0,
-    total_price: d.total_price ?? 0,
-    color_identity: d.color_identity || [],
-    created_date: d.created_date || d.created_at || '',
-    updated_date: d.updated_date || d.updated_at || '',
-  })) as Deck[]
-}
-
-export async function getDeck(deckId: number) {
-  return get<Deck>(`/api/decks/${deckId}`)
-}
-
-export async function createDeck(deck: { name: string; commander?: string; format?: string }) {
-  return post<Deck>('/api/decks', deck)
-}
-
-export async function updateDeck(deckId: number, updates: Partial<Deck>) {
-  return put<Deck>(`/api/decks/${deckId}`, updates)
-}
-
-export async function deleteDeck(deckId: number) {
-  return del<void>(`/api/decks/${deckId}`)
-}
-
-export async function deleteAllDecks() {
-  return del<void>('/api/decks')
-}
-
-export async function getDeckCards(deckId: number) {
-  const res = await get<{ cards: Record<string, unknown>[]; total: number } | Record<string, unknown>[]>(`/api/decks/${deckId}/cards`)
-  const raw = Array.isArray(res) ? res : (res as { cards: Record<string, unknown>[] }).cards || []
-  return raw.map(c => ({
-    ...c,
-    name: c.name || c.card_name || '',
-    category: c.category || c.role_tag || '',
-  })) as DeckCard[]
-}
-
-export async function addCardToDeck(deckId: number, card: { name: string; scryfall_id?: string; quantity?: number; category?: string }) {
-  return post<DeckCard>(`/api/decks/${deckId}/cards`, card)
-}
-
-export async function removeCardFromDeck(deckId: number, cardId: number) {
-  return del<void>(`/api/decks/${deckId}/cards/${cardId}`)
-}
-
-export async function updateDeckCard(deckId: number, cardId: number, updates: Partial<DeckCard>) {
-  return patch<DeckCard>(`/api/decks/${deckId}/cards/${cardId}`, updates)
-}
-
-export async function getDeckAnalysis(deckId: number) {
-  const raw = await get<Record<string, unknown>>(`/api/decks/${deckId}/analysis`)
-  // Backend returns: counts_by_type, targets, deltas, mana_curve, color_pips, total_cards, roles
-  // Frontend DeckAnalysis expects: card_count, land_count, creature_count, noncreature_count, avg_cmc, mana_curve, color_distribution, type_distribution, total_price, owned_count, missing_count
-  const counts = (raw.counts_by_type || {}) as Record<string, number>
-  const totalCards = (raw.total_cards as number) || 0
-  const landCount = counts['Land'] || 0
-  const creatureCount = counts['Creature'] || 0
-  const manaCurve = (raw.mana_curve || {}) as Record<string | number, number>
-  const colorPips = (raw.color_pips || {}) as Record<string, number>
-  // Compute avg CMC from mana curve
-  let totalMana = 0, nonLandCards = 0
-  for (const [cmc, count] of Object.entries(manaCurve)) {
-    const cmcNum = cmc === '6+' ? 6 : Number(cmc)
-    totalMana += cmcNum * count
-    nonLandCards += count
-  }
-  return {
-    card_count: totalCards,
-    land_count: landCount,
-    creature_count: creatureCount,
-    noncreature_count: totalCards - landCount - creatureCount,
-    avg_cmc: nonLandCards > 0 ? totalMana / nonLandCards : 0,
-    mana_curve: manaCurve as Record<number, number>,
-    color_distribution: colorPips,
-    type_distribution: counts,
-    total_price: 0,
-    owned_count: totalCards,
-    missing_count: 0,
-  } as DeckAnalysis
-}
-
-export async function getCollectionRecs(deckId: number) {
-  return get<CollectionRecsResponse>(`/api/decks/${deckId}/recommended-from-collection`)
-}
-
-export async function getEdhRecs(deckId: number, onlyOwned = false, maxResults = 30) {
-  return get<EdhRecsResponse>(`/api/decks/${deckId}/edh-recs?only_owned=${onlyOwned}&max_results=${maxResults}`)
-}
-
-export async function bulkAddCards(deckId: number, cards: { name: string; quantity?: number }[]) {
-  return post<{ added: number }>(`/api/decks/${deckId}/bulk-add`, { cards })
-}
-
-export async function bulkAddRecommended(deckId: number, cards: string[]) {
-  return post<{ added: number }>(`/api/decks/${deckId}/bulk-add-recommended`, { card_names: cards })
-}
-
-export async function exportDeckToSim(deckId: number) {
-  return post<{ path: string }>(`/api/decks/${deckId}/export-to-sim`)
-}
-
-export async function importDeckToDeck(deckId: number, data: { text?: string; url?: string }) {
-  return post<{ imported: number }>(`/api/decks/${deckId}/import`, data)
-}
-
-export async function importNewDeck(data: { text?: string; url?: string; name?: string }) {
-  return post<Deck>('/api/decks/import-new', data)
-}
-
-export async function getPplxStatus() {
-  return get<PplxStatus>('/api/pplx/status')
-}
-
-export async function deckResearch(payload: { commander: string; strategy?: string }) {
-  return post<{ research: string }>('/api/deck-research', payload)
-}
-
-export async function deckGenerate(payload: { commander: string; strategy?: string; bracket?: number }) {
-  return post<{ cards: Array<{ name: string; quantity: number; category: string }> }>('/api/deck-generate', payload)
-}

frontend/commander-ai-lab-ui/src/api/index.ts

@@ -1,7 +0,0 @@
-export * as collectionApi from './collection'
-export * as decksApi from './decks'
-export * as deckgenApi from './deckgen'
-export * as labApi from './lab'
-export * as simApi from './sim'
-export * as coachApi from './coach'
-export * as trainingApi from './training'

frontend/commander-ai-lab-ui/src/api/lab.ts

@@ -1,65 +0,0 @@
-import { get, post } from './client'
-import type { LabDeck, LabStatus, LabResult, LabHistoryEntry, PreconDeck } from '../types'
-
-export async function getLabDecks() {
-  const res = await get<{ decks: LabDeck[] } | LabDeck[]>('/api/lab/decks')
-  return Array.isArray(res) ? res : (res as { decks: LabDeck[] }).decks || []
-}
-
-export async function startBatchSim(config: { decks: string[]; games: number; threads?: number; use_deepseek?: boolean; deepseek_deck?: string }) {
-  return post<{ run_id: string }>('/api/lab/start', config)
-}
-
-export async function startDeepSeekSim(config: { decks: string[]; games: number; deepseek_deck: string }) {
-  return post<{ run_id: string }>('/api/lab/start-deepseek', config)
-}
-
-export async function getLabStatus() {
-  return get<LabStatus>('/api/lab/status')
-}
-
-export async function getLabResult() {
-  return get<LabResult>('/api/lab/result')
-}
-
-export async function getLabHistory() {
-  const res = await get<{ results: LabHistoryEntry[] } | LabHistoryEntry[]>('/api/lab/history')
-  return Array.isArray(res) ? res : (res as { results: LabHistoryEntry[] }).results || []
-}
-
-export async function getPrecons() {
-  const res = await get<{ precons: PreconDeck[] } | PreconDeck[]>('/api/lab/precons')
-  return Array.isArray(res) ? res : (res as { precons: PreconDeck[] }).precons || []
-}
-
-export async function installPrecon(name: string) {
-  return post<{ installed: boolean }>('/api/lab/precons/install', { name })
-}
-
-export async function installPreconBatch(names: string[]) {
-  return post<{ installed: number }>('/api/lab/precons/install-batch', { names })
-}
-
-export async function getMetaCommanders() {
-  return get<Array<{ name: string; color_identity: string[] }>>('/api/lab/meta/commanders')
-}
-
-export async function searchMetaCommanders(q: string) {
-  return get<Array<{ name: string; color_identity: string[]; source: string }>>('/api/lab/meta/search', { q })
-}
-
-export async function fetchMetaDeck(commander: string) {
-  return post<{ deck_name: string; cards: Array<{ name: string; quantity: number }> }>('/api/lab/meta/fetch', { commander })
-}
-
-export async function importDeckFromUrl(url: string) {
-  return post<{ deck_name: string }>('/api/lab/import/url', { url })
-}
-
-export async function importDeckFromText(text: string, name?: string) {
-  return post<{ deck_name: string }>('/api/lab/import/text', { text, name })
-}
-
-export async function getLabLog() {
-  return get<{ log: string }>('/api/lab/log')
-}

frontend/commander-ai-lab-ui/src/api/sim.ts

@@ -1,38 +0,0 @@
-import { get, post } from './client'
-import type { SimStatus, SimResult, DeepSeekStatus } from '../types'
-
-export async function runSim(config: { deck_a: string; deck_b: string; games: number }) {
-  return post<{ sim_id: string }>('/api/sim/run', config)
-}
-
-export async function runSimFromDeck(deckId: number, config: { opponent?: string; games?: number }) {
-  return post<{ sim_id: string }>('/api/sim/run-from-deck', { deck_id: deckId, ...config })
-}
-
-export async function runDeepSeekSim(config: { deck_a: string; deck_b: string; games: number; deepseek_deck: string }) {
-  return post<{ sim_id: string }>('/api/sim/run-deepseek', config)
-}
-
-export async function getSimStatus(simId: string) {
-  return get<SimStatus>('/api/sim/status', { simId })
-}
-
-export async function getSimResult(simId: string) {
-  return get<SimResult>('/api/sim/result', { simId })
-}
-
-export async function connectDeepSeek() {
-  return post<DeepSeekStatus>('/api/deepseek/connect')
-}
-
-export async function getDeepSeekStatus() {
-  return get<DeepSeekStatus>('/api/deepseek/status')
-}
-
-export async function configureDeepSeek(config: { model?: string; base_url?: string }) {
-  return post<DeepSeekStatus>('/api/deepseek/configure', config)
-}
-
-export async function getDeepSeekLogs() {
-  return get<Array<{ filename: string; size: number; modified: string }>>('/api/deepseek/logs')
-}

frontend/commander-ai-lab-ui/src/api/training.ts

@@ -1,92 +0,0 @@
-import { get, post } from './client'
-import type {
-  MLStatus, MLDataStatus, MLTrainStatus, PPOTrainStatus,
-  TournamentStatus, TournamentResults, MLModelInfo
-} from '../types'
-
-// ── ML Status & Toggle ──────────────────────────────────────
-export async function getMLStatus() {
-  return get<MLStatus>('/api/ml/status')
-}
-
-export async function toggleML(enable = true) {
-  return post<{ ml_logging_enabled: boolean; message: string }>(`/api/ml/toggle?enable=${enable}`)
-}
-
-// ── Data & Model Info ───────────────────────────────────────
-export async function getDataStatus() {
-  return get<MLDataStatus>('/api/ml/data/status')
-}
-
-export async function getModelInfo() {
-  return get<MLModelInfo>('/api/ml/model')
-}
-
-export async function reloadModel(checkpoint?: string) {
-  return post<{ success: boolean; status: MLModelInfo }>('/api/ml/reload', checkpoint ? { checkpoint } : undefined)
-}
-
-// ── Supervised Training ─────────────────────────────────────
-export async function startTraining(config: {
-  epochs?: number
-  lr?: number
-  batchSize?: number
-  patience?: number
-  rebuildDataset?: boolean
-}) {
-  return post<{ status: string; config: Record<string, unknown> }>('/api/ml/train', config)
-}
-
-export async function getTrainStatus() {
-  return get<MLTrainStatus>('/api/ml/train/status')
-}
-
-// ── PPO Training ────────────────────────────────────────────
-export async function startPPOTraining(config: {
-  iterations?: number
-  episodesPerIter?: number
-  ppoEpochs?: number
-  batchSize?: number
-  lr?: number
-  clipEpsilon?: number
-  entropyCoeff?: number
-  opponent?: string
-  playstyle?: string
-  loadSupervised?: string
-}) {
-  return post<{ status: string; iterations: number }>('/api/ml/train/ppo', config)
-}
-
-export async function getPPOStatus() {
-  return get<PPOTrainStatus>('/api/ml/train/ppo/status')
-}
-
-// ── Tournament ──────────────────────────────────────────────
-export async function startTournament(config: {
-  episodes?: number
-  playstyle?: string
-}) {
-  return post<{ status: string; episodes: number }>('/api/ml/tournament', config)
-}
-
-export async function getTournamentStatus() {
-  return get<TournamentStatus>('/api/ml/tournament/status')
-}
-
-export async function getTournamentResults() {
-  return get<TournamentResults>('/api/ml/tournament/results')
-}
-
-// ── Decision Files ──────────────────────────────────────────
-export async function getDecisionFile(filename: string, limit = 100, offset = 0) {
-  return get<{ file: string; offset: number; limit: number; count: number; decisions: Record<string, unknown>[] }>(
-    `/api/ml/decisions/${filename}?limit=${limit}&offset=${offset}`
-  )
-}
-
-// ── Prediction ──────────────────────────────────────────────
-export async function predict(state: Record<string, unknown>) {
-  return post<{ action: string; action_index: number; confidence: number; probabilities: Record<string, number>; inference_ms: number }>(
-    '/api/ml/predict', state
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/CardImage.tsx

@@ -1,49 +0,0 @@
-import { useState } from 'react'
-
-interface CardImageProps {
-  src: string
-  alt: string
-  size?: 'sm' | 'md' | 'lg' | 'xl'
-  className?: string
-  onClick?: () => void
-}
-
-const SIZES = {
-  sm: 'w-16 h-[22px]',    // tiny inline
-  md: 'w-[146px] h-[204px]',  // grid thumbnail
-  lg: 'w-[244px] h-[340px]',  // detail view
-  xl: 'w-[336px] h-[468px]',  // full size
-}
-
-export function CardImage({ src, alt, size = 'md', className = '', onClick }: CardImageProps) {
-  const [loaded, setLoaded] = useState(false)
-  const [error, setError] = useState(false)
-
-  // Build Scryfall URL if needed — ensure we get the right size
-  const imgUrl = src || ''
-
-  return (
-    <div
-      className={`relative overflow-hidden rounded-lg bg-bg-tertiary ${SIZES[size]} ${onClick ? 'cursor-pointer hover:ring-2 hover:ring-accent-blue/50 transition-all' : ''} ${className}`}
-      onClick={onClick}
-    >
-      {!error ? (
-        <img
-          src={imgUrl}
-          alt={alt}
-          className={`w-full h-full object-cover transition-opacity duration-200 ${loaded ? 'opacity-100' : 'opacity-0'}`}
-          onLoad={() => setLoaded(true)}
-          onError={() => setError(true)}
-          loading="lazy"
-        />
-      ) : (
-        <div className="flex items-center justify-center w-full h-full text-text-tertiary text-xs text-center p-2">
-          {alt}
-        </div>
-      )}
-      {!loaded && !error && (
-        <div className="absolute inset-0 bg-bg-tertiary animate-pulse" />
-      )}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/EmptyState.tsx

@@ -1,19 +0,0 @@
-import type { LucideIcon } from 'lucide-react'
-
-interface EmptyStateProps {
-  icon: LucideIcon
-  title: string
-  description?: string
-  action?: React.ReactNode
-}
-
-export function EmptyState({ icon: Icon, title, description, action }: EmptyStateProps) {
-  return (
-    <div className="flex flex-col items-center justify-center py-16 text-center">
-      <Icon className="w-12 h-12 text-text-tertiary mb-4" strokeWidth={1.5} />
-      <h3 className="text-lg font-semibold text-text-primary mb-1">{title}</h3>
-      {description && <p className="text-sm text-text-secondary max-w-md mb-4">{description}</p>}
-      {action}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/ManaSymbols.tsx

@@ -1,69 +0,0 @@
-// Parse MTG mana cost strings like "{2}{W}{U}" into colored symbols
-
-const MANA_COLORS: Record<string, string> = {
-  W: 'bg-mana-white text-black',
-  U: 'bg-mana-blue text-white',
-  B: 'bg-mana-black text-white',
-  R: 'bg-mana-red text-white',
-  G: 'bg-mana-green text-white',
-  C: 'bg-mana-colorless text-white',
-}
-
-interface ManaSymbolsProps {
-  cost: string
-  size?: 'sm' | 'md'
-}
-
-export function ManaSymbols({ cost, size = 'sm' }: ManaSymbolsProps) {
-  if (!cost) return null
-
-  const symbols = cost.match(/\{([^}]+)\}/g)
-  if (!symbols) return <span className="text-text-tertiary">{cost}</span>
-
-  const sz = size === 'sm' ? 'w-5 h-5 text-[10px]' : 'w-6 h-6 text-xs'
-
-  return (
-    <span className="inline-flex items-center gap-0.5">
-      {symbols.map((s, i) => {
-        const val = s.replace(/[{}]/g, '')
-        const colorClass = MANA_COLORS[val] || 'bg-mana-colorless text-white'
-        return (
-          <span
-            key={i}
-            className={`${sz} inline-flex items-center justify-center rounded-full font-bold ${colorClass}`}
-          >
-            {val}
-          </span>
-        )
-      })}
-    </span>
-  )
-}
-
-// Color identity dots
-interface ColorDotsProps {
-  colors: string[]
-  size?: 'sm' | 'md'
-}
-
-export function ColorDots({ colors, size = 'sm' }: ColorDotsProps) {
-  if (!colors || colors.length === 0) return <span className="text-text-tertiary text-xs">Colorless</span>
-
-  const sz = size === 'sm' ? 'w-3 h-3' : 'w-4 h-4'
-
-  const DOT_COLORS: Record<string, string> = {
-    W: 'bg-mana-white',
-    U: 'bg-mana-blue',
-    B: 'bg-mana-black border border-border-secondary',
-    R: 'bg-mana-red',
-    G: 'bg-mana-green',
-  }
-
-  return (
-    <span className="inline-flex items-center gap-0.5">
-      {colors.map(c => (
-        <span key={c} className={`${sz} rounded-full ${DOT_COLORS[c] || 'bg-mana-colorless'}`} />
-      ))}
-    </span>
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/Spinner.tsx

@@ -1,38 +0,0 @@
-interface SpinnerProps {
-  size?: 'sm' | 'md' | 'lg'
-  className?: string
-}
-
-const SIZES = { sm: 'w-4 h-4', md: 'w-6 h-6', lg: 'w-8 h-8' }
-
-export function Spinner({ size = 'md', className = '' }: SpinnerProps) {
-  return (
-    <svg
-      className={`animate-spin ${SIZES[size]} ${className}`}
-      viewBox="0 0 24 24"
-      fill="none"
-    >
-      <circle
-        cx="12" cy="12" r="10"
-        stroke="currentColor"
-        strokeWidth="3"
-        strokeLinecap="round"
-        className="opacity-20"
-      />
-      <path
-        d="M12 2a10 10 0 0 1 10 10"
-        stroke="currentColor"
-        strokeWidth="3"
-        strokeLinecap="round"
-      />
-    </svg>
-  )
-}
-
-export function PageSpinner() {
-  return (
-    <div className="flex items-center justify-center h-64">
-      <Spinner size="lg" className="text-accent-blue" />
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/StatusBadge.tsx

@@ -1,35 +0,0 @@
-import { CheckCircle, XCircle, AlertTriangle, Info } from 'lucide-react'
-
-type Variant = 'success' | 'error' | 'warning' | 'info' | 'neutral'
-
-interface StatusBadgeProps {
-  variant: Variant
-  label: string
-  className?: string
-}
-
-const STYLES: Record<Variant, string> = {
-  success: 'bg-status-success/15 text-status-success border-status-success/30',
-  error: 'bg-status-error/15 text-status-error border-status-error/30',
-  warning: 'bg-status-warning/15 text-status-warning border-status-warning/30',
-  info: 'bg-status-info/15 text-status-info border-status-info/30',
-  neutral: 'bg-bg-tertiary text-text-secondary border-border-primary',
-}
-
-const ICONS: Record<Variant, typeof CheckCircle> = {
-  success: CheckCircle,
-  error: XCircle,
-  warning: AlertTriangle,
-  info: Info,
-  neutral: Info,
-}
-
-export function StatusBadge({ variant, label, className = '' }: StatusBadgeProps) {
-  const Icon = ICONS[variant]
-  return (
-    <span className={`inline-flex items-center gap-1.5 px-2.5 py-1 text-xs font-medium rounded-md border ${STYLES[variant]} ${className}`}>
-      <Icon className="w-3.5 h-3.5" />
-      {label}
-    </span>
-  )
-}

frontend/commander-ai-lab-ui/src/components/common/index.ts

@@ -1,5 +0,0 @@
-export { Spinner, PageSpinner } from './Spinner'
-export { StatusBadge } from './StatusBadge'
-export { CardImage } from './CardImage'
-export { ManaSymbols, ColorDots } from './ManaSymbols'
-export { EmptyState } from './EmptyState'

frontend/commander-ai-lab-ui/src/components/layout/PageLayout.tsx

@@ -1,16 +0,0 @@
-import { Sidebar } from './Sidebar'
-
-interface PageLayoutProps {
-  children: React.ReactNode
-}
-
-export function PageLayout({ children }: PageLayoutProps) {
-  return (
-    <div className="min-h-screen bg-bg-primary">
-      <Sidebar />
-      <main className="ml-56 min-h-screen">
-        {children}
-      </main>
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/components/layout/Sidebar.tsx

@@ -1,51 +0,0 @@
-import { NavLink } from 'react-router-dom'
-
-const NAV_ITEMS = [
-  { to: '/',           label: 'Batch Sim',   icon: '⚗️' },
-  { to: '/collection', label: 'Collection',  icon: '📦' },
-  { to: '/decks',      label: 'Decks',       icon: '🃏' },
-  { to: '/autogen',    label: 'Auto Gen',    icon: '⚡' },
-  { to: '/simulator',  label: 'Simulator',   icon: '🎮' },
-  { to: '/coach',      label: 'Coach',       icon: '🧠' },
-  { to: '/training',   label: 'Training',    icon: '📈' },
-]
-
-export function Sidebar() {
-  return (
-    <aside className="fixed left-0 top-0 bottom-0 w-56 bg-bg-secondary border-r border-border-primary flex flex-col z-30">
-      {/* Logo */}
-      <div className="px-5 py-5 border-b border-border-primary">
-        <h1 className="text-base font-bold text-text-primary tracking-tight">
-          Commander AI Lab
-        </h1>
-        <p className="text-xs text-text-tertiary mt-0.5">MTG Intelligence Platform</p>
-      </div>
-
-      {/* Nav */}
-      <nav className="flex-1 py-3 overflow-y-auto">
-        {NAV_ITEMS.map(item => (
-          <NavLink
-            key={item.to}
-            to={item.to}
-            end={item.to === '/'}
-            className={({ isActive }) =>
-              `flex items-center gap-3 px-5 py-2.5 text-sm transition-colors duration-150 ${
-                isActive
-                  ? 'bg-accent-blue/10 text-accent-blue border-r-2 border-accent-blue'
-                  : 'text-text-secondary hover:text-text-primary hover:bg-bg-hover'
-              }`
-            }
-          >
-            <span className="text-base">{item.icon}</span>
-            <span>{item.label}</span>
-          </NavLink>
-        ))}
-      </nav>
-
-      {/* Footer */}
-      <div className="px-5 py-4 border-t border-border-primary">
-        <p className="text-xs text-text-tertiary">v4.0 — React + TypeScript</p>
-      </div>
-    </aside>
-  )
-}

frontend/commander-ai-lab-ui/src/hooks/usePolling.ts

@@ -1,13 +0,0 @@
-import { useEffect, useRef } from 'react'
-
-/** Polls a function at the given interval while `enabled` is true */
-export function usePolling(fn: () => void, intervalMs: number, enabled: boolean) {
-  const savedFn = useRef(fn)
-  savedFn.current = fn
-
-  useEffect(() => {
-    if (!enabled) return
-    const id = setInterval(() => savedFn.current(), intervalMs)
-    return () => clearInterval(id)
-  }, [intervalMs, enabled])
-}

frontend/commander-ai-lab-ui/src/index.css

@@ -1,114 +0,0 @@
-@import "tailwindcss";
-
-/* ═══════════════════════════════════════════════════════════
-   Commander AI Lab — Design Tokens (Tailwind v4 CSS Theme)
-   Fresh dark theme with MTG-inspired accents
-   ═══════════════════════════════════════════════════════════ */
-
-@theme {
-  /* ── Backgrounds ── */
-  --color-bg-primary: #0f1117;
-  --color-bg-secondary: #161921;
-  --color-bg-tertiary: #1c2030;
-  --color-bg-elevated: #222738;
-  --color-bg-hover: #2a3048;
-
-  /* ── Borders ── */
-  --color-border-primary: #2a2f42;
-  --color-border-secondary: #363c54;
-  --color-border-accent: #4f6ef7;
-
-  /* ── Text ── */
-  --color-text-primary: #e8eaf0;
-  --color-text-secondary: #9ba1b8;
-  --color-text-tertiary: #6b7190;
-  --color-text-inverse: #0f1117;
-
-  /* ── Accents ── */
-  --color-accent-blue: #4f6ef7;
-  --color-accent-blue-hover: #6b84ff;
-  --color-accent-teal: #2dd4bf;
-  --color-accent-teal-hover: #5eead4;
-  --color-accent-purple: #a78bfa;
-  --color-accent-amber: #fbbf24;
-  --color-accent-red: #f87171;
-  --color-accent-green: #4ade80;
-
-  /* ── MTG Mana Colors ── */
-  --color-mana-white: #f9faf4;
-  --color-mana-blue: #0e67ab;
-  --color-mana-black: #2b2424;
-  --color-mana-red: #d3202a;
-  --color-mana-green: #00733e;
-  --color-mana-colorless: #706f6f;
-
-  /* ── Status ── */
-  --color-status-success: #4ade80;
-  --color-status-warning: #fbbf24;
-  --color-status-error: #f87171;
-  --color-status-info: #60a5fa;
-
-  /* ── Radius ── */
-  --radius-sm: 0.375rem;
-  --radius-md: 0.5rem;
-  --radius-lg: 0.75rem;
-  --radius-xl: 1rem;
-
-  /* ── Shadows ── */
-  --shadow-sm: 0 1px 2px rgba(0, 0, 0, 0.3);
-  --shadow-md: 0 4px 12px rgba(0, 0, 0, 0.4);
-  --shadow-lg: 0 8px 24px rgba(0, 0, 0, 0.5);
-  --shadow-glow-blue: 0 0 20px rgba(79, 110, 247, 0.15);
-  --shadow-glow-teal: 0 0 20px rgba(45, 212, 191, 0.15);
-}
-
-/* ── Base Styles ── */
-html {
-  background-color: var(--color-bg-primary);
-  color: var(--color-text-primary);
-}
-
-body {
-  font-family: 'Inter', system-ui, -apple-system, sans-serif;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-
-/* ── Scrollbar ── */
-::-webkit-scrollbar {
-  width: 8px;
-  height: 8px;
-}
-::-webkit-scrollbar-track {
-  background: var(--color-bg-secondary);
-}
-::-webkit-scrollbar-thumb {
-  background: var(--color-border-secondary);
-  border-radius: 4px;
-}
-::-webkit-scrollbar-thumb:hover {
-  background: var(--color-text-tertiary);
-}
-
-/* ── Utility animations ── */
-@keyframes fade-in {
-  from { opacity: 0; transform: translateY(4px); }
-  to { opacity: 1; transform: translateY(0); }
-}
-@keyframes pulse-glow {
-  0%, 100% { box-shadow: 0 0 0 0 rgba(79, 110, 247, 0.2); }
-  50% { box-shadow: 0 0 16px 4px rgba(79, 110, 247, 0.15); }
-}
-@keyframes spin {
-  to { transform: rotate(360deg); }
-}
-
-.animate-fade-in {
-  animation: fade-in 0.2s ease-out;
-}
-.animate-pulse-glow {
-  animation: pulse-glow 2s infinite;
-}
-.animate-spin {
-  animation: spin 1s linear infinite;
-}

frontend/commander-ai-lab-ui/src/main.tsx

@@ -1,10 +0,0 @@
-import { StrictMode } from 'react'
-import { createRoot } from 'react-dom/client'
-import './index.css'
-import App from './App.tsx'
-
-createRoot(document.getElementById('root')!).render(
-  <StrictMode>
-    <App />
-  </StrictMode>,
-)

frontend/commander-ai-lab-ui/src/pages/AutoGenPage.tsx

@@ -1,436 +0,0 @@
-import { useState, useEffect, useRef } from 'react'
-import {
-  Search, Wand2, Save, AlertCircle,
-  ChevronDown, Sparkles, RefreshCw, Check
-} from 'lucide-react'
-import { Spinner, StatusBadge, ManaSymbols, CardImage, ColorDots } from '../components/common'
-import { deckgenApi } from '../api'
-import type { DeckGenV3Status, DeckGenV3Result, DeckGenV3Card, CommanderSearchResult } from '../types'
-import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
-
-// ── Commander Search ──────────────────────────────────────────
-function CommanderSearch({ onSelect }: { onSelect: (c: CommanderSearchResult) => void }) {
-  const [query, setQuery] = useState('')
-  const [results, setResults] = useState<CommanderSearchResult[]>([])
-  const [searching, setSearching] = useState(false)
-  const debounceRef = useRef<ReturnType<typeof setTimeout>>(undefined)
-
-  function handleSearch(q: string) {
-    setQuery(q)
-    clearTimeout(debounceRef.current)
-    if (q.length < 2) { setResults([]); return }
-    debounceRef.current = setTimeout(async () => {
-      setSearching(true)
-      try {
-        const r = await deckgenApi.searchCommanders(q)
-        setResults(r)
-      } catch { setResults([]) }
-      setSearching(false)
-    }, 300)
-  }
-
-  return (
-    <div className="relative">
-      <div className="relative">
-        <Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
-        <input
-          type="text"
-          placeholder="Search for a commander..."
-          value={query}
-          onChange={e => handleSearch(e.target.value)}
-          className="w-full pl-10 pr-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 focus:ring-1 focus:ring-accent-blue/30 transition-all"
-        />
-        {searching && <Spinner size="sm" className="absolute right-3 top-1/2 -translate-y-1/2 text-accent-blue" />}
-      </div>
-      {results.length > 0 && (
-        <div className="absolute z-20 top-full mt-1 w-full bg-bg-elevated border border-border-primary rounded-xl shadow-lg max-h-80 overflow-y-auto">
-          {results.map(c => (
-            <button
-              key={c.scryfall_id}
-              onClick={() => { onSelect(c); setQuery(c.name); setResults([]) }}
-              className="w-full flex items-center gap-3 px-4 py-3 hover:bg-bg-hover text-left transition-colors"
-            >
-              <CardImage src={c.image_url} alt={c.name} size="sm" />
-              <div className="flex-1 min-w-0">
-                <p className="text-sm font-medium text-text-primary truncate">{c.name}</p>
-                <p className="text-xs text-text-secondary truncate">{c.type_line}</p>
-              </div>
-              <ColorDots colors={c.color_identity} />
-              {c.in_collection && (
-                <span className="text-[10px] px-1.5 py-0.5 bg-accent-green/15 text-accent-green rounded">Owned</span>
-              )}
-            </button>
-          ))}
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ── Generated Deck View ───────────────────────────────────────
-function GeneratedDeckView({ result, onCommit }: { result: DeckGenV3Result; onCommit: () => void }) {
-  const [view, setView] = useState<'visual' | 'list'>('visual')
-  const [committing, setCommitting] = useState(false)
-  const [selectedCard, setSelectedCard] = useState<DeckGenV3Card | null>(null)
-
-  // Group by category
-  const grouped = result.cards.reduce<Record<string, DeckGenV3Card[]>>((acc, c) => {
-    const cat = c.category || 'Other'
-    if (!acc[cat]) acc[cat] = []
-    acc[cat].push(c)
-    return acc
-  }, {})
-
-  const curveData = Object.entries(result.stats.mana_curve)
-    .map(([cmc, count]) => ({ cmc: cmc === '7' ? '7+' : cmc, count }))
-    .sort((a, b) => Number(a.cmc) - Number(b.cmc))
-
-  async function handleCommit() {
-    setCommitting(true)
-    try { onCommit() } catch { /* ignore */ }
-    setCommitting(false)
-  }
-
-  return (
-    <div className="space-y-5 animate-fade-in">
-      {/* Commander header */}
-      <div className="flex gap-5 bg-bg-secondary rounded-xl border border-border-primary p-5">
-        <CardImage src={result.commander.image_url} alt={result.commander.name} size="lg" />
-        <div className="flex-1 space-y-3">
-          <div>
-            <h2 className="text-xl font-bold text-text-primary">{result.commander.name}</h2>
-            <p className="text-sm text-text-secondary">{result.commander.type_line}</p>
-            <div className="flex items-center gap-3 mt-2">
-              <ManaSymbols cost={result.commander.mana_cost} size="md" />
-              <ColorDots colors={result.commander.color_identity} size="md" />
-            </div>
-          </div>
-          <p className="text-sm text-text-primary whitespace-pre-wrap">{result.commander.oracle_text}</p>
-          <div className="flex items-center gap-4 text-xs text-text-secondary">
-            <span>Strategy: <span className="text-accent-blue font-medium">{result.strategy}</span></span>
-            <span>Bracket: <span className="text-accent-amber font-medium">{result.bracket}</span></span>
-            <span>Model: <span className="text-accent-purple font-medium">{result.model}</span></span>
-          </div>
-          {result.substitution && (
-            <div className="flex items-center gap-3 text-xs">
-              <span className="text-accent-green">{result.substitution.owned} owned</span>
-              <span className="text-accent-amber">{result.substitution.substituted} substituted</span>
-              <span className="text-accent-red">{result.substitution.missing} missing</span>
-            </div>
-          )}
-        </div>
-      </div>
-
-      {/* Actions */}
-      <div className="flex items-center gap-3">
-        <button onClick={handleCommit} disabled={committing}
-          className="flex items-center gap-2 px-4 py-2.5 bg-accent-green/15 text-accent-green text-sm font-medium rounded-lg hover:bg-accent-green/25 border border-accent-green/30 transition-colors disabled:opacity-50">
-          {committing ? <Spinner size="sm" /> : <Save className="w-4 h-4" />}
-          Save to Decks
-        </button>
-        <div className="flex items-center gap-1 bg-bg-secondary rounded-lg border border-border-primary p-0.5">
-          <button onClick={() => setView('visual')}
-            className={`px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${view === 'visual' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}>
-            Visual
-          </button>
-          <button onClick={() => setView('list')}
-            className={`px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${view === 'list' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}>
-            List
-          </button>
-        </div>
-        {/* Stats summary */}
-        <div className="flex-1" />
-        <span className="text-xs text-text-tertiary">{result.stats.card_count} cards</span>
-        <span className="text-xs text-accent-teal font-medium">${result.stats.total_price.toFixed(2)}</span>
-      </div>
-
-      {/* Mana curve mini */}
-      <div className="bg-bg-secondary rounded-xl border border-border-primary p-4">
-        <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-2">Mana Curve</h4>
-        <div className="h-28">
-          <ResponsiveContainer width="100%" height="100%">
-            <BarChart data={curveData} barSize={20}>
-              <XAxis dataKey="cmc" axisLine={false} tickLine={false} tick={{ fill: '#6b7190', fontSize: 10 }} />
-              <YAxis hide />
-              <Tooltip contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 11 }} />
-              <Bar dataKey="count" radius={[3, 3, 0, 0]}>
-                {curveData.map((_, i) => <Cell key={i} fill="#4f6ef7" fillOpacity={0.8} />)}
-              </Bar>
-            </BarChart>
-          </ResponsiveContainer>
-        </div>
-      </div>
-
-      {/* Card grid/list */}
-      {view === 'visual' ? (
-        <div className="space-y-5">
-          {Object.entries(grouped).map(([cat, cards]) => (
-            <div key={cat}>
-              <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-3">{cat} ({cards.length})</h4>
-              <div className="grid grid-cols-3 sm:grid-cols-4 md:grid-cols-5 lg:grid-cols-6 xl:grid-cols-8 gap-2">
-                {cards.map(card => (
-                  <div key={card.name} className="group cursor-pointer relative" onClick={() => setSelectedCard(card)}>
-                    <CardImage src={card.image_url} alt={card.name} size="md" className="w-full h-auto aspect-[5/7]" />
-                    {card.is_substitute && (
-                      <div className="absolute top-1 right-1 w-4 h-4 bg-accent-amber rounded-full flex items-center justify-center">
-                        <RefreshCw className="w-2.5 h-2.5 text-bg-primary" />
-                      </div>
-                    )}
-                    {card.owned && (
-                      <div className="absolute top-1 left-1 w-4 h-4 bg-accent-green rounded-full flex items-center justify-center">
-                        <Check className="w-2.5 h-2.5 text-bg-primary" />
-                      </div>
-                    )}
-                  </div>
-                ))}
-              </div>
-            </div>
-          ))}
-        </div>
-      ) : (
-        <div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
-          <table className="w-full text-sm">
-            <thead>
-              <tr className="border-b border-border-primary text-left">
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Card</th>
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Category</th>
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Role</th>
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Mana</th>
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider text-right">Price</th>
-                <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Status</th>
-              </tr>
-            </thead>
-            <tbody className="divide-y divide-border-primary">
-              {result.cards.map(card => (
-                <tr key={card.name} className="hover:bg-bg-hover transition-colors">
-                  <td className="px-4 py-2.5">
-                    <span className="font-medium text-text-primary">{card.quantity}x {card.name}</span>
-                    {card.is_substitute && (
-                      <span className="ml-2 text-[10px] text-accent-amber">sub for {card.original_name}</span>
-                    )}
-                  </td>
-                  <td className="px-4 py-2.5 text-xs text-text-secondary">{card.category}</td>
-                  <td className="px-4 py-2.5 text-xs text-text-secondary">{card.role}</td>
-                  <td className="px-4 py-2.5"><ManaSymbols cost={card.mana_cost} /></td>
-                  <td className="px-4 py-2.5 text-right text-accent-teal text-xs">{card.tcg_price != null ? `$${card.tcg_price.toFixed(2)}` : '—'}</td>
-                  <td className="px-4 py-2.5">
-                    <span className={`text-[10px] px-2 py-0.5 rounded ${card.owned ? 'bg-accent-green/15 text-accent-green' : 'bg-bg-tertiary text-text-tertiary'}`}>
-                      {card.owned ? `Owned (${card.owned_qty})` : 'Missing'}
-                    </span>
-                  </td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </div>
-      )}
-
-      {/* Card detail popup */}
-      {selectedCard && (
-        <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/70 backdrop-blur-sm" onClick={() => setSelectedCard(null)}>
-          <div className="bg-bg-secondary rounded-xl border border-border-primary shadow-lg max-w-2xl w-full mx-4 p-6 animate-fade-in" onClick={e => e.stopPropagation()}>
-            <div className="flex gap-5">
-              <CardImage src={selectedCard.image_url} alt={selectedCard.name} size="lg" />
-              <div className="flex-1 space-y-3">
-                <h3 className="text-lg font-bold text-text-primary">{selectedCard.name}</h3>
-                <p className="text-sm text-text-secondary">{selectedCard.type_line}</p>
-                <ManaSymbols cost={selectedCard.mana_cost} size="md" />
-                <p className="text-sm text-text-primary whitespace-pre-wrap">{selectedCard.reason}</p>
-                <div className="flex items-center gap-3 text-xs">
-                  <span className="text-text-tertiary">Role: <span className="text-accent-blue">{selectedCard.role}</span></span>
-                  {selectedCard.is_substitute && (
-                    <span className="text-accent-amber">Substituted for {selectedCard.original_name} ({(selectedCard.similarity_score ?? 0 * 100).toFixed(0)}%)</span>
-                  )}
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ── Main Auto Gen Page ────────────────────────────────────────
-export function AutoGenPage() {
-  const [status, setStatus] = useState<DeckGenV3Status | null>(null)
-  const [loading, setLoading] = useState(true)
-  const [generating, setGenerating] = useState(false)
-  const [result, setResult] = useState<DeckGenV3Result | null>(null)
-  const [commander, setCommander] = useState<CommanderSearchResult | null>(null)
-  const [strategy, setStrategy] = useState('')
-  const [bracket, setBracket] = useState(3)
-  const [budget, setBudget] = useState<string>('')
-  const [useCollection, setUseCollection] = useState(true)
-  const [runSub, setRunSub] = useState(true)
-  const [error, setError] = useState<string | null>(null)
-
-  useEffect(() => {
-    deckgenApi.getV3Status().then(s => { setStatus(s); setLoading(false) }).catch(() => setLoading(false))
-  }, [])
-
-  async function handleGenerate() {
-    if (!commander) return
-    setGenerating(true)
-    setError(null)
-    setResult(null)
-    try {
-      const res = await deckgenApi.generateDeckV3({
-        commander_name: commander.name,
-        strategy: strategy || undefined,
-        target_bracket: bracket,
-        budget_usd: budget ? Number(budget) : undefined,
-        use_collection: useCollection,
-        run_substitution: runSub,
-      })
-      setResult(res)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Generation failed')
-    }
-    setGenerating(false)
-  }
-
-  async function handleCommit() {
-    if (!commander || !result) return
-    try {
-      await deckgenApi.commitDeckV3({
-        commander_name: commander.name,
-        strategy: strategy || undefined,
-        target_bracket: bracket,
-        budget_usd: budget ? Number(budget) : undefined,
-        use_collection: useCollection,
-        run_substitution: runSub,
-      })
-    } catch { /* ignore */ }
-  }
-
-  if (loading) {
-    return <div className="p-6 flex items-center justify-center h-64"><Spinner size="lg" className="text-accent-blue" /></div>
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      <div>
-        <h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
-          <Sparkles className="w-6 h-6 text-accent-purple" />
-          Auto Gen V3
-        </h1>
-        <p className="text-sm text-text-secondary mt-0.5">AI-powered deck generation with Perplexity</p>
-      </div>
-
-      {/* Status bar */}
-      <div className="flex items-center gap-3">
-        <StatusBadge
-          variant={status?.initialized ? 'success' : 'error'}
-          label={status?.initialized ? 'Ready' : 'Not Initialized'}
-        />
-        <StatusBadge
-          variant={status?.pplx_configured ? 'success' : 'warning'}
-          label={status?.pplx_configured ? 'Perplexity Connected' : 'Perplexity Not Set'}
-        />
-        {status?.embeddings_loaded && (
-          <StatusBadge variant="info" label={`${status.embedding_cards.toLocaleString()} embeddings`} />
-        )}
-      </div>
-
-      {!result ? (
-        /* Config form */
-        <div className="max-w-2xl space-y-5">
-          <div>
-            <label className="text-sm font-medium text-text-primary block mb-2">Commander</label>
-            <CommanderSearch onSelect={setCommander} />
-            {commander && (
-              <div className="mt-2 flex items-center gap-3 p-3 bg-bg-secondary rounded-lg border border-border-primary">
-                <CardImage src={commander.image_url} alt={commander.name} size="sm" />
-                <div>
-                  <p className="text-sm font-medium text-text-primary">{commander.name}</p>
-                  <p className="text-xs text-text-secondary">{commander.type_line}</p>
-                </div>
-                <ColorDots colors={commander.color_identity} />
-              </div>
-            )}
-          </div>
-
-          <div>
-            <label className="text-sm font-medium text-text-primary block mb-2">Strategy (optional)</label>
-            <input
-              type="text"
-              placeholder="e.g. Voltron, Aristocrats, Storm..."
-              value={strategy}
-              onChange={e => setStrategy(e.target.value)}
-              className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-            />
-          </div>
-
-          <div className="grid grid-cols-2 gap-4">
-            <div>
-              <label className="text-sm font-medium text-text-primary block mb-2">Target Bracket</label>
-              <select
-                value={bracket}
-                onChange={e => setBracket(Number(e.target.value))}
-                className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-              >
-                <option value={1}>1 — Casual</option>
-                <option value={2}>2 — Low Power</option>
-                <option value={3}>3 — Mid Power</option>
-                <option value={4}>4 — High Power</option>
-              </select>
-            </div>
-            <div>
-              <label className="text-sm font-medium text-text-primary block mb-2">Budget (USD, optional)</label>
-              <input
-                type="number"
-                placeholder="No limit"
-                value={budget}
-                onChange={e => setBudget(e.target.value)}
-                className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-              />
-            </div>
-          </div>
-
-          <div className="flex items-center gap-6">
-            <label className="flex items-center gap-2 cursor-pointer">
-              <input type="checkbox" checked={useCollection} onChange={e => setUseCollection(e.target.checked)}
-                className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30" />
-              <span className="text-sm text-text-secondary">Use collection cards</span>
-            </label>
-            <label className="flex items-center gap-2 cursor-pointer">
-              <input type="checkbox" checked={runSub} onChange={e => setRunSub(e.target.checked)}
-                className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30" />
-              <span className="text-sm text-text-secondary">Run substitution pass</span>
-            </label>
-          </div>
-
-          {error && (
-            <div className="flex items-center gap-2 px-4 py-3 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
-              <AlertCircle className="w-4 h-4 flex-shrink-0" />
-              {error}
-            </div>
-          )}
-
-          <button
-            onClick={handleGenerate}
-            disabled={!commander || generating || !status?.initialized}
-            className="flex items-center gap-2 px-6 py-3 bg-accent-blue text-white text-sm font-semibold rounded-lg hover:bg-accent-blue-hover shadow-glow-blue transition-all disabled:opacity-50 disabled:shadow-none"
-          >
-            {generating ? (
-              <><Spinner size="sm" /> Generating...</>
-            ) : (
-              <><Wand2 className="w-4 h-4" /> Generate Deck</>
-            )}
-          </button>
-        </div>
-      ) : (
-        /* Results */
-        <div>
-          <button onClick={() => setResult(null)}
-            className="flex items-center gap-2 mb-4 text-sm text-text-secondary hover:text-text-primary transition-colors">
-            <ChevronDown className="w-4 h-4 rotate-90" />
-            Back to Config
-          </button>
-          <GeneratedDeckView result={result} onCommit={handleCommit} />
-        </div>
-      )}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/BatchSimPage.tsx

@@ -1,415 +0,0 @@
-import { useState, useEffect } from 'react'
-import {
-  FlaskConical, Play, Clock, Trophy,
-  Upload, Link, FileText, History, ChevronRight, AlertCircle,
-  Check
-} from 'lucide-react'
-import { Spinner } from '../components/common'
-import { labApi } from '../api'
-import { usePolling } from '../hooks/usePolling'
-import type { LabDeck, LabStatus, LabResult, LabHistoryEntry, PreconDeck } from '../types'
-import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
-
-// ── Results Chart ─────────────────────────────────────────────
-function ResultsChart({ result }: { result: LabResult }) {
-  const data = result.decks
-    .sort((a, b) => b.win_rate - a.win_rate)
-    .map(d => ({
-      name: d.name.length > 20 ? d.name.slice(0, 18) + '...' : d.name,
-      win_rate: Math.round(d.win_rate * 100),
-      fullName: d.name,
-    }))
-
-  return (
-    <div className="h-64">
-      <ResponsiveContainer width="100%" height="100%">
-        <BarChart data={data} layout="vertical" barSize={20} margin={{ left: 10, right: 20 }}>
-          <XAxis type="number" domain={[0, 100]} tick={{ fill: '#6b7190', fontSize: 11 }} axisLine={false} tickLine={false} />
-          <YAxis type="category" dataKey="name" width={150} tick={{ fill: '#9ba1b8', fontSize: 11 }} axisLine={false} tickLine={false} />
-          <Tooltip
-            contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
-            formatter={(value: unknown) => [`${value}%`, 'Win Rate']}
-            labelFormatter={(label: unknown) => data.find(d => d.name === String(label))?.fullName || String(label)}
-          />
-          <Bar dataKey="win_rate" radius={[0, 4, 4, 0]}>
-            {data.map((_, i) => (
-              <Cell key={i} fill={i === 0 ? '#4ade80' : i === 1 ? '#4f6ef7' : '#363c54'} />
-            ))}
-          </Bar>
-        </BarChart>
-      </ResponsiveContainer>
-    </div>
-  )
-}
-
-// ── Deck Selector ─────────────────────────────────────────────
-function DeckSelector({
-  decks,
-  selected,
-  onToggle,
-  precons,
-  onInstallPrecon,
-}: {
-  decks: LabDeck[]
-  selected: Set<string>
-  onToggle: (name: string) => void
-  precons: PreconDeck[]
-  onInstallPrecon: (name: string) => void
-}) {
-  const [showPrecons, setShowPrecons] = useState(false)
-  const [installing, setInstalling] = useState<string | null>(null)
-
-  async function handleInstall(name: string) {
-    setInstalling(name)
-    await onInstallPrecon(name)
-    setInstalling(null)
-  }
-
-  return (
-    <div className="space-y-3">
-      <h3 className="text-sm font-semibold text-text-primary">Available Decks</h3>
-      {decks.length === 0 ? (
-        <p className="text-xs text-text-tertiary">No decks available. Import or install precons.</p>
-      ) : (
-        <div className="space-y-1 max-h-64 overflow-y-auto">
-          {decks.map(d => (
-            <label key={d.name} className="flex items-center gap-3 px-3 py-2 rounded-lg hover:bg-bg-hover cursor-pointer transition-colors">
-              <input
-                type="checkbox"
-                checked={selected.has(d.name)}
-                onChange={() => onToggle(d.name)}
-                className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30"
-              />
-              <div className="flex-1 min-w-0">
-                <span className="text-sm text-text-primary truncate block">{d.name}</span>
-                <span className="text-[10px] text-text-tertiary">{d.source} · {d.card_count} cards</span>
-              </div>
-            </label>
-          ))}
-        </div>
-      )}
-
-      <button
-        onClick={() => setShowPrecons(!showPrecons)}
-        className="w-full flex items-center justify-between px-3 py-2 text-xs font-medium bg-bg-tertiary rounded-lg text-text-secondary hover:bg-bg-hover border border-border-primary transition-colors"
-      >
-        Precon Decks
-        <ChevronRight className={`w-3.5 h-3.5 transition-transform ${showPrecons ? 'rotate-90' : ''}`} />
-      </button>
-
-      {showPrecons && (
-        <div className="space-y-1 max-h-48 overflow-y-auto animate-fade-in">
-          {precons.map(p => (
-            <div key={p.name} className="flex items-center gap-3 px-3 py-2 rounded-lg bg-bg-tertiary">
-              <span className="flex-1 text-xs text-text-secondary truncate">{p.name}</span>
-              {p.installed ? (
-                <span className="text-[10px] text-accent-green flex items-center gap-1"><Check className="w-3 h-3" /> Installed</span>
-              ) : (
-                <button
-                  onClick={() => handleInstall(p.name)}
-                  disabled={installing === p.name}
-                  className="text-[10px] text-accent-blue hover:text-accent-blue-hover transition-colors"
-                >
-                  {installing === p.name ? <Spinner size="sm" /> : 'Install'}
-                </button>
-              )}
-            </div>
-          ))}
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ── Import Panel ──────────────────────────────────────────────
-function ImportPanel({ onImported }: { onImported: () => void }) {
-  const [importUrl, setImportUrl] = useState('')
-  const [importText, setImportText] = useState('')
-  const [importName, setImportName] = useState('')
-  const [importing, setImporting] = useState(false)
-  const [importMode, setImportMode] = useState<'url' | 'text'>('url')
-
-  async function handleImport() {
-    setImporting(true)
-    try {
-      if (importMode === 'url' && importUrl.trim()) {
-        await labApi.importDeckFromUrl(importUrl.trim())
-      } else if (importMode === 'text' && importText.trim()) {
-        await labApi.importDeckFromText(importText.trim(), importName || undefined)
-      }
-      setImportUrl('')
-      setImportText('')
-      setImportName('')
-      onImported()
-    } catch { /* ignore */ }
-    setImporting(false)
-  }
-
-  return (
-    <div className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3">
-      <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-        <Upload className="w-4 h-4 text-accent-blue" />
-        Import Deck
-      </h3>
-      <div className="flex gap-1">
-        <button onClick={() => setImportMode('url')}
-          className={`flex-1 px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${importMode === 'url' ? 'bg-accent-blue/15 text-accent-blue' : 'bg-bg-tertiary text-text-tertiary'}`}>
-          <Link className="w-3 h-3 inline mr-1" />URL
-        </button>
-        <button onClick={() => setImportMode('text')}
-          className={`flex-1 px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${importMode === 'text' ? 'bg-accent-blue/15 text-accent-blue' : 'bg-bg-tertiary text-text-tertiary'}`}>
-          <FileText className="w-3 h-3 inline mr-1" />Text
-        </button>
-      </div>
-      {importMode === 'url' ? (
-        <input
-          type="text"
-          placeholder="Moxfield, Archidekt, or deck URL..."
-          value={importUrl}
-          onChange={e => setImportUrl(e.target.value)}
-          className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-        />
-      ) : (
-        <>
-          <input
-            type="text"
-            placeholder="Deck name"
-            value={importName}
-            onChange={e => setImportName(e.target.value)}
-            className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-          />
-          <textarea
-            placeholder={"1x Sol Ring\n1x Command Tower\n..."}
-            value={importText}
-            onChange={e => setImportText(e.target.value)}
-            rows={5}
-            className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary font-mono focus:outline-none focus:border-accent-blue/50 transition-all resize-none"
-          />
-        </>
-      )}
-      <button onClick={handleImport} disabled={importing}
-        className="w-full px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50">
-        {importing ? <Spinner size="sm" /> : 'Import'}
-      </button>
-    </div>
-  )
-}
-
-// ── Main Batch Sim Page ───────────────────────────────────────
-export function BatchSimPage() {
-  const [decks, setDecks] = useState<LabDeck[]>([])
-  const [precons, setPrecons] = useState<PreconDeck[]>([])
-  const [selected, setSelected] = useState<Set<string>>(new Set())
-  const [games, setGames] = useState(100)
-  const [status, setStatus] = useState<LabStatus | null>(null)
-  const [result, setResult] = useState<LabResult | null>(null)
-  const [history, setHistory] = useState<LabHistoryEntry[]>([])
-  const [loading, setLoading] = useState(true)
-  const [starting, setStarting] = useState(false)
-  const [error, setError] = useState<string | null>(null)
-  const [showHistory, setShowHistory] = useState(false)
-
-  async function fetchDecks() {
-    try {
-      const [d, p] = await Promise.all([labApi.getLabDecks(), labApi.getPrecons()])
-      setDecks(d)
-      setPrecons(p)
-    } catch { /* ignore */ }
-    setLoading(false)
-  }
-
-  useEffect(() => {
-    fetchDecks()
-    labApi.getLabStatus().then(setStatus).catch(() => {})
-    labApi.getLabHistory().then(setHistory).catch(() => {})
-  }, [])
-
-  // Poll while running
-  usePolling(async () => {
-    try {
-      const s = await labApi.getLabStatus()
-      setStatus(s)
-      if (!s.running && s.run_id) {
-        const r = await labApi.getLabResult()
-        setResult(r)
-        labApi.getLabHistory().then(setHistory).catch(() => {})
-      }
-    } catch { /* ignore */ }
-  }, 2000, status?.running || false)
-
-  function toggleDeck(name: string) {
-    setSelected(prev => {
-      const next = new Set(prev)
-      if (next.has(name)) next.delete(name)
-      else next.add(name)
-      return next
-    })
-  }
-
-  async function handleStart() {
-    if (selected.size < 2) { setError('Select at least 2 decks'); return }
-    setStarting(true)
-    setError(null)
-    setResult(null)
-    try {
-      await labApi.startBatchSim({ decks: [...selected], games })
-      const s = await labApi.getLabStatus()
-      setStatus(s)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to start')
-    }
-    setStarting(false)
-  }
-
-  async function handleInstallPrecon(name: string) {
-    try {
-      await labApi.installPrecon(name)
-      fetchDecks()
-    } catch { /* ignore */ }
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      <div>
-        <h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
-          <FlaskConical className="w-6 h-6 text-accent-teal" />
-          Batch Simulator
-        </h1>
-        <p className="text-sm text-text-secondary mt-0.5">Run mass simulations between decks</p>
-      </div>
-
-      <div className="flex gap-5">
-        {/* Left sidebar: deck selection + import */}
-        <div className="w-72 flex-shrink-0 space-y-4">
-          {loading ? (
-            <div className="flex items-center justify-center h-32"><Spinner size="md" className="text-accent-blue" /></div>
-          ) : (
-            <DeckSelector
-              decks={decks}
-              selected={selected}
-              onToggle={toggleDeck}
-              precons={precons}
-              onInstallPrecon={handleInstallPrecon}
-            />
-          )}
-          <ImportPanel onImported={fetchDecks} />
-        </div>
-
-        {/* Main content */}
-        <div className="flex-1 space-y-5">
-          {/* Config + Start */}
-          <div className="bg-bg-secondary rounded-xl border border-border-primary p-5">
-            <div className="flex items-center gap-4">
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Games</label>
-                <input
-                  type="number"
-                  min={10}
-                  max={10000}
-                  step={10}
-                  value={games}
-                  onChange={e => setGames(Number(e.target.value))}
-                  className="mt-1 w-28 px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-                />
-              </div>
-              <div className="flex-1">
-                <p className="text-xs text-text-tertiary">Selected: {selected.size} decks</p>
-              </div>
-              {status?.running ? (
-                <div className="flex items-center gap-3">
-                  <div className="flex items-center gap-2">
-                    <Spinner size="sm" className="text-accent-blue" />
-                    <span className="text-sm text-text-secondary">
-                      {status.games_completed} / {status.total_games} games
-                    </span>
-                  </div>
-                  <div className="w-48 h-2 bg-bg-tertiary rounded-full overflow-hidden">
-                    <div
-                      className="h-full bg-accent-blue rounded-full transition-all"
-                      style={{ width: `${status.total_games ? (status.games_completed / status.total_games) * 100 : 0}%` }}
-                    />
-                  </div>
-                </div>
-              ) : (
-                <button onClick={handleStart} disabled={starting || selected.size < 2}
-                  className="flex items-center gap-2 px-5 py-2.5 bg-accent-teal/15 text-accent-teal text-sm font-semibold rounded-lg hover:bg-accent-teal/25 border border-accent-teal/30 transition-colors disabled:opacity-50">
-                  {starting ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
-                  Start Simulation
-                </button>
-              )}
-            </div>
-            {error && (
-              <div className="mt-3 flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
-                <AlertCircle className="w-4 h-4" />{error}
-              </div>
-            )}
-          </div>
-
-          {/* Results */}
-          {result && (
-            <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 animate-fade-in">
-              <div className="flex items-center justify-between">
-                <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-                  <Trophy className="w-4 h-4 text-accent-amber" />
-                  Results
-                </h3>
-                <span className="text-xs text-text-tertiary">{result.total_games} games · {new Date(result.timestamp).toLocaleString()}</span>
-              </div>
-              <ResultsChart result={result} />
-              <div className="overflow-x-auto">
-                <table className="w-full text-sm">
-                  <thead>
-                    <tr className="border-b border-border-primary">
-                      <th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium uppercase tracking-wider">Deck</th>
-                      <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Wins</th>
-                      <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Games</th>
-                      <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Win Rate</th>
-                      <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Avg Turns</th>
-                    </tr>
-                  </thead>
-                  <tbody className="divide-y divide-border-primary">
-                    {result.decks.sort((a, b) => b.win_rate - a.win_rate).map((d, i) => (
-                      <tr key={d.name} className="hover:bg-bg-hover transition-colors">
-                        <td className="px-3 py-2.5 flex items-center gap-2">
-                          {i === 0 && <Trophy className="w-3.5 h-3.5 text-accent-amber" />}
-                          <span className="font-medium text-text-primary">{d.name}</span>
-                        </td>
-                        <td className="px-3 py-2.5 text-right text-text-secondary">{d.wins}</td>
-                        <td className="px-3 py-2.5 text-right text-text-secondary">{d.games}</td>
-                        <td className="px-3 py-2.5 text-right font-semibold text-accent-blue">{(d.win_rate * 100).toFixed(1)}%</td>
-                        <td className="px-3 py-2.5 text-right text-text-secondary">{d.avg_turns.toFixed(1)}</td>
-                      </tr>
-                    ))}
-                  </tbody>
-                </table>
-              </div>
-            </div>
-          )}
-
-          {/* History */}
-          <div>
-            <button onClick={() => setShowHistory(!showHistory)}
-              className="flex items-center gap-2 text-sm text-text-secondary hover:text-text-primary transition-colors">
-              <History className="w-4 h-4" />
-              Run History ({history.length})
-              <ChevronRight className={`w-3.5 h-3.5 transition-transform ${showHistory ? 'rotate-90' : ''}`} />
-            </button>
-            {showHistory && history.length > 0 && (
-              <div className="mt-3 space-y-2 animate-fade-in">
-                {history.map(h => (
-                  <div key={h.run_id} className="flex items-center gap-4 px-4 py-3 bg-bg-secondary rounded-lg border border-border-primary">
-                    <Clock className="w-4 h-4 text-text-tertiary" />
-                    <div className="flex-1">
-                      <p className="text-sm text-text-primary">{h.decks.join(' vs ')}</p>
-                      <p className="text-xs text-text-tertiary">{new Date(h.timestamp).toLocaleString()} · {h.total_games} games</p>
-                    </div>
-                  </div>
-                ))}
-              </div>
-            )}
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/CoachPage.tsx

@@ -1,949 +0,0 @@
-import { useState, useEffect, useRef, useCallback } from 'react'
-import {
-  GraduationCap, MessageSquare, Send, Sparkles, AlertCircle, X, Check,
-  ChevronRight, ChevronDown, Download, Settings2, Search, Zap,
-  Minus, Plus, History, ArrowRightLeft, Lightbulb, DollarSign, Loader2,
-  ClipboardList, Brain, BarChart3
-} from 'lucide-react'
-import { Spinner, StatusBadge, EmptyState } from '../components/common'
-import { coachApi } from '../api'
-import type {
-  CoachStatus, CoachDeck, CoachSession, CoachSessionSummary,
-  CoachMessage, CoachGoals, CoachApplyResult, CoachCardLikeResult
-} from '../types'
-
-// ════════════════════════════════════════════════════════════
-// Goals Config Panel
-// ════════════════════════════════════════════════════════════
-function GoalsPanel({
-  goals, onChange, collapsed, onToggle
-}: {
-  goals: CoachGoals
-  onChange: (g: CoachGoals) => void
-  collapsed: boolean
-  onToggle: () => void
-}) {
-  const strategies = ['aggro', 'control', 'combo', 'midrange', 'stax', 'voltron', 'aristocrats', 'tokens']
-  const budgets = [
-    { value: 'budget', label: 'Budget', desc: 'Under $5/card' },
-    { value: 'medium', label: 'Medium', desc: 'Under $20/card' },
-    { value: 'no-limit', label: 'No Limit', desc: 'Any price' },
-  ]
-  const focusOptions = ['ramp', 'card draw', 'removal', 'protection', 'threats', 'mana base', 'combos', 'synergy']
-
-  return (
-    <div className="bg-bg-secondary rounded-xl border border-border-primary">
-      <button
-        onClick={onToggle}
-        className="w-full flex items-center gap-2 px-4 py-3 text-left hover:bg-bg-hover transition-colors rounded-xl"
-      >
-        <Settings2 className="w-4 h-4 text-accent-teal" />
-        <span className="text-sm font-medium text-text-primary flex-1">Coaching Goals</span>
-        {collapsed ? <ChevronRight className="w-4 h-4 text-text-tertiary" /> : <ChevronDown className="w-4 h-4 text-text-tertiary" />}
-      </button>
-
-      {!collapsed && (
-        <div className="px-4 pb-4 space-y-4 border-t border-border-primary pt-3">
-          {/* Power Level Slider */}
-          <div>
-            <label className="text-xs font-medium text-text-secondary mb-1 block">
-              Power Level: {goals.targetPowerLevel ?? 'Any'}
-            </label>
-            <input
-              type="range" min={1} max={10}
-              value={goals.targetPowerLevel ?? 5}
-              onChange={e => onChange({ ...goals, targetPowerLevel: parseInt(e.target.value) })}
-              className="w-full accent-accent-teal"
-            />
-            <div className="flex justify-between text-[10px] text-text-tertiary">
-              <span>Casual</span><span>Focused</span><span>cEDH</span>
-            </div>
-          </div>
-
-          {/* Strategy */}
-          <div>
-            <label className="text-xs font-medium text-text-secondary mb-1.5 block">Strategy Focus</label>
-            <div className="flex flex-wrap gap-1.5">
-              {strategies.map(s => (
-                <button
-                  key={s}
-                  onClick={() => onChange({ ...goals, metaFocus: goals.metaFocus === s ? null : s })}
-                  className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
-                    goals.metaFocus === s
-                      ? 'bg-accent-teal/20 border-accent-teal/40 text-accent-teal'
-                      : 'border-border-primary text-text-tertiary hover:border-border-secondary'
-                  }`}
-                >
-                  {s}
-                </button>
-              ))}
-            </div>
-          </div>
-
-          {/* Budget */}
-          <div>
-            <label className="text-xs font-medium text-text-secondary mb-1.5 block">Budget</label>
-            <div className="grid grid-cols-3 gap-2">
-              {budgets.map(b => (
-                <button
-                  key={b.value}
-                  onClick={() => onChange({ ...goals, budget: goals.budget === b.value ? null : b.value })}
-                  className={`px-3 py-2 text-xs rounded-lg border transition-colors ${
-                    goals.budget === b.value
-                      ? 'bg-accent-teal/20 border-accent-teal/40 text-accent-teal'
-                      : 'border-border-primary text-text-tertiary hover:border-border-secondary'
-                  }`}
-                >
-                  <div className="font-medium">{b.label}</div>
-                  <div className="text-[10px] opacity-60">{b.desc}</div>
-                </button>
-              ))}
-            </div>
-          </div>
-
-          {/* Focus Areas */}
-          <div>
-            <label className="text-xs font-medium text-text-secondary mb-1.5 block">Focus Areas</label>
-            <div className="flex flex-wrap gap-1.5">
-              {focusOptions.map(f => {
-                const active = (goals.focusAreas || []).includes(f)
-                return (
-                  <button
-                    key={f}
-                    onClick={() => {
-                      const areas = goals.focusAreas || []
-                      onChange({
-                        ...goals,
-                        focusAreas: active ? areas.filter(a => a !== f) : [...areas, f]
-                      })
-                    }}
-                    className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
-                      active
-                        ? 'bg-accent-blue/20 border-accent-blue/40 text-accent-blue'
-                        : 'border-border-primary text-text-tertiary hover:border-border-secondary'
-                    }`}
-                  >
-                    {f}
-                  </button>
-                )
-              })}
-            </div>
-          </div>
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ════════════════════════════════════════════════════════════
-// Session View — Shows coaching results with apply functionality
-// ════════════════════════════════════════════════════════════
-function SessionView({
-  session, deckId, onClose, onApplied
-}: {
-  session: CoachSession
-  deckId: number
-  onClose: () => void
-  onApplied?: () => void
-}) {
-  const [selectedCuts, setSelectedCuts] = useState<Set<string>>(new Set())
-  const [selectedAdds, setSelectedAdds] = useState<Set<string>>(new Set())
-  const [applying, setApplying] = useState(false)
-  const [applyResult, setApplyResult] = useState<CoachApplyResult | null>(null)
-
-  const toggleCut = (name: string) => {
-    const s = new Set(selectedCuts)
-    s.has(name) ? s.delete(name) : s.add(name)
-    setSelectedCuts(s)
-  }
-  const toggleAdd = (name: string) => {
-    const s = new Set(selectedAdds)
-    s.has(name) ? s.delete(name) : s.add(name)
-    setSelectedAdds(s)
-  }
-
-  const handleApply = async () => {
-    if (selectedCuts.size === 0 && selectedAdds.size === 0) return
-    setApplying(true)
-    try {
-      const result = await coachApi.applyCoachSuggestions(
-        session.sessionId, deckId,
-        [...selectedCuts], [...selectedAdds]
-      )
-      setApplyResult(result)
-      onApplied?.()
-    } catch (e) {
-      console.error('Apply failed:', e)
-    }
-    setApplying(false)
-  }
-
-  return (
-    <div className="space-y-5">
-      <div className="flex items-center justify-between">
-        <div>
-          <h3 className="text-lg font-semibold text-text-primary">Coaching Session</h3>
-          <p className="text-xs text-text-tertiary">
-            {new Date(session.timestamp).toLocaleString()} · {session.modelUsed}
-            {session.promptTokens > 0 && ` · ${session.promptTokens + session.completionTokens} tokens`}
-          </p>
-        </div>
-        <button onClick={onClose} className="p-1.5 rounded-lg hover:bg-bg-hover text-text-tertiary">
-          <X className="w-4 h-4" />
-        </button>
-      </div>
-
-      {/* Summary */}
-      {session.summary && (
-        <div className="bg-bg-secondary rounded-xl border border-border-primary p-4">
-          <h4 className="text-xs font-medium text-text-secondary mb-2 flex items-center gap-1.5">
-            <Brain className="w-3.5 h-3.5" /> Summary
-          </h4>
-          <p className="text-sm text-text-primary leading-relaxed">{session.summary}</p>
-        </div>
-      )}
-
-      {/* Heuristic Hints */}
-      {session.heuristicHints?.length > 0 && (
-        <div className="bg-accent-amber/5 rounded-xl border border-accent-amber/20 p-4">
-          <h4 className="text-xs font-medium text-accent-amber mb-2 flex items-center gap-1.5">
-            <Lightbulb className="w-3.5 h-3.5" /> Strategic Tips
-          </h4>
-          <ul className="space-y-1.5">
-            {session.heuristicHints.map((h, i) => (
-              <li key={i} className="text-sm text-text-primary flex items-start gap-2">
-                <span className="text-accent-amber mt-0.5">•</span> {h}
-              </li>
-            ))}
-          </ul>
-        </div>
-      )}
-
-      {/* Mana Base Advice */}
-      {session.manaBaseAdvice && (
-        <div className="bg-accent-blue/5 rounded-xl border border-accent-blue/20 p-4">
-          <h4 className="text-xs font-medium text-accent-blue mb-2 flex items-center gap-1.5">
-            <BarChart3 className="w-3.5 h-3.5" /> Mana Base
-          </h4>
-          <p className="text-sm text-text-primary">{session.manaBaseAdvice}</p>
-        </div>
-      )}
-
-      {/* Suggested Cuts */}
-      {session.suggestedCuts?.length > 0 && (
-        <div>
-          <h4 className="text-sm font-medium text-status-error mb-2 flex items-center gap-1.5">
-            <Minus className="w-4 h-4" /> Suggested Cuts ({session.suggestedCuts.length})
-          </h4>
-          <div className="space-y-2">
-            {session.suggestedCuts.map((cut, i) => (
-              <div
-                key={i}
-                onClick={() => toggleCut(cut.cardName)}
-                className={`flex items-start gap-3 p-3 rounded-lg border cursor-pointer transition-colors ${
-                  selectedCuts.has(cut.cardName)
-                    ? 'bg-status-error/10 border-status-error/30'
-                    : 'bg-bg-secondary border-border-primary hover:border-border-secondary'
-                }`}
-              >
-                <div className={`mt-0.5 w-4 h-4 rounded border flex items-center justify-center flex-shrink-0 ${
-                  selectedCuts.has(cut.cardName)
-                    ? 'bg-status-error border-status-error text-white'
-                    : 'border-border-secondary'
-                }`}>
-                  {selectedCuts.has(cut.cardName) && <Check className="w-3 h-3" />}
-                </div>
-                <div className="flex-1 min-w-0">
-                  <p className="text-sm font-medium text-text-primary">{cut.cardName}</p>
-                  <p className="text-xs text-text-secondary mt-0.5">{cut.reason}</p>
-                  {cut.replacementOptions?.length > 0 && (
-                    <p className="text-[10px] text-text-tertiary mt-1">
-                      Replace with: {cut.replacementOptions.join(', ')}
-                    </p>
-                  )}
-                </div>
-              </div>
-            ))}
-          </div>
-        </div>
-      )}
-
-      {/* Suggested Adds */}
-      {session.suggestedAdds?.length > 0 && (
-        <div>
-          <h4 className="text-sm font-medium text-status-success mb-2 flex items-center gap-1.5">
-            <Plus className="w-4 h-4" /> Suggested Adds ({session.suggestedAdds.length})
-          </h4>
-          <div className="space-y-2">
-            {session.suggestedAdds.map((add, i) => (
-              <div
-                key={i}
-                onClick={() => toggleAdd(add.cardName)}
-                className={`flex items-start gap-3 p-3 rounded-lg border cursor-pointer transition-colors ${
-                  selectedAdds.has(add.cardName)
-                    ? 'bg-status-success/10 border-status-success/30'
-                    : 'bg-bg-secondary border-border-primary hover:border-border-secondary'
-                }`}
-              >
-                <div className={`mt-0.5 w-4 h-4 rounded border flex items-center justify-center flex-shrink-0 ${
-                  selectedAdds.has(add.cardName)
-                    ? 'bg-status-success border-status-success text-white'
-                    : 'border-border-secondary'
-                }`}>
-                  {selectedAdds.has(add.cardName) && <Check className="w-3 h-3" />}
-                </div>
-                <div className="flex-1 min-w-0">
-                  <div className="flex items-center gap-2">
-                    <p className="text-sm font-medium text-text-primary">{add.cardName}</p>
-                    {add.role && (
-                      <span className="px-1.5 py-0.5 text-[10px] bg-accent-blue/15 text-accent-blue rounded">
-                        {add.role}
-                      </span>
-                    )}
-                  </div>
-                  <p className="text-xs text-text-secondary mt-0.5">{add.reason}</p>
-                  {add.synergyWith?.length > 0 && (
-                    <p className="text-[10px] text-text-tertiary mt-1">
-                      Synergy: {add.synergyWith.join(', ')}
-                    </p>
-                  )}
-                </div>
-              </div>
-            ))}
-          </div>
-        </div>
-      )}
-
-      {/* Apply Button */}
-      {!applyResult && (selectedCuts.size > 0 || selectedAdds.size > 0) && (
-        <div className="sticky bottom-0 bg-bg-primary/95 backdrop-blur-sm border-t border-border-primary py-3 -mx-6 px-6">
-          <button
-            onClick={handleApply}
-            disabled={applying}
-            className="w-full flex items-center justify-center gap-2 px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50"
-          >
-            {applying ? <Spinner size="sm" /> : <ArrowRightLeft className="w-4 h-4" />}
-            Apply {selectedCuts.size > 0 && `${selectedCuts.size} cuts`}
-            {selectedCuts.size > 0 && selectedAdds.size > 0 && ' + '}
-            {selectedAdds.size > 0 && `${selectedAdds.size} adds`} to Deck
-          </button>
-        </div>
-      )}
-
-      {/* Apply Results */}
-      {applyResult && (
-        <div className="bg-status-success/5 rounded-xl border border-status-success/20 p-4">
-          <h4 className="text-sm font-medium text-status-success mb-2 flex items-center gap-1.5">
-            <Check className="w-4 h-4" /> Changes Applied
-          </h4>
-          <p className="text-sm text-text-primary">
-            {applyResult.total_cuts > 0 && `Removed ${applyResult.total_cuts} card(s). `}
-            {applyResult.total_adds > 0 && `Added ${applyResult.total_adds} card(s). `}
-            {applyResult.errors.length > 0 && `${applyResult.errors.length} error(s).`}
-          </p>
-          {applyResult.errors.length > 0 && (
-            <ul className="mt-2 text-xs text-status-error">
-              {applyResult.errors.map((e, i) => (
-                <li key={i}>{e.name}: {e.error}</li>
-              ))}
-            </ul>
-          )}
-        </div>
-      )}
-
-      {/* Raw Explanation */}
-      {session.rawTextExplanation && (
-        <details className="group">
-          <summary className="text-xs text-text-tertiary cursor-pointer hover:text-text-secondary">
-            Full analysis...
-          </summary>
-          <p className="mt-2 text-sm text-text-secondary whitespace-pre-wrap leading-relaxed bg-bg-secondary rounded-lg p-4 border border-border-primary">
-            {session.rawTextExplanation}
-          </p>
-        </details>
-      )}
-    </div>
-  )
-}
-
-// ════════════════════════════════════════════════════════════
-// Cards Like Search
-// ════════════════════════════════════════════════════════════
-function CardsLikePanel() {
-  const [query, setQuery] = useState('')
-  const [results, setResults] = useState<CoachCardLikeResult[]>([])
-  const [searching, setSearching] = useState(false)
-
-  const handleSearch = async () => {
-    if (!query.trim()) return
-    setSearching(true)
-    try {
-      const data = await coachApi.searchCardsLike(query.trim(), undefined, 12)
-      setResults(data.results)
-    } catch { /* ignore */ }
-    setSearching(false)
-  }
-
-  return (
-    <div className="space-y-4">
-      <div className="flex gap-2">
-        <div className="relative flex-1">
-          <Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
-          <input
-            value={query}
-            onChange={e => setQuery(e.target.value)}
-            onKeyDown={e => e.key === 'Enter' && handleSearch()}
-            placeholder="Enter a card name..."
-            className="w-full pl-9 pr-3 py-2 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue"
-          />
-        </div>
-        <button
-          onClick={handleSearch}
-          disabled={searching || !query.trim()}
-          className="px-4 py-2 bg-accent-teal/15 text-accent-teal text-sm font-medium rounded-lg hover:bg-accent-teal/25 border border-accent-teal/30 transition-colors disabled:opacity-50"
-        >
-          {searching ? <Spinner size="sm" /> : 'Search'}
-        </button>
-      </div>
-
-      {results.length > 0 && (
-        <div className="grid grid-cols-1 md:grid-cols-2 gap-2">
-          {results.map((card, i) => (
-            <div key={i} className="flex items-start gap-3 p-3 bg-bg-secondary rounded-lg border border-border-primary">
-              <div className="flex-1 min-w-0">
-                <div className="flex items-center gap-2">
-                  <p className="text-sm font-medium text-text-primary truncate">{card.name}</p>
-                  <span className="text-[10px] text-text-tertiary whitespace-nowrap">
-                    {(card.similarity * 100).toFixed(0)}%
-                  </span>
-                </div>
-                <p className="text-[11px] text-text-secondary mt-0.5">{card.types}</p>
-                {card.text && (
-                  <p className="text-[10px] text-text-tertiary mt-1 line-clamp-2">{card.text}</p>
-                )}
-                <div className="flex items-center gap-3 mt-1.5 text-[10px]">
-                  {card.tcg_price != null && (
-                    <span className="text-accent-amber flex items-center gap-0.5">
-                      <DollarSign className="w-2.5 h-2.5" />{card.tcg_price.toFixed(2)}
-                    </span>
-                  )}
-                  {card.owned_qty > 0 && (
-                    <span className="text-status-success">Owned: {card.owned_qty}</span>
-                  )}
-                  <span className="text-text-tertiary">MV: {card.mana_value}</span>
-                </div>
-              </div>
-              {card.image_url && (
-                <img src={card.image_url} alt={card.name} className="w-14 h-auto rounded" />
-              )}
-            </div>
-          ))}
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ════════════════════════════════════════════════════════════
-// Live Chat View
-// ════════════════════════════════════════════════════════════
-function LiveChat({
-  deckId, deckName, goals, onClose
-}: {
-  deckId: string
-  deckName: string
-  goals: CoachGoals
-  onClose: () => void
-}) {
-  const [messages, setMessages] = useState<CoachMessage[]>([])
-  const [input, setInput] = useState('')
-  const [streaming, setStreaming] = useState(false)
-  const [streamText, setStreamText] = useState('')
-  const messagesEndRef = useRef<HTMLDivElement>(null)
-  const abortRef = useRef<AbortController | null>(null)
-
-  useEffect(() => {
-    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
-  }, [messages, streamText])
-
-  const sendMessage = async () => {
-    if (!input.trim() || streaming) return
-    const userMsg: CoachMessage = { role: 'user', content: input.trim() }
-    const allMessages = [...messages, userMsg]
-    setMessages(allMessages)
-    setInput('')
-    setStreaming(true)
-    setStreamText('')
-
-    const goalsDict = goals.targetPowerLevel || goals.metaFocus || goals.budget || (goals.focusAreas || []).length > 0
-      ? {
-          targetPowerLevel: goals.targetPowerLevel,
-          metaFocus: goals.metaFocus,
-          budget: goals.budget,
-          focusAreas: goals.focusAreas,
-        }
-      : undefined
-
-    try {
-      abortRef.current = new AbortController()
-      const fullText = await coachApi.coachChatStream(
-        deckId,
-        allMessages.map(m => ({ role: m.role, content: m.content })),
-        goalsDict,
-        (text) => setStreamText(text),
-        abortRef.current.signal
-      )
-
-      setMessages(prev => [...prev, { role: 'assistant', content: fullText }])
-      setStreamText('')
-    } catch (e) {
-      if ((e as Error).name !== 'AbortError') {
-        // Fallback to non-streaming
-        try {
-          const resp = await coachApi.coachChat(
-            deckId,
-            allMessages.map(m => ({ role: m.role, content: m.content })),
-            goalsDict
-          )
-          setMessages(prev => [...prev, { role: 'assistant', content: resp.content }])
-        } catch (e2) {
-          setMessages(prev => [...prev, {
-            role: 'assistant',
-            content: `Error: ${(e2 as Error).message || 'Failed to get response'}`
-          }])
-        }
-      }
-      setStreamText('')
-    }
-    setStreaming(false)
-  }
-
-  return (
-    <div className="flex flex-col h-[calc(100vh-180px)]">
-      {/* Header */}
-      <div className="flex items-center justify-between px-4 py-3 border-b border-border-primary">
-        <div>
-          <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-            <MessageSquare className="w-4 h-4 text-accent-blue" />
-            Chat: {deckName}
-          </h3>
-          <p className="text-[10px] text-text-tertiary">
-            {goals.metaFocus && `${goals.metaFocus} · `}
-            {goals.targetPowerLevel && `Power ${goals.targetPowerLevel} · `}
-            {goals.budget && `${goals.budget}`}
-          </p>
-        </div>
-        <button onClick={onClose} className="p-1.5 rounded-lg hover:bg-bg-hover text-text-tertiary">
-          <X className="w-4 h-4" />
-        </button>
-      </div>
-
-      {/* Messages */}
-      <div className="flex-1 overflow-y-auto p-4 space-y-3">
-        {messages.length === 0 && !streaming && (
-          <div className="text-center py-12">
-            <GraduationCap className="w-10 h-10 text-text-tertiary mx-auto mb-3" />
-            <p className="text-sm text-text-secondary">Ask your coach anything about this deck.</p>
-            <div className="mt-4 flex flex-wrap gap-2 justify-center">
-              {[
-                'What should I cut?',
-                'How is my mana curve?',
-                'Suggest win conditions',
-                'Rate my removal suite',
-              ].map(q => (
-                <button
-                  key={q}
-                  onClick={() => { setInput(q); }}
-                  className="px-3 py-1.5 text-xs bg-bg-secondary border border-border-primary rounded-full text-text-secondary hover:text-text-primary hover:border-border-secondary transition-colors"
-                >
-                  {q}
-                </button>
-              ))}
-            </div>
-          </div>
-        )}
-
-        {messages.map((msg, i) => (
-          <div key={i} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
-            <div className={`max-w-[85%] px-4 py-3 rounded-xl text-sm whitespace-pre-wrap leading-relaxed ${
-              msg.role === 'user'
-                ? 'bg-accent-blue/15 text-text-primary border border-accent-blue/20'
-                : 'bg-bg-secondary text-text-primary border border-border-primary'
-            }`}>
-              {msg.content}
-            </div>
-          </div>
-        ))}
-
-        {/* Streaming indicator */}
-        {streaming && (
-          <div className="flex justify-start">
-            <div className="max-w-[85%] px-4 py-3 rounded-xl text-sm whitespace-pre-wrap leading-relaxed bg-bg-secondary text-text-primary border border-border-primary">
-              {streamText || (
-                <span className="flex items-center gap-2 text-text-tertiary">
-                  <Loader2 className="w-3.5 h-3.5 animate-spin" />
-                  Thinking...
-                </span>
-              )}
-            </div>
-          </div>
-        )}
-        <div ref={messagesEndRef} />
-      </div>
-
-      {/* Input */}
-      <div className="border-t border-border-primary p-3">
-        <div className="flex gap-2">
-          <input
-            value={input}
-            onChange={e => setInput(e.target.value)}
-            onKeyDown={e => e.key === 'Enter' && !e.shiftKey && sendMessage()}
-            placeholder="Ask the coach..."
-            disabled={streaming}
-            className="flex-1 px-3 py-2 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue disabled:opacity-50"
-          />
-          <button
-            onClick={sendMessage}
-            disabled={streaming || !input.trim()}
-            className="px-3 py-2 bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50"
-          >
-            <Send className="w-4 h-4" />
-          </button>
-        </div>
-      </div>
-    </div>
-  )
-}
-
-// ════════════════════════════════════════════════════════════
-// Main Coach Page
-// ════════════════════════════════════════════════════════════
-type Tab = 'decks' | 'chat' | 'session' | 'history' | 'search'
-
-export function CoachPage() {
-  const [status, setStatus] = useState<CoachStatus | null>(null)
-  const [decks, setDecks] = useState<CoachDeck[]>([])
-  const [sessions, setSessions] = useState<CoachSessionSummary[]>([])
-  const [loading, setLoading] = useState(true)
-  const [error, setError] = useState<string | null>(null)
-
-  // UI state
-  const [tab, setTab] = useState<Tab>('decks')
-  const [selectedDeck, setSelectedDeck] = useState<CoachDeck | null>(null)
-  const [selectedSession, setSelectedSession] = useState<CoachSession | null>(null)
-  const [goals, setGoals] = useState<CoachGoals>({})
-  const [goalsCollapsed, setGoalsCollapsed] = useState(true)
-  const [generatingReport, setGeneratingReport] = useState(false)
-  const [runningSession, setRunningSession] = useState<number | null>(null)
-  const [downloadingEmbeddings, setDownloadingEmbeddings] = useState(false)
-
-  const loadData = useCallback(async () => {
-    try {
-      const [s, d, sess] = await Promise.all([
-        coachApi.getCoachStatus(),
-        coachApi.getCoachDecks().catch(() => []),
-        coachApi.getCoachSessions().catch(() => ({ sessions: [] })),
-      ])
-      setStatus(s)
-      setDecks(Array.isArray(d) ? d : [])
-      setSessions(Array.isArray(sess) ? sess : (sess as { sessions: CoachSessionSummary[] }).sessions || [])
-    } catch { /* ignore */ }
-    setLoading(false)
-  }, [])
-
-  useEffect(() => { loadData() }, [loadData])
-
-  const handleDownloadEmbeddings = async () => {
-    setDownloadingEmbeddings(true)
-    try {
-      await coachApi.downloadEmbeddings()
-      const s = await coachApi.getCoachStatus()
-      setStatus(s)
-    } catch { /* ignore */ }
-    setDownloadingEmbeddings(false)
-  }
-
-  const handleGenerateReports = async () => {
-    setGeneratingReport(true)
-    setError(null)
-    try {
-      await coachApi.generateReport()
-      await loadData()
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Report generation failed')
-    }
-    setGeneratingReport(false)
-  }
-
-  const handleRunSession = async (deck: CoachDeck) => {
-    setRunningSession(deck.deck_id)
-    setError(null)
-    try {
-      const deckSlug = deck.deck_name.toLowerCase().replace(/\s+/g, '-')
-      const goalsDict = goals.targetPowerLevel || goals.metaFocus || goals.budget || (goals.focusAreas || []).length > 0
-        ? goals : undefined
-      const session = await coachApi.runCoachSession(deckSlug, goalsDict as Record<string, unknown>)
-      setSelectedSession(session)
-      setSelectedDeck(deck)
-      setTab('session')
-      await loadData()
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Session failed')
-    }
-    setRunningSession(null)
-  }
-
-  const handleOpenChat = (deck: CoachDeck) => {
-    setSelectedDeck(deck)
-    setTab('chat')
-  }
-
-  const handleViewSession = async (sessionId: string) => {
-    try {
-      const s = await coachApi.getCoachSession(sessionId)
-      setSelectedSession(s)
-      // Find the matching deck
-      const matchDeck = decks.find(d =>
-        d.deck_name.toLowerCase().replace(/\s+/g, '-') === s.deckId.toLowerCase() ||
-        d.deck_name.toLowerCase() === s.deckId.toLowerCase()
-      )
-      if (matchDeck) setSelectedDeck(matchDeck)
-      setTab('session')
-    } catch { /* ignore */ }
-  }
-
-  if (loading) {
-    return (
-      <div className="p-6 flex items-center justify-center h-64">
-        <Spinner size="lg" className="text-accent-blue" />
-      </div>
-    )
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      {/* Header */}
-      <div className="flex items-center justify-between">
-        <div>
-          <h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
-            <GraduationCap className="w-6 h-6 text-accent-amber" />
-            Deck Coach
-          </h1>
-          <p className="text-sm text-text-secondary mt-0.5">
-            AI-powered deck analysis, coaching, and live chat
-          </p>
-        </div>
-      </div>
-
-      {/* Status Bar */}
-      <div className="flex items-center gap-3 flex-wrap">
-        <StatusBadge
-          variant={status?.llmConnected ? 'success' : 'warning'}
-          label={status?.llmConnected ? `LLM: ${status.llmModel || 'Connected'}` : 'LLM: Disconnected'}
-        />
-        <StatusBadge
-          variant={status?.embeddingsLoaded ? 'success' : 'neutral'}
-          label={status?.embeddingsLoaded ? `${(status.embeddingCards || 0).toLocaleString()} embeddings` : 'No embeddings'}
-        />
-        {status?.deckReportsAvailable != null && status.deckReportsAvailable > 0 && (
-          <StatusBadge variant="success" label={`${status.deckReportsAvailable} reports`} />
-        )}
-        {!status?.embeddingsLoaded && (
-          <button onClick={handleDownloadEmbeddings} disabled={downloadingEmbeddings}
-            className="flex items-center gap-1.5 text-xs text-accent-blue hover:text-accent-blue-hover transition-colors disabled:opacity-50">
-            {downloadingEmbeddings ? <Spinner size="sm" /> : <Download className="w-3 h-3" />}
-            Download Embeddings
-          </button>
-        )}
-      </div>
-
-      {error && (
-        <div className="flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
-          <AlertCircle className="w-4 h-4 flex-shrink-0" />{error}
-          <button onClick={() => setError(null)} className="ml-auto"><X className="w-3 h-3" /></button>
-        </div>
-      )}
-
-      {/* Tab Nav */}
-      <div className="flex gap-1 bg-bg-secondary rounded-lg p-1 border border-border-primary">
-        {([
-          { id: 'decks', label: 'Decks', icon: ClipboardList },
-          { id: 'history', label: 'Sessions', icon: History },
-          { id: 'search', label: 'Cards Like', icon: Search },
-        ] as { id: Tab; label: string; icon: typeof ClipboardList }[]).map(t => (
-          <button
-            key={t.id}
-            onClick={() => setTab(t.id)}
-            className={`flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${
-              tab === t.id
-                ? 'bg-bg-primary text-text-primary shadow-sm'
-                : 'text-text-tertiary hover:text-text-secondary'
-            }`}
-          >
-            <t.icon className="w-3.5 h-3.5" />
-            {t.label}
-            {t.id === 'history' && sessions.length > 0 && (
-              <span className="text-[10px] bg-bg-tertiary px-1.5 rounded-full">{sessions.length}</span>
-            )}
-          </button>
-        ))}
-        {tab === 'chat' && selectedDeck && (
-          <div className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md bg-bg-primary text-accent-blue shadow-sm">
-            <MessageSquare className="w-3.5 h-3.5" />
-            Chat
-          </div>
-        )}
-        {tab === 'session' && selectedSession && (
-          <div className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md bg-bg-primary text-accent-amber shadow-sm">
-            <Sparkles className="w-3.5 h-3.5" />
-            Results
-          </div>
-        )}
-      </div>
-
-      {/* Goals Panel (shown when on decks tab) */}
-      {tab === 'decks' && (
-        <GoalsPanel
-          goals={goals}
-          onChange={setGoals}
-          collapsed={goalsCollapsed}
-          onToggle={() => setGoalsCollapsed(!goalsCollapsed)}
-        />
-      )}
-
-      {/* ── Decks Tab ─────────────────────────────────────── */}
-      {tab === 'decks' && (
-        <div className="space-y-4">
-          <div className="flex items-center justify-between">
-            <h2 className="text-sm font-semibold text-text-primary">Your Decks</h2>
-            <button
-              onClick={handleGenerateReports}
-              disabled={generatingReport}
-              className="flex items-center gap-1.5 text-xs text-accent-amber hover:text-accent-amber transition-colors disabled:opacity-50"
-            >
-              {generatingReport ? <Spinner size="sm" /> : <Zap className="w-3 h-3" />}
-              Generate Reports
-            </button>
-          </div>
-
-          {decks.length === 0 ? (
-            <EmptyState
-              icon={GraduationCap}
-              title="No decks available"
-              description="Create decks in the Deck Builder to start coaching."
-            />
-          ) : (
-            <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-3">
-              {decks.map(deck => (
-                <div key={deck.deck_id} className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3">
-                  <div>
-                    <h3 className="text-sm font-semibold text-text-primary">{deck.deck_name}</h3>
-                    <p className="text-xs text-text-secondary">{deck.commander}</p>
-                  </div>
-                  <div className="flex items-center gap-3 text-[10px] text-text-tertiary">
-                    <span>{deck.card_count} cards</span>
-                    {deck.has_report && (
-                      <span className="flex items-center gap-1 text-status-success">
-                        <Check className="w-2.5 h-2.5" /> Report
-                      </span>
-                    )}
-                  </div>
-                  <div className="flex items-center gap-2">
-                    <button
-                      onClick={() => handleRunSession(deck)}
-                      disabled={runningSession === deck.deck_id || !status?.llmConnected}
-                      className="flex-1 flex items-center justify-center gap-1.5 px-3 py-2 bg-accent-amber/15 text-accent-amber text-xs font-medium rounded-lg hover:bg-accent-amber/25 border border-accent-amber/30 transition-colors disabled:opacity-50"
-                    >
-                      {runningSession === deck.deck_id ? <Spinner size="sm" /> : <Sparkles className="w-3 h-3" />}
-                      Analyze
-                    </button>
-                    <button
-                      onClick={() => handleOpenChat(deck)}
-                      disabled={!status?.llmConnected}
-                      className="flex-1 flex items-center justify-center gap-1.5 px-3 py-2 bg-accent-blue/15 text-accent-blue text-xs font-medium rounded-lg hover:bg-accent-blue/25 border border-accent-blue/30 transition-colors disabled:opacity-50"
-                    >
-                      <MessageSquare className="w-3 h-3" />
-                      Chat
-                    </button>
-                  </div>
-                </div>
-              ))}
-            </div>
-          )}
-        </div>
-      )}
-
-      {/* ── Chat Tab ──────────────────────────────────────── */}
-      {tab === 'chat' && selectedDeck && (
-        <LiveChat
-          deckId={selectedDeck.deck_name.toLowerCase().replace(/\s+/g, '-')}
-          deckName={selectedDeck.deck_name}
-          goals={goals}
-          onClose={() => setTab('decks')}
-        />
-      )}
-
-      {/* ── Session Results Tab ───────────────────────────── */}
-      {tab === 'session' && selectedSession && selectedDeck && (
-        <SessionView
-          session={selectedSession}
-          deckId={selectedDeck.deck_id}
-          onClose={() => setTab('decks')}
-          onApplied={() => loadData()}
-        />
-      )}
-
-      {/* ── History Tab ───────────────────────────────────── */}
-      {tab === 'history' && (
-        <div className="space-y-3">
-          <h2 className="text-sm font-semibold text-text-primary">Coaching Sessions</h2>
-          {sessions.length === 0 ? (
-            <EmptyState
-              icon={History}
-              title="No sessions yet"
-              description="Run an analysis on a deck to create your first coaching session."
-            />
-          ) : (
-            <div className="space-y-2">
-              {sessions.map(s => (
-                <button
-                  key={s.sessionId}
-                  onClick={() => handleViewSession(s.sessionId)}
-                  className="w-full flex items-center gap-4 px-4 py-3 bg-bg-secondary rounded-lg border border-border-primary hover:border-border-secondary text-left transition-colors"
-                >
-                  <Sparkles className="w-4 h-4 text-accent-amber flex-shrink-0" />
-                  <div className="flex-1 min-w-0">
-                    <div className="flex items-center gap-2">
-                      <p className="text-sm font-medium text-text-primary">{s.deckId}</p>
-                      <span className="text-[10px] text-text-tertiary">
-                        {s.cutsCount > 0 && `${s.cutsCount} cuts`}
-                        {s.cutsCount > 0 && s.addsCount > 0 && ' · '}
-                        {s.addsCount > 0 && `${s.addsCount} adds`}
-                      </span>
-                    </div>
-                    <p className="text-xs text-text-tertiary mt-0.5 truncate">{s.summary || 'No summary'}</p>
-                    <p className="text-[10px] text-text-tertiary mt-0.5">{new Date(s.timestamp).toLocaleString()}</p>
-                  </div>
-                  <ChevronRight className="w-4 h-4 text-text-tertiary flex-shrink-0" />
-                </button>
-              ))}
-            </div>
-          )}
-        </div>
-      )}
-
-      {/* ── Cards Like Tab ────────────────────────────────── */}
-      {tab === 'search' && (
-        <div className="space-y-4">
-          <div>
-            <h2 className="text-sm font-semibold text-text-primary mb-1">Cards Like This</h2>
-            <p className="text-xs text-text-secondary">
-              Find similar cards using AI embeddings. Search by card name to discover alternatives.
-            </p>
-          </div>
-          <CardsLikePanel />
-        </div>
-      )}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/CollectionPage.tsx

@@ -1,484 +0,0 @@
-import { useState, useEffect, useCallback, useRef } from 'react'
-import {
-  Search, Filter, Upload, Download, Camera, RefreshCw,
-  ChevronLeft, ChevronRight, X, Package, SlidersHorizontal,
-  Plus, Grid3x3, List
-} from 'lucide-react'
-import { Spinner, EmptyState, ManaSymbols, CardImage } from '../components/common'
-import { collectionApi } from '../api'
-import type { CollectionCard, CollectionFilters, SetInfo } from '../types'
-
-// ── Card Detail Modal ─────────────────────────────────────────
-function CardDetailModal({ card, onClose }: { card: CollectionCard; onClose: () => void }) {
-  return (
-    <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/70 backdrop-blur-sm" onClick={onClose}>
-      <div
-        className="bg-bg-secondary rounded-xl border border-border-primary shadow-lg max-w-4xl w-full mx-4 max-h-[90vh] overflow-y-auto animate-fade-in"
-        onClick={e => e.stopPropagation()}
-      >
-        <div className="flex items-start justify-between p-5 border-b border-border-primary">
-          <div>
-            <h2 className="text-xl font-bold text-text-primary">{card.name}</h2>
-            <p className="text-sm text-text-secondary mt-0.5">{card.type_line}</p>
-          </div>
-          <button onClick={onClose} className="p-1 rounded hover:bg-bg-hover text-text-tertiary">
-            <X className="w-5 h-5" />
-          </button>
-        </div>
-        <div className="flex flex-col md:flex-row gap-6 p-5">
-          {/* Large card image */}
-          <div className="flex-shrink-0">
-            <CardImage src={card.image_url} alt={card.name} size="xl" />
-          </div>
-          {/* Card info */}
-          <div className="flex-1 space-y-4">
-            <div>
-              <label className="text-xs text-text-tertiary uppercase tracking-wider">Mana Cost</label>
-              <div className="mt-1"><ManaSymbols cost={card.mana_cost} size="md" /></div>
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary uppercase tracking-wider">Oracle Text</label>
-              <p className="mt-1 text-sm text-text-primary whitespace-pre-wrap leading-relaxed">{card.oracle_text}</p>
-            </div>
-            <div className="grid grid-cols-2 gap-4">
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Set</label>
-                <p className="mt-1 text-sm text-text-primary">{card.set_name} ({card.set_code.toUpperCase()})</p>
-              </div>
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Rarity</label>
-                <p className={`mt-1 text-sm font-medium capitalize ${
-                  card.rarity === 'mythic' ? 'text-accent-amber' :
-                  card.rarity === 'rare' ? 'text-accent-amber/80' :
-                  card.rarity === 'uncommon' ? 'text-accent-teal' : 'text-text-secondary'
-                }`}>{card.rarity}</p>
-              </div>
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">CMC</label>
-                <p className="mt-1 text-sm text-text-primary">{card.cmc}</p>
-              </div>
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">EDHREC Rank</label>
-                <p className="mt-1 text-sm text-text-primary">{card.edhrec_rank ?? 'N/A'}</p>
-              </div>
-            </div>
-            <div className="grid grid-cols-2 gap-4">
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Quantity</label>
-                <p className="mt-1 text-sm text-text-primary">{card.quantity} (+ {card.foil_quantity} foil)</p>
-              </div>
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Price</label>
-                <p className="mt-1 text-sm text-accent-teal font-semibold">
-                  ${card.tcg_price?.toFixed(2) ?? '—'}
-                  {card.foil_price ? <span className="text-text-tertiary ml-2">Foil: ${card.foil_price.toFixed(2)}</span> : null}
-                </p>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
-
-// ── Scanner Panel ─────────────────────────────────────────────
-function ScannerPanel({ onCardAdded }: { onCardAdded: () => void }) {
-  const [scanning, setScanning] = useState(false)
-  const [results, setResults] = useState<import('../types').ScanMatch[]>([])
-  const [adding, setAdding] = useState<string | null>(null)
-  const fileRef = useRef<HTMLInputElement>(null)
-
-  async function handleScan(file: File) {
-    setScanning(true)
-    setResults([])
-    try {
-      const res = await collectionApi.scanCard(file)
-      setResults(res.matches || [])
-    } catch {
-      setResults([])
-    }
-    setScanning(false)
-  }
-
-  async function handleAdd(match: import('../types').ScanMatch) {
-    setAdding(match.scryfall_id)
-    try {
-      await collectionApi.addScanResult({
-        name: match.name,
-        set_code: match.set_code,
-        scryfall_id: match.scryfall_id,
-      })
-      onCardAdded()
-    } catch { /* ignore */ }
-    setAdding(null)
-  }
-
-  return (
-    <div className="bg-bg-secondary rounded-xl border border-border-primary p-5">
-      <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2 mb-3">
-        <Camera className="w-4 h-4 text-accent-blue" />
-        Card Scanner
-      </h3>
-      <input
-        ref={fileRef}
-        type="file"
-        accept="image/*"
-        className="hidden"
-        onChange={e => e.target.files?.[0] && handleScan(e.target.files[0])}
-      />
-      <button
-        onClick={() => fileRef.current?.click()}
-        disabled={scanning}
-        className="w-full flex items-center justify-center gap-2 px-4 py-2.5 bg-accent-blue/10 text-accent-blue border border-accent-blue/30 rounded-lg text-sm font-medium hover:bg-accent-blue/20 transition-colors disabled:opacity-50"
-      >
-        {scanning ? <Spinner size="sm" /> : <Camera className="w-4 h-4" />}
-        {scanning ? 'Scanning...' : 'Scan a Card'}
-      </button>
-      {results.length > 0 && (
-        <div className="mt-3 space-y-2 max-h-60 overflow-y-auto">
-          {results.map(m => (
-            <div key={m.scryfall_id} className="flex items-center gap-3 p-2 rounded-lg bg-bg-tertiary">
-              <CardImage src={m.image_url} alt={m.name} size="sm" />
-              <div className="flex-1 min-w-0">
-                <p className="text-sm font-medium text-text-primary truncate">{m.name}</p>
-                <p className="text-xs text-text-tertiary">{m.set_name} — {(m.confidence * 100).toFixed(0)}%</p>
-              </div>
-              <button
-                onClick={() => handleAdd(m)}
-                disabled={adding === m.scryfall_id}
-                className="p-1.5 rounded-lg bg-accent-green/15 text-accent-green hover:bg-accent-green/25 transition-colors"
-              >
-                {adding === m.scryfall_id ? <Spinner size="sm" /> : <Plus className="w-4 h-4" />}
-              </button>
-            </div>
-          ))}
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ── Main Collection Page ──────────────────────────────────────
-export function CollectionPage() {
-  const [cards, setCards] = useState<CollectionCard[]>([])
-  const [total, setTotal] = useState(0)
-  const [loading, setLoading] = useState(true)
-  const [sets, setSets] = useState<SetInfo[]>([])
-  const [selectedCard, setSelectedCard] = useState<CollectionCard | null>(null)
-  const [viewMode, setViewMode] = useState<'grid' | 'list'>('grid')
-  const [showFilters, setShowFilters] = useState(false)
-  const [importing, setImporting] = useState(false)
-  const [enriching, setEnriching] = useState(false)
-  const importRef = useRef<HTMLInputElement>(null)
-
-  const [filters, setFilters] = useState<CollectionFilters>({
-    q: '',
-    page: 1,
-    page_size: 48,
-    sort_by: 'name',
-    sort_dir: 'asc',
-  })
-
-  const fetchCards = useCallback(async () => {
-    setLoading(true)
-    try {
-      const res = await collectionApi.searchCollection(filters)
-      setCards(res.cards)
-      setTotal(res.total)
-    } catch { /* ignore */ }
-    setLoading(false)
-  }, [filters])
-
-  useEffect(() => { fetchCards() }, [fetchCards])
-
-  useEffect(() => {
-    collectionApi.getSets().then(setSets).catch(() => {})
-  }, [])
-
-  const totalPages = Math.ceil(total / (filters.page_size || 48))
-
-  function updateFilter(key: keyof CollectionFilters, value: string | number | boolean | undefined) {
-    setFilters(prev => ({ ...prev, [key]: value, page: 1 }))
-  }
-
-  async function handleExport() {
-    const blob = await collectionApi.exportCollection('csv')
-    const url = URL.createObjectURL(blob)
-    const a = document.createElement('a')
-    a.href = url
-    a.download = 'collection.csv'
-    a.click()
-    URL.revokeObjectURL(url)
-  }
-
-  async function handleImport(file: File) {
-    setImporting(true)
-    try {
-      await collectionApi.importCollection(file)
-      fetchCards()
-    } catch { /* ignore */ }
-    setImporting(false)
-  }
-
-  async function handleReEnrich() {
-    setEnriching(true)
-    try {
-      await collectionApi.reEnrichCards()
-      fetchCards()
-    } catch { /* ignore */ }
-    setEnriching(false)
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      {/* Header */}
-      <div className="flex items-center justify-between">
-        <div>
-          <h1 className="text-2xl font-bold text-text-primary">Collection</h1>
-          <p className="text-sm text-text-secondary mt-0.5">{total.toLocaleString()} cards in your collection</p>
-        </div>
-        <div className="flex items-center gap-2">
-          <button onClick={handleReEnrich} disabled={enriching}
-            className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50">
-            {enriching ? <Spinner size="sm" /> : <RefreshCw className="w-3.5 h-3.5" />}
-            Re-Enrich
-          </button>
-          <input ref={importRef} type="file" accept=".csv,.txt" className="hidden"
-            onChange={e => e.target.files?.[0] && handleImport(e.target.files[0])} />
-          <button onClick={() => importRef.current?.click()} disabled={importing}
-            className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50">
-            {importing ? <Spinner size="sm" /> : <Upload className="w-3.5 h-3.5" />}
-            Import
-          </button>
-          <button onClick={handleExport}
-            className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
-            <Download className="w-3.5 h-3.5" />
-            Export
-          </button>
-        </div>
-      </div>
-
-      <div className="flex gap-5">
-        {/* Left sidebar: search + filters + scanner */}
-        <div className="w-72 flex-shrink-0 space-y-4">
-          {/* Search */}
-          <div className="relative">
-            <Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
-            <input
-              type="text"
-              placeholder="Search cards..."
-              value={filters.q || ''}
-              onChange={e => updateFilter('q', e.target.value)}
-              className="w-full pl-10 pr-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 focus:ring-1 focus:ring-accent-blue/30 transition-all"
-            />
-          </div>
-
-          {/* Filter toggle */}
-          <button
-            onClick={() => setShowFilters(!showFilters)}
-            className="w-full flex items-center justify-between px-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-secondary hover:bg-bg-hover transition-colors"
-          >
-            <span className="flex items-center gap-2">
-              <SlidersHorizontal className="w-4 h-4" />
-              Filters
-            </span>
-            <Filter className={`w-3.5 h-3.5 transition-transform ${showFilters ? 'rotate-180' : ''}`} />
-          </button>
-
-          {showFilters && (
-            <div className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3 animate-fade-in">
-              {/* Rarity */}
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Rarity</label>
-                <select
-                  value={filters.rarity || ''}
-                  onChange={e => updateFilter('rarity', e.target.value || undefined)}
-                  className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                >
-                  <option value="">All</option>
-                  <option value="common">Common</option>
-                  <option value="uncommon">Uncommon</option>
-                  <option value="rare">Rare</option>
-                  <option value="mythic">Mythic</option>
-                </select>
-              </div>
-              {/* Color */}
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Color</label>
-                <select
-                  value={filters.color || ''}
-                  onChange={e => updateFilter('color', e.target.value || undefined)}
-                  className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                >
-                  <option value="">All</option>
-                  <option value="W">White</option>
-                  <option value="U">Blue</option>
-                  <option value="B">Black</option>
-                  <option value="R">Red</option>
-                  <option value="G">Green</option>
-                  <option value="C">Colorless</option>
-                </select>
-              </div>
-              {/* Set */}
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Set</label>
-                <select
-                  value={filters.set_code || ''}
-                  onChange={e => updateFilter('set_code', e.target.value || undefined)}
-                  className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                >
-                  <option value="">All Sets</option>
-                  {sets.map(s => (
-                    <option key={s.code} value={s.code}>{s.name} ({s.card_count})</option>
-                  ))}
-                </select>
-              </div>
-              {/* CMC Range */}
-              <div className="grid grid-cols-2 gap-2">
-                <div>
-                  <label className="text-xs text-text-tertiary uppercase tracking-wider">CMC Min</label>
-                  <input type="number" min="0" max="20"
-                    value={filters.cmc_min ?? ''}
-                    onChange={e => updateFilter('cmc_min', e.target.value ? Number(e.target.value) : undefined)}
-                    className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                  />
-                </div>
-                <div>
-                  <label className="text-xs text-text-tertiary uppercase tracking-wider">CMC Max</label>
-                  <input type="number" min="0" max="20"
-                    value={filters.cmc_max ?? ''}
-                    onChange={e => updateFilter('cmc_max', e.target.value ? Number(e.target.value) : undefined)}
-                    className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                  />
-                </div>
-              </div>
-              {/* Sort */}
-              <div>
-                <label className="text-xs text-text-tertiary uppercase tracking-wider">Sort By</label>
-                <select
-                  value={filters.sort_by || 'name'}
-                  onChange={e => updateFilter('sort_by', e.target.value)}
-                  className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
-                >
-                  <option value="name">Name</option>
-                  <option value="cmc">CMC</option>
-                  <option value="tcg_price">Price</option>
-                  <option value="rarity">Rarity</option>
-                  <option value="set_code">Set</option>
-                  <option value="added_date">Date Added</option>
-                </select>
-              </div>
-            </div>
-          )}
-
-          {/* Scanner */}
-          <ScannerPanel onCardAdded={fetchCards} />
-        </div>
-
-        {/* Main content: card grid/list */}
-        <div className="flex-1 min-w-0">
-          {/* View toggle + pagination */}
-          <div className="flex items-center justify-between mb-4">
-            <div className="flex items-center gap-1 bg-bg-secondary rounded-lg border border-border-primary p-0.5">
-              <button
-                onClick={() => setViewMode('grid')}
-                className={`p-1.5 rounded-md transition-colors ${viewMode === 'grid' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}
-              >
-                <Grid3x3 className="w-4 h-4" />
-              </button>
-              <button
-                onClick={() => setViewMode('list')}
-                className={`p-1.5 rounded-md transition-colors ${viewMode === 'list' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}
-              >
-                <List className="w-4 h-4" />
-              </button>
-            </div>
-            <div className="flex items-center gap-2 text-sm text-text-secondary">
-              <span>Page {filters.page} of {totalPages || 1}</span>
-              <button
-                onClick={() => setFilters(p => ({ ...p, page: Math.max(1, (p.page || 1) - 1) }))}
-                disabled={!filters.page || filters.page <= 1}
-                className="p-1 rounded hover:bg-bg-hover disabled:opacity-30"
-              >
-                <ChevronLeft className="w-4 h-4" />
-              </button>
-              <button
-                onClick={() => setFilters(p => ({ ...p, page: Math.min(totalPages, (p.page || 1) + 1) }))}
-                disabled={(filters.page || 1) >= totalPages}
-                className="p-1 rounded hover:bg-bg-hover disabled:opacity-30"
-              >
-                <ChevronRight className="w-4 h-4" />
-              </button>
-            </div>
-          </div>
-
-          {loading ? (
-            <div className="flex items-center justify-center h-64">
-              <Spinner size="lg" className="text-accent-blue" />
-            </div>
-          ) : cards.length === 0 ? (
-            <EmptyState
-              icon={Package}
-              title="No cards found"
-              description="Try adjusting your search or filters, or import a collection."
-            />
-          ) : viewMode === 'grid' ? (
-            <div className="grid grid-cols-2 sm:grid-cols-3 md:grid-cols-4 lg:grid-cols-5 xl:grid-cols-6 gap-3">
-              {cards.map(card => (
-                <div
-                  key={card.id}
-                  className="group cursor-pointer"
-                  onClick={() => setSelectedCard(card)}
-                >
-                  <CardImage src={card.image_url} alt={card.name} size="md" className="w-full h-auto aspect-[5/7]" />
-                  <div className="mt-1.5 px-0.5">
-                    <p className="text-xs font-medium text-text-primary truncate group-hover:text-accent-blue transition-colors">{card.name}</p>
-                    <div className="flex items-center justify-between">
-                      <span className="text-[10px] text-text-tertiary">{card.set_code.toUpperCase()}</span>
-                      {card.tcg_price != null && (
-                        <span className="text-[10px] text-accent-teal font-medium">${card.tcg_price.toFixed(2)}</span>
-                      )}
-                    </div>
-                  </div>
-                </div>
-              ))}
-            </div>
-          ) : (
-            <div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
-              <table className="w-full text-sm">
-                <thead>
-                  <tr className="border-b border-border-primary text-left">
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Card</th>
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Type</th>
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Mana</th>
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Set</th>
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Qty</th>
-                    <th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider text-right">Price</th>
-                  </tr>
-                </thead>
-                <tbody className="divide-y divide-border-primary">
-                  {cards.map(card => (
-                    <tr key={card.id} className="hover:bg-bg-hover cursor-pointer transition-colors" onClick={() => setSelectedCard(card)}>
-                      <td className="px-4 py-2.5">
-                        <span className="font-medium text-text-primary">{card.name}</span>
-                      </td>
-                      <td className="px-4 py-2.5 text-text-secondary text-xs">{card.type_line}</td>
-                      <td className="px-4 py-2.5"><ManaSymbols cost={card.mana_cost} /></td>
-                      <td className="px-4 py-2.5 text-text-secondary text-xs">{card.set_code.toUpperCase()}</td>
-                      <td className="px-4 py-2.5 text-text-secondary">{card.quantity}</td>
-                      <td className="px-4 py-2.5 text-right text-accent-teal font-medium">{card.tcg_price != null ? `$${card.tcg_price.toFixed(2)}` : '—'}</td>
-                    </tr>
-                  ))}
-                </tbody>
-              </table>
-            </div>
-          )}
-        </div>
-      </div>
-
-      {/* Card detail modal */}
-      {selectedCard && <CardDetailModal card={selectedCard} onClose={() => setSelectedCard(null)} />}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/DecksPage.tsx

@@ -1,967 +0,0 @@
-import { useState, useEffect } from 'react'
-import {
-  Plus, Trash2, Download, ChevronRight,
-  BarChart3, Lightbulb, Layers, X, Library,
-  Globe, XCircle, ShoppingCart,
-  ChevronDown, ChevronUp, Sparkles
-} from 'lucide-react'
-import { Spinner, EmptyState, ManaSymbols, ColorDots } from '../components/common'
-import { decksApi } from '../api'
-import type {
-  Deck, DeckCard, DeckAnalysis,
-  EdhRecCard, EdhRecsResponse,
-  CollectionRecsResponse
-} from '../types'
-import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
-
-// ── Role Badge ──────────────────────────────────────────────
-const ROLE_COLORS: Record<string, string> = {
-  Ramp: 'bg-green-500/15 text-green-400 border-green-500/25',
-  Draw: 'bg-blue-500/15 text-blue-400 border-blue-500/25',
-  Removal: 'bg-red-500/15 text-red-400 border-red-500/25',
-  BoardWipe: 'bg-orange-500/15 text-orange-400 border-orange-500/25',
-  Tutor: 'bg-purple-500/15 text-purple-400 border-purple-500/25',
-  Protection: 'bg-cyan-500/15 text-cyan-400 border-cyan-500/25',
-  Finisher: 'bg-yellow-500/15 text-yellow-400 border-yellow-500/25',
-  Recursion: 'bg-emerald-500/15 text-emerald-400 border-emerald-500/25',
-  Other: 'bg-slate-500/15 text-slate-400 border-slate-500/25',
-}
-
-function RoleBadge({ role }: { role: string }) {
-  const colors = ROLE_COLORS[role] || ROLE_COLORS.Other
-  return (
-    <span className={'text-[10px] font-medium px-1.5 py-0.5 rounded border ' + colors}>
-      {role}
-    </span>
-  )
-}
-
-// ── Deck Card List ────────────────────────────────────────────
-function DeckCardList({ deckId, cards, onUpdate }: { deckId: number; cards: DeckCard[]; onUpdate: () => void }) {
-  const [removing, setRemoving] = useState<number | null>(null)
-
-  // Group cards by category
-  const grouped = cards.reduce<Record<string, DeckCard[]>>((acc, c) => {
-    const cat = c.category || 'Other'
-    if (!acc[cat]) acc[cat] = []
-    acc[cat].push(c)
-    return acc
-  }, {})
-
-  const categoryOrder = ['Commander', 'Creature', 'Instant', 'Sorcery', 'Artifact', 'Enchantment', 'Planeswalker', 'Land', 'Other']
-  const sortedCategories = Object.keys(grouped).sort((a, b) => {
-    const ia = categoryOrder.indexOf(a)
-    const ib = categoryOrder.indexOf(b)
-    return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
-  })
-
-  async function handleRemove(cardId: number) {
-    setRemoving(cardId)
-    try {
-      await decksApi.removeCardFromDeck(deckId, cardId)
-      onUpdate()
-    } catch { /* ignore */ }
-    setRemoving(null)
-  }
-
-  return (
-    <div className="space-y-4">
-      {sortedCategories.map(cat => (
-        <div key={cat}>
-          <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-2">
-            {cat} ({grouped[cat].length})
-          </h4>
-          <div className="space-y-1">
-            {grouped[cat].map(card => (
-              <div key={card.id} className="flex items-center gap-3 px-3 py-2 rounded-lg hover:bg-bg-hover group transition-colors">
-                <span className="text-xs text-text-tertiary w-5 text-right">{card.quantity}x</span>
-                <ManaSymbols cost={card.mana_cost} />
-                <span className="flex-1 text-sm text-text-primary truncate">{card.name}</span>
-                {card.tcg_price != null && (
-                  <span className="text-xs text-accent-teal">${card.tcg_price.toFixed(2)}</span>
-                )}
-                {!card.is_commander && (
-                  <button
-                    onClick={() => handleRemove(card.id)}
-                    className="opacity-0 group-hover:opacity-100 p-1 text-text-tertiary hover:text-accent-red transition-all"
-                  >
-                    {removing === card.id ? <Spinner size="sm" /> : <Trash2 className="w-3.5 h-3.5" />}
-                  </button>
-                )}
-              </div>
-            ))}
-          </div>
-        </div>
-      ))}
-    </div>
-  )
-}
-
-// ── Mana Curve Chart ──────────────────────────────────────────
-function ManaCurveChart({ analysis }: { analysis: DeckAnalysis }) {
-  const data = Object.entries(analysis.mana_curve)
-    .map(([cmc, count]) => ({ cmc: cmc === '7' ? '7+' : cmc, count }))
-    .sort((a, b) => Number(a.cmc) - Number(b.cmc))
-
-  return (
-    <div className="h-40">
-      <ResponsiveContainer width="100%" height="100%">
-        <BarChart data={data} barSize={24}>
-          <XAxis dataKey="cmc" axisLine={false} tickLine={false} tick={{ fill: '#6b7190', fontSize: 11 }} />
-          <YAxis hide />
-          <Tooltip
-            contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
-            labelStyle={{ color: '#9ba1b8' }}
-            itemStyle={{ color: '#e8eaf0' }}
-          />
-          <Bar dataKey="count" radius={[4, 4, 0, 0]}>
-            {data.map((_, i) => (
-              <Cell key={i} fill="#4f6ef7" fillOpacity={0.8} />
-            ))}
-          </Bar>
-        </BarChart>
-      </ResponsiveContainer>
-    </div>
-  )
-}
-
-// ── EDHREC Recommendations Sub-tab ──────────────────────────
-function EdhRecTab({ deckId, onAddedCards }: { deckId: number; onAddedCards: () => void }) {
-  const [data, setData] = useState<EdhRecsResponse | null>(null)
-  const [loading, setLoading] = useState(true)
-  const [error, setError] = useState<string | null>(null)
-  const [onlyOwned, setOnlyOwned] = useState(false)
-  const [selectedCards, setSelectedCards] = useState<Set<string>>(new Set())
-  const [addingCards, setAddingCards] = useState<Set<string>>(new Set())
-  const [bulkAdding, setBulkAdding] = useState(false)
-  const [hoveredCard, setHoveredCard] = useState<EdhRecCard | null>(null)
-  const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set())
-
-  async function fetchRecs() {
-    setLoading(true)
-    setError(null)
-    try {
-      const result = await decksApi.getEdhRecs(deckId, onlyOwned)
-      setData(result)
-    } catch (e: any) {
-      setError(e?.message || 'Failed to load EDHREC recommendations')
-    }
-    setLoading(false)
-  }
-
-  useEffect(() => { fetchRecs() }, [deckId, onlyOwned])
-
-  function toggleSelect(name: string) {
-    setSelectedCards(prev => {
-      const next = new Set(prev)
-      if (next.has(name)) next.delete(name)
-      else next.add(name)
-      return next
-    })
-  }
-
-  function selectAll() {
-    if (!data) return
-    setSelectedCards(new Set(data.recommendations.map(r => r.name)))
-  }
-
-  function selectNone() { setSelectedCards(new Set()) }
-
-  function selectOwned() {
-    if (!data) return
-    setSelectedCards(new Set(data.recommendations.filter(r => r.owned).map(r => r.name)))
-  }
-
-  async function handleAddSingle(name: string) {
-    setAddingCards(prev => new Set(prev).add(name))
-    try {
-      await decksApi.bulkAddRecommended(deckId, [name])
-      onAddedCards()
-      // Remove from list
-      if (data) {
-        setData({
-          ...data,
-          recommendations: data.recommendations.filter(r => r.name !== name),
-          total: data.total - 1,
-        })
-      }
-      selectedCards.delete(name)
-      setSelectedCards(new Set(selectedCards))
-    } catch { /* ignore */ }
-    setAddingCards(prev => { const n = new Set(prev); n.delete(name); return n })
-  }
-
-  async function handleBulkAdd() {
-    if (selectedCards.size === 0) return
-    setBulkAdding(true)
-    try {
-      await decksApi.bulkAddRecommended(deckId, Array.from(selectedCards))
-      onAddedCards()
-      if (data) {
-        setData({
-          ...data,
-          recommendations: data.recommendations.filter(r => !selectedCards.has(r.name)),
-          total: data.total - selectedCards.size,
-        })
-      }
-      setSelectedCards(new Set())
-    } catch { /* ignore */ }
-    setBulkAdding(false)
-  }
-
-  function toggleGroup(groupName: string) {
-    setCollapsedGroups(prev => {
-      const next = new Set(prev)
-      if (next.has(groupName)) next.delete(groupName)
-      else next.add(groupName)
-      return next
-    })
-  }
-
-  // Group recommendations by role
-  function groupByRole(recs: EdhRecCard[]): Record<string, EdhRecCard[]> {
-    const groups: Record<string, EdhRecCard[]> = {}
-    for (const rec of recs) {
-      const role = rec.role || 'Other'
-      if (!groups[role]) groups[role] = []
-      groups[role].push(rec)
-    }
-    return groups
-  }
-
-  if (loading) {
-    return <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
-  }
-
-  if (error) {
-    return (
-      <div className="bg-accent-red/10 border border-accent-red/20 rounded-xl p-5 text-center">
-        <XCircle className="w-6 h-6 text-accent-red mx-auto mb-2" />
-        <p className="text-sm text-accent-red font-medium">{error}</p>
-        <button onClick={fetchRecs} className="mt-3 px-4 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
-          Retry
-        </button>
-      </div>
-    )
-  }
-
-  if (!data || data.recommendations.length === 0) {
-    return <EmptyState icon={Globe} title="No EDHREC data" description="No recommendations found for this commander on EDHREC." />
-  }
-
-  const grouped = groupByRole(data.recommendations)
-  const roleOrder = ['Ramp', 'Draw', 'Removal', 'BoardWipe', 'Tutor', 'Protection', 'Finisher', 'Recursion', 'Other']
-  const sortedRoles = Object.keys(grouped).sort((a, b) => {
-    const ia = roleOrder.indexOf(a)
-    const ib = roleOrder.indexOf(b)
-    return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
-  })
-
-  const ownedCount = data.recommendations.filter(r => r.owned).length
-  const missingCount = data.recommendations.length - ownedCount
-
-  return (
-    <div className="space-y-4">
-      {/* Header bar */}
-      <div className="flex items-center justify-between flex-wrap gap-3">
-        <div className="flex items-center gap-3">
-          <div className="flex items-center gap-2 text-sm">
-            <Sparkles className="w-4 h-4 text-accent-blue" />
-            <span className="text-text-primary font-medium">{data.commander}</span>
-            <span className="text-text-tertiary">via {data.source}</span>
-          </div>
-          <span className="text-xs px-2 py-0.5 rounded bg-accent-green/15 text-accent-green">{ownedCount} owned</span>
-          <span className="text-xs px-2 py-0.5 rounded bg-bg-tertiary text-text-tertiary">{missingCount} missing</span>
-        </div>
-        <label className="flex items-center gap-2 text-xs text-text-secondary cursor-pointer select-none">
-          <input
-            type="checkbox"
-            checked={onlyOwned}
-            onChange={e => setOnlyOwned(e.target.checked)}
-            className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5"
-          />
-          Show only owned
-        </label>
-      </div>
-
-      {/* Selection toolbar */}
-      <div className="flex items-center justify-between bg-bg-secondary rounded-xl border border-border-primary px-4 py-3">
-        <div className="flex items-center gap-2">
-          <span className="text-xs text-text-tertiary">Select:</span>
-          <button onClick={selectAll} className="text-xs text-accent-blue hover:underline">All</button>
-          <span className="text-text-tertiary">/</span>
-          <button onClick={selectNone} className="text-xs text-accent-blue hover:underline">None</button>
-          <span className="text-text-tertiary">/</span>
-          <button onClick={selectOwned} className="text-xs text-accent-blue hover:underline">Owned</button>
-          {selectedCards.size > 0 && (
-            <span className="text-xs text-text-secondary ml-2">({selectedCards.size} selected)</span>
-          )}
-        </div>
-        <button
-          onClick={handleBulkAdd}
-          disabled={selectedCards.size === 0 || bulkAdding}
-          className="flex items-center gap-2 px-3 py-1.5 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
-        >
-          {bulkAdding ? <Spinner size="sm" /> : <ShoppingCart className="w-3.5 h-3.5" />}
-          Add {selectedCards.size > 0 ? selectedCards.size : ''} to Deck
-        </button>
-      </div>
-
-      {/* Grouped cards */}
-      {sortedRoles.map(role => {
-        const cards = grouped[role]
-        const isCollapsed = collapsedGroups.has(role)
-        return (
-          <div key={role} className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
-            {/* Group header */}
-            <button
-              onClick={() => toggleGroup(role)}
-              className="w-full flex items-center justify-between px-4 py-3 hover:bg-bg-hover transition-colors"
-            >
-              <div className="flex items-center gap-2">
-                <RoleBadge role={role} />
-                <span className="text-xs text-text-tertiary">({cards.length})</span>
-              </div>
-              {isCollapsed ? <ChevronDown className="w-4 h-4 text-text-tertiary" /> : <ChevronUp className="w-4 h-4 text-text-tertiary" />}
-            </button>
-            {/* Card list */}
-            {!isCollapsed && (
-              <div className="border-t border-border-primary divide-y divide-border-primary">
-                {cards.map(rec => (
-                  <div
-                    key={rec.name}
-                    className="flex items-center gap-3 px-4 py-2.5 hover:bg-bg-hover transition-colors group relative"
-                    onMouseEnter={() => setHoveredCard(rec)}
-                    onMouseLeave={() => setHoveredCard(null)}
-                  >
-                    {/* Checkbox */}
-                    <input
-                      type="checkbox"
-                      checked={selectedCards.has(rec.name)}
-                      onChange={() => toggleSelect(rec.name)}
-                      className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5 flex-shrink-0"
-                    />
-                    {/* Card image mini */}
-                    {rec.image_url && (
-                      <div className="w-8 h-11 rounded overflow-hidden bg-bg-tertiary flex-shrink-0">
-                        <img src={rec.image_url} alt={rec.name} className="w-full h-full object-cover" loading="lazy" />
-                      </div>
-                    )}
-                    {/* Card info */}
-                    <div className="flex-1 min-w-0">
-                      <p className="text-sm font-medium text-text-primary truncate">{rec.name}</p>
-                      <div className="flex items-center gap-1.5 mt-0.5">
-                        <span className="text-[10px] text-text-tertiary truncate">{rec.type_line}</span>
-                        {rec.roles.length > 0 && rec.roles.map(r => <RoleBadge key={r} role={r} />)}
-                      </div>
-                    </div>
-                    {/* Owned badge */}
-                    <span className={'text-xs px-2 py-0.5 rounded flex-shrink-0 ' + (rec.owned ? 'bg-accent-green/15 text-accent-green' : 'bg-bg-tertiary text-text-tertiary')}>
-                      {rec.owned ? (rec.owned_qty + 'x owned') : 'Missing'}
-                    </span>
-                    {/* Add button */}
-                    <button
-                      onClick={e => { e.stopPropagation(); handleAddSingle(rec.name) }}
-                      disabled={addingCards.has(rec.name)}
-                      className="opacity-0 group-hover:opacity-100 px-2.5 py-1 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-all disabled:opacity-50 flex-shrink-0"
-                    >
-                      {addingCards.has(rec.name) ? <Spinner size="sm" /> : '+ Add'}
-                    </button>
-                  </div>
-                ))}
-              </div>
-            )}
-          </div>
-        )
-      })}
-
-      {/* Hover preview */}
-      {hoveredCard && hoveredCard.image_url && (
-        <div className="fixed top-20 right-8 z-50 pointer-events-none animate-fade-in">
-          <img
-            src={hoveredCard.image_url}
-            alt={hoveredCard.name}
-            className="w-[244px] rounded-xl shadow-2xl shadow-black/60 border border-border-primary"
-          />
-        </div>
-      )}
-    </div>
-  )
-}
-
-// ── Collection Recommendations Sub-tab ──────────────────────
-function CollectionRecTab({ deckId, onAddedCards }: { deckId: number; onAddedCards: () => void }) {
-  const [data, setData] = useState<CollectionRecsResponse | null>(null)
-  const [loading, setLoading] = useState(true)
-  const [error, setError] = useState<string | null>(null)
-  const [selectedCards, setSelectedCards] = useState<Set<string>>(new Set())
-  const [addingCards, setAddingCards] = useState<Set<string>>(new Set())
-  const [bulkAdding, setBulkAdding] = useState(false)
-  const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set())
-
-  async function fetchRecs() {
-    setLoading(true)
-    setError(null)
-    try {
-      const result = await decksApi.getCollectionRecs(deckId)
-      setData(result)
-    } catch (e: any) {
-      setError(e?.message || 'Failed to load collection recommendations')
-    }
-    setLoading(false)
-  }
-
-  useEffect(() => { fetchRecs() }, [deckId])
-
-  function toggleSelect(name: string) {
-    setSelectedCards(prev => {
-      const next = new Set(prev)
-      if (next.has(name)) next.delete(name)
-      else next.add(name)
-      return next
-    })
-  }
-
-  function selectAll() {
-    if (!data) return
-    const all = new Set<string>()
-    Object.values(data.grouped).flat().forEach(c => all.add(c.name))
-    setSelectedCards(all)
-  }
-
-  function selectNone() { setSelectedCards(new Set()) }
-
-  async function handleAddSingle(name: string) {
-    setAddingCards(prev => new Set(prev).add(name))
-    try {
-      await decksApi.bulkAddRecommended(deckId, [name])
-      onAddedCards()
-      // Remove from groupings
-      if (data) {
-        const newGrouped = { ...data.grouped }
-        for (const key of Object.keys(newGrouped)) {
-          newGrouped[key] = newGrouped[key].filter(c => c.name !== name)
-          if (newGrouped[key].length === 0) delete newGrouped[key]
-        }
-        setData({ ...data, grouped: newGrouped, total: data.total - 1 })
-      }
-      selectedCards.delete(name)
-      setSelectedCards(new Set(selectedCards))
-    } catch { /* ignore */ }
-    setAddingCards(prev => { const n = new Set(prev); n.delete(name); return n })
-  }
-
-  async function handleBulkAdd() {
-    if (selectedCards.size === 0) return
-    setBulkAdding(true)
-    try {
-      await decksApi.bulkAddRecommended(deckId, Array.from(selectedCards))
-      onAddedCards()
-      if (data) {
-        const newGrouped = { ...data.grouped }
-        for (const key of Object.keys(newGrouped)) {
-          newGrouped[key] = newGrouped[key].filter(c => !selectedCards.has(c.name))
-          if (newGrouped[key].length === 0) delete newGrouped[key]
-        }
-        setData({ ...data, grouped: newGrouped, total: data.total - selectedCards.size })
-      }
-      setSelectedCards(new Set())
-    } catch { /* ignore */ }
-    setBulkAdding(false)
-  }
-
-  function toggleGroup(groupName: string) {
-    setCollapsedGroups(prev => {
-      const next = new Set(prev)
-      if (next.has(groupName)) next.delete(groupName)
-      else next.add(groupName)
-      return next
-    })
-  }
-
-  if (loading) {
-    return <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
-  }
-
-  if (error) {
-    return (
-      <div className="bg-accent-red/10 border border-accent-red/20 rounded-xl p-5 text-center">
-        <XCircle className="w-6 h-6 text-accent-red mx-auto mb-2" />
-        <p className="text-sm text-accent-red font-medium">{error}</p>
-        <button onClick={fetchRecs} className="mt-3 px-4 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
-          Retry
-        </button>
-      </div>
-    )
-  }
-
-  if (!data || data.total === 0) {
-    return <EmptyState icon={Library} title="No collection matches" description="No cards in your collection match this deck's needs." />
-  }
-
-  const typeOrder = ['Creature', 'Instant', 'Sorcery', 'Artifact', 'Enchantment', 'Planeswalker', 'Land', 'Other']
-  const sortedTypes = Object.keys(data.grouped).sort((a, b) => {
-    const ia = typeOrder.indexOf(a)
-    const ib = typeOrder.indexOf(b)
-    return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
-  })
-
-  return (
-    <div className="space-y-4">
-      {/* Shortfall alert */}
-      {data.shortfall_types.length > 0 && (
-        <div className="flex items-start gap-3 bg-accent-blue/8 border border-accent-blue/20 rounded-xl px-4 py-3">
-          <Lightbulb className="w-4 h-4 text-accent-blue mt-0.5 flex-shrink-0" />
-          <div>
-            <p className="text-xs font-medium text-accent-blue">Deck needs more:</p>
-            <div className="flex flex-wrap gap-1.5 mt-1">
-              {data.shortfall_types.map(t => (
-                <span key={t} className="text-[10px] font-medium px-2 py-0.5 rounded bg-accent-blue/15 text-accent-blue border border-accent-blue/25">{t}</span>
-              ))}
-            </div>
-          </div>
-        </div>
-      )}
-
-      {/* Selection toolbar */}
-      <div className="flex items-center justify-between bg-bg-secondary rounded-xl border border-border-primary px-4 py-3">
-        <div className="flex items-center gap-2">
-          <span className="text-xs text-text-tertiary">Select:</span>
-          <button onClick={selectAll} className="text-xs text-accent-blue hover:underline">All</button>
-          <span className="text-text-tertiary">/</span>
-          <button onClick={selectNone} className="text-xs text-accent-blue hover:underline">None</button>
-          {selectedCards.size > 0 && (
-            <span className="text-xs text-text-secondary ml-2">({selectedCards.size} selected)</span>
-          )}
-        </div>
-        <button
-          onClick={handleBulkAdd}
-          disabled={selectedCards.size === 0 || bulkAdding}
-          className="flex items-center gap-2 px-3 py-1.5 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
-        >
-          {bulkAdding ? <Spinner size="sm" /> : <ShoppingCart className="w-3.5 h-3.5" />}
-          Add {selectedCards.size > 0 ? selectedCards.size : ''} to Deck
-        </button>
-      </div>
-
-      {/* Grouped cards */}
-      {sortedTypes.map(type => {
-        const cards = data.grouped[type]
-        const isCollapsed = collapsedGroups.has(type)
-        const isShortfall = data.shortfall_types.includes(type)
-        return (
-          <div key={type} className={'bg-bg-secondary rounded-xl border overflow-hidden ' + (isShortfall ? 'border-accent-blue/30' : 'border-border-primary')}>
-            {/* Group header */}
-            <button
-              onClick={() => toggleGroup(type)}
-              className="w-full flex items-center justify-between px-4 py-3 hover:bg-bg-hover transition-colors"
-            >
-              <div className="flex items-center gap-2">
-                <span className="text-xs font-semibold text-text-primary uppercase tracking-wider">{type}</span>
-                <span className="text-xs text-text-tertiary">({cards.length})</span>
-                {isShortfall && <span className="text-[10px] font-medium px-1.5 py-0.5 rounded bg-accent-blue/15 text-accent-blue border border-accent-blue/25">Shortfall</span>}
-              </div>
-              {isCollapsed ? <ChevronDown className="w-4 h-4 text-text-tertiary" /> : <ChevronUp className="w-4 h-4 text-text-tertiary" />}
-            </button>
-            {/* Card list */}
-            {!isCollapsed && (
-              <div className="border-t border-border-primary divide-y divide-border-primary">
-                {cards.map(rec => (
-                  <div key={rec.scryfall_id} className="flex items-center gap-3 px-4 py-2.5 hover:bg-bg-hover transition-colors group">
-                    {/* Checkbox */}
-                    <input
-                      type="checkbox"
-                      checked={selectedCards.has(rec.name)}
-                      onChange={() => toggleSelect(rec.name)}
-                      className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5 flex-shrink-0"
-                    />
-                    {/* Card image mini */}
-                    {rec.image_url && (
-                      <div className="w-8 h-11 rounded overflow-hidden bg-bg-tertiary flex-shrink-0">
-                        <img src={rec.image_url} alt={rec.name} className="w-full h-full object-cover" loading="lazy" />
-                      </div>
-                    )}
-                    {/* Card info */}
-                    <div className="flex-1 min-w-0">
-                      <p className="text-sm font-medium text-text-primary truncate">{rec.name}</p>
-                      <div className="flex items-center gap-1.5 mt-0.5">
-                        <span className="text-[10px] text-text-tertiary">{rec.cmc} CMC</span>
-                        {rec.roles.length > 0 && rec.roles.map(r => <RoleBadge key={r} role={r} />)}
-                      </div>
-                    </div>
-                    {/* Score */}
-                    <span className="text-xs text-accent-teal font-medium flex-shrink-0">Score: {rec.score}</span>
-                    {/* Owned qty */}
-                    <span className="text-xs px-2 py-0.5 rounded bg-accent-green/15 text-accent-green flex-shrink-0">{rec.owned_qty}x</span>
-                    {/* Add button */}
-                    <button
-                      onClick={e => { e.stopPropagation(); handleAddSingle(rec.name) }}
-                      disabled={addingCards.has(rec.name)}
-                      className="opacity-0 group-hover:opacity-100 px-2.5 py-1 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-all disabled:opacity-50 flex-shrink-0"
-                    >
-                      {addingCards.has(rec.name) ? <Spinner size="sm" /> : '+ Add'}
-                    </button>
-                  </div>
-                ))}
-              </div>
-            )}
-          </div>
-        )
-      })}
-    </div>
-  )
-}
-
-// ── Deck Detail View ──────────────────────────────────────────
-function DeckDetail({ deck, onBack }: { deck: Deck; onBack: () => void }) {
-  const [cards, setCards] = useState<DeckCard[]>([])
-  const [analysis, setAnalysis] = useState<DeckAnalysis | null>(null)
-  const [loading, setLoading] = useState(true)
-  const [tab, setTab] = useState<'cards' | 'analysis' | 'recs'>('cards')
-  const [recSource, setRecSource] = useState<'edhrec' | 'collection'>('edhrec')
-  const [addingCard, setAddingCard] = useState('')
-  const [bulkText, setBulkText] = useState('')
-  const [showBulk, setShowBulk] = useState(false)
-
-  async function fetchAll() {
-    setLoading(true)
-    try {
-      const [c, a] = await Promise.all([
-        decksApi.getDeckCards(deck.id),
-        decksApi.getDeckAnalysis(deck.id),
-      ])
-      setCards(c)
-      setAnalysis(a)
-    } catch { /* ignore */ }
-    setLoading(false)
-  }
-
-  useEffect(() => { fetchAll() }, [deck.id])
-
-  async function handleAddCard() {
-    if (!addingCard.trim()) return
-    try {
-      await decksApi.addCardToDeck(deck.id, { name: addingCard.trim() })
-      setAddingCard('')
-      fetchAll()
-    } catch { /* ignore */ }
-  }
-
-  async function handleBulkAdd() {
-    if (!bulkText.trim()) return
-    const cardLines = bulkText.split('\n').filter(l => l.trim())
-    const cardParsed = cardLines.map(l => {
-      const match = l.match(/^(\d+)x?\s+(.+)$/)
-      return match ? { name: match[2].trim(), quantity: Number(match[1]) } : { name: l.trim() }
-    })
-    try {
-      await decksApi.bulkAddCards(deck.id, cardParsed)
-      setBulkText('')
-      setShowBulk(false)
-      fetchAll()
-    } catch { /* ignore */ }
-  }
-
-  async function handleExportToSim() {
-    try {
-      await decksApi.exportDeckToSim(deck.id)
-    } catch { /* ignore */ }
-  }
-
-  return (
-    <div className="space-y-5">
-      {/* Header */}
-      <div className="flex items-center gap-4">
-        <button onClick={onBack} className="p-2 rounded-lg hover:bg-bg-hover text-text-secondary transition-colors">
-          <ChevronRight className="w-5 h-5 rotate-180" />
-        </button>
-        <div className="flex-1">
-          <h2 className="text-xl font-bold text-text-primary">{deck.name}</h2>
-          <div className="flex items-center gap-3 mt-1">
-            <span className="text-sm text-text-secondary">{deck.commander}</span>
-            <ColorDots colors={deck.color_identity} />
-            <span className="text-xs text-text-tertiary">{deck.card_count} cards</span>
-            <span className="text-xs text-accent-teal">${deck.total_price.toFixed(2)}</span>
-          </div>
-        </div>
-        <button onClick={handleExportToSim}
-          className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
-          <Download className="w-3.5 h-3.5" />
-          Export to Sim
-        </button>
-      </div>
-
-      {/* Tabs */}
-      <div className="flex gap-1 border-b border-border-primary">
-        {(['cards', 'analysis', 'recs'] as const).map(t => (
-          <button
-            key={t}
-            onClick={() => setTab(t)}
-            className={`px-4 py-2.5 text-sm font-medium border-b-2 transition-colors capitalize ${
-              tab === t
-                ? 'border-accent-blue text-accent-blue'
-                : 'border-transparent text-text-tertiary hover:text-text-secondary'
-            }`}
-          >
-            {t === 'recs' ? 'Recommendations' : t}
-          </button>
-        ))}
-      </div>
-
-      {loading ? (
-        <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
-      ) : tab === 'cards' ? (
-        <div className="space-y-4">
-          {/* Add card */}
-          <div className="flex items-center gap-2">
-            <input
-              type="text"
-              placeholder="Add card by name..."
-              value={addingCard}
-              onChange={e => setAddingCard(e.target.value)}
-              onKeyDown={e => e.key === 'Enter' && handleAddCard()}
-              className="flex-1 px-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-            />
-            <button onClick={handleAddCard}
-              className="px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
-              Add
-            </button>
-            <button onClick={() => setShowBulk(!showBulk)}
-              className="px-3 py-2.5 bg-bg-tertiary text-text-secondary text-sm rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
-              <Layers className="w-4 h-4" />
-            </button>
-          </div>
-          {showBulk && (
-            <div className="bg-bg-secondary rounded-xl border border-border-primary p-4 animate-fade-in space-y-3">
-              <textarea
-                placeholder={"1x Sol Ring\n1x Command Tower\n2x Counterspell"}
-                value={bulkText}
-                onChange={e => setBulkText(e.target.value)}
-                rows={6}
-                className="w-full px-4 py-3 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary font-mono focus:outline-none focus:border-accent-blue/50 transition-all resize-none"
-              />
-              <button onClick={handleBulkAdd}
-                className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
-                Bulk Add
-              </button>
-            </div>
-          )}
-          <DeckCardList deckId={deck.id} cards={cards} onUpdate={fetchAll} />
-        </div>
-      ) : tab === 'analysis' && analysis ? (
-        <div className="grid grid-cols-1 md:grid-cols-2 gap-5">
-          {/* Stats */}
-          <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4">
-            <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-              <BarChart3 className="w-4 h-4 text-accent-blue" />
-              Deck Stats
-            </h3>
-            <div className="grid grid-cols-2 gap-3">
-              {[
-                { label: 'Cards', value: analysis.card_count },
-                { label: 'Lands', value: analysis.land_count },
-                { label: 'Creatures', value: analysis.creature_count },
-                { label: 'Non-Creature', value: analysis.noncreature_count },
-                { label: 'Avg CMC', value: analysis.avg_cmc.toFixed(2) },
-                { label: 'Owned', value: `${analysis.owned_count} / ${analysis.card_count}` },
-              ].map(stat => (
-                <div key={stat.label} className="px-3 py-2 rounded-lg bg-bg-tertiary">
-                  <p className="text-xs text-text-tertiary">{stat.label}</p>
-                  <p className="text-lg font-bold text-text-primary">{stat.value}</p>
-                </div>
-              ))}
-            </div>
-          </div>
-          {/* Mana Curve */}
-          <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4">
-            <h3 className="text-sm font-semibold text-text-primary">Mana Curve</h3>
-            <ManaCurveChart analysis={analysis} />
-          </div>
-          {/* Type Distribution */}
-          <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-3 md:col-span-2">
-            <h3 className="text-sm font-semibold text-text-primary">Type Distribution</h3>
-            <div className="flex flex-wrap gap-2">
-              {Object.entries(analysis.type_distribution).map(([type, count]) => (
-                <span key={type} className="px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs text-text-secondary">
-                  {type}: <span className="font-semibold text-text-primary">{count}</span>
-                </span>
-              ))}
-            </div>
-          </div>
-        </div>
-      ) : tab === 'recs' ? (
-        <div className="space-y-4">
-          {/* Sub-tab toggle */}
-          <div className="flex items-center gap-1 bg-bg-tertiary rounded-lg p-1 w-fit">
-            <button
-              onClick={() => setRecSource('edhrec')}
-              className={'flex items-center gap-1.5 px-3.5 py-2 text-xs font-medium rounded-md transition-all ' +
-                (recSource === 'edhrec'
-                  ? 'bg-accent-blue text-white shadow-sm'
-                  : 'text-text-tertiary hover:text-text-secondary')}
-            >
-              <Globe className="w-3.5 h-3.5" />
-              EDHREC
-            </button>
-            <button
-              onClick={() => setRecSource('collection')}
-              className={'flex items-center gap-1.5 px-3.5 py-2 text-xs font-medium rounded-md transition-all ' +
-                (recSource === 'collection'
-                  ? 'bg-accent-blue text-white shadow-sm'
-                  : 'text-text-tertiary hover:text-text-secondary')}
-            >
-              <Library className="w-3.5 h-3.5" />
-              From Collection
-            </button>
-          </div>
-
-          {/* Sub-tab content */}
-          {recSource === 'edhrec' ? (
-            <EdhRecTab deckId={deck.id} onAddedCards={fetchAll} />
-          ) : (
-            <CollectionRecTab deckId={deck.id} onAddedCards={fetchAll} />
-          )}
-        </div>
-      ) : null}
-    </div>
-  )
-}
-
-// ── Main Decks Page ───────────────────────────────────────────
-export function DecksPage() {
-  const [decks, setDecks] = useState<Deck[]>([])
-  const [loading, setLoading] = useState(true)
-  const [selectedDeck, setSelectedDeck] = useState<Deck | null>(null)
-  const [showCreate, setShowCreate] = useState(false)
-  const [newDeckName, setNewDeckName] = useState('')
-  const [newCommander, setNewCommander] = useState('')
-  const [creating, setCreating] = useState(false)
-
-  async function fetchDecks() {
-    setLoading(true)
-    try {
-      const d = await decksApi.listDecks()
-      setDecks(d)
-    } catch { /* ignore */ }
-    setLoading(false)
-  }
-
-  useEffect(() => { fetchDecks() }, [])
-
-  async function handleCreate() {
-    if (!newDeckName.trim()) return
-    setCreating(true)
-    try {
-      const deck = await decksApi.createDeck({ name: newDeckName.trim(), commander: newCommander.trim() || undefined })
-      setNewDeckName('')
-      setNewCommander('')
-      setShowCreate(false)
-      setSelectedDeck(deck)
-      fetchDecks()
-    } catch { /* ignore */ }
-    setCreating(false)
-  }
-
-  async function handleDelete(deckId: number) {
-    try {
-      await decksApi.deleteDeck(deckId)
-      fetchDecks()
-    } catch { /* ignore */ }
-  }
-
-  if (selectedDeck) {
-    return (
-      <div className="p-6">
-        <DeckDetail deck={selectedDeck} onBack={() => { setSelectedDeck(null); fetchDecks() }} />
-      </div>
-    )
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      {/* Header */}
-      <div className="flex items-center justify-between">
-        <div>
-          <h1 className="text-2xl font-bold text-text-primary">Decks</h1>
-          <p className="text-sm text-text-secondary mt-0.5">{decks.length} deck{decks.length !== 1 ? 's' : ''}</p>
-        </div>
-        <button
-          onClick={() => setShowCreate(true)}
-          className="flex items-center gap-2 px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors"
-        >
-          <Plus className="w-4 h-4" />
-          New Deck
-        </button>
-      </div>
-
-      {/* Create form */}
-      {showCreate && (
-        <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 animate-fade-in space-y-3">
-          <div className="flex items-center justify-between">
-            <h3 className="text-sm font-semibold text-text-primary">Create New Deck</h3>
-            <button onClick={() => setShowCreate(false)} className="text-text-tertiary hover:text-text-secondary">
-              <X className="w-4 h-4" />
-            </button>
-          </div>
-          <div className="grid grid-cols-2 gap-3">
-            <input
-              type="text"
-              placeholder="Deck name"
-              value={newDeckName}
-              onChange={e => setNewDeckName(e.target.value)}
-              className="px-4 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-            />
-            <input
-              type="text"
-              placeholder="Commander (optional)"
-              value={newCommander}
-              onChange={e => setNewCommander(e.target.value)}
-              className="px-4 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-            />
-          </div>
-          <button onClick={handleCreate} disabled={creating}
-            className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50">
-            {creating ? <Spinner size="sm" /> : 'Create'}
-          </button>
-        </div>
-      )}
-
-      {/* Deck list */}
-      {loading ? (
-        <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
-      ) : decks.length === 0 ? (
-        <EmptyState icon={Layers} title="No decks yet" description="Create your first deck to get started." />
-      ) : (
-        <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
-          {decks.map(deck => (
-            <div
-              key={deck.id}
-              className="bg-bg-secondary rounded-xl border border-border-primary hover:border-border-secondary p-5 cursor-pointer transition-all group"
-              onClick={() => setSelectedDeck(deck)}
-            >
-              <div className="flex items-start justify-between mb-3">
-                <div className="min-w-0 flex-1">
-                  <h3 className="text-base font-semibold text-text-primary truncate group-hover:text-accent-blue transition-colors">{deck.name}</h3>
-                  <p className="text-sm text-text-secondary mt-0.5 truncate">{deck.commander}</p>
-                </div>
-                <button
-                  onClick={e => { e.stopPropagation(); handleDelete(deck.id) }}
-                  className="opacity-0 group-hover:opacity-100 p-1.5 text-text-tertiary hover:text-accent-red transition-all"
-                >
-                  <Trash2 className="w-3.5 h-3.5" />
-                </button>
-              </div>
-              <div className="flex items-center justify-between">
-                <div className="flex items-center gap-3">
-                  <ColorDots colors={deck.color_identity} />
-                  <span className="text-xs text-text-tertiary">{deck.card_count} cards</span>
-                </div>
-                <span className="text-xs font-medium text-accent-teal">${deck.total_price.toFixed(2)}</span>
-              </div>
-            </div>
-          ))}
-        </div>
-      )}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/SimulatorPage.tsx

@@ -1,284 +0,0 @@
-import { useState, useEffect } from 'react'
-import {
-  Gamepad2, Play, Wifi, WifiOff, Settings,
-  AlertCircle, Trophy, Clock, Brain
-} from 'lucide-react'
-import { Spinner, StatusBadge } from '../components/common'
-import { simApi, labApi } from '../api'
-import { usePolling } from '../hooks/usePolling'
-import type { SimStatus, SimResult, DeepSeekStatus, LabDeck } from '../types'
-import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
-
-export function SimulatorPage() {
-  const [decks, setDecks] = useState<LabDeck[]>([])
-  const [deckA, setDeckA] = useState('')
-  const [deckB, setDeckB] = useState('')
-  const [games, setGames] = useState(50)
-  const [useDeepSeek, setUseDeepSeek] = useState(false)
-  const [deepSeekDeck, setDeepSeekDeck] = useState('')
-  const [dsStatus, setDsStatus] = useState<DeepSeekStatus | null>(null)
-  const [running, setRunning] = useState(false)
-  const [simId, setSimId] = useState<string | null>(null)
-  const [simStatus, setSimStatus] = useState<SimStatus | null>(null)
-  const [result, setResult] = useState<SimResult | null>(null)
-  const [error, setError] = useState<string | null>(null)
-  const [starting, setStarting] = useState(false)
-
-  // DS config
-  const [showDsConfig, setShowDsConfig] = useState(false)
-  const [dsModel, setDsModel] = useState('')
-  const [dsUrl, setDsUrl] = useState('')
-
-  useEffect(() => {
-    labApi.getLabDecks().then(setDecks).catch(() => {})
-    simApi.getDeepSeekStatus().then(s => {
-      setDsStatus(s)
-      if (s.model) setDsModel(s.model)
-      if (s.base_url) setDsUrl(s.base_url)
-    }).catch(() => {})
-  }, [])
-
-  // Poll while running
-  usePolling(async () => {
-    if (!simId) return
-    try {
-      const s = await simApi.getSimStatus(simId)
-      setSimStatus(s)
-      if (!s.running) {
-        const r = await simApi.getSimResult(simId)
-        setResult(r)
-        setRunning(false)
-      }
-    } catch { /* ignore */ }
-  }, 2000, running)
-
-  async function handleStart() {
-    if (!deckA || !deckB) { setError('Select both decks'); return }
-    setStarting(true)
-    setError(null)
-    setResult(null)
-    try {
-      let res
-      if (useDeepSeek && deepSeekDeck) {
-        res = await simApi.runDeepSeekSim({ deck_a: deckA, deck_b: deckB, games, deepseek_deck: deepSeekDeck })
-      } else {
-        res = await simApi.runSim({ deck_a: deckA, deck_b: deckB, games })
-      }
-      setSimId(res.sim_id)
-      setRunning(true)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to start')
-    }
-    setStarting(false)
-  }
-
-  async function handleDsConnect() {
-    try {
-      const s = await simApi.connectDeepSeek()
-      setDsStatus(s)
-    } catch { /* ignore */ }
-  }
-
-  async function handleDsConfigure() {
-    try {
-      const s = await simApi.configureDeepSeek({
-        model: dsModel || undefined,
-        base_url: dsUrl || undefined,
-      })
-      setDsStatus(s)
-      setShowDsConfig(false)
-    } catch { /* ignore */ }
-  }
-
-  return (
-    <div className="p-6 space-y-5">
-      <div>
-        <h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
-          <Gamepad2 className="w-6 h-6 text-accent-purple" />
-          Simulator
-        </h1>
-        <p className="text-sm text-text-secondary mt-0.5">Run head-to-head deck simulations</p>
-      </div>
-
-      {/* DeepSeek status */}
-      <div className="flex items-center gap-3">
-        <StatusBadge
-          variant={dsStatus?.connected ? 'success' : 'warning'}
-          label={dsStatus?.connected ? `DeepSeek: ${dsStatus.model}` : 'DeepSeek: Disconnected'}
-        />
-        <button onClick={handleDsConnect}
-          className="flex items-center gap-1.5 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
-          {dsStatus?.connected ? <Wifi className="w-3 h-3" /> : <WifiOff className="w-3 h-3" />}
-          {dsStatus?.connected ? 'Reconnect' : 'Connect'}
-        </button>
-        <button onClick={() => setShowDsConfig(!showDsConfig)}
-          className="flex items-center gap-1.5 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
-          <Settings className="w-3 h-3" />
-          Configure
-        </button>
-      </div>
-
-      {showDsConfig && (
-        <div className="bg-bg-secondary rounded-xl border border-border-primary p-4 max-w-md animate-fade-in space-y-3">
-          <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-            <Brain className="w-4 h-4 text-accent-purple" />
-            DeepSeek Configuration
-          </h3>
-          <input
-            type="text"
-            placeholder="Model name"
-            value={dsModel}
-            onChange={e => setDsModel(e.target.value)}
-            className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-          />
-          <input
-            type="text"
-            placeholder="Base URL (e.g. http://192.168.0.122:1234)"
-            value={dsUrl}
-            onChange={e => setDsUrl(e.target.value)}
-            className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
-          />
-          <button onClick={handleDsConfigure}
-            className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
-            Save
-          </button>
-        </div>
-      )}
-
-      {/* Sim config */}
-      <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 max-w-2xl">
-        <div className="grid grid-cols-2 gap-4">
-          <div>
-            <label className="text-xs text-text-tertiary uppercase tracking-wider">Deck A</label>
-            <select
-              value={deckA}
-              onChange={e => setDeckA(e.target.value)}
-              className="mt-1 w-full px-3 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-            >
-              <option value="">Select deck...</option>
-              {decks.map(d => <option key={d.name} value={d.name}>{d.name}</option>)}
-            </select>
-          </div>
-          <div>
-            <label className="text-xs text-text-tertiary uppercase tracking-wider">Deck B</label>
-            <select
-              value={deckB}
-              onChange={e => setDeckB(e.target.value)}
-              className="mt-1 w-full px-3 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-            >
-              <option value="">Select deck...</option>
-              {decks.map(d => <option key={d.name} value={d.name}>{d.name}</option>)}
-            </select>
-          </div>
-        </div>
-
-        <div className="flex items-center gap-6">
-          <div>
-            <label className="text-xs text-text-tertiary uppercase tracking-wider">Games</label>
-            <input
-              type="number" min={1} max={1000}
-              value={games}
-              onChange={e => setGames(Number(e.target.value))}
-              className="mt-1 w-24 px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-            />
-          </div>
-          <label className="flex items-center gap-2 cursor-pointer mt-5">
-            <input type="checkbox" checked={useDeepSeek} onChange={e => setUseDeepSeek(e.target.checked)}
-              className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-purple focus:ring-accent-purple/30" />
-            <span className="text-sm text-text-secondary">Use DeepSeek AI pilot</span>
-          </label>
-          {useDeepSeek && (
-            <div className="mt-5">
-              <select
-                value={deepSeekDeck}
-                onChange={e => setDeepSeekDeck(e.target.value)}
-                className="px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
-              >
-                <option value="">AI pilots which deck?</option>
-                {[deckA, deckB].filter(Boolean).map(d => <option key={d} value={d}>{d}</option>)}
-              </select>
-            </div>
-          )}
-        </div>
-
-        {error && (
-          <div className="flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
-            <AlertCircle className="w-4 h-4" />{error}
-          </div>
-        )}
-
-        {running ? (
-          <div className="flex items-center gap-3">
-            <Spinner size="md" className="text-accent-blue" />
-            <div>
-              <p className="text-sm text-text-primary">Simulation running...</p>
-              {simStatus && (
-                <p className="text-xs text-text-tertiary">
-                  {simStatus.games_completed ?? 0} / {simStatus.total_games ?? games} games
-                </p>
-              )}
-            </div>
-          </div>
-        ) : (
-          <button onClick={handleStart} disabled={starting || !deckA || !deckB}
-            className="flex items-center gap-2 px-5 py-2.5 bg-accent-purple/15 text-accent-purple text-sm font-semibold rounded-lg hover:bg-accent-purple/25 border border-accent-purple/30 transition-colors disabled:opacity-50">
-            {starting ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
-            Run Simulation
-          </button>
-        )}
-      </div>
-
-      {/* Results */}
-      {result && (
-        <div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 animate-fade-in">
-          <div className="flex items-center justify-between">
-            <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-              <Trophy className="w-4 h-4 text-accent-amber" />
-              Results
-            </h3>
-            <span className="text-xs text-text-tertiary flex items-center gap-1.5">
-              <Clock className="w-3 h-3" />
-              {(result.duration_ms / 1000).toFixed(1)}s · {result.total_games} games
-            </span>
-          </div>
-          <div className="h-48">
-            <ResponsiveContainer width="100%" height="100%">
-              <BarChart data={result.decks.map(d => ({ name: d.name, win_rate: Math.round(d.win_rate * 100) }))} barSize={40}>
-                <XAxis dataKey="name" tick={{ fill: '#9ba1b8', fontSize: 12 }} axisLine={false} tickLine={false} />
-                <YAxis domain={[0, 100]} tick={{ fill: '#6b7190', fontSize: 11 }} axisLine={false} tickLine={false} />
-                <Tooltip contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
-                  formatter={(value: unknown) => [`${value}%`, 'Win Rate']} />
-                <Bar dataKey="win_rate" radius={[4, 4, 0, 0]}>
-                  {result.decks.map((d, i) => (
-                    <Cell key={i} fill={d.win_rate === Math.max(...result.decks.map(x => x.win_rate)) ? '#4ade80' : '#4f6ef7'} />
-                  ))}
-                </Bar>
-              </BarChart>
-            </ResponsiveContainer>
-          </div>
-          <div className="grid grid-cols-2 gap-4">
-            {result.decks.map(d => (
-              <div key={d.name} className="px-4 py-3 rounded-lg bg-bg-tertiary">
-                <p className="text-sm font-semibold text-text-primary">{d.name}</p>
-                <div className="grid grid-cols-3 gap-2 mt-2">
-                  <div>
-                    <p className="text-[10px] text-text-tertiary uppercase">Wins</p>
-                    <p className="text-lg font-bold text-accent-green">{d.wins}</p>
-                  </div>
-                  <div>
-                    <p className="text-[10px] text-text-tertiary uppercase">Win Rate</p>
-                    <p className="text-lg font-bold text-accent-blue">{(d.win_rate * 100).toFixed(1)}%</p>
-                  </div>
-                  <div>
-                    <p className="text-[10px] text-text-tertiary uppercase">Avg Turn</p>
-                    <p className="text-lg font-bold text-text-primary">{d.avg_turn.toFixed(1)}</p>
-                  </div>
-                </div>
-              </div>
-            ))}
-          </div>
-        </div>
-      )}
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/pages/TrainingPage.tsx

@@ -1,822 +0,0 @@
-import { useState, useEffect, useCallback } from 'react'
-import {
-  Brain, Play, RotateCw, Database, Trophy, AlertCircle,
-  Activity, Zap, Swords, FileText, Cpu, CheckCircle,
-  ChevronDown, ChevronUp, Settings
-} from 'lucide-react'
-import { Spinner, StatusBadge } from '../components/common'
-import { trainingApi } from '../api'
-import { usePolling } from '../hooks/usePolling'
-import type {
-  MLStatus, MLDataStatus, MLTrainStatus, PPOTrainStatus,
-  TournamentStatus, MLModelInfo
-} from '../types'
-
-// ── Progress Bar ──────────────────────────────────────────────
-function ProgressBar({ value, max, label, color = 'bg-accent-blue' }: { value: number; max: number; label: string; color?: string }) {
-  const pct = max > 0 ? Math.min((value / max) * 100, 100) : 0
-  return (
-    <div>
-      <div className="flex items-center justify-between text-xs mb-1">
-        <span className="text-text-secondary">{label}</span>
-        <span className="text-text-tertiary">{value} / {max} ({pct.toFixed(0)}%)</span>
-      </div>
-      <div className="w-full h-2 bg-bg-tertiary rounded-full overflow-hidden">
-        <div className={'h-full rounded-full transition-all duration-500 ' + color} style={{ width: `${pct}%` }} />
-      </div>
-    </div>
-  )
-}
-
-// ── Phase Badge ──────────────────────────────────────────────
-const PHASE_STYLES: Record<string, string> = {
-  idle: 'bg-bg-tertiary text-text-tertiary',
-  starting: 'bg-accent-blue/15 text-accent-blue animate-pulse',
-  building: 'bg-accent-purple/15 text-accent-purple animate-pulse',
-  training: 'bg-accent-blue/15 text-accent-blue animate-pulse',
-  evaluating: 'bg-accent-teal/15 text-accent-teal animate-pulse',
-  running: 'bg-accent-amber/15 text-accent-amber animate-pulse',
-  done: 'bg-accent-green/15 text-accent-green',
-  error: 'bg-accent-red/15 text-accent-red',
-}
-
-function PhaseBadge({ phase }: { phase: string }) {
-  const cls = PHASE_STYLES[phase] || PHASE_STYLES.idle
-  return (
-    <span className={'text-[10px] font-semibold uppercase tracking-wider px-2 py-0.5 rounded ' + cls}>
-      {phase}
-    </span>
-  )
-}
-
-// ── Metric Card ──────────────────────────────────────────────
-function MetricCard({ label, value, accent }: { label: string; value: string | number; accent?: string }) {
-  return (
-    <div className="px-3 py-2 bg-bg-tertiary rounded-lg">
-      <p className="text-[10px] text-text-tertiary">{label}</p>
-      <p className={'text-sm font-bold ' + (accent || 'text-text-primary')}>{value}</p>
-    </div>
-  )
-}
-
-// ── Collapsible Section ─────────────────────────────────────
-function Section({ title, icon, children, defaultOpen = true, color = 'text-accent-blue' }: {
-  title: string; icon: React.ReactNode; children: React.ReactNode; defaultOpen?: boolean; color?: string
-}) {
-  const [open, setOpen] = useState(defaultOpen)
-  return (
-    <div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
-      <button
-        onClick={() => setOpen(!open)}
-        className="w-full flex items-center justify-between px-5 py-4 hover:bg-bg-hover transition-colors"
-      >
-        <h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
-          <span className={color}>{icon}</span>
-          {title}
-        </h3>
-        {open ? <ChevronUp className="w-4 h-4 text-text-tertiary" /> : <ChevronDown className="w-4 h-4 text-text-tertiary" />}
-      </button>
-      {open && <div className="px-5 pb-5 space-y-4 border-t border-border-primary pt-4">{children}</div>}
-    </div>
-  )
-}
-
-// ══════════════════════════════════════════════════════════════
-// Main Training Page
-// ══════════════════════════════════════════════════════════════
-export function TrainingPage() {
-  // Status states
-  const [mlStatus, setMlStatus] = useState<MLStatus | null>(null)
-  const [dataStatus, setDataStatus] = useState<MLDataStatus | null>(null)
-  const [modelInfo, setModelInfo] = useState<MLModelInfo | null>(null)
-  const [trainStatus, setTrainStatus] = useState<MLTrainStatus | null>(null)
-  const [ppoStatus, setPpoStatus] = useState<PPOTrainStatus | null>(null)
-  const [tournStatus, setTournStatus] = useState<TournamentStatus | null>(null)
-  const [loading, setLoading] = useState(true)
-  const [error, setError] = useState<string | null>(null)
-
-  // Supervised training config
-  const [epochs, setEpochs] = useState(50)
-  const [lr, setLr] = useState(0.001)
-  const [batchSize, setBatchSize] = useState(256)
-  const [patience, setPatience] = useState(10)
-  const [rebuildDataset, setRebuildDataset] = useState(true)
-
-  // PPO config
-  const [ppoIterations, setPpoIterations] = useState(100)
-  const [ppoEpisodes, setPpoEpisodes] = useState(64)
-  const [ppoEpochs, setPpoEpochs] = useState(4)
-  const [ppoBatchSize, setPpoBatchSize] = useState(256)
-  const [ppoLr, setPpoLr] = useState(0.0003)
-  const [ppoClipEps, setPpoClipEps] = useState(0.2)
-  const [ppoEntropy, setPpoEntropy] = useState(0.01)
-  const [ppoOpponent, setPpoOpponent] = useState('heuristic')
-  const [ppoPlaystyle, _setPpoPlaystyle] = useState('midrange') // eslint-disable-line
-
-  // Tournament config
-  const [tournEpisodes, setTournEpisodes] = useState(50)
-  const [tournPlaystyle, setTournPlaystyle] = useState('midrange')
-
-  // Action states
-  const [startingTrain, setStartingTrain] = useState(false)
-  const [startingPpo, setStartingPpo] = useState(false)
-  const [startingTourn, setStartingTourn] = useState(false)
-  const [reloading, setReloading] = useState(false)
-  const [showSupAdvanced, setShowSupAdvanced] = useState(false)
-  const [showPpoAdvanced, setShowPpoAdvanced] = useState(false)
-
-  // ── Initial Load ──────────────────────────────────────────
-  const loadAll = useCallback(async () => {
-    try {
-      const [ml, data, model, train, ppo, tourn] = await Promise.all([
-        trainingApi.getMLStatus().catch(() => null),
-        trainingApi.getDataStatus().catch(() => null),
-        trainingApi.getModelInfo().catch(() => null),
-        trainingApi.getTrainStatus().catch(() => null),
-        trainingApi.getPPOStatus().catch(() => null),
-        trainingApi.getTournamentStatus().catch(() => null),
-      ])
-      if (ml) setMlStatus(ml)
-      if (data) setDataStatus(data)
-      if (model) setModelInfo(model)
-      if (train) setTrainStatus(train)
-      if (ppo) setPpoStatus(ppo)
-      if (tourn) setTournStatus(tourn)
-    } catch { /* ignore */ }
-    setLoading(false)
-  }, [])
-
-  useEffect(() => { loadAll() }, [loadAll])
-
-  // ── Polling ───────────────────────────────────────────────
-  const isTraining = trainStatus?.running || false
-  const isPpo = ppoStatus?.running || false
-  const isTourn = tournStatus?.running || false
-  const anyRunning = isTraining || isPpo || isTourn
-
-  usePolling(async () => {
-    try {
-      const s = await trainingApi.getTrainStatus()
-      setTrainStatus(s)
-      if (!s.running && s.phase === 'done') {
-        // Refresh model info + data when training completes
-        const [model, data] = await Promise.all([
-          trainingApi.getModelInfo().catch(() => null),
-          trainingApi.getDataStatus().catch(() => null),
-        ])
-        if (model) setModelInfo(model)
-        if (data) setDataStatus(data)
-      }
-    } catch { /* ignore */ }
-  }, 2000, isTraining)
-
-  usePolling(async () => {
-    try {
-      const s = await trainingApi.getPPOStatus()
-      setPpoStatus(s)
-      if (!s.running && s.phase === 'done') {
-        const [model, data] = await Promise.all([
-          trainingApi.getModelInfo().catch(() => null),
-          trainingApi.getDataStatus().catch(() => null),
-        ])
-        if (model) setModelInfo(model)
-        if (data) setDataStatus(data)
-      }
-    } catch { /* ignore */ }
-  }, 2000, isPpo)
-
-  usePolling(async () => {
-    try {
-      const s = await trainingApi.getTournamentStatus()
-      setTournStatus(s)
-    } catch { /* ignore */ }
-  }, 2000, isTourn)
-
-  // ── Actions ───────────────────────────────────────────────
-  async function handleToggleLogging() {
-    try {
-      const newState = !(mlStatus?.ml_logging_enabled ?? false)
-      await trainingApi.toggleML(newState)
-      const ml = await trainingApi.getMLStatus()
-      setMlStatus(ml)
-    } catch { /* ignore */ }
-  }
-
-  async function handleReloadModel() {
-    setReloading(true)
-    try {
-      const res = await trainingApi.reloadModel()
-      setModelInfo(res.status)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to reload model')
-    }
-    setReloading(false)
-  }
-
-  async function handleStartTrain() {
-    setStartingTrain(true)
-    setError(null)
-    try {
-      await trainingApi.startTraining({ epochs, lr, batchSize, patience, rebuildDataset })
-      const s = await trainingApi.getTrainStatus()
-      setTrainStatus(s)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to start training')
-    }
-    setStartingTrain(false)
-  }
-
-  async function handleStartPpo() {
-    setStartingPpo(true)
-    setError(null)
-    try {
-      await trainingApi.startPPOTraining({
-        iterations: ppoIterations,
-        episodesPerIter: ppoEpisodes,
-        ppoEpochs,
-        batchSize: ppoBatchSize,
-        lr: ppoLr,
-        clipEpsilon: ppoClipEps,
-        entropyCoeff: ppoEntropy,
-        opponent: ppoOpponent,
-        playstyle: ppoPlaystyle,
-      })
-      const s = await trainingApi.getPPOStatus()
-      setPpoStatus(s)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to start PPO training')
-    }
-    setStartingPpo(false)
-  }
-
-  async function handleStartTournament() {
-    setStartingTourn(true)
-    setError(null)
-    try {
-      await trainingApi.startTournament({ episodes: tournEpisodes, playstyle: tournPlaystyle })
-      const s = await trainingApi.getTournamentStatus()
-      setTournStatus(s)
-    } catch (e) {
-      setError(e instanceof Error ? e.message : 'Failed to start tournament')
-    }
-    setStartingTourn(false)
-  }
-
-  // ── Render ────────────────────────────────────────────────
-  if (loading) {
-    return <div className="p-6 flex items-center justify-center h-64"><Spinner size="lg" className="text-accent-blue" /></div>
-  }
-
-  const trainDataset = dataStatus?.datasets?.['train']
-  const hasDecisions = (mlStatus?.total_decisions ?? 0) > 0
-  const hasModel = modelInfo?.loaded === true
-
-  return (
-    <div className="p-6 space-y-5">
-      {/* Page Header */}
-      <div>
-        <h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
-          <Brain className="w-6 h-6 text-accent-green" />
-          ML Training Pipeline
-        </h1>
-        <p className="text-sm text-text-secondary mt-0.5">Collect data, train models, and evaluate performance</p>
-      </div>
-
-      {/* Error banner */}
-      {error && (
-        <div className="flex items-center gap-2 px-4 py-2 bg-accent-red/10 border border-accent-red/30 rounded-lg text-sm text-accent-red">
-          <AlertCircle className="w-4 h-4 flex-shrink-0" />
-          <span className="flex-1">{error}</span>
-          <button onClick={() => setError(null)} className="text-accent-red/60 hover:text-accent-red text-xs">dismiss</button>
-        </div>
-      )}
-
-      {/* Pipeline overview bar */}
-      <div className="flex flex-wrap items-center gap-3 bg-bg-secondary rounded-xl border border-border-primary px-5 py-3">
-        <div className="flex items-center gap-2">
-          <div className={'w-2 h-2 rounded-full ' + (mlStatus?.ml_logging_enabled ? 'bg-accent-green' : 'bg-text-tertiary')} />
-          <span className="text-xs text-text-secondary">Logging {mlStatus?.ml_logging_enabled ? 'On' : 'Off'}</span>
-        </div>
-        <span className="text-border-primary">|</span>
-        <span className="text-xs text-text-secondary">{mlStatus?.total_decisions ?? 0} decisions</span>
-        <span className="text-border-primary">|</span>
-        <span className="text-xs text-text-secondary">{trainDataset ? `${trainDataset.samples.toLocaleString()} training samples` : 'No dataset'}</span>
-        <span className="text-border-primary">|</span>
-        <span className="text-xs text-text-secondary">{(dataStatus?.checkpoints?.length ?? 0)} checkpoints</span>
-        <span className="text-border-primary">|</span>
-        <StatusBadge
-          variant={hasModel ? 'success' : 'neutral'}
-          label={hasModel ? `Model on ${modelInfo?.device ?? '?'}` : 'No model'}
-        />
-        {anyRunning && (
-          <>
-            <span className="text-border-primary">|</span>
-            <span className="text-xs font-medium text-accent-blue animate-pulse">Pipeline running...</span>
-          </>
-        )}
-      </div>
-
-      {/* ═══ Phase 1: Data Collection ═══ */}
-      <Section title="Phase 1 — Data Collection" icon={<FileText className="w-4 h-4" />} color="text-accent-teal">
-        <div className="flex items-center justify-between">
-          <p className="text-xs text-text-secondary">
-            Run batch simulations with ML logging enabled to collect decision data.
-          </p>
-          <button
-            onClick={handleToggleLogging}
-            className={'flex items-center gap-2 px-3 py-1.5 text-xs font-medium rounded-lg border transition-colors ' +
-              (mlStatus?.ml_logging_enabled
-                ? 'bg-accent-green/15 text-accent-green border-accent-green/30 hover:bg-accent-green/25'
-                : 'bg-bg-tertiary text-text-secondary border-border-primary hover:bg-bg-hover'
-              )}
-          >
-            <div className={'w-2 h-2 rounded-full ' + (mlStatus?.ml_logging_enabled ? 'bg-accent-green' : 'bg-text-tertiary')} />
-            {mlStatus?.ml_logging_enabled ? 'Logging Enabled' : 'Enable Logging'}
-          </button>
-        </div>
-
-        {/* Decision files */}
-        {(dataStatus?.decisionFiles?.length ?? 0) > 0 ? (
-          <div className="space-y-1">
-            <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Decision Files</h4>
-            <div className="max-h-36 overflow-y-auto space-y-1">
-              {dataStatus!.decisionFiles.map(f => (
-                <div key={f.name} className="flex items-center justify-between px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs">
-                  <span className="text-text-secondary font-mono truncate">{f.name}</span>
-                  <div className="flex items-center gap-3 text-text-tertiary">
-                    <span>{f.decisions.toLocaleString()} decisions</span>
-                    <span>{(f.size / 1024).toFixed(0)} KB</span>
-                  </div>
-                </div>
-              ))}
-            </div>
-          </div>
-        ) : (
-          <div className="text-xs text-text-tertiary italic">No decision files yet. Run batch sims with logging enabled.</div>
-        )}
-      </Section>
-
-      {/* ═══ Phase 2: Dataset & Checkpoints ═══ */}
-      <Section title="Phase 2 — Dataset & Model Artifacts" icon={<Database className="w-4 h-4" />} color="text-accent-purple">
-        <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
-          {/* Dataset splits */}
-          <div className="space-y-2">
-            <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Dataset Splits</h4>
-            {Object.keys(dataStatus?.datasets ?? {}).length > 0 ? (
-              <div className="grid grid-cols-3 gap-2">
-                {(['train', 'val', 'test'] as const).map(split => {
-                  const ds = dataStatus?.datasets?.[split]
-                  return (
-                    <div key={split} className="px-3 py-2 bg-bg-tertiary rounded-lg">
-                      <p className="text-[10px] text-text-tertiary uppercase">{split}</p>
-                      {ds ? (
-                        <>
-                          <p className="text-sm font-bold text-text-primary">{ds.samples.toLocaleString()}</p>
-                          <p className="text-[10px] text-text-tertiary">{ds.features} features</p>
-                        </>
-                      ) : (
-                        <p className="text-xs text-text-tertiary italic">Not built</p>
-                      )}
-                    </div>
-                  )
-                })}
-              </div>
-            ) : (
-              <p className="text-xs text-text-tertiary italic">No dataset built yet. Training will build it automatically.</p>
-            )}
-          </div>
-
-          {/* Checkpoints */}
-          <div className="space-y-2">
-            <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Checkpoints</h4>
-            {(dataStatus?.checkpoints?.length ?? 0) > 0 ? (
-              <div className="max-h-28 overflow-y-auto space-y-1">
-                {dataStatus!.checkpoints.map(ckpt => (
-                  <div key={ckpt.name} className="flex items-center justify-between px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs">
-                    <span className="text-text-secondary font-mono truncate">{ckpt.name}</span>
-                    <span className="text-text-tertiary">{(ckpt.size / 1024 / 1024).toFixed(1)} MB</span>
-                  </div>
-                ))}
-              </div>
-            ) : (
-              <p className="text-xs text-text-tertiary italic">No checkpoints yet. Train a model to generate them.</p>
-            )}
-          </div>
-        </div>
-
-        {/* Model info + reload */}
-        <div className="flex items-center justify-between pt-2 border-t border-border-primary">
-          <div className="flex items-center gap-3">
-            <Cpu className="w-4 h-4 text-text-tertiary" />
-            <div className="text-xs">
-              {hasModel ? (
-                <span className="text-accent-green font-medium">
-                  Model loaded on {modelInfo?.device} | {modelInfo?.input_dim} features | {modelInfo?.num_actions} actions
-                </span>
-              ) : (
-                <span className="text-text-tertiary">No model loaded{modelInfo?.error ? ` — ${modelInfo.error}` : ''}</span>
-              )}
-            </div>
-          </div>
-          <button
-            onClick={handleReloadModel}
-            disabled={reloading}
-            className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50"
-          >
-            {reloading ? <Spinner size="sm" /> : <RotateCw className="w-3 h-3" />}
-            Reload Model
-          </button>
-        </div>
-
-        {/* Eval results */}
-        {dataStatus?.evalResults && (
-          <div className="pt-2 border-t border-border-primary space-y-2">
-            <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider flex items-center gap-2">
-              <CheckCircle className="w-3.5 h-3.5 text-accent-green" />
-              Latest Evaluation
-            </h4>
-            <div className="grid grid-cols-2 md:grid-cols-4 gap-2">
-              {Object.entries(dataStatus.evalResults).map(([k, v]) => (
-                <MetricCard
-                  key={k}
-                  label={k.replace(/_/g, ' ')}
-                  value={typeof v === 'number' ? (k.includes('accuracy') || k.includes('rate') ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)) : String(v)}
-                  accent={k.includes('accuracy') ? 'text-accent-green' : undefined}
-                />
-              ))}
-            </div>
-          </div>
-        )}
-      </Section>
-
-      {/* ═══ Phase 3: Supervised Training ═══ */}
-      <Section title="Phase 3 — Supervised Training" icon={<Activity className="w-4 h-4" />}>
-        {/* Config */}
-        <div className="space-y-3">
-          <div className="grid grid-cols-2 gap-3">
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Epochs</label>
-              <input type="number" min={1} max={1000} value={epochs} onChange={e => setEpochs(Number(e.target.value))}
-                disabled={isTraining}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Learning Rate</label>
-              <input type="number" min={0.00001} max={1} step={0.0001} value={lr} onChange={e => setLr(Number(e.target.value))}
-                disabled={isTraining}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-          </div>
-
-          {/* Advanced toggle */}
-          <button onClick={() => setShowSupAdvanced(!showSupAdvanced)} className="flex items-center gap-1 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
-            <Settings className="w-3 h-3" />
-            {showSupAdvanced ? 'Hide' : 'Show'} advanced options
-          </button>
-          {showSupAdvanced && (
-            <div className="grid grid-cols-3 gap-3 animate-fade-in">
-              <div>
-                <label className="text-xs text-text-tertiary block mb-1">Batch Size</label>
-                <input type="number" min={16} max={2048} value={batchSize} onChange={e => setBatchSize(Number(e.target.value))}
-                  disabled={isTraining}
-                  className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-              </div>
-              <div>
-                <label className="text-xs text-text-tertiary block mb-1">Early Stop Patience</label>
-                <input type="number" min={1} max={100} value={patience} onChange={e => setPatience(Number(e.target.value))}
-                  disabled={isTraining}
-                  className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-              </div>
-              <div className="flex items-end pb-1">
-                <label className="flex items-center gap-2 text-xs text-text-secondary cursor-pointer select-none">
-                  <input type="checkbox" checked={rebuildDataset} onChange={e => setRebuildDataset(e.target.checked)}
-                    disabled={isTraining}
-                    className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5" />
-                  Rebuild dataset
-                </label>
-              </div>
-            </div>
-          )}
-        </div>
-
-        {/* Training status / start button */}
-        {isTraining || (trainStatus?.phase && trainStatus.phase !== 'idle') ? (
-          <div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
-            <div className="flex items-center justify-between">
-              <div className="flex items-center gap-2">
-                <PhaseBadge phase={trainStatus!.phase} />
-                <span className="text-xs text-text-secondary">{trainStatus!.message}</span>
-              </div>
-              {trainStatus!.started_at && (
-                <span className="text-[10px] text-text-tertiary">Started: {new Date(trainStatus!.started_at).toLocaleTimeString()}</span>
-              )}
-            </div>
-
-            {/* Progress bar — only in training phase */}
-            {(trainStatus!.phase === 'training' || trainStatus!.phase === 'done') && trainStatus!.total_epochs > 0 && (
-              <ProgressBar
-                value={trainStatus!.current_epoch}
-                max={trainStatus!.total_epochs}
-                label="Epoch progress"
-              />
-            )}
-
-            {/* Live metrics during training */}
-            {trainStatus!.metrics && (
-              <div className="grid grid-cols-2 md:grid-cols-4 gap-2">
-                {Object.entries(trainStatus!.metrics).map(([k, v]) => {
-                  if (typeof v !== 'number') return null
-                  const isAcc = k.includes('accuracy') || k.includes('acc')
-                  const isLoss = k.includes('loss')
-                  return (
-                    <MetricCard
-                      key={k}
-                      label={k.replace(/_/g, ' ')}
-                      value={isAcc ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)}
-                      accent={isAcc ? 'text-accent-green' : isLoss ? 'text-accent-amber' : undefined}
-                    />
-                  )
-                })}
-              </div>
-            )}
-
-            {/* Final result */}
-            {trainStatus!.phase === 'done' && trainStatus!.result && (
-              <div className="pt-2 border-t border-border-primary space-y-2">
-                <h4 className="text-xs font-semibold text-accent-green flex items-center gap-1.5">
-                  <CheckCircle className="w-3.5 h-3.5" /> Training Complete
-                </h4>
-                <div className="grid grid-cols-2 md:grid-cols-4 gap-2">
-                  <MetricCard label="Device" value={trainStatus!.result.device} />
-                  <MetricCard label="Checkpoint" value={trainStatus!.result.checkpoint.split('/').pop() || '—'} />
-                  {trainStatus!.result.evaluation && Object.entries(trainStatus!.result.evaluation).map(([k, v]) => (
-                    typeof v === 'number' ? (
-                      <MetricCard key={k} label={k.replace(/_/g, ' ')}
-                        value={k.includes('accuracy') ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)}
-                        accent={k.includes('accuracy') ? 'text-accent-green' : undefined} />
-                    ) : null
-                  ))}
-                </div>
-              </div>
-            )}
-
-            {/* Error state */}
-            {trainStatus!.phase === 'error' && trainStatus!.error && (
-              <div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
-                <AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
-                <span className="text-xs text-accent-red">{trainStatus!.error}</span>
-              </div>
-            )}
-          </div>
-        ) : (
-          <div className="flex items-center gap-3">
-            <button
-              onClick={handleStartTrain}
-              disabled={startingTrain || !hasDecisions || isPpo}
-              className="flex items-center gap-2 px-4 py-2.5 bg-accent-blue/15 text-accent-blue text-sm font-medium rounded-lg hover:bg-accent-blue/25 border border-accent-blue/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
-            >
-              {startingTrain ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
-              Start Training
-            </button>
-            {!hasDecisions && (
-              <span className="text-xs text-text-tertiary italic">Enable logging and run batch sims first to generate decision data.</span>
-            )}
-          </div>
-        )}
-      </Section>
-
-      {/* ═══ Phase 4: PPO Reinforcement Learning ═══ */}
-      <Section title="Phase 4 — PPO Reinforcement Learning" icon={<Zap className="w-4 h-4" />} color="text-accent-purple" defaultOpen={false}>
-        <p className="text-xs text-text-secondary">
-          Fine-tune the supervised model using PPO self-play against an opponent policy.
-        </p>
-
-        {/* Config */}
-        <div className="grid grid-cols-2 gap-3">
-          <div>
-            <label className="text-xs text-text-tertiary block mb-1">Iterations</label>
-            <input type="number" min={1} max={1000} value={ppoIterations} onChange={e => setPpoIterations(Number(e.target.value))}
-              disabled={isPpo}
-              className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-          </div>
-          <div>
-            <label className="text-xs text-text-tertiary block mb-1">Opponent</label>
-            <select value={ppoOpponent} onChange={e => setPpoOpponent(e.target.value)}
-              disabled={isPpo}
-              className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50">
-              <option value="heuristic">Heuristic</option>
-              <option value="random">Random</option>
-              <option value="self">Self-Play</option>
-            </select>
-          </div>
-        </div>
-
-        <button onClick={() => setShowPpoAdvanced(!showPpoAdvanced)} className="flex items-center gap-1 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
-          <Settings className="w-3 h-3" />
-          {showPpoAdvanced ? 'Hide' : 'Show'} advanced options
-        </button>
-        {showPpoAdvanced && (
-          <div className="grid grid-cols-3 gap-3 animate-fade-in">
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Episodes / Iter</label>
-              <input type="number" min={1} max={512} value={ppoEpisodes} onChange={e => setPpoEpisodes(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">PPO Epochs</label>
-              <input type="number" min={1} max={20} value={ppoEpochs} onChange={e => setPpoEpochs(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Batch Size</label>
-              <input type="number" min={16} max={2048} value={ppoBatchSize} onChange={e => setPpoBatchSize(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Learning Rate</label>
-              <input type="number" min={0.000001} max={0.01} step={0.0001} value={ppoLr} onChange={e => setPpoLr(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Clip Epsilon</label>
-              <input type="number" min={0.05} max={0.5} step={0.05} value={ppoClipEps} onChange={e => setPpoClipEps(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-            <div>
-              <label className="text-xs text-text-tertiary block mb-1">Entropy Coeff</label>
-              <input type="number" min={0} max={0.1} step={0.005} value={ppoEntropy} onChange={e => setPpoEntropy(Number(e.target.value))}
-                disabled={isPpo}
-                className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-            </div>
-          </div>
-        )}
-
-        {/* PPO running status */}
-        {isPpo || (ppoStatus?.phase && ppoStatus.phase !== 'idle') ? (
-          <div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
-            <div className="flex items-center gap-2">
-              <PhaseBadge phase={ppoStatus!.phase} />
-              <span className="text-xs text-text-secondary">{ppoStatus!.message}</span>
-            </div>
-
-            {(ppoStatus!.phase === 'training' || ppoStatus!.phase === 'done') && ppoStatus!.total_iterations > 0 && (
-              <ProgressBar
-                value={ppoStatus!.iteration}
-                max={ppoStatus!.total_iterations}
-                label="PPO iteration"
-                color="bg-accent-purple"
-              />
-            )}
-
-            {ppoStatus!.metrics && (
-              <div className="grid grid-cols-2 md:grid-cols-4 gap-2">
-                {ppoStatus!.metrics.win_rate != null && (
-                  <MetricCard label="Win Rate" value={`${(ppoStatus!.metrics.win_rate * 100).toFixed(1)}%`} accent="text-accent-green" />
-                )}
-                {ppoStatus!.metrics.avg_reward != null && (
-                  <MetricCard label="Avg Reward" value={ppoStatus!.metrics.avg_reward.toFixed(3)} accent="text-accent-amber" />
-                )}
-                {ppoStatus!.metrics.policy_loss != null && (
-                  <MetricCard label="Policy Loss" value={ppoStatus!.metrics.policy_loss.toFixed(4)} />
-                )}
-                {ppoStatus!.metrics.entropy != null && (
-                  <MetricCard label="Entropy" value={ppoStatus!.metrics.entropy.toFixed(4)} />
-                )}
-              </div>
-            )}
-
-            {ppoStatus!.phase === 'done' && ppoStatus!.result && (
-              <div className="flex items-center gap-2 pt-2 border-t border-border-primary">
-                <CheckCircle className="w-3.5 h-3.5 text-accent-green" />
-                <span className="text-xs text-accent-green font-medium">
-                  PPO Training Complete — Best Win Rate: {
-                    typeof ppoStatus!.result.best_win_rate === 'number'
-                      ? `${(ppoStatus!.result.best_win_rate as number * 100).toFixed(1)}%`
-                      : '—'
-                  }
-                </span>
-              </div>
-            )}
-
-            {ppoStatus!.phase === 'error' && ppoStatus!.error && (
-              <div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
-                <AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
-                <span className="text-xs text-accent-red">{ppoStatus!.error}</span>
-              </div>
-            )}
-          </div>
-        ) : (
-          <button
-            onClick={handleStartPpo}
-            disabled={startingPpo || isTraining || !hasModel}
-            className="flex items-center gap-2 px-4 py-2.5 bg-accent-purple/15 text-accent-purple text-sm font-medium rounded-lg hover:bg-accent-purple/25 border border-accent-purple/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
-          >
-            {startingPpo ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
-            Start PPO Training
-          </button>
-        )}
-        {!hasModel && !(isPpo || (ppoStatus?.phase && ppoStatus.phase !== 'idle')) && (
-          <p className="text-xs text-text-tertiary italic">Train a supervised model first (Phase 3) before running PPO.</p>
-        )}
-      </Section>
-
-      {/* ═══ Phase 5: Tournament Evaluation ═══ */}
-      <Section title="Phase 5 — Tournament Evaluation" icon={<Swords className="w-4 h-4" />} color="text-accent-amber" defaultOpen={false}>
-        <p className="text-xs text-text-secondary">
-          Pit the trained model against heuristic, random, and other policy variants.
-        </p>
-
-        <div className="grid grid-cols-2 gap-3">
-          <div>
-            <label className="text-xs text-text-tertiary block mb-1">Episodes per Matchup</label>
-            <input type="number" min={10} max={1000} value={tournEpisodes} onChange={e => setTournEpisodes(Number(e.target.value))}
-              disabled={isTourn}
-              className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
-          </div>
-          <div>
-            <label className="text-xs text-text-tertiary block mb-1">Playstyle</label>
-            <select value={tournPlaystyle} onChange={e => setTournPlaystyle(e.target.value)}
-              disabled={isTourn}
-              className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50">
-              <option value="midrange">Midrange</option>
-              <option value="aggro">Aggro</option>
-              <option value="control">Control</option>
-              <option value="combo">Combo</option>
-            </select>
-          </div>
-        </div>
-
-        {/* Tournament status */}
-        {isTourn || (tournStatus?.phase && tournStatus.phase !== 'idle') ? (
-          <div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
-            <div className="flex items-center gap-2">
-              <PhaseBadge phase={tournStatus!.phase} />
-              <span className="text-xs text-text-secondary">{tournStatus!.message}</span>
-            </div>
-
-            {tournStatus!.phase === 'error' && tournStatus!.error && (
-              <div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
-                <AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
-                <span className="text-xs text-accent-red">{tournStatus!.error}</span>
-              </div>
-            )}
-          </div>
-        ) : (
-          <button
-            onClick={handleStartTournament}
-            disabled={startingTourn || isTraining || isPpo}
-            className="flex items-center gap-2 px-4 py-2.5 bg-accent-amber/15 text-accent-amber text-sm font-medium rounded-lg hover:bg-accent-amber/25 border border-accent-amber/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
-          >
-            {startingTourn ? <Spinner size="sm" /> : <Trophy className="w-4 h-4" />}
-            Start Tournament
-          </button>
-        )}
-
-        {/* Tournament results */}
-        {(tournStatus?.result?.players?.length ?? 0) > 0 && (
-          <div className="pt-2 border-t border-border-primary space-y-2">
-            <h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider flex items-center gap-2">
-              <Trophy className="w-3.5 h-3.5 text-accent-amber" />
-              Leaderboard
-            </h4>
-            <table className="w-full text-sm">
-              <thead>
-                <tr className="border-b border-border-primary">
-                  <th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium w-8">#</th>
-                  <th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium">Policy</th>
-                  <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Wins</th>
-                  <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Games</th>
-                  <th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Win Rate</th>
-                </tr>
-              </thead>
-              <tbody className="divide-y divide-border-primary">
-                {tournStatus!.result!.players
-                  .sort((a, b) => b.win_rate - a.win_rate)
-                  .map((p, i) => (
-                    <tr key={p.name} className="hover:bg-bg-hover transition-colors">
-                      <td className="px-3 py-2 text-text-tertiary text-xs">
-                        {i === 0 ? <Trophy className="w-3.5 h-3.5 text-accent-amber" /> : i + 1}
-                      </td>
-                      <td className="px-3 py-2">
-                        <span className="text-text-primary font-medium capitalize">{p.name}</span>
-                      </td>
-                      <td className="px-3 py-2 text-right text-text-secondary">{p.wins}</td>
-                      <td className="px-3 py-2 text-right text-text-secondary">{p.games}</td>
-                      <td className={'px-3 py-2 text-right font-semibold ' + (i === 0 ? 'text-accent-green' : 'text-accent-blue')}>
-                        {(p.win_rate * 100).toFixed(1)}%
-                      </td>
-                    </tr>
-                  ))}
-              </tbody>
-            </table>
-          </div>
-        )}
-      </Section>
-    </div>
-  )
-}

frontend/commander-ai-lab-ui/src/types/index.ts

@@ -1,563 +0,0 @@
-// ═══════════════════════════════════════════════════════════
-// Commander AI Lab — TypeScript Type Definitions
-// Derived from the FastAPI backend's Pydantic models + SQLite schema
-// ═══════════════════════════════════════════════════════════
-
-// ── Cards & Collection ──────────────────────────────────────
-
-export interface CollectionCard {
-  id: number
-  name: string
-  scryfall_id: string
-  set_code: string
-  set_name: string
-  collector_number: string
-  rarity: string
-  type_line: string
-  mana_cost: string
-  cmc: number
-  oracle_text: string
-  color_identity: string[]
-  quantity: number
-  foil_quantity: number
-  condition: string
-  tcg_price: number | null
-  foil_price: number | null
-  image_url: string
-  edhrec_rank: number | null
-  keywords: string
-  added_date: string
-  updated_date: string
-}
-
-export interface CollectionFilters {
-  q?: string
-  type_line?: string
-  color?: string
-  rarity?: string
-  set_code?: string
-  cmc_min?: number
-  cmc_max?: number
-  price_min?: number
-  price_max?: number
-  owned_only?: boolean
-  sort_by?: string
-  sort_dir?: 'asc' | 'desc'
-  page?: number
-  page_size?: number
-}
-
-export interface CollectionStats {
-  total_cards: number
-  total_unique: number
-  total_value: number
-  sets_count: number
-}
-
-export interface SetInfo {
-  code: string
-  name: string
-  card_count?: number
-}
-
-// ── Scan ─────────────────────────────────────────────────────
-
-export interface ScanResult {
-  matches: ScanMatch[]
-  image_url?: string
-}
-
-export interface ScanMatch {
-  name: string
-  set_code: string
-  set_name: string
-  collector_number: string
-  scryfall_id: string
-  confidence: number
-  image_url: string
-  type_line: string
-  oracle_text: string
-  mana_cost: string
-  tcg_price: number | null
-}
-
-// ── Decks ────────────────────────────────────────────────────
-
-export interface Deck {
-  id: number
-  name: string
-  commander: string
-  commander_scryfall_id?: string
-  commander_image_url?: string
-  color_identity: string[]
-  format: string
-  card_count: number
-  total_price: number
-  created_date: string
-  updated_date: string
-}
-
-export interface DeckCard {
-  id: number
-  deck_id: number
-  name: string
-  scryfall_id: string
-  quantity: number
-  is_commander: boolean
-  category: string
-  type_line: string
-  mana_cost: string
-  cmc: number
-  oracle_text: string
-  tcg_price: number | null
-  image_url: string
-  owned_qty: number
-  set_code: string
-}
-
-export interface DeckAnalysis {
-  card_count: number
-  land_count: number
-  creature_count: number
-  noncreature_count: number
-  avg_cmc: number
-  mana_curve: Record<number, number>
-  color_distribution: Record<string, number>
-  type_distribution: Record<string, number>
-  total_price: number
-  owned_count: number
-  missing_count: number
-}
-
-export interface DeckRecommendation {
-  name: string
-  scryfall_id: string
-  type_line: string
-  mana_cost: string
-  oracle_text: string
-  image_url: string
-  tcg_price: number | null
-  reason: string
-  score: number
-  owned: boolean
-}
-
-// ── EDHREC Recommendations ──────────────────────────────────
-
-export interface EdhRecCard {
-  name: string
-  type_line: string
-  role: string
-  roles: string[]
-  inclusion_pct: number | null
-  synergy_score: number | null
-  owned: boolean
-  owned_qty: number
-  scryfall_id: string
-  image_url: string | null
-}
-
-export interface EdhRecsResponse {
-  commander: string
-  source: string
-  total: number
-  recommendations: EdhRecCard[]
-}
-
-// ── Collection Recommendations ──────────────────────────────
-
-export interface CollectionRecCard {
-  id: number
-  scryfall_id: string
-  name: string
-  type_line: string
-  card_type: string
-  cmc: number
-  color_identity: string[]
-  owned_qty: number
-  roles: string[]
-  score: number
-  image_url: string | null
-}
-
-export interface CollectionRecsResponse {
-  shortfall_types: string[]
-  role_filter: string[]
-  grouped: Record<string, CollectionRecCard[]>
-  total: number
-}
-
-// ── V3 Deck Generator ────────────────────────────────────────
-
-export interface DeckGenV3Request {
-  commander_name: string
-  strategy?: string
-  target_bracket: number
-  budget_usd?: number
-  budget_mode?: 'total' | 'per_card'
-  omit_cards?: string[]
-  use_collection?: boolean
-  run_substitution?: boolean
-  model?: string
-  deck_name?: string
-}
-
-export interface DeckGenV3Status {
-  initialized: boolean
-  pplx_configured: boolean
-  model: string | null
-  embeddings_loaded: boolean
-  embedding_cards: number
-  error: string | null
-}
-
-export interface DeckGenV3Card {
-  name: string
-  quantity: number
-  category: string
-  type_line: string
-  mana_cost: string
-  cmc: number
-  role: string
-  reason: string
-  tcg_price: number | null
-  image_url: string
-  owned: boolean
-  owned_qty: number
-  scryfall_id: string
-  is_substitute: boolean
-  original_name: string | null
-  substitution_reason: string | null
-  similarity_score: number | null
-}
-
-export interface DeckGenV3Result {
-  commander: {
-    name: string
-    image_url: string
-    type_line: string
-    color_identity: string[]
-    mana_cost: string
-    oracle_text: string
-  }
-  cards: DeckGenV3Card[]
-  stats: DeckAnalysis
-  strategy: string
-  bracket: number
-  model: string
-  substitution?: {
-    owned: number
-    substituted: number
-    missing: number
-  }
-}
-
-// ── Simulation ───────────────────────────────────────────────
-
-export interface SimConfig {
-  deck_names: string[]
-  num_games: number
-  threads: number
-  use_deepseek?: boolean
-}
-
-export interface SimStatus {
-  running: boolean
-  sim_id: string
-  progress?: number
-  games_completed?: number
-  total_games?: number
-}
-
-export interface SimResult {
-  sim_id: string
-  decks: SimDeckResult[]
-  total_games: number
-  duration_ms: number
-  timestamp: string
-}
-
-export interface SimDeckResult {
-  name: string
-  wins: number
-  losses: number
-  win_rate: number
-  avg_turn: number
-}
-
-// ── Batch Sim (Lab) ──────────────────────────────────────────
-
-export interface LabDeck {
-  name: string
-  source: string
-  path: string
-  card_count: number
-}
-
-export interface LabStartRequest {
-  decks: string[]
-  games: number
-  threads?: number
-  use_deepseek?: boolean
-  deepseek_deck?: string
-}
-
-export interface LabStatus {
-  running: boolean
-  run_id: string
-  games_completed: number
-  total_games: number
-  current_decks: string[]
-  error: string | null
-}
-
-export interface LabResult {
-  run_id: string
-  decks: LabDeckResult[]
-  total_games: number
-  timestamp: string
-}
-
-export interface LabDeckResult {
-  name: string
-  wins: number
-  games: number
-  win_rate: number
-  avg_turns: number
-  avg_life_remaining: number
-}
-
-export interface LabHistoryEntry {
-  run_id: string
-  timestamp: string
-  decks: string[]
-  total_games: number
-}
-
-export interface PreconDeck {
-  name: string
-  filename: string
-  release_date?: string
-  set_name?: string
-  installed: boolean
-}
-
-// ── Coach ────────────────────────────────────────────────────
-
-export interface CoachStatus {
-  llmConnected: boolean
-  llmModel: string | null
-  llmModels: string[]
-  embeddingsLoaded: boolean
-  embeddingCards: number
-  deckReportsAvailable: number
-  error: string | null
-}
-
-export interface CoachDeck {
-  deck_id: number
-  deck_name: string
-  commander: string
-  card_count: number
-  has_report: boolean
-  report_count: number
-  last_report_date: string | null
-}
-
-export interface CoachGoals {
-  targetPowerLevel?: number | null
-  metaFocus?: string | null
-  budget?: string | null
-  focusAreas?: string[]
-}
-
-export interface CoachMessage {
-  role: 'user' | 'assistant'
-  content: string
-}
-
-export interface CoachChatResponse {
-  content: string
-  model: string
-  prompt_tokens: number
-  completion_tokens: number
-}
-
-export interface CoachSession {
-  sessionId: string
-  deckId: string
-  timestamp: string
-  summary: string
-  suggestedCuts: CoachSuggestedCut[]
-  suggestedAdds: CoachSuggestedAdd[]
-  heuristicHints: string[]
-  manaBaseAdvice: string | null
-  rawTextExplanation: string
-  modelUsed: string
-  promptTokens: number
-  completionTokens: number
-  goals: CoachGoals | null
-}
-
-export interface CoachSuggestedCut {
-  cardName: string
-  reason: string
-  replacementOptions: string[]
-  currentImpactScore: number
-}
-
-export interface CoachSuggestedAdd {
-  cardName: string
-  role: string
-  reason: string
-  synergyWith: string[]
-  estimatedManaValue: number | null
-}
-
-export interface CoachSessionSummary {
-  sessionId: string
-  deckId: string
-  timestamp: string
-  summary: string
-  cutsCount: number
-  addsCount: number
-}
-
-export interface CoachApplyResult {
-  cuts: { name: string; status: string }[]
-  adds: { name: string; scryfall_id?: string; status: string }[]
-  errors: { name: string; error: string }[]
-  total_cuts: number
-  total_adds: number
-}
-
-export interface CoachCardLikeResult {
-  name: string
-  similarity: number
-  types: string
-  mana_value: number
-  mana_cost: string
-  text: string
-  owned_qty: number
-  image_url: string | null
-  tcg_price: number | null
-}
-
-// ── ML Training ──────────────────────────────────────────────
-
-/** GET /api/ml/status */
-export interface MLStatus {
-  ml_logging_enabled: boolean
-  training_files: { file: string; decisions: number; size_kb: number }[]
-  total_decisions: number
-  total_files: number
-}
-
-/** GET /api/ml/data/status */
-export interface MLDataStatus {
-  decisionFiles: { name: string; size: number; decisions: number }[]
-  totalDecisions: number
-  datasets: Record<string, { samples: number; features: number; size: number }>
-  checkpoints: { name: string; size: number; modified: string }[]
-  evalResults: Record<string, unknown> | null
-  policyLoaded: boolean
-}
-
-/** GET /api/ml/train/status */
-export interface MLTrainStatus {
-  running: boolean
-  progress: number
-  total_epochs: number
-  current_epoch: number
-  phase: 'idle' | 'starting' | 'building' | 'training' | 'evaluating' | 'done' | 'error'
-  message: string
-  metrics: Record<string, unknown> | null
-  result: {
-    training: Record<string, unknown>
-    evaluation: Record<string, unknown> | null
-    checkpoint: string
-    device: string
-  } | null
-  error: string | null
-  started_at: string | null
-}
-
-/** GET /api/ml/train/ppo/status */
-export interface PPOTrainStatus {
-  running: boolean
-  iteration: number
-  total_iterations: number
-  phase: 'idle' | 'starting' | 'training' | 'done' | 'error'
-  message: string
-  metrics: {
-    avg_reward?: number
-    win_rate?: number
-    policy_loss?: number
-    value_loss?: number
-    entropy?: number
-    [key: string]: unknown
-  } | null
-  result: Record<string, unknown> | null
-  error: string | null
-}
-
-/** GET /api/ml/tournament/status */
-export interface TournamentStatus {
-  running: boolean
-  phase: 'idle' | 'starting' | 'running' | 'done' | 'error'
-  message: string
-  result: TournamentResults | null
-  error: string | null
-}
-
-export interface TournamentResults {
-  players: TournamentPlayer[]
-  total_matches: number
-  matchups?: Record<string, unknown>
-}
-
-export interface TournamentPlayer {
-  name: string
-  wins: number
-  games: number
-  win_rate: number
-}
-
-/** GET /api/ml/model */
-export interface MLModelInfo {
-  loaded: boolean
-  device?: string
-  input_dim?: number
-  num_actions?: number
-  checkpoint_path?: string
-  error?: string
-  torch_available?: boolean
-}
-
-// ── DeepSeek ─────────────────────────────────────────────────
-
-export interface DeepSeekStatus {
-  connected: boolean
-  model: string | null
-  base_url: string
-}
-
-// ── Commander Search ─────────────────────────────────────────
-
-export interface CommanderSearchResult {
-  name: string
-  type_line: string
-  color_identity: string[]
-  mana_cost: string
-  image_url: string
-  oracle_text: string
-  scryfall_id: string
-  in_collection: boolean
-}
-
-// ── Perplexity ───────────────────────────────────────────────
-
-export interface PplxStatus {
-  configured: boolean
-  key_prefix: string
-}

frontend/commander-ai-lab-ui/tsconfig.app.json

@@ -1,28 +0,0 @@
-{
-  "compilerOptions": {
-    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
-    "target": "ES2022",
-    "useDefineForClassFields": true,
-    "lib": ["ES2022", "DOM", "DOM.Iterable"],
-    "module": "ESNext",
-    "types": ["vite/client"],
-    "skipLibCheck": true,
-
-    /* Bundler mode */
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "verbatimModuleSyntax": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-    "jsx": "react-jsx",
-
-    /* Linting */
-    "strict": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
-    "erasableSyntaxOnly": true,
-    "noFallthroughCasesInSwitch": true,
-    "noUncheckedSideEffectImports": true
-  },
-  "include": ["src"]
-}

frontend/commander-ai-lab-ui/tsconfig.json

@@ -1,7 +0,0 @@
-{
-  "files": [],
-  "references": [
-    { "path": "./tsconfig.app.json" },
-    { "path": "./tsconfig.node.json" }
-  ]
-}

frontend/commander-ai-lab-ui/tsconfig.node.json

@@ -1,26 +0,0 @@
-{
-  "compilerOptions": {
-    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
-    "target": "ES2023",
-    "lib": ["ES2023"],
-    "module": "ESNext",
-    "types": ["node"],
-    "skipLibCheck": true,
-
-    /* Bundler mode */
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "verbatimModuleSyntax": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-
-    /* Linting */
-    "strict": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
-    "erasableSyntaxOnly": true,
-    "noFallthroughCasesInSwitch": true,
-    "noUncheckedSideEffectImports": true
-  },
-  "include": ["vite.config.ts"]
-}

frontend/commander-ai-lab-ui/vite.config.ts

@@ -1,28 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-import tailwindcss from '@tailwindcss/vite'
-
-export default defineConfig({
-  plugins: [react(), tailwindcss()],
-  server: {
-    port: 5173,
-    proxy: {
-      '/api': {
-        target: 'http://localhost:8080',
-        changeOrigin: true,
-      },
-    },
-  },
-  build: {
-    outDir: 'dist',
-    sourcemap: false,
-    rollupOptions: {
-      output: {
-        manualChunks: {
-          recharts: ['recharts'],
-          react: ['react', 'react-dom', 'react-router-dom'],
-        },
-      },
-    },
-  },
-})

lan-client/index.html

@@ -0,0 +1,651 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>MTG Commander Battlefield</title>
+<link rel="stylesheet" href="style.css?v=20260403_0300">
+</head>
+<body>
+
+<!-- ====================================================
+     SETUP SCREEN
+     ==================================================== -->
+<div id="setup-screen">
+  <!-- Populated by ui.js initSetupScreen() -->
+</div>
+
+<!-- ====================================================
+     TABLE SURFACE — 4-Quadrant Battlefield
+     ==================================================== -->
+<div class="table-surface" id="game-battlefield" style="display:none;">
+
+  <!-- TOP BAR: Phase tracker + controls -->
+  <div id="top-bar">
+    <span class="top-bar-logo">Commander</span>
+
+    <div class="turn-counter-badge" id="turn-counter">Turn <span>1</span></div>
+
+    <div class="active-player-indicator" id="active-player-indicator">
+      <span class="active-player-dot" id="active-dot" style="background:#c0392b;"></span>
+      Player 1's Turn
+    </div>
+
+    <!-- Phase track -->
+    <div class="phase-track-wrapper">
+      <div class="phase-track" id="phase-track"></div>
+    </div>
+
+    <!-- Phase/turn action buttons -->
+    <div class="top-bar-actions">
+      <button class="btn-phase" onclick="nextPhase()" title="Advance to next phase (N)">Next Phase</button>
+      <button class="btn-phase btn-next-turn" onclick="nextTurn()" title="End turn, advance to next player (T)">Next Turn</button>
+    </div>
+
+    <!-- Utility buttons -->
+    <div class="top-bar-utils">
+      <button class="btn-util" id="btn-undo" onclick="undo()" title="Undo (Ctrl+Z)" disabled>&#x21A9;</button>
+      <button class="btn-util" id="btn-redo" onclick="redo()" title="Redo (Ctrl+Y)" disabled>&#x21AA;</button>
+      <button class="btn-util" id="btn-triggers" onclick="toggleTriggerStack()" title="Trigger Stack">&#x2747;</button>
+      <button class="btn-util" onclick="openDiceModal()" title="Dice &amp; Coins (G)">&#x1F3B2;</button>
+      <button class="btn-util" onclick="checkStateBasedActions()" title="Check State-Based Actions">SBA</button>
+      <button class="btn-util" onclick="toggleSettingsPanel()" title="Settings">&#x2699;</button>
+      <button class="btn-util" id="btn-search-toggle" onclick="document.getElementById('card-search-sidebar').classList.toggle('collapsed')" title="Card Search (S)">&#x1F50D;</button>
+    </div>
+
+    <!-- Storm counter -->
+    <span id="storm-badge" style="display:none;font-size:10px;color:var(--mtg-gold);background:rgba(200,168,67,0.15);border:1px solid rgba(200,168,67,0.3);border-radius:4px;padding:2px 7px;white-space:nowrap;">Storm: 0</span>
+  </div>
+
+  <!-- DIVIDER LINES -->
+  <div class="divider-h"></div>
+  <div class="divider-v"></div>
+
+  <!-- CORNER ACCENTS -->
+  <div class="corner-accent tl"></div>
+  <div class="corner-accent tr"></div>
+  <div class="corner-accent bl"></div>
+  <div class="corner-accent br"></div>
+
+  <!-- CENTER EMBLEM -->
+  <div class="center-emblem">
+    <svg viewBox="0 0 100 100" fill="none">
+      <circle cx="50" cy="50" r="42" stroke="rgba(255,255,255,0.2)" stroke-width="1.5"/>
+      <circle cx="50" cy="50" r="28" stroke="rgba(255,255,255,0.1)" stroke-width="1"/>
+      <polygon points="50,15 58.5,37 82,37 63,51 69,74 50,60 31,74 37,51 18,37 41.5,37"
+               fill="rgba(255,255,255,0.1)" stroke="rgba(255,255,255,0.2)" stroke-width="0.8"/>
+    </svg>
+  </div>
+
+  <!-- ================================================
+       4-QUADRANT BATTLEFIELD
+       ================================================ -->
+  <div class="battlefield">
+
+    <!-- PLAYER 1: Top-Left (Crimson) -->
+    <div class="player-area p1" id="player-area-1">
+      <span class="player-label" id="player-label-1">Player 1</span>
+      <div class="life-counter" id="life-counter-1">
+        <button onclick="adjustLife(0,-1)" aria-label="Decrease P1 life">&#x2212;</button>
+        <span class="life-counter-value" id="life-0" onclick="showSetLifeDialog(0)">40</span>
+        <button onclick="adjustLife(0,1)" aria-label="Increase P1 life">+</button>
+      </div>
+      <div class="poison-badge" id="poison-0" style="display:none;" onclick="adjustPoison(0,1)" title="Poison counters (click +1, right-click -1)" oncontextmenu="event.preventDefault();adjustPoison(0,-1)">0 poison</div>
+
+      <!-- Zone Row: Library, Exile, Graveyard, Hand -->
+      <div class="zone-row" id="zone-row-1">
+        <div class="zone library" id="lib-zone-1" data-player="1" data-zone="library"
+             onclick="openZoneModal(0,'library')" data-tooltip="Library">
+          <span class="card-count" id="lib-count-1">0</span>
+          <span class="zone-icon">&#x1F4DA;</span>
+          <span class="zone-name">Library</span>
+        </div>
+        <div class="zone exile" id="exile-zone-1" data-player="1" data-zone="exile"
+             onclick="openZoneModal(0,'exile')" data-tooltip="Exile">
+          <span class="card-count" id="exile-count-1" style="display:none;">0</span>
+          <span class="zone-icon">&#x2B21;</span>
+          <span class="zone-name">Exile</span>
+        </div>
+        <div class="zone graveyard" id="gv-zone-1" data-player="1" data-zone="graveyard"
+             onclick="openZoneModal(0,'graveyard')" data-tooltip="Graveyard">
+          <span class="card-count" id="gv-count-1" style="display:none;">0</span>
+          <span class="zone-icon">&#x2620;</span>
+          <span class="zone-name">Grave</span>
+        </div>
+        <div class="zone hand" id="hand-zone-1" data-player="1" data-zone="hand"
+             onclick="openZoneModal(0,'hand')" data-tooltip="Hand">
+          <span class="zone-icon">&#x1F0CF;</span>
+          <span class="zone-name">Hand (<span id="hand-count-1">0</span>)</span>
+        </div>
+      </div>
+
+      <!-- Commander Row -->
+      <div class="commander-row" id="cmd-row-1">
+        <div class="zone commander" id="cmd-zone-1" data-player="1" data-zone="command"
+             onclick="openZoneModal(0,'command')" data-tooltip="Command Zone">
+          <span class="zone-icon">&#x1F451;</span>
+          <span class="zone-name">Commander</span>
+        </div>
+      </div>
+
+      <!-- Battle Zone -->
+      <div class="battle-zone" id="battle-zone-1">
+        <div class="battle-zone-inner"></div>
+        <span class="battle-zone-label">Battlefield</span>
+        <!-- Battlefield cards injected here -->
+      </div>
+    </div>
+
+    <!-- PLAYER 2: Top-Right (Sapphire) -->
+    <div class="player-area p2" id="player-area-2">
+      <span class="player-label" id="player-label-2">Player 2</span>
+      <div class="life-counter" id="life-counter-2">
+        <button onclick="adjustLife(1,-1)" aria-label="Decrease P2 life">&#x2212;</button>
+        <span class="life-counter-value" id="life-1" onclick="showSetLifeDialog(1)">40</span>
+        <button onclick="adjustLife(1,1)" aria-label="Increase P2 life">+</button>
+      </div>
+      <div class="poison-badge" id="poison-1" style="display:none;" onclick="adjustPoison(1,1)" oncontextmenu="event.preventDefault();adjustPoison(1,-1)">0 poison</div>
+
+      <div class="zone-row" id="zone-row-2">
+        <div class="zone library" id="lib-zone-2" data-player="2" data-zone="library"
+             onclick="openZoneModal(1,'library')" data-tooltip="Library">
+          <span class="card-count" id="lib-count-2">0</span>
+          <span class="zone-icon">&#x1F4DA;</span>
+          <span class="zone-name">Library</span>
+        </div>
+        <div class="zone exile" id="exile-zone-2" data-player="2" data-zone="exile"
+             onclick="openZoneModal(1,'exile')" data-tooltip="Exile">
+          <span class="card-count" id="exile-count-2" style="display:none;">0</span>
+          <span class="zone-icon">&#x2B21;</span>
+          <span class="zone-name">Exile</span>
+        </div>
+        <div class="zone graveyard" id="gv-zone-2" data-player="2" data-zone="graveyard"
+             onclick="openZoneModal(1,'graveyard')" data-tooltip="Graveyard">
+          <span class="card-count" id="gv-count-2" style="display:none;">0</span>
+          <span class="zone-icon">&#x2620;</span>
+          <span class="zone-name">Grave</span>
+        </div>
+        <div class="zone hand" id="hand-zone-2" data-player="2" data-zone="hand"
+             onclick="openZoneModal(1,'hand')" data-tooltip="Hand">
+          <span class="zone-icon">&#x1F0CF;</span>
+          <span class="zone-name">Hand (<span id="hand-count-2">0</span>)</span>
+        </div>
+      </div>
+
+      <div class="commander-row" id="cmd-row-2">
+        <div class="zone commander" id="cmd-zone-2" data-player="2" data-zone="command"
+             onclick="openZoneModal(1,'command')" data-tooltip="Command Zone">
+          <span class="zone-icon">&#x1F451;</span>
+          <span class="zone-name">Commander</span>
+        </div>
+      </div>
+
+      <div class="battle-zone" id="battle-zone-2">
+        <div class="battle-zone-inner"></div>
+        <span class="battle-zone-label">Battlefield</span>
+      </div>
+    </div>
+
+    <!-- PLAYER 3: Bottom-Left (Emerald) -->
+    <div class="player-area p3" id="player-area-3">
+      <!-- Battle zone on top for bottom players -->
+      <div class="battle-zone" id="battle-zone-3">
+        <div class="battle-zone-inner"></div>
+        <span class="battle-zone-label">Battlefield</span>
+      </div>
+
+      <div class="commander-row" id="cmd-row-3">
+        <div class="zone commander" id="cmd-zone-3" data-player="3" data-zone="command"
+             onclick="openZoneModal(2,'command')" data-tooltip="Command Zone">
+          <span class="zone-icon">&#x1F451;</span>
+          <span class="zone-name">Commander</span>
+        </div>
+      </div>
+
+      <div class="zone-row" id="zone-row-3">
+        <div class="zone library" id="lib-zone-3" data-player="3" data-zone="library"
+             onclick="openZoneModal(2,'library')" data-tooltip="Library">
+          <span class="card-count" id="lib-count-3">0</span>
+          <span class="zone-icon">&#x1F4DA;</span>
+          <span class="zone-name">Library</span>
+        </div>
+        <div class="zone exile" id="exile-zone-3" data-player="3" data-zone="exile"
+             onclick="openZoneModal(2,'exile')" data-tooltip="Exile">
+          <span class="card-count" id="exile-count-3" style="display:none;">0</span>
+          <span class="zone-icon">&#x2B21;</span>
+          <span class="zone-name">Exile</span>
+        </div>
+        <div class="zone graveyard" id="gv-zone-3" data-player="3" data-zone="graveyard"
+             onclick="openZoneModal(2,'graveyard')" data-tooltip="Graveyard">
+          <span class="card-count" id="gv-count-3" style="display:none;">0</span>
+          <span class="zone-icon">&#x2620;</span>
+          <span class="zone-name">Grave</span>
+        </div>
+        <div class="zone hand" id="hand-zone-3" data-player="3" data-zone="hand"
+             onclick="openZoneModal(2,'hand')" data-tooltip="Hand">
+          <span class="zone-icon">&#x1F0CF;</span>
+          <span class="zone-name">Hand (<span id="hand-count-3">0</span>)</span>
+        </div>
+      </div>
+
+      <span class="player-label" id="player-label-3">Player 3</span>
+      <div class="life-counter" id="life-counter-3">
+        <button onclick="adjustLife(2,-1)" aria-label="Decrease P3 life">&#x2212;</button>
+        <span class="life-counter-value" id="life-2" onclick="showSetLifeDialog(2)">40</span>
+        <button onclick="adjustLife(2,1)" aria-label="Increase P3 life">+</button>
+      </div>
+      <div class="poison-badge" id="poison-2" style="display:none;" onclick="adjustPoison(2,1)" oncontextmenu="event.preventDefault();adjustPoison(2,-1)">0 poison</div>
+    </div>
+
+    <!-- PLAYER 4: Bottom-Right (Gold) -->
+    <div class="player-area p4" id="player-area-4">
+      <div class="battle-zone" id="battle-zone-4">
+        <div class="battle-zone-inner"></div>
+        <span class="battle-zone-label">Battlefield</span>
+      </div>
+
+      <div class="commander-row" id="cmd-row-4">
+        <div class="zone commander" id="cmd-zone-4" data-player="4" data-zone="command"
+             onclick="openZoneModal(3,'command')" data-tooltip="Command Zone">
+          <span class="zone-icon">&#x1F451;</span>
+          <span class="zone-name">Commander</span>
+        </div>
+      </div>
+
+      <div class="zone-row" id="zone-row-4">
+        <div class="zone library" id="lib-zone-4" data-player="4" data-zone="library"
+             onclick="openZoneModal(3,'library')" data-tooltip="Library">
+          <span class="card-count" id="lib-count-4">0</span>
+          <span class="zone-icon">&#x1F4DA;</span>
+          <span class="zone-name">Library</span>
+        </div>
+        <div class="zone exile" id="exile-zone-4" data-player="4" data-zone="exile"
+             onclick="openZoneModal(3,'exile')" data-tooltip="Exile">
+          <span class="card-count" id="exile-count-4" style="display:none;">0</span>
+          <span class="zone-icon">&#x2B21;</span>
+          <span class="zone-name">Exile</span>
+        </div>
+        <div class="zone graveyard" id="gv-zone-4" data-player="4" data-zone="graveyard"
+             onclick="openZoneModal(3,'graveyard')" data-tooltip="Graveyard">
+          <span class="card-count" id="gv-count-4" style="display:none;">0</span>
+          <span class="zone-icon">&#x2620;</span>
+          <span class="zone-name">Grave</span>
+        </div>
+        <div class="zone hand" id="hand-zone-4" data-player="4" data-zone="hand"
+             onclick="openZoneModal(3,'hand')" data-tooltip="Hand">
+          <span class="zone-icon">&#x1F0CF;</span>
+          <span class="zone-name">Hand (<span id="hand-count-4">0</span>)</span>
+        </div>
+      </div>
+
+      <span class="player-label" id="player-label-4">Player 4</span>
+      <div class="life-counter" id="life-counter-4">
+        <button onclick="adjustLife(3,-1)" aria-label="Decrease P4 life">&#x2212;</button>
+        <span class="life-counter-value" id="life-3" onclick="showSetLifeDialog(3)">40</span>
+        <button onclick="adjustLife(3,1)" aria-label="Increase P4 life">+</button>
+      </div>
+      <div class="poison-badge" id="poison-3" style="display:none;" onclick="adjustPoison(3,1)" oncontextmenu="event.preventDefault();adjustPoison(3,-1)">0 poison</div>
+    </div>
+
+  </div><!-- /.battlefield -->
+
+  <!-- COMBAT SVG OVERLAY (arrows) -->
+  <svg id="combat-overlay" xmlns="http://www.w3.org/2000/svg" style="position:absolute;inset:44px 0 0 0;pointer-events:none;z-index:30;">
+    <defs>
+      <marker id="arrowhead" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
+        <polygon points="0 0, 10 3.5, 0 7" fill="rgba(211,32,42,0.7)"/>
+      </marker>
+    </defs>
+    <g id="combat-arrows-svg"></g>
+  </svg>
+
+  <!-- FLOATING LOG -->
+  <div id="floating-log" class="floating-log"></div>
+
+  <!-- SHORTCUT HINT -->
+  <div class="shortcut-hint"></div>
+
+  <!-- CARD SEARCH SIDEBAR (right side, collapsible) -->
+  <div id="card-search-sidebar" class="collapsed">
+    <div class="sidebar-header">
+      <span class="sidebar-title">Card Search</span>
+      <button class="sidebar-toggle" onclick="document.getElementById('card-search-sidebar').classList.toggle('collapsed')" title="Close search">&#x2715;</button>
+    </div>
+    <div class="search-input-wrap">
+      <input class="search-input" id="card-search-input" type="text" placeholder="Search Scryfall..." autocomplete="off"/>
+    </div>
+    <div class="search-status" id="search-status">Type to search cards...</div>
+    <div class="card-results" id="card-results"></div>
+  </div>
+
+  <!-- Sidebar edge tab -->
+  <div id="sidebar-edge-tab" class="sidebar-edge-tab" title="Toggle card search">&#x2039;</div>
+
+</div><!-- /.table-surface / #game-battlefield -->
+
+<!-- ====================================================
+     CONTEXT MENU
+     ==================================================== -->
+<div id="context-menu"></div>
+
+<!-- ====================================================
+     FLOATING CARD PREVIEW
+     ==================================================== -->
+<div id="card-preview">
+  <img src="" alt="Card Preview"/>
+  <div class="card-preview-pt-overlay" id="card-preview-pt-overlay"></div>
+  <div class="card-preview-info">
+    <div class="card-preview-name"></div>
+    <div class="card-preview-type"></div>
+    <div class="card-preview-oracle"></div>
+    <div class="card-preview-pt"></div>
+  </div>
+</div>
+
+<!-- ====================================================
+     ZONE MODAL
+     ==================================================== -->
+<div id="zone-modal">
+  <div class="zone-modal-card">
+    <div class="zone-modal-header">
+      <span class="zone-modal-title" id="zone-modal-title">Player Zones</span>
+      <button class="btn-close" onclick="closeZoneModal()">&#x2715;</button>
+    </div>
+    <div class="zone-modal-tabs" id="zone-modal-tabs">
+      <!-- Populated by renderZoneModal() -->
+    </div>
+    <div class="zone-modal-body" id="zone-modal-body">
+      <!-- Populated by renderZoneModal() -->
+    </div>
+  </div>
+</div>
+
+<!-- ====================================================
+     DICE MODAL
+     ==================================================== -->
+<div id="dice-modal">
+  <div class="dice-modal-card">
+    <div style="display:flex;justify-content:space-between;align-items:center;margin-bottom:1rem;">
+      <span style="font-family:var(--font-display);font-size:14px;font-weight:700;color:var(--color-text-muted);text-transform:uppercase;letter-spacing:0.08em;">Dice &amp; Coins</span>
+      <button class="btn-close" onclick="closeDiceModal()">&#x2715;</button>
+    </div>
+    <div class="dice-result-display" id="dice-result-display">&#x2014;</div>
+    <div class="dice-label" id="dice-label">Choose a die</div>
+    <div class="dice-buttons">
+      <button class="btn-dice" onclick="rollDice(4)">d4</button>
+      <button class="btn-dice" onclick="rollDice(6)">d6</button>
+      <button class="btn-dice" onclick="rollDice(8)">d8</button>
+      <button class="btn-dice" onclick="rollDice(10)">d10</button>
+      <button class="btn-dice" onclick="rollDice(12)">d12</button>
+      <button class="btn-dice" onclick="rollDice(20)">d20</button>
+      <button class="btn-dice" onclick="rollDice(100)">d100</button>
+      <button class="btn-dice coin" onclick="flipCoin()">Coin Flip</button>
+    </div>
+  </div>
+</div>
+
+<!-- ====================================================
+     TOKEN MODAL
+     ==================================================== -->
+<div id="token-modal">
+  <div class="token-modal-card">
+    <div class="modal-header">
+      <span class="modal-title">Create Token</span>
+      <button class="btn-close" onclick="closeTokenModal()">&#x2715;</button>
+    </div>
+    <div class="modal-body" id="token-modal-body">
+      <div style="display:grid;grid-template-columns:1fr 1fr;gap:10px;margin-bottom:12px;">
+        <div>
+          <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Token Name</label>
+          <input class="game-input" id="token-name" type="text" placeholder="e.g. 1/1 White Soldier"/>
+        </div>
+        <div>
+          <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Type Line</label>
+          <input class="game-input" id="token-type" type="text" placeholder="Creature — Soldier"/>
+        </div>
+        <div>
+          <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Power/Toughness</label>
+          <input class="game-input" id="token-pt" type="text" placeholder="1/1"/>
+        </div>
+        <div>
+          <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Count</label>
+          <input class="game-input" id="token-count" type="number" value="1" min="1" max="20"/>
+        </div>
+      </div>
+      <div style="margin-bottom:10px;">
+        <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Keywords (comma-separated)</label>
+        <input class="game-input" id="token-keywords" type="text" placeholder="Flying, Vigilance"/>
+      </div>
+      <div style="margin-bottom:12px;">
+        <label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Player</label>
+        <select class="game-input" id="token-player-select" style="height:28px;">
+          <option value="0">Player 1</option>
+          <option value="1">Player 2</option>
+          <option value="2">Player 3</option>
+          <option value="3">Player 4</option>
+        </select>
+      </div>
+      <div style="display:flex;gap:8px;">
+        <button class="btn btn-primary" onclick="createTokenFromModal()">Create Token(s)</button>
+        <button class="btn btn-cancel" onclick="closeTokenModal()">Cancel</button>
+      </div>
+    </div>
+  </div>
+</div>
+
+<!-- ====================================================
+     DECK MODAL
+     ==================================================== -->
+<div id="deck-modal">
+  <div class="deck-modal-card">
+    <div class="modal-header">
+      <span class="modal-title">Deck Manager</span>
+      <button class="btn-close" onclick="closeDeckModal()">&#x2715;</button>
+    </div>
+    <div class="modal-body" id="deck-modal-body">
+      <!-- Populated by renderDeckModalContent() -->
+    </div>
+  </div>
+</div>
+
+<!-- ====================================================
+     COMMANDER DAMAGE MATRIX MODAL
+     ==================================================== -->
+<div id="cmd-dmg-matrix-modal">
+  <!-- Populated by renderCmdDmgMatrix() -->
+</div>
+
+<!-- ====================================================
+     TRIGGER STACK PANEL
+     ==================================================== -->
+<div id="trigger-stack-panel">
+  <div class="trigger-stack-card">
+    <div class="trigger-stack-header">
+      <span class="trigger-stack-title">Trigger Stack</span>
+      <button class="btn-close" style="font-size:14px;width:20px;height:20px;" onclick="closeTriggerStack()">&#x2715;</button>
+    </div>
+    <div class="trigger-stack-body" id="trigger-stack-body">
+      <div class="trigger-stack-empty">No pending triggers.</div>
+    </div>
+    <div class="trigger-stack-actions">
+      <button class="btn-resolve" onclick="resolveNextTrigger()">Resolve Next</button>
+      <button class="btn-resolve-all" onclick="resolveAllTriggers()">Resolve All</button>
+    </div>
+  </div>
+</div>
+
+<!-- ====================================================
+     SETTINGS PANEL
+     ==================================================== -->
+<div id="settings-panel">
+  <div class="modal-header" style="flex-shrink:0;">
+    <span class="modal-title">Settings</span>
+    <button class="btn-close" onclick="toggleSettingsPanel()">&#x2715;</button>
+  </div>
+  <div class="settings-body" id="settings-body" style="flex:1;overflow-y:auto;padding:12px;">
+    <!-- Populated by renderSettingsPanel() -->
+  </div>
+  <div style="padding:10px 14px;border-top:1px solid var(--color-border);display:flex;gap:8px;flex-wrap:wrap;flex-shrink:0;">
+    <button class="btn btn-primary" onclick="openDiceModal()">&#x1F3B2; Dice</button>
+    <button class="btn btn-gold" onclick="openCmdDmgMatrix()">CMD Damage Matrix</button>
+    <button class="btn btn-cancel" onclick="checkStateBasedActions()">Check SBA</button>
+    <button class="btn btn-cancel" onclick="autoArrangeBattlefield()">Auto-Arrange</button>
+  </div>
+</div>
+
+<!-- ====================================================
+     PRIORITY BANNER
+     ==================================================== -->
+<div id="priority-banner">
+  <span class="priority-text" id="priority-text">Waiting for priority...</span>
+  <button class="btn-priority-pass" onclick="passPriority()">Pass Priority</button>
+  <button class="btn-close" style="font-size:13px;" onclick="closePriorityWindow()">&#x2715;</button>
+</div>
+
+<!-- ====================================================
+     LEGAL ACTIONS BAR
+     ==================================================== -->
+<div id="legal-actions-bar">
+  <!-- Populated by renderLegalActionsBar() -->
+</div>
+
+<!-- ====================================================
+     UNDO HISTORY PANEL
+     ==================================================== -->
+<div id="undo-history-panel">
+  <!-- Populated by renderUndoHistory() -->
+</div>
+
+<!-- ====================================================
+     MANA COLOR PICKER (dynamically appended) -->
+<!-- Zone card context menu (dynamically appended) -->
+<!-- SBA alert panel (dynamically appended) -->
+<!-- Set-life dialog (dynamically appended) -->
+
+<!-- ====================================================
+     SCRIPTS
+     Load order matters:
+       1. precons.js      — static PRECON_DATA fallback
+       2. engine.js       — game state & core logic
+       3. ai-bridge.js    — backend connector & DeckSourceClient
+       4. precon-loader.js — startGameWithDecks() + loadPreconDeckFromDck()
+       5. player-panels.js — player panel rendering
+       6. ui.js           — setup screen, deck picker, UI wiring
+     ==================================================== -->
+<script src="precons.js?v=20260403_0300"></script>
+<script src="engine.js?v=20260403_0300"></script>
+<script src="ai-bridge.js?v=20260403_0300"></script>
+<script src="precon-loader.js?v=20260403_0300"></script>
+<script src="player-panels.js?v=20260403_0300"></script>
+<script src="ui.js?v=20260403_0300"></script>
+
+<script>
+// ============================================================
+// STARTUP & BRIDGE FUNCTIONS
+// ============================================================
+
+// Token creation from modal
+function createTokenFromModal() {
+  if (!gameState) return;
+  const name = document.getElementById('token-name').value.trim() || '1/1 Token';
+  const typeLine = document.getElementById('token-type').value.trim() || 'Creature';
+  const pt = document.getElementById('token-pt').value.trim() || '1/1';
+  const count = parseInt(document.getElementById('token-count').value) || 1;
+  const keywords = document.getElementById('token-keywords').value.trim();
+  const playerIdx = parseInt(document.getElementById('token-player-select').value) || 0;
+
+  const parts = pt.split('/');
+  const power = parts[0] ? parts[0].trim() : '1';
+  const toughness = parts[1] ? parts[1].trim() : '1';
+
+  for (let i = 0; i < count; i++) {
+    addCardToBattlefield(name, '', typeLine, playerIdx, undefined, undefined,
+      keywords ? keywords.replace(/,/g, ' ') : '',
+      { power, toughness, pt, isToken: true });
+  }
+  addLogEntry('Created ' + count + 'x <strong>' + name + '</strong> token(s) for Player ' + (playerIdx + 1));
+  closeTokenModal();
+}
+
+// Pass priority
+function passPriority() {
+  addLogEntry('Priority passed.');
+  closePriorityWindow();
+}
+
+// Open precon browser (stub - opens deck modal for current player)
+function openPreconBrowser() {
+  if (gameState) openDeckModal(gameState.currentPlayerIndex);
+}
+
+// Open collection modal (stub)
+function openCollectionModal() { showToast('Collection tracker available in full version.'); }
+
+// Scry dialog stub
+function showScryDialog(playerId) {
+  if (!gameState) return;
+  const player = gameState.players[playerId];
+  if (!player || player.zones.library.length === 0) {
+    addLogEntry('!! ' + (player ? player.name : 'Player') + ' has no cards to scry.');
+    return;
+  }
+  const count = Math.min(parseInt(prompt('Scry how many? (1-7)', '1') || '1'), 7, player.zones.library.length);
+  if (isNaN(count) || count < 1) return;
+  const topCards = player.zones.library.slice(0, count);
+  const names = topCards.map(c => c.name || '?').join(', ');
+  addLogEntry('<strong>' + player.name + '</strong> scrys ' + count + ': [' + names + ']');
+}
+
+// Mill dialog stub
+function showMillDialog(playerId) {
+  if (!gameState) return;
+  const count = parseInt(prompt('Mill how many cards?', '1') || '1');
+  if (isNaN(count) || count < 1) return;
+  millCards(playerId, count);
+}
+
+// Become monarch
+function becomeMonarch(playerId) {
+  if (!gameState) return;
+  gameState.monarchPlayerIndex = playerId;
+  const player = gameState.players[playerId];
+  addLogEntry('&#x1F451; <strong>' + (player ? player.name : 'Player') + '</strong> becomes the Monarch!');
+  updateMonarchBadges();
+}
+
+// Empower the life counter right-click on the quadrant labels
+document.addEventListener('DOMContentLoaded', function() {
+  // Wire right-click on life values to set-life dialog
+  for (var i = 0; i < 4; i++) {
+    (function(idx) {
+      var lifeEl = document.getElementById('life-' + idx);
+      if (lifeEl) {
+        lifeEl.addEventListener('contextmenu', function(e) {
+          e.preventDefault();
+          showSetLifeDialog(idx);
+        });
+      }
+    })(i);
+  }
+
+  // Life counter right-click on quadrant buttons too
+  // (already handled via inline onclick in HTML)
+
+  // Auto-detect Commander-AI-Lab backend on page load
+  if (typeof AIBridge !== 'undefined') {
+    AIBridge.init().then(function(status) {
+      var indicator = document.getElementById('backend-status-indicator');
+      if (indicator) {
+        indicator.textContent = status.connected
+          ? (status.modelLoaded ? '\u{1F9E0} ML Backend Connected' : '\u26a1 Backend Connected (no ML model)')
+          : '\u{1F4E1} Local Mode (no backend)';
+        indicator.className = 'backend-status ' +
+          (status.connected ? (status.modelLoaded ? 'connected-ml' : 'connected') : 'disconnected');
+      }
+    });
+  }
+});
+</script>
+
+</body>
+</html>

lan-client/player-panels.js

@@ -0,0 +1,270 @@
+// player-panels.js — Enhanced Player Panel Component
+// Provides collapsible panels with life, commander damage, counters, and emblems.
+
+'use strict';
+
+/**
+ * Create an enhanced player panel widget for a quadrant.
+ * Replaces the basic life counter with a full tracking panel.
+ *
+ * @param {object} player - Player object from gameState
+ * @param {number} quadrant - 1-4 (matches CSS class p1-p4)
+ * @param {boolean} isActive - Whether this is the active player
+ */
+function createEnhancedPlayerPanel(player, quadrant, isActive) {
+  var panel = document.createElement('div');
+  panel.className = 'enhanced-player-panel' +
+    (isActive ? ' active-turn' : '') +
+    (player.eliminated ? ' eliminated' : '');
+  panel.id = 'enhanced-panel-' + player.id;
+  panel.setAttribute('data-player-id', player.id);
+
+  // Track collapse state
+  var collapseKey = 'panel-collapsed-' + player.id;
+  var isCollapsed = localStorage.getItem(collapseKey) === 'true';
+
+  // ── Header Row ──────────────────────────────────────────
+  var headerHtml =
+    '<div class="epp-header" onclick="togglePlayerPanel(' + player.id + ')">' +
+      '<span class="epp-collapse-arrow">' + (isCollapsed ? '\u25b8' : '\u25be') + '</span>' +
+      '<div class="epp-name-dot" style="background:' + player.color + '"></div>' +
+      '<span class="epp-name">' + player.name + '</span>' +
+      (player.isAI ? '<span class="epp-ai-badge">AI</span>' : '') +
+      '<span class="epp-life-badge">' +
+        '\u2665 ' + player.life +
+        (player.life < player.startingLife
+          ? ' / ' + player.startingLife
+          : '') +
+      '</span>' +
+    '</div>';
+
+  // ── Life Counter Row ────────────────────────────────────
+  var lifeHtml =
+    '<div class="epp-life-row">' +
+      '<button class="epp-life-btn minus" onclick="adjustLife(' + player.id + ',-1)" ' +
+        'oncontextmenu="event.preventDefault();adjustLife(' + player.id + ',-5)" ' +
+        'title="Left: -1 | Right: -5">\u2212</button>' +
+      '<div class="epp-life-value" id="epp-life-' + player.id + '" ' +
+        'onclick="showSetLifeDialog(' + player.id + ')">' +
+        player.life +
+      '</div>' +
+      '<button class="epp-life-btn plus" onclick="adjustLife(' + player.id + ',1)" ' +
+        'oncontextmenu="event.preventDefault();adjustLife(' + player.id + ',5)" ' +
+        'title="Left: +1 | Right: +5">+</button>' +
+    '</div>';
+
+  // ── Commander Damage Section ────────────────────────────
+  var otherPlayers = gameState.players.filter(function(p) {
+    return p.id !== player.id && !p.eliminated;
+  });
+
+  var cmdDmgHtml =
+    '<div class="epp-section">' +
+      '<div class="epp-section-label">Commander Damage</div>' +
+      '<div class="epp-cmd-dmg-grid">';
+
+  otherPlayers.forEach(function(op) {
+    var dmg = player.commanderDamage[op.id] || 0;
+    var pct = Math.round((dmg / 21) * 100);
+    var dangerClass = dmg >= 18 ? 'critical' : dmg >= 10 ? 'warning' : '';
+
+    cmdDmgHtml +=
+      '<div class="epp-cmd-dmg-item ' + dangerClass + '">' +
+        '<div class="epp-cmd-dmg-bar" style="width:' + pct + '%"></div>' +
+        '<div class="epp-cmd-dmg-dot" style="background:' + op.color + '"></div>' +
+        '<span class="epp-cmd-dmg-name">' + op.name + '</span>' +
+        '<span class="epp-cmd-dmg-value">' + dmg + '/21</span>' +
+        '<button class="epp-counter-btn" ' +
+          'onclick="event.stopPropagation();adjustCommanderDamage(' + player.id + ',' + op.id + ',1)" ' +
+          'title="Add commander damage">+</button>' +
+        '<button class="epp-counter-btn" ' +
+          'onclick="event.stopPropagation();adjustCommanderDamage(' + player.id + ',' + op.id + ',-1)" ' +
+          'title="Remove commander damage">\u2212</button>' +
+      '</div>';
+  });
+
+  cmdDmgHtml += '</div></div>';
+
+  // ── Counters Section ────────────────────────────────────
+  var poison = player.poison || 0;
+  var energy = player.energy || 0;
+  var experience = player.experience || 0;
+  var rad = player.rad || 0;
+
+  var countersHtml =
+    '<div class="epp-section">' +
+      '<div class="epp-section-label">Counters</div>' +
+      '<div class="epp-counters-grid">' +
+        _buildCounterWidget('Poison', '\u2620', poison, 10,
+          'adjustPoison(' + player.id + ',1)',
+          'adjustPoison(' + player.id + ',-1)',
+          poison >= 8 ? 'critical' : poison >= 5 ? 'warning' : '') +
+        _buildCounterWidget('Energy', '\u269b', energy, 0,
+          'adjustEnergy(' + player.id + ',1)',
+          'adjustEnergy(' + player.id + ',-1)', '') +
+        _buildCounterWidget('Experience', '\u2605', experience, 0,
+          'adjustExperience(' + player.id + ',1)',
+          'adjustExperience(' + player.id + ',-1)', '') +
+        _buildCounterWidget('Rad', '\u2622', rad, 0,
+          'adjustRad(' + player.id + ',1)',
+          'adjustRad(' + player.id + ',-1)', '') +
+      '</div>' +
+    '</div>';
+
+  // ── Emblems Section ─────────────────────────────────────
+  var emblems = player.emblems || [];
+  var emblemsHtml =
+    '<div class="epp-section">' +
+      '<div class="epp-section-label">Emblems</div>' +
+      '<div class="epp-emblems">';
+
+  if (emblems.length === 0) {
+    emblemsHtml += '<span class="epp-no-emblems">None</span>';
+  } else {
+    emblems.forEach(function(emb, idx) {
+      emblemsHtml +=
+        '<div class="epp-emblem-chip" title="' + emb.name + '">' +
+          '<span class="epp-emblem-name">' + emb.name + '</span>' +
+          '<button class="epp-emblem-remove" ' +
+            'onclick="event.stopPropagation();removeEmblem(' + player.id + ',' + idx + ')">\u00d7</button>' +
+        '</div>';
+    });
+  }
+
+  emblemsHtml +=
+      '<button class="epp-add-emblem-btn" ' +
+        'onclick="event.stopPropagation();showAddEmblemDialog(' + player.id + ')" ' +
+        'title="Add an emblem">+ Emblem</button>' +
+      '</div>' +
+    '</div>';
+
+  // ── Assemble Panel ──────────────────────────────────────
+  var bodyHtml =
+    '<div class="epp-body" id="epp-body-' + player.id + '" ' +
+      'style="' + (isCollapsed ? 'display:none;' : '') + '">' +
+      lifeHtml + cmdDmgHtml + countersHtml + emblemsHtml +
+    '</div>';
+
+  panel.innerHTML = headerHtml + bodyHtml;
+  return panel;
+}
+
+function _buildCounterWidget(label, icon, value, threshold, onInc, onDec, dangerClass) {
+  return '<div class="epp-counter ' + dangerClass + '">' +
+    '<div class="epp-counter-icon">' + icon + '</div>' +
+    '<div class="epp-counter-info">' +
+      '<span class="epp-counter-value">' + value + '</span>' +
+      '<span class="epp-counter-label">' + label + '</span>' +
+    '</div>' +
+    '<div class="epp-counter-btns">' +
+      '<button class="epp-counter-btn" onclick="event.stopPropagation();' + onInc + '">+</button>' +
+      '<button class="epp-counter-btn" onclick="event.stopPropagation();' + onDec + '">\u2212</button>' +
+    '</div>' +
+  '</div>';
+}
+
+function togglePlayerPanel(playerId) {
+  var body = document.getElementById('epp-body-' + playerId);
+  var panel = document.getElementById('enhanced-panel-' + playerId);
+  if (!body || !panel) return;
+
+  var isCollapsed = body.style.display === 'none';
+  body.style.display = isCollapsed ? '' : 'none';
+
+  var arrow = panel.querySelector('.epp-collapse-arrow');
+  if (arrow) arrow.textContent = isCollapsed ? '\u25be' : '\u25b8';
+
+  localStorage.setItem('panel-collapsed-' + playerId, !isCollapsed);
+}
+
+// Counter adjustment functions
+function adjustEnergy(playerId, amount) {
+  if (!gameState) return;
+  var player = gameState.players[playerId];
+  if (!player) return;
+  player.energy = Math.max(0, (player.energy || 0) + amount);
+  addLogEntry('<strong>' + player.name + '</strong> energy: ' + player.energy);
+  renderEnhancedPanels();
+}
+
+function adjustExperience(playerId, amount) {
+  if (!gameState) return;
+  var player = gameState.players[playerId];
+  if (!player) return;
+  player.experience = Math.max(0, (player.experience || 0) + amount);
+  addLogEntry('<strong>' + player.name + '</strong> experience: ' + player.experience);
+  renderEnhancedPanels();
+}
+
+function adjustRad(playerId, amount) {
+  if (!gameState) return;
+  var player = gameState.players[playerId];
+  if (!player) return;
+  player.rad = Math.max(0, (player.rad || 0) + amount);
+  addLogEntry('<strong>' + player.name + '</strong> rad counters: ' + player.rad);
+  renderEnhancedPanels();
+}
+
+function showAddEmblemDialog(playerId) {
+  var name = prompt('Enter emblem name (e.g., "Sorin, Vengeful Bloodlord"):');
+  if (!name || !name.trim()) return;
+  if (!gameState) return;
+  var player = gameState.players[playerId];
+  if (!player) return;
+  if (!player.emblems) player.emblems = [];
+  player.emblems.push({ name: name.trim() });
+  addLogEntry('<strong>' + player.name + '</strong> gained emblem: ' + name.trim());
+  renderEnhancedPanels();
+}
+
+function removeEmblem(playerId, idx) {
+  if (!gameState) return;
+  var player = gameState.players[playerId];
+  if (!player || !player.emblems) return;
+  var removed = player.emblems.splice(idx, 1);
+  if (removed.length > 0) {
+    addLogEntry('<strong>' + player.name + '</strong> lost emblem: ' + removed[0].name);
+  }
+  renderEnhancedPanels();
+}
+
+/**
+ * Re-render all enhanced player panels in their quadrant positions.
+ */
+function renderEnhancedPanels() {
+  if (!gameState) return;
+
+  for (var i = 0; i < gameState.players.length; i++) {
+    var player = gameState.players[i];
+    var quadrant = i + 1;
+    var areaEl = document.getElementById('player-area-' + quadrant);
+    if (!areaEl) continue;
+
+    // Find or create the enhanced panel container
+    var container = areaEl.querySelector('.epp-container');
+    if (!container) {
+      container = document.createElement('div');
+      container.className = 'epp-container';
+      // Insert before the zone row
+      var zoneRow = areaEl.querySelector('.zone-row');
+      if (zoneRow) {
+        areaEl.insertBefore(container, zoneRow);
+      } else {
+        areaEl.appendChild(container);
+      }
+    }
+
+    var isActive = gameState.currentPlayerIndex === i;
+    var panel = createEnhancedPlayerPanel(player, quadrant, isActive);
+    container.innerHTML = '';
+    container.appendChild(panel);
+
+    // Hide the old life counter (we have our own now)
+    var oldLifeCounter = areaEl.querySelector('.life-counter');
+    if (oldLifeCounter) oldLifeCounter.style.display = 'none';
+
+    // Hide old poison badge (we have counters section)
+    var oldPoison = areaEl.querySelector('.poison-badge');
+    if (oldPoison) oldPoison.style.display = 'none';
+  }
+}

lan-client/precon-loader.js

@@ -0,0 +1,299 @@
+/**
+ * precon-loader.js
+ * Handles loading precon .dck files from the backend into the lan-client simulator.
+ * Depends on: engine.js (gameState, initGame, addLogEntry, renderPlayerPanels)
+ *             ai-bridge.js (AIBridge.deckSource.fetchPreconDeck)
+ *             ui.js (setupDeckChoices, setupPlayerCount, setupStartingLife)
+ */
+
+'use strict';
+
+// In-memory Scryfall image cache: cardName -> { imageUrl, typeLine, oracleText, manaCost, power, toughness }
+const _scryfallCache = {};
+
+// Scryfall collection endpoint — accepts up to 75 card identifiers per call
+const SCRYFALL_COLLECTION_URL = 'https://api.scryfall.com/cards/collection';
+
+// ============================================================
+// startGameWithDecks
+// Called by ui.js startGame() when players have selected precon decks.
+// ============================================================
+async function startGameWithDecks() {
+  const playerNames = [];
+  for (let i = 0; i < (setupPlayerCount || 4); i++) {
+    const inp = document.getElementById('player-name-' + i);
+    playerNames.push(inp && inp.value.trim() ? inp.value.trim() : 'Player ' + (i + 1));
+  }
+
+  initGame(setupPlayerCount || 4, playerNames, setupStartingLife || 40);
+
+  const choices = typeof setupDeckChoices !== 'undefined' ? setupDeckChoices : {};
+  const loadPromises = Object.entries(choices).map(async ([idxStr, choice]) => {
+    const playerIdx = parseInt(idxStr, 10);
+    if (choice.type !== 'precon' || !choice.preconId) return;
+    try {
+      const fileName = choice.preconId.endsWith('.dck') ? choice.preconId : choice.preconId + '.dck';
+      const data = await AIBridge.deckSource.fetchPreconDeck(fileName);
+      if (!data || !data.decklist) {
+        addLogEntry('\u26A0 Could not load deck for Player ' + (playerIdx + 1) + ' (no decklist returned)');
+        return;
+      }
+      loadPreconDeckFromDck(playerIdx, data.decklist, choice.preconId);
+    } catch (e) {
+      console.warn('[precon-loader] Failed to load deck for player', playerIdx, e);
+      addLogEntry('\u26A0 Deck load failed for Player ' + (playerIdx + 1) + ': ' + e.message);
+    }
+  });
+
+  await Promise.all(loadPromises);
+
+  if (typeof renderPlayerPanels === 'function') renderPlayerPanels();
+  if (typeof updateActivePlayerHighlight === 'function') updateActivePlayerHighlight();
+
+  // Kick off image enrichment for all players in the background (non-blocking)
+  enrichAllZonesWithScryfall();
+}
+
+
+// ============================================================
+// loadPreconDeckFromDck
+// Parses a Forge-format .dck file string and populates a player's zones.
+// ============================================================
+function loadPreconDeckFromDck(playerIdx, dckContent, deckLabel) {
+  if (!gameState) return;
+  const player = gameState.players[playerIdx];
+  if (!player) return;
+
+  let commander = '';
+  let section = '';
+  const mainCards = [];
+
+  for (const rawLine of dckContent.split('\n')) {
+    const line = rawLine.trim();
+    if (!line || line.startsWith('//')) continue;
+
+    if (line.startsWith('[') && line.endsWith(']')) {
+      section = line.slice(1, -1);
+      continue;
+    }
+    if (line.includes('=') && !line.match(/^\d/)) continue;
+
+    const m = line.match(/^(\d+)\s+(.+?)(?:\|.*)?$/);
+    if (!m) continue;
+
+    const qty = parseInt(m[1], 10);
+    const name = m[2].trim();
+
+    if (section === 'Commander') {
+      commander = name;
+    } else if (section === 'Main') {
+      for (let q = 0; q < qty; q++) mainCards.push(name);
+    }
+  }
+
+  player.zones.command   = [];
+  player.zones.library   = [];
+  player.zones.hand      = [];
+  player.zones.graveyard = [];
+  player.zones.exile     = [];
+
+  if (commander) {
+    player.zones.command.push({
+      id: 'cmd-' + playerIdx + '-' + Date.now(),
+      name: commander,
+      imageUrl: '',
+      typeLine: 'Legendary Creature',
+      oracleText: '',
+      manaCost: '',
+    });
+    player.commanderCardName = commander;
+  }
+
+  for (const cardName of mainCards) {
+    player.zones.library.push({
+      id: 'lib-' + playerIdx + '-' + cardName + '-' + Math.random().toString(36).slice(2, 6),
+      name: cardName,
+      imageUrl: '',
+      typeLine: '',
+      oracleText: '',
+      manaCost: '',
+    });
+  }
+
+  // Fisher-Yates shuffle
+  const lib = player.zones.library;
+  for (let i = lib.length - 1; i > 0; i--) {
+    const j = Math.floor(Math.random() * (i + 1));
+    [lib[i], lib[j]] = [lib[j], lib[i]];
+  }
+
+  // Draw opening hand
+  for (let d = 0; d < 7 && lib.length > 0; d++) {
+    player.zones.hand.push(lib.shift());
+  }
+
+  if (typeof updateZoneCountsForPlayer === 'function') updateZoneCountsForPlayer(playerIdx);
+  if (typeof renderCommanderZone === 'function') renderCommanderZone(playerIdx);
+  if (typeof renderHandZone === 'function') renderHandZone(playerIdx);
+
+  const label = deckLabel ? deckLabel.replace('.dck', '') : 'Unknown Deck';
+  addLogEntry(
+    '\uD83C\uDCCF <strong>' + player.name + '</strong> loaded: <em>' + label + '</em>' +
+    (commander ? ' \u2014 Commander: <strong>' + commander + '</strong>' : '') +
+    ' (' + lib.length + ' in library, 7 in hand)'
+  );
+}
+
+
+// ============================================================
+// SCRYFALL IMAGE ENRICHMENT
+// After zones are populated with card names, batch-fetch card
+// data from Scryfall and backfill imageUrl on every card object.
+// Uses the /cards/collection endpoint (75 cards per request).
+// Results are cached in _scryfallCache so re-renders are instant.
+// ============================================================
+
+async function enrichAllZonesWithScryfall() {
+  if (!gameState) return;
+
+  // Gather all unique card names across all players and all zones
+  const nameSet = new Set();
+  for (const player of gameState.players) {
+    for (const zone of Object.values(player.zones)) {
+      for (const card of zone) {
+        if (card.name && !_scryfallCache[card.name]) nameSet.add(card.name);
+      }
+    }
+  }
+
+  if (nameSet.size === 0) return;
+
+  const names = Array.from(nameSet);
+  addLogEntry('\uD83C\uDF0D Fetching card images from Scryfall (' + names.length + ' unique cards)...');
+
+  // Split into batches of 75 (Scryfall max)
+  const BATCH = 75;
+  for (let i = 0; i < names.length; i += BATCH) {
+    const batch = names.slice(i, i + BATCH);
+    try {
+      await _fetchScryfallBatch(batch);
+    } catch (e) {
+      console.warn('[precon-loader] Scryfall batch failed', e);
+    }
+    // Polite delay between batches to respect Scryfall rate limit (50-100ms)
+    if (i + BATCH < names.length) await _sleep(100);
+  }
+
+  // Apply cached data to every card object in every zone
+  _applyScryfallDataToAllCards();
+
+  addLogEntry('\u2705 Card images loaded.');
+}
+
+async function _fetchScryfallBatch(names) {
+  const identifiers = names.map(name => ({ name }));
+  const resp = await fetch(SCRYFALL_COLLECTION_URL, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ identifiers }),
+  });
+  if (!resp.ok) throw new Error('Scryfall HTTP ' + resp.status);
+  const json = await resp.json();
+
+  for (const card of (json.data || [])) {
+    const name = card.name;
+    // Pick the best image: normal > large > small, handle double-faced
+    let imageUrl = '';
+    if (card.image_uris) {
+      imageUrl = card.image_uris.normal || card.image_uris.large || card.image_uris.small || '';
+    } else if (card.card_faces && card.card_faces[0] && card.card_faces[0].image_uris) {
+      imageUrl = card.card_faces[0].image_uris.normal || card.card_faces[0].image_uris.large || '';
+    }
+    _scryfallCache[name] = {
+      imageUrl,
+      typeLine:   card.type_line   || '',
+      oracleText: card.oracle_text || (card.card_faces && card.card_faces[0] ? card.card_faces[0].oracle_text : '') || '',
+      manaCost:   card.mana_cost   || (card.card_faces && card.card_faces[0] ? card.card_faces[0].mana_cost : '') || '',
+      power:      card.power       || '',
+      toughness:  card.toughness   || '',
+    };
+  }
+
+  // Mark not-found cards so we don't re-fetch them on next enrichment
+  for (const name of names) {
+    if (!_scryfallCache[name]) _scryfallCache[name] = { imageUrl: '', typeLine: '', oracleText: '', manaCost: '', power: '', toughness: '' };
+  }
+}
+
+function _applyScryfallDataToAllCards() {
+  if (!gameState) return;
+  for (const player of gameState.players) {
+    for (const zone of Object.values(player.zones)) {
+      for (const card of zone) {
+        const data = _scryfallCache[card.name];
+        if (data) {
+          card.imageUrl   = data.imageUrl   || card.imageUrl;
+          card.typeLine   = data.typeLine   || card.typeLine;
+          card.oracleText = data.oracleText || card.oracleText;
+          card.manaCost   = data.manaCost   || card.manaCost;
+          if (data.power)     card.power     = data.power;
+          if (data.toughness) card.toughness = data.toughness;
+        }
+      }
+    }
+    // Also enrich battlefield cards owned by this player
+    if (gameState.battlefieldCards) {
+      for (const card of gameState.battlefieldCards) {
+        if (card.ownerIndex !== player.id) continue;
+        const data = _scryfallCache[card.name];
+        if (data && !card.imageUrl) {
+          card.imageUrl   = data.imageUrl   || '';
+          card.typeLine   = data.typeLine   || card.typeLine;
+          card.oracleText = data.oracleText || card.oracleText;
+          card.manaCost   = data.manaCost   || card.manaCost;
+        }
+      }
+    }
+  }
+
+  // Trigger UI refresh so images appear without a full reload
+  if (typeof renderPlayerPanels === 'function') renderPlayerPanels();
+  if (typeof renderCommanderZone === 'function') {
+    for (let i = 0; i < gameState.players.length; i++) renderCommanderZone(i);
+  }
+  if (typeof renderHandZone === 'function') {
+    for (let i = 0; i < gameState.players.length; i++) renderHandZone(i);
+  }
+  if (typeof updateZoneCounts === 'function') updateZoneCounts();
+}
+
+/**
+ * Enrich a single card object on-demand (useful for cards drawn or played after initial load).
+ * Returns a Promise that resolves when the card's imageUrl is set.
+ */
+async function enrichCardWithScryfall(card) {
+  if (!card || !card.name) return;
+  if (card.imageUrl) return; // already has image
+
+  const cached = _scryfallCache[card.name];
+  if (cached) {
+    card.imageUrl   = cached.imageUrl   || '';
+    card.typeLine   = cached.typeLine   || card.typeLine;
+    card.oracleText = cached.oracleText || card.oracleText;
+    card.manaCost   = cached.manaCost   || card.manaCost;
+    return;
+  }
+
+  await _fetchScryfallBatch([card.name]);
+  const data = _scryfallCache[card.name];
+  if (data) {
+    card.imageUrl   = data.imageUrl   || '';
+    card.typeLine   = data.typeLine   || card.typeLine;
+    card.oracleText = data.oracleText || card.oracleText;
+    card.manaCost   = data.manaCost   || card.manaCost;
+  }
+}
+
+function _sleep(ms) {
+  return new Promise(resolve => setTimeout(resolve, ms));
+}

lan-client/precons.js

@@ -0,0 +1,167 @@
+// Auto-generated from precon-decks/precon-index.json
+// Run: node scripts/generate-precons-js.js to regenerate
+var PRECON_DATA = [
+  {"id":"Counterpunch","name":"Counterpunch","commander":"Ghave, Guru of Spores","set":"Commander 2011","fileName":"Counterpunch.dck","colors":[],"year":2011},
+  {"id":"Devour_for_Power_CMD","name":"Devour for Power","commander":"The Mimeoplasm","set":"Commander 2011","fileName":"Devour_for_Power_CMD.dck","colors":[],"year":2011},
+  {"id":"Heavenly_Inferno_CMD","name":"Heavenly Inferno","commander":"Kaalia of the Vast","set":"Commander 2011","fileName":"Heavenly_Inferno_CMD.dck","colors":[],"year":2011},
+  {"id":"Mirror_Mastery","name":"Mirror Mastery","commander":"Riku of Two Reflections","set":"Commander 2011","fileName":"Mirror_Mastery.dck","colors":[],"year":2011},
+  {"id":"Political_Puppets","name":"Political Puppets","commander":"Zedruu the Greathearted","set":"Commander 2011","fileName":"Political_Puppets.dck","colors":[],"year":2011},
+  {"id":"Eternal_Bargain","name":"Eternal Bargain","commander":"Oloro, Ageless Ascetic","set":"Commander 2013","fileName":"Eternal_Bargain.dck","colors":[],"year":2013},
+  {"id":"Evasive_Maneuvers_C13","name":"Evasive Maneuvers","commander":"Derevi, Empyrial Tactician","set":"Commander 2013","fileName":"Evasive_Maneuvers_C13.dck","colors":[],"year":2013},
+  {"id":"Mind_Seize","name":"Mind Seize","commander":"Jeleva, Nephalia's Scourge","set":"Commander 2013","fileName":"Mind_Seize.dck","colors":[],"year":2013},
+  {"id":"Nature_of_the_Beast","name":"Nature of the Beast","commander":"Marath, Will of the Wild","set":"Commander 2013","fileName":"Nature_of_the_Beast.dck","colors":[],"year":2013},
+  {"id":"Power_Hungry","name":"Power Hungry","commander":"Prossh, Skyraider of Kher","set":"Commander 2013","fileName":"Power_Hungry.dck","colors":[],"year":2013},
+  {"id":"Built_From_Scratch_C14","name":"Built From Scratch","commander":"Daretti, Scrap Savant","set":"Commander 2014","fileName":"Built_From_Scratch_C14.dck","colors":[],"year":2014},
+  {"id":"Forged_In_Stone","name":"Forged In Stone","commander":"Nahiri, the Lithomancer","set":"Commander 2014","fileName":"Forged_In_Stone.dck","colors":[],"year":2014},
+  {"id":"Guided_By_Nature","name":"Guided By Nature","commander":"Freyalise, Llanowar's Fury","set":"Commander 2014","fileName":"Guided_By_Nature.dck","colors":[],"year":2014},
+  {"id":"Peer_Through_Time","name":"Peer Through Time","commander":"Teferi, Temporal Archmage","set":"Commander 2014","fileName":"Peer_Through_Time.dck","colors":[],"year":2014},
+  {"id":"Sworn_To_Darkness","name":"Sworn To Darkness","commander":"Ob Nixilis of the Black Oath","set":"Commander 2014","fileName":"Sworn_To_Darkness.dck","colors":[],"year":2014},
+  {"id":"Call_the_Spirits","name":"Call the Spirits","commander":"Daxos the Returned","set":"Commander 2015","fileName":"Call_the_Spirits.dck","colors":[],"year":2015},
+  {"id":"Plunder_the_Graves_C15","name":"Plunder the Graves","commander":"Meren of Clan Nel Toth","set":"Commander 2015","fileName":"Plunder_the_Graves_C15.dck","colors":[],"year":2015},
+  {"id":"Seize_Control","name":"Seize Control","commander":"Mizzix of the Izmagnus","set":"Commander 2015","fileName":"Seize_Control.dck","colors":[],"year":2015},
+  {"id":"Swell_the_Host","name":"Swell the Host","commander":"Ezuri, Claw of Progress","set":"Commander 2015","fileName":"Swell_the_Host.dck","colors":[],"year":2015},
+  {"id":"Wade_into_Battle_C15","name":"Wade into Battle","commander":"Kalemne, Disciple of Iroas","set":"Commander 2015","fileName":"Wade_into_Battle_C15.dck","colors":[],"year":2015},
+  {"id":"Breed_Lethality_C16","name":"Breed Lethality","commander":"Atraxa, Praetors' Voice","set":"Commander 2016","fileName":"Breed_Lethality_C16.dck","colors":[],"year":2016},
+  {"id":"Entropic_Uprising","name":"Entropic Uprising","commander":"Yidris, Maelstrom Wielder","set":"Commander 2016","fileName":"Entropic_Uprising.dck","colors":[],"year":2016},
+  {"id":"Invent_Superiority","name":"Invent Superiority","commander":"Breya, Etherium Shaper","set":"Commander 2016","fileName":"Invent_Superiority.dck","colors":[],"year":2016},
+  {"id":"Open_Hostility","name":"Open Hostility","commander":"Saskia the Unyielding","set":"Commander 2016","fileName":"Open_Hostility.dck","colors":[],"year":2016},
+  {"id":"Stalwart_Unity","name":"Stalwart Unity","commander":"Kynaios and Tiro of Meletis","set":"Commander 2016","fileName":"Stalwart_Unity.dck","colors":[],"year":2016},
+  {"id":"Evasive_Maneuvers_CMA","name":"Evasive Maneuvers","commander":"Derevi, Empyrial Tactician","set":"Commander Anthology","fileName":"Evasive_Maneuvers_CMA.dck","colors":[],"year":2017},
+  {"id":"Guided_by_Nature","name":"Guided by Nature","commander":"Freyalise, Llanowar's Fury","set":"Commander Anthology","fileName":"Guided_by_Nature.dck","colors":[],"year":2017},
+  {"id":"Heavenly_Inferno_CMA","name":"Heavenly Inferno","commander":"Kaalia of the Vast","set":"Commander Anthology","fileName":"Heavenly_Inferno_CMA.dck","colors":[],"year":2017},
+  {"id":"Plunder_the_Graves_CMA","name":"Plunder the Graves","commander":"Meren of Clan Nel Toth","set":"Commander Anthology","fileName":"Plunder_the_Graves_CMA.dck","colors":[],"year":2017},
+  {"id":"Arcane_Wizardry","name":"Arcane Wizardry","commander":"Inalla, Archmage Ritualist","set":"Commander 2017","fileName":"Arcane_Wizardry.dck","colors":[],"year":2017},
+  {"id":"Draconic_Domination","name":"Draconic Domination","commander":"The Ur-Dragon","set":"Commander 2017","fileName":"Draconic_Domination.dck","colors":[],"year":2017},
+  {"id":"Feline_Ferocity","name":"Feline Ferocity","commander":"Arahbo, Roar of the World","set":"Commander 2017","fileName":"Feline_Ferocity.dck","colors":[],"year":2017},
+  {"id":"Vampiric_Bloodlust","name":"Vampiric Bloodlust","commander":"Edgar Markov","set":"Commander 2017","fileName":"Vampiric_Bloodlust.dck","colors":[],"year":2017},
+  {"id":"Breed_Lethality_CM2","name":"Breed Lethality","commander":"Atraxa, Praetors' Voice","set":"Commander Anthology Volume II","fileName":"Breed_Lethality_CM2.dck","colors":[],"year":2018},
+  {"id":"Built_From_Scratch_CM2","name":"Built From Scratch","commander":"Daretti, Scrap Savant","set":"Commander Anthology Volume II","fileName":"Built_From_Scratch_CM2.dck","colors":[],"year":2018},
+  {"id":"Devour_for_Power_CM2","name":"Devour for Power","commander":"The Mimeoplasm","set":"Commander Anthology Volume II","fileName":"Devour_for_Power_CM2.dck","colors":[],"year":2018},
+  {"id":"Wade_into_Battle_CM2","name":"Wade into Battle","commander":"Kalemne, Disciple of Iroas","set":"Commander Anthology Volume II","fileName":"Wade_into_Battle_CM2.dck","colors":[],"year":2018},
+  {"id":"Adaptive_Enchantment","name":"Adaptive Enchantment","commander":"Estrid, the Masked","set":"Commander 2018","fileName":"Adaptive_Enchantment.dck","colors":[],"year":2018},
+  {"id":"Exquisite_Invention","name":"Exquisite Invention","commander":"Saheeli, the Gifted","set":"Commander 2018","fileName":"Exquisite_Invention.dck","colors":[],"year":2018},
+  {"id":"Natures_Vengeance","name":"Nature's Vengeance","commander":"Lord Windgrace","set":"Commander 2018","fileName":"Natures_Vengeance.dck","colors":[],"year":2018},
+  {"id":"Subjective_Reality","name":"Subjective Reality","commander":"Aminatou, the Fateshifter","set":"Commander 2018","fileName":"Subjective_Reality.dck","colors":[],"year":2018},
+  {"id":"Faceless_Menace","name":"Faceless Menace","commander":"Kadena, Slinking Sorcerer","set":"Commander 2019","fileName":"Faceless_Menace.dck","colors":[],"year":2019},
+  {"id":"Merciless_Rage","name":"Merciless Rage","commander":"Anje Falkenrath","set":"Commander 2019","fileName":"Merciless_Rage.dck","colors":[],"year":2019},
+  {"id":"Mystic_Intellect","name":"Mystic Intellect","commander":"Sevinne, the Chronoclasm","set":"Commander 2019","fileName":"Mystic_Intellect.dck","colors":[],"year":2019},
+  {"id":"Primal_Genesis","name":"Primal Genesis","commander":"Ghired, Conclave Exile","set":"Commander 2019","fileName":"Primal_Genesis.dck","colors":[],"year":2019},
+  {"id":"Arcane_Maelstrom","name":"Arcane Maelstrom","commander":"Kalamax, the Stormsire","set":"Commander 2020","fileName":"Arcane_Maelstrom.dck","colors":[],"year":2020},
+  {"id":"Enhanced_Evolution","name":"Enhanced Evolution","commander":"Otrimi, the Ever-Playful","set":"Commander 2020","fileName":"Enhanced_Evolution.dck","colors":[],"year":2020},
+  {"id":"Ruthless_Regiment","name":"Ruthless Regiment","commander":"Jirina Kudro","set":"Commander 2020","fileName":"Ruthless_Regiment.dck","colors":[],"year":2020},
+  {"id":"Symbiotic_Swarm","name":"Symbiotic Swarm","commander":"Kathril, Aspect Warper","set":"Commander 2020","fileName":"Symbiotic_Swarm.dck","colors":[],"year":2020},
+  {"id":"Timeless_Wisdom","name":"Timeless Wisdom","commander":"Gavi, Nest Warden","set":"Commander 2020","fileName":"Timeless_Wisdom.dck","colors":[],"year":2020},
+  {"id":"Lands_Wrath","name":"Land's Wrath","commander":"Obuun, Mul Daya Ancestor","set":"Zendikar Rising Commander","fileName":"Lands_Wrath.dck","colors":[],"year":2020},
+  {"id":"Sneak_Attack","name":"Sneak Attack","commander":"Anowon, the Ruin Thief","set":"Zendikar Rising Commander","fileName":"Sneak_Attack.dck","colors":[],"year":2020},
+  {"id":"Arm_for_Battle","name":"Arm for Battle","commander":"Wyleth, Soul of Steel","set":"Commander Legends","fileName":"Arm_for_Battle.dck","colors":[],"year":2020},
+  {"id":"Reap_the_Tides","name":"Reap the Tides","commander":"Aesi, Tyrant of Gyre Strait","set":"Commander Legends","fileName":"Reap_the_Tides.dck","colors":[],"year":2020},
+  {"id":"Elven_Empire","name":"Elven Empire","commander":"Lathril, Blade of the Elves","set":"Kaldheim Commander","fileName":"Elven_Empire.dck","colors":[],"year":2021},
+  {"id":"Phantom_Premonition","name":"Phantom Premonition","commander":"Ranar the Ever-Watchful","set":"Kaldheim Commander","fileName":"Phantom_Premonition.dck","colors":[],"year":2021},
+  {"id":"Lorehold_Legacies","name":"Lorehold Legacies","commander":"Osgir, the Reconstructor","set":"Commander 2021","fileName":"Lorehold_Legacies.dck","colors":[],"year":2021},
+  {"id":"Prismari_Performance","name":"Prismari Performance","commander":"Zaffai, Thunder Conductor","set":"Commander 2021","fileName":"Prismari_Performance.dck","colors":[],"year":2021},
+  {"id":"Quantum_Quandrix","name":"Quantum Quandrix","commander":"Adrix and Nev, Twincasters","set":"Commander 2021","fileName":"Quantum_Quandrix.dck","colors":[],"year":2021},
+  {"id":"Silverquill_Statement","name":"Silverquill Statement","commander":"Breena, the Demagogue","set":"Commander 2021","fileName":"Silverquill_Statement.dck","colors":[],"year":2021},
+  {"id":"Witherbloom_Witchcraft","name":"Witherbloom Witchcraft","commander":"Willowdusk, Essence Seer","set":"Commander 2021","fileName":"Witherbloom_Witchcraft.dck","colors":[],"year":2021},
+  {"id":"Aura_of_Courage","name":"Aura of Courage","commander":"Galea, Kindler of Hope","set":"Forgotten Realms Commander","fileName":"Aura_of_Courage.dck","colors":[],"year":2021},
+  {"id":"Draconic_Rage","name":"Draconic Rage","commander":"Vrondiss, Rage of Ancients","set":"Forgotten Realms Commander","fileName":"Draconic_Rage.dck","colors":[],"year":2021},
+  {"id":"Dungeons_of_Death","name":"Dungeons of Death","commander":"Sefris of the Hidden Ways","set":"Forgotten Realms Commander","fileName":"Dungeons_of_Death.dck","colors":[],"year":2021},
+  {"id":"Planar_Portal","name":"Planar Portal","commander":"Prosper, Tome-Bound","set":"Forgotten Realms Commander","fileName":"Planar_Portal.dck","colors":[],"year":2021},
+  {"id":"Coven_Counters","name":"Coven Counters","commander":"Leinore, Autumn Sovereign","set":"Midnight Hunt Commander","fileName":"Coven_Counters.dck","colors":[],"year":2021},
+  {"id":"Undead_Unleashed","name":"Undead Unleashed","commander":"Wilhelt, the Rotcleaver","set":"Midnight Hunt Commander","fileName":"Undead_Unleashed.dck","colors":[],"year":2021},
+  {"id":"Spirit_Squadron","name":"Spirit Squadron","commander":"Millicent, Restless Revenant","set":"Crimson Vow Commander","fileName":"Spirit_Squadron.dck","colors":[],"year":2021},
+  {"id":"Vampiric_Bloodline","name":"Vampiric Bloodline","commander":"Strefan, Maurer Progenitor","set":"Crimson Vow Commander","fileName":"Vampiric_Bloodline.dck","colors":[],"year":2021},
+  {"id":"Buckle_Up","name":"Buckle Up","commander":"Kotori, Pilot Prodigy","set":"Neon Dynasty Commander","fileName":"Buckle_Up.dck","colors":[],"year":2022},
+  {"id":"Upgrades_Unleashed","name":"Upgrades Unleashed","commander":"Chishiro, the Shattered Blade","set":"Neon Dynasty Commander","fileName":"Upgrades_Unleashed.dck","colors":[],"year":2022},
+  {"id":"Heads_I_Win,_Tails_You_Lose","name":"Heads I Win, Tails You Lose","commander":"Zndrsplt, Eye of Wisdom","set":"Secret Lair Drop","fileName":"Heads_I_Win,_Tails_You_Lose.dck","colors":[],"year":2022},
+  {"id":"Bedecked_Brokers","name":"Bedecked Brokers","commander":"Perrie, the Pulverizer","set":"New Capenna Commander","fileName":"Bedecked_Brokers.dck","colors":[],"year":2022},
+  {"id":"Cabaretti_Cacophony","name":"Cabaretti Cacophony","commander":"Kitt Kanto, Mayhem Diva","set":"New Capenna Commander","fileName":"Cabaretti_Cacophony.dck","colors":[],"year":2022},
+  {"id":"Maestros_Massacre","name":"Maestros Massacre","commander":"Anhelo, the Painter","set":"New Capenna Commander","fileName":"Maestros_Massacre.dck","colors":[],"year":2022},
+  {"id":"Obscura_Operation","name":"Obscura Operation","commander":"Kamiz, Obscura Oculus","set":"New Capenna Commander","fileName":"Obscura_Operation.dck","colors":[],"year":2022},
+  {"id":"Riveteers_Rampage","name":"Riveteers Rampage","commander":"Henzie \"Toolbox\" Torre","set":"New Capenna Commander","fileName":"Riveteers_Rampage.dck","colors":[],"year":2022},
+  {"id":"Draconic_Dissent","name":"Draconic Dissent","commander":"Firkraag, Cunning Instigator","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Draconic_Dissent.dck","colors":[],"year":2022},
+  {"id":"Exit_from_Exile","name":"Exit from Exile","commander":"Faldorn, Dread Wolf Herald","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Exit_from_Exile.dck","colors":[],"year":2022},
+  {"id":"Mind_Flayarrrs","name":"Mind Flayarrrs","commander":"Captain N'ghathrod","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Mind_Flayarrrs.dck","colors":[],"year":2022},
+  {"id":"Party_Time","name":"Party Time","commander":"Nalia de'Arnise","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Party_Time.dck","colors":[],"year":2022},
+  {"id":"Legends_Legacy","name":"Legends' Legacy","commander":"Dihada, Binder of Wills","set":"Dominaria United Commander","fileName":"Legends_Legacy.dck","colors":[],"year":2022},
+  {"id":"Painbow","name":"Painbow","commander":"Jared Carthalion","set":"Dominaria United Commander","fileName":"Painbow.dck","colors":[],"year":2022},
+  {"id":"Forces_of_the_Imperium","name":"Forces of the Imperium","commander":"Inquisitor Greyfax","set":"Warhammer 40,000 Commander","fileName":"Forces_of_the_Imperium.dck","colors":[],"year":2022},
+  {"id":"Forces_of_the_Imperium_Collectors_Edition","name":"Forces of the Imperium Collector's Edition","commander":"Inquisitor Greyfax","set":"Warhammer 40,000 Commander","fileName":"Forces_of_the_Imperium_Collectors_Edition.dck","colors":[],"year":2022},
+  {"id":"Necron_Dynasties","name":"Necron Dynasties","commander":"Szarekh, the Silent King","set":"Warhammer 40,000 Commander","fileName":"Necron_Dynasties.dck","colors":[],"year":2022},
+  {"id":"Necron_Dynasties_Collectors_Edition","name":"Necron Dynasties Collector's Edition","commander":"Szarekh, the Silent King","set":"Warhammer 40,000 Commander","fileName":"Necron_Dynasties_Collectors_Edition.dck","colors":[],"year":2022},
+  {"id":"The_Ruinous_Powers","name":"The Ruinous Powers","commander":"Abaddon the Despoiler","set":"Warhammer 40,000 Commander","fileName":"The_Ruinous_Powers.dck","colors":[],"year":2022},
+  {"id":"The_Ruinous_Powers_Collectors_Edition","name":"The Ruinous Powers Collector's Edition","commander":"Abaddon the Despoiler","set":"Warhammer 40,000 Commander","fileName":"The_Ruinous_Powers_Collectors_Edition.dck","colors":[],"year":2022},
+  {"id":"Tyranid_Swarm","name":"Tyranid Swarm","commander":"The Swarmlord","set":"Warhammer 40,000 Commander","fileName":"Tyranid_Swarm.dck","colors":[],"year":2022},
+  {"id":"Tyranid_Swarm_Collectors_Edition","name":"Tyranid Swarm Collector's Edition","commander":"The Swarmlord","set":"Warhammer 40,000 Commander","fileName":"Tyranid_Swarm_Collectors_Edition.dck","colors":[],"year":2022},
+  {"id":"Mishras_Burnished_Banner","name":"Mishra's Burnished Banner","commander":"Mishra, Eminent One","set":"The Brothers' War Commander","fileName":"Mishras_Burnished_Banner.dck","colors":[],"year":2022},
+  {"id":"Urzas_Iron_Alliance","name":"Urza's Iron Alliance","commander":"Urza, Chief Artificer","set":"The Brothers' War Commander","fileName":"Urzas_Iron_Alliance.dck","colors":[],"year":2022},
+  {"id":"Chaos_Incarnate","name":"Chaos Incarnate","commander":"Kardur, Doomscourge","set":"Starter Commander Decks","fileName":"Chaos_Incarnate.dck","colors":[],"year":2022},
+  {"id":"Draconic_Destruction","name":"Draconic Destruction","commander":"Atarka, World Render","set":"Starter Commander Decks","fileName":"Draconic_Destruction.dck","colors":[],"year":2022},
+  {"id":"First_Flight","name":"First Flight","commander":"Isperia, Supreme Judge","set":"Starter Commander Decks","fileName":"First_Flight.dck","colors":[],"year":2022},
+  {"id":"Grave_Danger","name":"Grave Danger","commander":"Gisa and Geralf","set":"Starter Commander Decks","fileName":"Grave_Danger.dck","colors":[],"year":2022},
+  {"id":"Token_Triumph","name":"Token Triumph","commander":"Emmara, Soul of the Accord","set":"Starter Commander Decks","fileName":"Token_Triumph.dck","colors":[],"year":2022},
+  {"id":"Corrupting_Influence","name":"Corrupting Influence","commander":"Ixhel, Scion of Atraxa","set":"Phyrexia: All Will Be One Commander","fileName":"Corrupting_Influence.dck","colors":[],"year":2023},
+  {"id":"Rebellion_Rising","name":"Rebellion Rising","commander":"Neyali, Suns' Vanguard","set":"Phyrexia: All Will Be One Commander","fileName":"Rebellion_Rising.dck","colors":[],"year":2023},
+  {"id":"From_Cute_to_Brute","name":"From Cute to Brute","commander":"Esika, God of the Tree","set":"Secret Lair Drop","fileName":"From_Cute_to_Brute.dck","colors":[],"year":2023},
+  {"id":"Elven_Council","name":"Elven Council","commander":"Galadriel, Elven-Queen","set":"Tales of Middle-earth Commander","fileName":"Elven_Council.dck","colors":[],"year":2023},
+  {"id":"Food_and_Fellowship","name":"Food and Fellowship","commander":"Frodo, Adventurous Hobbit","set":"Tales of Middle-earth Commander","fileName":"Food_and_Fellowship.dck","colors":[],"year":2023},
+  {"id":"Riders_of_Rohan","name":"Riders of Rohan","commander":"Éowyn, Shieldmaiden","set":"Tales of Middle-earth Commander","fileName":"Riders_of_Rohan.dck","colors":[],"year":2023},
+  {"id":"The_Hosts_of_Mordor","name":"The Hosts of Mordor","commander":"Sauron, Lord of the Rings","set":"Tales of Middle-earth Commander","fileName":"The_Hosts_of_Mordor.dck","colors":[],"year":2023},
+  {"id":"Eldrazi_Unbound","name":"Eldrazi Unbound","commander":"Zhulodok, Void Gorger","set":"Commander Masters","fileName":"Eldrazi_Unbound.dck","colors":[],"year":2023},
+  {"id":"Enduring_Enchantments","name":"Enduring Enchantments","commander":"Anikthea, Hand of Erebos","set":"Commander Masters","fileName":"Enduring_Enchantments.dck","colors":[],"year":2023},
+  {"id":"Planeswalker_Party","name":"Planeswalker Party","commander":"Commodore Guff","set":"Commander Masters","fileName":"Planeswalker_Party.dck","colors":[],"year":2023},
+  {"id":"Sliver_Swarm","name":"Sliver Swarm","commander":"Sliver Gravemother","set":"Commander Masters","fileName":"Sliver_Swarm.dck","colors":[],"year":2023},
+  {"id":"Angels_Theyre_Just_Like_Us_but_Cooler_and_with_Wings","name":"Angels: They're Just Like Us but Cooler and with Wings","commander":"Gisela, the Broken Blade","set":"Secret Lair Drop","fileName":"Angels_Theyre_Just_Like_Us_but_Cooler_and_with_Wings.dck","colors":[],"year":2023},
+  {"id":"Fae_Dominion","name":"Fae Dominion","commander":"Tegwyll, Duke of Splendor","set":"Wilds of Eldraine Commander","fileName":"Fae_Dominion.dck","colors":[],"year":2023},
+  {"id":"Virtue_and_Valor","name":"Virtue and Valor","commander":"Ellivere of the Wild Court","set":"Wilds of Eldraine Commander","fileName":"Virtue_and_Valor.dck","colors":[],"year":2023},
+  {"id":"Ahoy_Mateys","name":"Ahoy Mateys","commander":"Admiral Brass, Unsinkable","set":"The Lost Caverns of Ixalan Commander","fileName":"Ahoy_Mateys.dck","colors":[],"year":2023},
+  {"id":"Blood_Rites","name":"Blood Rites","commander":"Clavileño, First of the Blessed","set":"The Lost Caverns of Ixalan Commander","fileName":"Blood_Rites.dck","colors":[],"year":2023},
+  {"id":"Explorers_of_the_Deep","name":"Explorers of the Deep","commander":"Hakbal of the Surging Soul","set":"The Lost Caverns of Ixalan Commander","fileName":"Explorers_of_the_Deep.dck","colors":[],"year":2023},
+  {"id":"Veloci-Ramp-Tor","name":"Veloci-Ramp-Tor","commander":"Pantlaza, Sun-Favored","set":"The Lost Caverns of Ixalan Commander","fileName":"Veloci-Ramp-Tor.dck","colors":[],"year":2023},
+  {"id":"Raining_Cats_and_Dogs","name":"Raining Cats and Dogs","commander":"Rin and Seri, Inseparable","set":"Secret Lair Drop","fileName":"Raining_Cats_and_Dogs.dck","colors":[],"year":2024},
+  {"id":"Blame_Game","name":"Blame Game","commander":"Nelly Borca, Impulsive Accuser","set":"Murders at Karlov Manor Commander","fileName":"Blame_Game.dck","colors":[],"year":2024},
+  {"id":"Deadly_Disguise","name":"Deadly Disguise","commander":"Kaust, Eyes of the Glade","set":"Murders at Karlov Manor Commander","fileName":"Deadly_Disguise.dck","colors":[],"year":2024},
+  {"id":"Deep_Clue_Sea","name":"Deep Clue Sea","commander":"Morska, Undersea Sleuth","set":"Murders at Karlov Manor Commander","fileName":"Deep_Clue_Sea.dck","colors":[],"year":2024},
+  {"id":"Revenant_Recon","name":"Revenant Recon","commander":"Mirko, Obsessive Theorist","set":"Murders at Karlov Manor Commander","fileName":"Revenant_Recon.dck","colors":[],"year":2024},
+  {"id":"Hail,_Caesar","name":"Hail, Caesar","commander":"Caesar, Legion's Emperor","set":"Fallout","fileName":"Hail,_Caesar.dck","colors":[],"year":2024},
+  {"id":"Mutant_Menace","name":"Mutant Menace","commander":"The Wise Mothman","set":"Fallout","fileName":"Mutant_Menace.dck","colors":[],"year":2024},
+  {"id":"Science","name":"Science!","commander":"Dr. Madison Li","set":"Fallout","fileName":"Science.dck","colors":[],"year":2024},
+  {"id":"Scrappy_Survivors","name":"Scrappy Survivors","commander":"Dogmeat, Ever Loyal","set":"Fallout","fileName":"Scrappy_Survivors.dck","colors":[],"year":2024},
+  {"id":"Desert_Bloom","name":"Desert Bloom","commander":"Yuma, Proud Protector","set":"Outlaws of Thunder Junction Commander","fileName":"Desert_Bloom.dck","colors":[],"year":2024},
+  {"id":"Grand_Larceny","name":"Grand Larceny","commander":"Gonti, Canny Acquisitor","set":"Outlaws of Thunder Junction Commander","fileName":"Grand_Larceny.dck","colors":[],"year":2024},
+  {"id":"Most_Wanted","name":"Most Wanted","commander":"Olivia, Opulent Outlaw","set":"Outlaws of Thunder Junction Commander","fileName":"Most_Wanted.dck","colors":[],"year":2024},
+  {"id":"Quick_Draw","name":"Quick Draw","commander":"Stella Lee, Wild Card","set":"Outlaws of Thunder Junction Commander","fileName":"Quick_Draw.dck","colors":[],"year":2024},
+  {"id":"Creative_Energy","name":"Creative Energy","commander":"Satya, Aetherflux Genius","set":"Modern Horizons 3 Commander","fileName":"Creative_Energy.dck","colors":[],"year":2024},
+  {"id":"Creative_Energy_Collectors_Edition","name":"Creative Energy Collector's Edition","commander":"Satya, Aetherflux Genius","set":"Modern Horizons 3 Commander","fileName":"Creative_Energy_Collectors_Edition.dck","colors":[],"year":2024},
+  {"id":"Eldrazi_Incursion","name":"Eldrazi Incursion","commander":"Ulalek, Fused Atrocity","set":"Modern Horizons 3 Commander","fileName":"Eldrazi_Incursion.dck","colors":[],"year":2024},
+  {"id":"Eldrazi_Incursion_Collectors_Edition","name":"Eldrazi Incursion Collector's Edition","commander":"Ulalek, Fused Atrocity","set":"Modern Horizons 3 Commander","fileName":"Eldrazi_Incursion_Collectors_Edition.dck","colors":[],"year":2024},
+  {"id":"Graveyard_Overdrive","name":"Graveyard Overdrive","commander":"Disa the Restless","set":"Modern Horizons 3 Commander","fileName":"Graveyard_Overdrive.dck","colors":[],"year":2024},
+  {"id":"Graveyard_Overdrive_Collectors_Edition","name":"Graveyard Overdrive Collector's Edition","commander":"Disa the Restless","set":"Modern Horizons 3 Commander","fileName":"Graveyard_Overdrive_Collectors_Edition.dck","colors":[],"year":2024},
+  {"id":"Tricky_Terrain","name":"Tricky Terrain","commander":"Omo, Queen of Vesuva","set":"Modern Horizons 3 Commander","fileName":"Tricky_Terrain.dck","colors":[],"year":2024},
+  {"id":"Tricky_Terrain_Collectors_Edition","name":"Tricky Terrain Collector's Edition","commander":"Omo, Queen of Vesuva","set":"Modern Horizons 3 Commander","fileName":"Tricky_Terrain_Collectors_Edition.dck","colors":[],"year":2024},
+  {"id":"Animated_Army","name":"Animated Army","commander":"Bello, Bard of the Brambles","set":"Bloomburrow Commander","fileName":"Animated_Army.dck","colors":[],"year":2024},
+  {"id":"Family_Matters","name":"Family Matters","commander":"Zinnia, Valley's Voice","set":"Bloomburrow Commander","fileName":"Family_Matters.dck","colors":[],"year":2024},
+  {"id":"Peace_Offering","name":"Peace Offering","commander":"Ms. Bumbleflower","set":"Bloomburrow Commander","fileName":"Peace_Offering.dck","colors":[],"year":2024},
+  {"id":"Squirreled_Away","name":"Squirreled Away","commander":"Hazel of the Rootbloom","set":"Bloomburrow Commander","fileName":"Squirreled_Away.dck","colors":[],"year":2024},
+  {"id":"20_Ways_to_Win","name":"20 Ways to Win","commander":"Go-Shintai of Life's Origin","set":"Secret Lair Drop","fileName":"20_Ways_to_Win.dck","colors":[],"year":2024},
+  {"id":"Eternal_Might","name":"Eternal Might","commander":"Temmet, Naktamun's Will","set":"Aetherdrift Commander","fileName":"Eternal_Might.dck","colors":[],"year":2025},
+  {"id":"Living_Energy","name":"Living Energy","commander":"Saheeli, Radiant Creator","set":"Aetherdrift Commander","fileName":"Living_Energy.dck","colors":[],"year":2025},
+  {"id":"Abzan_Armor","name":"Abzan Armor","commander":"Felothar the Steadfast","set":"Tarkir: Dragonstorm Commander","fileName":"Abzan_Armor.dck","colors":[],"year":2025},
+  {"id":"Jeskai_Striker","name":"Jeskai Striker","commander":"Shiko and Narset, Unified","set":"Tarkir: Dragonstorm Commander","fileName":"Jeskai_Striker.dck","colors":[],"year":2025},
+  {"id":"Mardu_Surge","name":"Mardu Surge","commander":"Zurgo Stormrender","set":"Tarkir: Dragonstorm Commander","fileName":"Mardu_Surge.dck","colors":[],"year":2025},
+  {"id":"Sultai_Arisen","name":"Sultai Arisen","commander":"Teval, the Balanced Scale","set":"Tarkir: Dragonstorm Commander","fileName":"Sultai_Arisen.dck","colors":[],"year":2025},
+  {"id":"Temur_Roar","name":"Temur Roar","commander":"Eshki, Temur's Roar","set":"Tarkir: Dragonstorm Commander","fileName":"Temur_Roar.dck","colors":[],"year":2025},
+  {"id":"Everyones_Invited","name":"Everyone's Invited!","commander":"Morophon, the Boundless","set":"Secret Lair Drop","fileName":"Everyones_Invited.dck","colors":[],"year":2025},
+  {"id":"Counter_Blitz_(FINAL_FANTASY_X)","name":"Counter Blitz (FINAL FANTASY X)","commander":"Tidus, Yuna's Guardian","set":"Final Fantasy Commander","fileName":"Counter_Blitz_(FINAL_FANTASY_X).dck","colors":[],"year":2025},
+  {"id":"Counter_Blitz_Collectors_Edition_(FINAL_FANTASY_X)","name":"Counter Blitz Collector's Edition (FINAL FANTASY X)","commander":"Tidus, Yuna's Guardian","set":"Final Fantasy Commander","fileName":"Counter_Blitz_Collectors_Edition_(FINAL_FANTASY_X).dck","colors":[],"year":2025},
+  {"id":"Limit_Break_(FINAL_FANTASY_VII)","name":"Limit Break (FINAL FANTASY VII)","commander":"Cloud, Ex-SOLDIER","set":"Final Fantasy Commander","fileName":"Limit_Break_(FINAL_FANTASY_VII).dck","colors":[],"year":2025},
+  {"id":"Limit_Break_Collectors_Edition_(FINAL_FANTASY_VII)","name":"Limit Break Collector's Edition (FINAL FANTASY VII)","commander":"Cloud, Ex-SOLDIER","set":"Final Fantasy Commander","fileName":"Limit_Break_Collectors_Edition_(FINAL_FANTASY_VII).dck","colors":[],"year":2025},
+  {"id":"Revival_Trance_(FINAL_FANTASY_VI)","name":"Revival Trance (FINAL FANTASY VI)","commander":"Terra, Herald of Hope","set":"Final Fantasy Commander","fileName":"Revival_Trance_(FINAL_FANTASY_VI).dck","colors":[],"year":2025},
+  {"id":"Revival_Trance_Collectors_Edition_(FINAL_FANTASY_VI)","name":"Revival Trance Collector's Edition (FINAL FANTASY VI)","commander":"Terra, Herald of Hope","set":"Final Fantasy Commander","fileName":"Revival_Trance_Collectors_Edition_(FINAL_FANTASY_VI).dck","colors":[],"year":2025},
+  {"id":"Scions_&_Spellcraft_(FINAL_FANTASY_XIV)","name":"Scions & Spellcraft (FINAL FANTASY XIV)","commander":"Y'shtola, Night's Blessed","set":"Final Fantasy Commander","fileName":"Scions_&_Spellcraft_(FINAL_FANTASY_XIV).dck","colors":[],"year":2025},
+  {"id":"Scions_&_Spellcraft_Collectors_Edition_(FINAL_FANTASY_XIV)","name":"Scions & Spellcraft Collector's Edition (FINAL FANTASY XIV)","commander":"Y'shtola, Night's Blessed","set":"Final Fantasy Commander","fileName":"Scions_&_Spellcraft_Collectors_Edition_(FINAL_FANTASY_XIV).dck","colors":[],"year":2025},
+  {"id":"Counter_Intelligence","name":"Counter Intelligence","commander":"Inspirit, Flagship Vessel","set":"Edge of Eternities Commander","fileName":"Counter_Intelligence.dck","colors":[],"year":2025},
+  {"id":"World_Shaper","name":"World Shaper","commander":"Hearthhull, the Worldseed","set":"Edge of Eternities Commander","fileName":"World_Shaper.dck","colors":[],"year":2025},
+  {"id":"Blight_Curse","name":"Blight Curse","commander":"Auntie Ool, Cursewretch","set":"Lorwyn Eclipsed Commander","fileName":"Blight_Curse.dck","colors":[],"year":2026},
+  {"id":"Dance_of_the_Elements","name":"Dance of the Elements","commander":"Ashling, the Limitless","set":"Lorwyn Eclipsed Commander","fileName":"Dance_of_the_Elements.dck","colors":[],"year":2026},
+  {"id":"Turtle_Power","name":"Turtle Power!","commander":"Heroes in a Half Shell","set":"Teenage Mutant Ninja Turtles Eternal","fileName":"Turtle_Power.dck","colors":[],"year":2026}
+];

ml/actions/labeler.py

@@ -4,10 +4,15 @@ Commander AI Lab — Action Labeler
 Maps raw Forge action records (from DecisionSnapshot JSONL)
 to one of 8 macro-action labels for supervised policy learning.
 
-Classification logic:
-  1. Check action.type (cast, attack, land, cast_commander, pass)
-  2. For "cast" actions, classify by card oracle text / card type keywords
-  3. For ambiguous cases, use the raw log line and card name heuristics
+Classification logic (updated):
+  1. Check action.type for unambiguous types (attack, land, pass, hold,
+     cast_commander)
+  2. For "cast" actions:
+     a. If action.card is present, match directly against name databases
+     b. If action.card is absent (Forge omits it), scan the active
+        player's hand against name databases to infer the cast type.
+        Priority: removal > ramp > draw > commander > creature (default)
+  3. Fallback: PASS
 
 Macro-actions (from ml.config.scope):
   0: CAST_CREATURE    — Play a creature
@@ -41,11 +46,6 @@ logger = logging.getLogger("ml.labeler")
 # Card Type Keyword Databases
 # ══════════════════════════════════════════════════════════
 
-# Known removal spells (partial list — expanded from common Commander staples)
-REMOVAL_KEYWORDS = {
-    "destroy", "exile", "sacrifice", "counter", "bounce", "return.*to.*hand",
-    "-\\d+/-\\d+", "damage to", "fight", "deathtouch",
-}
 REMOVAL_CARD_NAMES = {
     "swords to plowshares", "path to exile", "beast within", "chaos warp",
     "counterspell", "swan song", "negate", "arcane denial", "doomblade",
@@ -62,45 +62,52 @@ REMOVAL_CARD_NAMES = {
     "decree of pain", "in garruk's wake", "plague wind",
     "fire covenant", "imprisoned in the moon", "darksteel mutation",
     "kenrith's transformation", "song of the dryads",
+    "abrade", "disenchant", "naturalize", "wear", "tear",
+    "shattering spree", "shatter", "smash to smithereens",
+    "lightning bolt", "lightning helix", "condemn", "oust",
+    "pongify", "reality shift", "turn to frog", "polymorph",
+    "boros charm", "utter end", "anguished unmaking", "mortify",
+    "putrefy", "maelstrom pulse", "void rend", "render silent",
+    "overwhelming denial", "test of talents", "dispel", "spell pierce",
+    "flusterstorm", "mental misstep", "pact of negation",
+    "opposition agent", "hullbreacher", "narset parter of veils",
+    "grafdigger's cage", "rest in peace", "leyline of the void",
+    "tormod's crypt", "relic of progenitus",
 }
 
-# Known draw spells
-DRAW_KEYWORDS = {
-    "draw", "scry", "look at the top", "reveal.*library",
-    "search your library.*hand", "impulse", "brainstorm",
-}
 DRAW_CARD_NAMES = {
     "rhystic study", "mystic remora", "phyrexian arena", "sylvan library",
     "harmonize", "blue sun's zenith", "pull from tomorrow",
     "windfall", "wheel of fortune", "timetwister", "brainstorm",
     "ponder", "preordain", "opt", "consider", "treasure cruise",
-    "dig through time", "fact or fiction", "nights whisper",
-    "nights whisper", "sign in blood", "read the bones",
-    "painful truths", "ambition's cost", "promise of power",
-    "necropotence", "dark confidant", "toski bearer of secrets",
-    "beast whisperer", "guardian project", "the great henge",
-    "skullclamp", "esper sentinel", "archivist of oghma",
-    "mind's eye", "consecrated sphinx", "ancient craving",
-    "faithless looting", "cathartic reunion", "thrill of possibility",
-    "jeska's will", "valakut awakening", "stinging study",
-    "night's whisper", "notion thief", "tymna the weaver",
-    "shamanic revelation", "rishkar's expertise", "return of the wildspeaker",
-    "garruk's uprising", "elemental bond", "kindred discovery",
-    "coastal piracy", "reconnaissance mission", "bident of thassa",
+    "dig through time", "fact or fiction", "night's whisper",
+    "sign in blood", "read the bones", "painful truths",
+    "ambition's cost", "promise of power", "necropotence",
+    "dark confidant", "toski bearer of secrets", "beast whisperer",
+    "guardian project", "the great henge", "skullclamp",
+    "esper sentinel", "archivist of oghma", "mind's eye",
+    "consecrated sphinx", "ancient craving", "faithless looting",
+    "cathartic reunion", "thrill of possibility", "jeska's will",
+    "valakut awakening", "stinging study", "notion thief",
+    "tymna the weaver", "shamanic revelation", "rishkar's expertise",
+    "return of the wildspeaker", "garruk's uprising", "elemental bond",
+    "kindred discovery", "coastal piracy", "reconnaissance mission",
+    "bident of thassa", "distant melody", "slate of ancestry",
+    "wheel of fate", "reforge the soul", "magus of the wheel",
+    "river's rebuke", "prosperity", "intellectual offering",
+    "peer into the abyss", "mind spring", "stroke of genius",
+    "jace's ingenuity", "tidings", "concentrate", "divination",
+    "inspiration", "counsel of the soratami", "accumulated knowledge",
+    "predict", "pressure point", "quicken", "serum visions",
+    "gitaxian probe", "mental note", "thought scour",
 }
 
-# Known ramp cards
-RAMP_KEYWORDS = {
-    "add {", "add one mana", "mana of any color",
-    "land.*onto the battlefield", "search.*basic land.*battlefield",
-}
 RAMP_CARD_NAMES = {
     "sol ring", "mana crypt", "mana vault", "arcane signet",
     "commander's sphere", "chromatic lantern", "fellwar stone",
     "mind stone", "thought vessel", "thran dynamo", "worn powerstone",
     "gilded lotus", "hedron archive", "everflowing chalice",
-    "coalition relic", "prismatic lens", "signets",  # catch-all partial
-    "talisman",  # catch-all for talisman cycle
+    "coalition relic", "prismatic lens",
     "cultivate", "kodama's reach", "rampant growth", "farseek",
     "nature's lore", "three visits", "skyshroud claim",
     "explosive vegetation", "migration path", "circuitous route",
@@ -112,13 +119,144 @@ RAMP_CARD_NAMES = {
     "birds of paradise", "llanowar elves", "elvish mystic",
     "fyndhorn elves", "avacyn's pilgrim", "elves of deep shadow",
     "bloom tender", "priest of titania", "selvala heart of the wilds",
+    "boreal druid", "green sun's zenith", "eternal witness",
+    "reclamation sage", "mystic snake", "fierce empath",
+    "signal the clans", "crop rotation", "harrow", "kodama's reach",
+    "traverse the ulvenwald", "search for tomorrow", "primal growth",
+    "land grant", "untamed wilds", "rangers path", "rangers' path",
+    "nissa's pilgrimage", "forest grove", "grove of the burnwillows",
+    "cabal stronghold", "urborg tomb of yawgmoth", "urborg, tomb of yawgmoth",
+    "bojuka bog", "ghost quarter", "field of ruin", "strip mine",
+    "wasteland", "tectonic edge", "dust bowl",
+    "worn powerstone", "basalt monolith", "grim monolith",
+    "mana geode", "darksteel ingot", "manalith", "opaline unicorn",
+    "vessel of endless rest", "corrupted grafstone", "dreamstone hedron",
+    "sisay's ring", "ur-golem's eye", "blinkmoth urn",
+    "astral cornucopia", "druids' repository", "black market",
+    "cryptolith rite", "earthcraft", "citanul hierophants",
+    "zendikar resurgent", "mirari's wake", "mana reflection",
+    "vorinclex voice of hunger", "vorinclex, voice of hunger",
 }
 
-# Known creature-type indicators
-CREATURE_KEYWORDS = {
-    "creature", "token", "enters the battlefield",
+# Partial-match prefixes (for cycles like Signets, Talismans)
+RAMP_PARTIAL_PREFIXES = {
+    "talisman", "signet", "locket", "banner", "obelisk",
+    "keyrune", "cluestone", "monolith", "medallion",
 }
 
+DRAW_PARTIAL_PREFIXES = {
+    "scroll", "tome", "journal",
+}
+
+
+# ══════════════════════════════════════════════════════════
+# Core classification helpers
+# ══════════════════════════════════════════════════════════
+
+def _classify_card_name(name: str) -> Optional[MacroAction]:
+    """
+    Classify a single card name into a MacroAction, or None if unknown.
+
+    Checks exact matches first, then partial prefix matches.
+    Returns None for unrecognised cards (caller handles the default).
+    """
+    n = name.lower().strip()
+    if not n:
+        return None
+
+    if n in REMOVAL_CARD_NAMES:
+        return MacroAction.CAST_REMOVAL
+    if n in DRAW_CARD_NAMES:
+        return MacroAction.CAST_DRAW
+    if n in RAMP_CARD_NAMES:
+        return MacroAction.CAST_RAMP
+
+    # Partial prefix match for cycles (Talismans, Signets, etc.)
+    for prefix in RAMP_PARTIAL_PREFIXES:
+        if n.startswith(prefix):
+            return MacroAction.CAST_RAMP
+    for prefix in DRAW_PARTIAL_PREFIXES:
+        if n.startswith(prefix):
+            return MacroAction.CAST_DRAW
+
+    # Substring match (catches "Doom Blade" inside longer names etc.)
+    for known in REMOVAL_CARD_NAMES:
+        if known in n or n in known:
+            return MacroAction.CAST_REMOVAL
+    for known in DRAW_CARD_NAMES:
+        if known in n or n in known:
+            return MacroAction.CAST_DRAW
+    for known in RAMP_CARD_NAMES:
+        if known in n or n in known:
+            return MacroAction.CAST_RAMP
+
+    return None  # Unknown → caller defaults to CAST_CREATURE
+
+
+def _classify_from_hand(
+    players: list,
+    active_seat: int,
+    commander_names: Optional[set] = None,
+) -> MacroAction:
+    """
+    Infer the cast action type by scanning the active player's hand.
+
+    When action.card is absent (Forge omits it), the hand snapshot
+    is the best available signal.  We scan all hand cards and return
+    the highest-priority match:
+
+        removal > ramp > draw > commander > creature (default)
+
+    Priority rationale: Forge's AI plays the highest-impact available
+    action, so if any removal/ramp/draw spell is in hand during a cast
+    decision, it's the most likely card played.
+    """
+    if active_seat >= len(players):
+        return MacroAction.CAST_CREATURE
+
+    hand: List[str] = players[active_seat].get("hand", [])
+    command_zone: List[str] = players[active_seat].get("command_zone", [])
+
+    # Build commander name set from command_zone if not provided
+    if commander_names is None:
+        commander_names = {c.lower().strip() for c in command_zone if c}
+
+    found_removal = False
+    found_ramp = False
+    found_draw = False
+    found_commander = False
+
+    for card in hand:
+        card_lower = card.lower().strip()
+
+        # Commander check: hand card matches a command zone card
+        if card_lower in commander_names:
+            found_commander = True
+            continue
+
+        result = _classify_card_name(card_lower)
+        if result == MacroAction.CAST_REMOVAL:
+            found_removal = True
+        elif result == MacroAction.CAST_RAMP:
+            found_ramp = True
+        elif result == MacroAction.CAST_DRAW:
+            found_draw = True
+
+    # Return highest-priority found
+    if found_removal:
+        return MacroAction.CAST_REMOVAL
+    if found_ramp:
+        return MacroAction.CAST_RAMP
+    if found_draw:
+        return MacroAction.CAST_DRAW
+    if found_commander:
+        return MacroAction.CAST_COMMANDER
+    return MacroAction.CAST_CREATURE
+
+
+# ══════════════════════════════════════════════════════════
+# Main label_action entry point
+# ══════════════════════════════════════════════════════════
 
 def label_action(decision: dict) -> int:
     """
@@ -133,74 +271,52 @@ def label_action(decision: dict) -> int:
     action = decision.get("action", {})
     action_type = action.get("type", "").lower()
     card_name = (action.get("card", "") or "").lower().strip()
-    raw_line = (action.get("raw", "") or "").lower()
 
-    # ── Direct type matching ────────────────────────────
+    players = decision.get("players", [])
+    active_seat = decision.get("active_seat", 0)
+
+    # ── Unambiguous direct-type matches ──────────────────────
 
-    # Commander cast is unambiguous
     if action_type == "cast_commander":
         return ACTION_TO_IDX[MacroAction.CAST_COMMANDER]
 
-    # Attack is unambiguous
     if action_type == "attack":
         return ACTION_TO_IDX[MacroAction.ATTACK_OPPONENT]
 
-    # Land play → classify as CAST_RAMP (developing mana is ramp)
+    # Land play → developing mana base = ramp
     if action_type == "land":
         return ACTION_TO_IDX[MacroAction.CAST_RAMP]
 
-    # Pass / hold mana
+    # Pass / hold mana — check available mana to distinguish
     if action_type in ("pass", "hold"):
-        # Check if player had mana open (from state)
-        active_seat = decision.get("active_seat", 0)
-        players = decision.get("players", [])
         if active_seat < len(players):
             mana = players[active_seat].get("mana", 0)
             if mana >= 2:
                 return ACTION_TO_IDX[MacroAction.HOLD_MANA]
         return ACTION_TO_IDX[MacroAction.PASS]
 
-    # ── Cast actions: classify by card identity ─────────
+    # ── Cast actions ──────────────────────────────────────
 
     if action_type == "cast":
-        # 1. Check known card name databases
-        if card_name in REMOVAL_CARD_NAMES or _partial_match(card_name, REMOVAL_CARD_NAMES):
-            return ACTION_TO_IDX[MacroAction.CAST_REMOVAL]
+        # Path A: action.card is present — classify it directly
+        if card_name:
+            result = _classify_card_name(card_name)
+            if result is not None:
+                return ACTION_TO_IDX[result]
+            # Unrecognised named card → default creature
+            return ACTION_TO_IDX[MacroAction.CAST_CREATURE]
 
-        if card_name in DRAW_CARD_NAMES or _partial_match(card_name, DRAW_CARD_NAMES):
-            return ACTION_TO_IDX[MacroAction.CAST_DRAW]
+        # Path B: action.card absent (Forge omits it) — infer from hand
+        inferred = _classify_from_hand(players, active_seat)
+        return ACTION_TO_IDX[inferred]
 
-        if card_name in RAMP_CARD_NAMES or _partial_match(card_name, RAMP_CARD_NAMES):
-            return ACTION_TO_IDX[MacroAction.CAST_RAMP]
-
-        # 2. Check raw log line for keyword patterns
-        for keyword in REMOVAL_KEYWORDS:
-            if re.search(keyword, raw_line):
-                return ACTION_TO_IDX[MacroAction.CAST_REMOVAL]
-
-        for keyword in DRAW_KEYWORDS:
-            if re.search(keyword, raw_line):
-                return ACTION_TO_IDX[MacroAction.CAST_DRAW]
-
-        for keyword in RAMP_KEYWORDS:
-            if re.search(keyword, raw_line):
-                return ACTION_TO_IDX[MacroAction.CAST_RAMP]
-
-        # 3. Default: if it's a spell cast and not recognized, assume creature
-        #    (most casts in Commander are creatures or creature-adjacent)
-        return ACTION_TO_IDX[MacroAction.CAST_CREATURE]
-
-    # ── Fallback ────────────────────────────────────────
+    # ── Fallback ──────────────────────────────────────────
     return ACTION_TO_IDX[MacroAction.PASS]
 
 
-def _partial_match(name: str, name_set: set) -> bool:
-    """Check if card name partially matches any name in the set."""
-    for known in name_set:
-        if known in name or name in known:
-            return True
-    return False
-
+# ══════════════════════════════════════════════════════════
+# File-level helpers
+# ══════════════════════════════════════════════════════════
 
 def label_decisions_file(jsonl_path: str) -> Tuple[np.ndarray, List[dict]]:
     """
@@ -232,7 +348,11 @@ def label_decisions_file(jsonl_path: str) -> Tuple[np.ndarray, List[dict]]:
 
 
 def print_label_distribution(labels: np.ndarray) -> Dict[str, int]:
-    """Print and return the distribution of action labels."""
+    """Print and return the distribution of action labels.
+
+    Iterates MacroAction enum in index order so the display always
+    matches ACTION_TO_IDX regardless of dict insertion order.
+    """
     dist = {}
     for action in MacroAction:
         idx = ACTION_TO_IDX[action]
@@ -240,12 +360,13 @@ def print_label_distribution(labels: np.ndarray) -> Dict[str, int]:
         dist[action.value] = count
 
     total = len(labels)
-    print(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
-    print("─" * 43)
+    logger.info(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
+    logger.info("─" * 43)
+    # Sort descending by count for readability
     for action_name, count in sorted(dist.items(), key=lambda x: -x[1]):
         pct = (count / total * 100) if total > 0 else 0
-        print(f"  {action_name:<23} {count:>8} {pct:>7.1f}%")
-    print(f"{'─' * 43}")
-    print(f"  {'TOTAL':<23} {total:>8}")
+        logger.info(f"  {action_name:<23} {count:>8} {pct:>7.1f}%")
+    logger.info("─" * 43)
+    logger.info(f"  {'TOTAL':<23} {total:>8}")
 
     return dist

ml/config/scope.py

@@ -155,15 +155,25 @@ class StateDimensions:
     2 players = 28 global features
     + turn_number = 1
     Total global = 29
+
+    Zone embedding boundary (architectural decision — Phase 5 follow-up):
+      Zone dims are **always** 768 per zone slot, zero-padded when a zone
+      is empty (no cards).  The fixed 6144-dim zone block is the contract
+      between ForgeEpisodeGenerator (upstream) and the policy network
+      (downstream).  All downstream consumers (PolicyNetwork, PPOTrainer,
+      dataset builder) depend on this shape.  If a future change adds
+      attention pooling or variable-length zone representations, the
+      boundary must be updated here *and* in StateEncoder._encode_zones.
     """
     card_embedding_dim: int = 768       # From mtg-embeddings
-    zone_pool_dim: int = 768            # Mean-pooled per zone
+    zone_pool_dim: int = 768            # Mean-pooled per zone (zero-vec when empty)
 
     # Zones per player: hand, battlefield, graveyard, command_zone
     zones_per_player: int = 4
     num_players: int = 2                # 1v1
 
     # Zone vectors: 4 zones × 2 players × 768 = 6144
+    # This is a fixed-size block; empty zones produce zero vectors.
     total_zone_dim: int = 4 * 2 * 768  # 6144
 
     # Global scalar features
@@ -256,3 +266,85 @@ class TrainingConfig:
     log_every: int = 100               # Log every N batches
 
 TRAINING_CONFIG = TrainingConfig()
+
+
+# ══════════════════════════════════════════════════════════
+# Distillation Loop Defaults (Phase 4)
+# ══════════════════════════════════════════════════════════
+
+@dataclass
+class DistillationDefaults:
+    """Default hyperparameters for the closed-loop distillation pipeline.
+
+    These are consumed by ml.training.distillation_loop.DistillationConfig
+    when no explicit override is provided.
+
+    Default weights: Forge-only (forge_weight=1.0, ppo_weight=0.0).
+    The baseline trains exclusively on Forge simulation data.  Use
+    ``MIXED_MODE_PRESETS`` below for blended Forge+PPO configurations.
+    """
+    max_iterations: int = 10
+    convergence_window: int = 3
+    convergence_threshold: float = 0.01
+
+    # PPO self-play per generation
+    ppo_iterations: int = 50
+    ppo_episodes_per_iter: int = 64
+    ppo_lr: float = 3e-4
+    ppo_clip_epsilon: float = 0.2
+    ppo_entropy_coeff: float = 0.01
+    ppo_eval_episodes: int = 100
+
+    # Dataset mixing — Forge-only by default (Phase 5 follow-up decision)
+    forge_weight: float = 1.0
+    ppo_weight: float = 0.0
+    min_reward_threshold: float = 0.0
+
+    # Quality gate thresholds
+    min_forge_accuracy: float = 0.35
+    max_accuracy_drop: float = 0.03
+    min_ppo_win_rate: float = 0.30
+
+    # Retry behaviour on gate failure
+    max_retries_per_generation: int = 2
+    retry_lr_factor: float = 0.5
+    retry_entropy_boost: float = 0.005
+
+    # Output paths
+    results_dir: str = "data/results"
+    history_dir: str = "data/results/distillation-history"
+
+
+DISTILLATION_DEFAULTS = DistillationDefaults()
+
+
+# ══════════════════════════════════════════════════════════
+# Mixed-Mode Presets (Phase 5 follow-up)
+# ══════════════════════════════════════════════════════════
+
+# Pre-defined Forge/PPO weight combinations for common scenarios.
+# Select a preset by name when starting a distillation run to
+# override the Forge-only defaults without specifying raw weights.
+#
+# "forge_only"  — baseline: 100% Forge data, ignore PPO
+# "forge_90_10" — light PPO blending: 90% Forge, 10% PPO
+# "forge_80_20" — heavier PPO blending: 80% Forge, 20% PPO
+MIXED_MODE_PRESETS: Dict[str, Dict[str, float]] = {
+    "forge_only": {"forge_weight": 1.0, "ppo_weight": 0.0},
+    "forge_90_10": {"forge_weight": 1.0, "ppo_weight": 0.11},  # ~10% PPO
+    "forge_80_20": {"forge_weight": 1.0, "ppo_weight": 0.25},  # ~20% PPO
+}
+
+
+def get_preset_weights(preset_name: str) -> Dict[str, float]:
+    """Look up a mixed-mode preset by name.
+
+    Returns a dict with ``forge_weight`` and ``ppo_weight`` keys.
+    Raises ``KeyError`` if the preset name is unknown.
+    """
+    if preset_name not in MIXED_MODE_PRESETS:
+        raise KeyError(
+            f"Unknown preset '{preset_name}'. "
+            f"Available: {list(MIXED_MODE_PRESETS.keys())}"
+        )
+    return dict(MIXED_MODE_PRESETS[preset_name])

ml/data/dataset_builder.py

@@ -1,9 +1,18 @@
 """
 Commander AI Lab — Training Dataset Builder
 ════════════════════════════════════════════
+
 Reads ML decision JSONL files, encodes states, labels actions,
 and produces a training-ready dataset saved as NPZ.
 
+Phase 2: Source Awareness (Issue #66)
+  - Scans Forge sim files  (ml-decisions-sim-*.jsonl)  → source "forge"
+  - Scans PPO self-play    (ml-decisions-ppo-*.jsonl)  → source "ppo"
+  - Scans DeepSeek files   (ml-decisions-ds-*.jsonl)   → source "ds"
+  - Configurable source_weights for sampling ratios
+  - min_reward_threshold filter for PPO data quality
+  - Each sample tagged with source for downstream analysis
+
 Usage:
     python -m ml.data.dataset_builder --results-dir results/ --output ml/models/dataset.npz
 
@@ -13,6 +22,7 @@ Output NPZ structure:
     game_ids:   object  (N,)      — game ID for each sample
     outcomes:   object  (N,)      — game outcome per sample
     playstyles: object  (N,)      — deck archetype per sample
+    sources:    object  (N,)      — data source ("forge", "ppo", or "ds")
 """
 
 import argparse
@@ -20,8 +30,9 @@ import json
 import logging
 import os
 import sys
+from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 
@@ -40,22 +51,176 @@ logging.basicConfig(level=logging.INFO, format="[%(name)s] %(message)s")
 logger = logging.getLogger("ml.dataset")
 
 
+# ---------------------------------------------------------------------------
+# Configuration (Phase 2)
+# ---------------------------------------------------------------------------
+
+@dataclass
+class DatasetConfig:
+    """Configuration for dataset building with source awareness."""
+
+    results_dir: str = "results"
+    embeddings_dir: Optional[str] = None
+    max_samples: Optional[int] = None
+
+    # Source weights control sampling ratios between Forge, PPO, and DS data.
+    # ds=4.0 oversamples the 8 DeepSeek files from ~5.8% → ~20% of effective
+    # training tokens, matching their higher signal quality relative to sim data.
+    source_weights: Dict[str, float] = field(
+        default_factory=lambda: {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
+    )
+
+    # Minimum reward threshold for PPO data — only include decisions
+    # from games where the episode return exceeds this value.
+    # Set to None to disable filtering.
+    min_reward_threshold: Optional[float] = 0.0
+
+
+# ---------------------------------------------------------------------------
+# Source-aware file discovery (Phase 2 — Task 1)
+# ---------------------------------------------------------------------------
+
+def _discover_jsonl_files(results_dir: str) -> Dict[str, List[Path]]:
+    """
+    Scan results directory for Forge sim, PPO, and DeepSeek decision files.
+
+    Returns:
+        Dict mapping source name to list of JSONL file paths.
+        Keys: "forge" for ml-decisions-sim-*.jsonl
+              "ppo"   for ml-decisions-ppo-*.jsonl
+              "ds"    for ml-decisions-ds-*.jsonl
+    """
+    results_path = Path(results_dir)
+    sources = {}
+
+    # Forge batch sim files
+    forge_files = sorted(results_path.glob("ml-decisions-sim-*.jsonl"))
+    if forge_files:
+        sources["forge"] = forge_files
+
+    # PPO self-play files (from Phase 1 DecisionExporter)
+    ppo_files = sorted(results_path.glob("ml-decisions-ppo-*.jsonl"))
+    if ppo_files:
+        sources["ppo"] = ppo_files
+
+    # DeepSeek high-quality decisions (upweighted by default)
+    ds_files = sorted(results_path.glob("ml-decisions-ds-*.jsonl"))
+    if ds_files:
+        sources["ds"] = ds_files
+
+    # Legacy files that don't match any known prefix — treat as forge
+    known = set(forge_files + ppo_files + ds_files)
+    legacy_files = sorted(
+        f for f in results_path.glob("ml-decisions-*.jsonl")
+        if f not in known
+    )
+    if legacy_files:
+        sources.setdefault("forge", []).extend(legacy_files)
+
+    return sources
+
+
+# ---------------------------------------------------------------------------
+# PPO reward filter (Phase 2 — Task 3)
+# ---------------------------------------------------------------------------
+
+def _passes_reward_filter(
+    decision: dict,
+    source: str,
+    min_reward_threshold: Optional[float],
+) -> bool:
+    """
+    Filter PPO decisions by reward threshold.
+    Forge and DS data always passes. PPO data must meet the threshold.
+    """
+    if source != "ppo":
+        return True
+    if min_reward_threshold is None:
+        return True
+    episode_return = decision.get("episode_return", 0.0)
+    return episode_return >= min_reward_threshold
+
+
+# ---------------------------------------------------------------------------
+# Source-aware sampling (Phase 2 — Task 2)
+# ---------------------------------------------------------------------------
+
+def _apply_source_weights(
+    samples_by_source: Dict[str, List[dict]],
+    source_weights: Dict[str, float],
+    rng: np.random.Generator,
+) -> List[dict]:
+    """
+    Apply source weights to control the mixing ratio.
+
+    A weight of 1.0 keeps all samples from that source.
+    A weight of 0.5 randomly keeps ~50% of samples.
+    A weight > 1.0 oversamples (with replacement).
+    """
+    combined = []
+    for source, samples in samples_by_source.items():
+        weight = source_weights.get(source, 1.0)
+        if weight <= 0:
+            logger.info("  Skipping %d %s samples (weight=0)", len(samples), source)
+            continue
+        if weight == 1.0:
+            combined.extend(samples)
+        elif weight < 1.0:
+            # Subsample
+            n_keep = max(1, int(len(samples) * weight))
+            indices = rng.choice(len(samples), size=n_keep, replace=False)
+            combined.extend(samples[i] for i in indices)
+            logger.info(
+                "  Subsampled %s: %d → %d (weight=%.2f)",
+                source, len(samples), n_keep, weight,
+            )
+        else:
+            # Oversample (weight > 1.0)
+            n_target = int(len(samples) * weight)
+            indices = rng.choice(len(samples), size=n_target, replace=True)
+            combined.extend(samples[i] for i in indices)
+            logger.info(
+                "  Oversampled %s: %d → %d (weight=%.2f)",
+                source, len(samples), n_target, weight,
+            )
+    return combined
+
+
+# ---------------------------------------------------------------------------
+# Main dataset builder (updated for Phase 2)
+# ---------------------------------------------------------------------------
+
 def build_dataset(
     results_dir: str,
     embeddings_dir: str = None,
     max_samples: int = None,
+    source_weights: Dict[str, float] = None,
+    min_reward_threshold: float = 0.0,
 ) -> Dict[str, np.ndarray]:
     """
     Build complete training dataset from all ML decision files.
 
+    Phase 2 additions:
+      - Scans Forge, PPO, and DeepSeek (ds) JSONL files
+      - Applies source_weights for configurable mixing
+        (default: ds=4.0 to oversample high-quality DeepSeek decisions)
+      - Filters PPO data by min_reward_threshold
+      - Tags each sample with its source
+
     Args:
-        results_dir: Path to results/ directory containing ml-decisions-*.jsonl
+        results_dir: Path to results/ directory containing JSONL files
         embeddings_dir: Path to embeddings/ directory (default: auto-detect)
         max_samples: Cap total samples (None = unlimited)
+        source_weights: Dict of source → sampling weight
+                        (default: forge=1.0, ppo=0.0, ds=4.0)
+        min_reward_threshold: Min episode_return for PPO data (default: 0.0)
 
     Returns:
-        Dict with keys: states, labels, game_ids, outcomes, playstyles
+        Dict with keys: states, labels, game_ids, outcomes, playstyles, sources
     """
+    if source_weights is None:
+        source_weights = {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
+
     # Load card embeddings
     card_index = CardEmbeddingIndex(embeddings_dir)
     if not card_index.load():
@@ -70,10 +235,9 @@ def build_dataset(
 
     encoder = StateEncoder(card_index)
 
-    # Find all JSONL files
-    results_path = Path(results_dir)
-    jsonl_files = sorted(results_path.glob("ml-decisions-*.jsonl"))
-    if not jsonl_files:
+    # --- Phase 2 Task 1: Discover files from all sources ---
+    source_files = _discover_jsonl_files(results_dir)
+    if not source_files:
         logger.error("No ML decision files found in %s", results_dir)
         logger.error("Run batch simulations with --ml-log or enable ML logging in the UI first.")
         raise RuntimeError(
@@ -81,60 +245,114 @@ def build_dataset(
             "Run batch simulations with ML logging enabled first."
         )
 
-    logger.info("Found %d ML decision files", len(jsonl_files))
+    total_files = sum(len(files) for files in source_files.values())
+    logger.info(
+        "Found %d ML decision files across %d sources: %s",
+        total_files,
+        len(source_files),
+        {src: len(files) for src, files in source_files.items()},
+    )
 
+    # --- Read and filter all decisions, grouped by source ---
+    rng = np.random.default_rng(42)
+    samples_by_source: Dict[str, List[dict]] = {}
+    total_skipped = 0
+    total_filtered = 0
+
+    for source, jsonl_files in source_files.items():
+        source_samples = []
+
+        for jsonl_file in jsonl_files:
+            logger.info("Processing %s [%s] ...", jsonl_file.name, source)
+            file_count = 0
+
+            with open(jsonl_file, "r") as f:
+                for line_num, line in enumerate(f):
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        decision = json.loads(line)
+
+                        # --- Phase 2 Task 3: PPO reward filter ---
+                        if not _passes_reward_filter(
+                            decision, source, min_reward_threshold
+                        ):
+                            total_filtered += 1
+                            continue
+
+                        # --- Phase 2 Task 4: Tag with source ---
+                        decision["_source"] = source
+
+                        source_samples.append(decision)
+                        file_count += 1
+
+                    except Exception as e:
+                        total_skipped += 1
+                        if total_skipped <= 10:
+                            logger.warning(
+                                "Skipping %s line %d: %s", jsonl_file.name, line_num, e
+                            )
+
+            logger.info("  → %d samples from %s", file_count, jsonl_file.name)
+
+        if source_samples:
+            samples_by_source[source] = source_samples
+
+    if not samples_by_source:
+        logger.error("No valid samples found.")
+        return {}
+
+    # Log per-source counts before weighting
+    for src, samples in samples_by_source.items():
+        logger.info("Source '%s': %d raw samples", src, len(samples))
+    if total_filtered > 0:
+        logger.info("PPO reward filter removed %d samples (threshold=%.2f)",
+                     total_filtered, min_reward_threshold or 0.0)
+
+    # --- Phase 2 Task 2: Apply source weights ---
+    combined_decisions = _apply_source_weights(
+        samples_by_source, source_weights, rng
+    )
+
+    # Apply max_samples cap after weighting
+    if max_samples and len(combined_decisions) > max_samples:
+        rng.shuffle(combined_decisions)
+        combined_decisions = combined_decisions[:max_samples]
+        logger.info("Capped to %d samples", max_samples)
+
+    # --- Encode all decisions ---
     all_states = []
     all_labels = []
     all_game_ids = []
     all_outcomes = []
     all_playstyles = []
-    total_skipped = 0
+    all_sources = []
+    encode_errors = 0
 
-    for jsonl_file in jsonl_files:
-        logger.info("Processing %s ...", jsonl_file.name)
-        file_count = 0
+    for decision in combined_decisions:
+        try:
+            playstyle = decision.get("archetype", "midrange")
+            state_vec = encoder.encode(decision, playstyle)
+            action_idx = label_action(decision)
+            game_id = decision.get("game_id", "unknown")
+            outcome = decision.get("game_outcome", "unknown")
+            source = decision.get("_source", "forge")
 
-        with open(jsonl_file, "r") as f:
-            for line_num, line in enumerate(f):
-                if max_samples and len(all_states) >= max_samples:
-                    break
+            all_states.append(state_vec)
+            all_labels.append(action_idx)
+            all_game_ids.append(game_id)
+            all_outcomes.append(outcome)
+            all_playstyles.append(playstyle)
+            all_sources.append(source)
 
-                line = line.strip()
-                if not line:
-                    continue
-
-                try:
-                    decision = json.loads(line)
-
-                    # Encode state
-                    playstyle = decision.get("archetype", "midrange")
-                    state_vec = encoder.encode(decision, playstyle)
-
-                    # Label action
-                    action_idx = label_action(decision)
-
-                    # Metadata
-                    game_id = decision.get("game_id", f"unknown-{line_num}")
-                    outcome = decision.get("game_outcome", "unknown")
-
-                    all_states.append(state_vec)
-                    all_labels.append(action_idx)
-                    all_game_ids.append(game_id)
-                    all_outcomes.append(outcome)
-                    all_playstyles.append(playstyle)
-                    file_count += 1
-
-                except Exception as e:
-                    total_skipped += 1
-                    if total_skipped <= 10:
-                        logger.warning(
-                            "Skipping %s line %d: %s", jsonl_file.name, line_num, e
-                        )
-
-        logger.info("  → %d samples from %s", file_count, jsonl_file.name)
+        except Exception as e:
+            encode_errors += 1
+            if encode_errors <= 5:
+                logger.warning("Encode error: %s", e)
 
     if not all_states:
-        logger.error("No valid samples found.")
+        logger.error("No valid samples after encoding.")
         return {}
 
     # Convert to arrays
@@ -143,10 +361,11 @@ def build_dataset(
     game_ids = np.array(all_game_ids, dtype=object)
     outcomes = np.array(all_outcomes, dtype=object)
     playstyles = np.array(all_playstyles, dtype=object)
+    sources = np.array(all_sources, dtype=object)
 
     logger.info(
-        "Dataset built: %d samples, %d features, %d actions, %d skipped",
-        len(states), states.shape[1], NUM_ACTIONS, total_skipped,
+        "Dataset built: %d samples, %d features, %d actions, %d skipped, %d encode errors",
+        len(states), states.shape[1], NUM_ACTIONS, total_skipped, encode_errors,
     )
 
     # Print label distribution
@@ -154,17 +373,25 @@ def build_dataset(
 
     # Print game outcome distribution
     unique_outcomes, outcome_counts = np.unique(outcomes, return_counts=True)
-    print(f"\n{'Outcome':<25} {'Count':>8}")
-    print("─" * 35)
+    logger.info(f"\n{'Outcome':<25} {'Count':>8}")
+    logger.info("─" * 35)
     for o, c in sorted(zip(unique_outcomes, outcome_counts), key=lambda x: -x[1]):
-        print(f"  {str(o):<23} {c:>8}")
+        logger.info(f"  {str(o):<23} {c:>8}")
 
     # Print playstyle distribution
     unique_styles, style_counts = np.unique(playstyles, return_counts=True)
-    print(f"\n{'Playstyle':<25} {'Count':>8}")
-    print("─" * 35)
+    logger.info(f"\n{'Playstyle':<25} {'Count':>8}")
+    logger.info("─" * 35)
     for s, c in sorted(zip(unique_styles, style_counts), key=lambda x: -x[1]):
-        print(f"  {str(s):<23} {c:>8}")
+        logger.info(f"  {str(s):<23} {c:>8}")
+
+    # --- Phase 2 Task 4: Print source distribution ---
+    unique_sources, source_counts = np.unique(sources, return_counts=True)
+    logger.info(f"\n{'Source':<25} {'Count':>8} {'%':>7}")
+    logger.info("─" * 42)
+    for s, c in sorted(zip(unique_sources, source_counts), key=lambda x: -x[1]):
+        pct = 100.0 * c / len(sources)
+        logger.info(f"  {str(s):<23} {c:>8} {pct:>6.1f}%")
 
     return {
         "states": states,
@@ -172,6 +399,7 @@ def build_dataset(
         "game_ids": game_ids,
         "outcomes": outcomes,
         "playstyles": playstyles,
+        "sources": sources,
     }
 
 
@@ -240,7 +468,6 @@ def split_dataset(
         len(train["states"]), len(val["states"]), len(test["states"]),
         split_by_game,
     )
-
     return train, val, test
 
 
@@ -279,16 +506,43 @@ def main():
         "--no-split", action="store_true",
         help="Don't split into train/val/test (save single file)",
     )
+    # Phase 2 CLI arguments
+    parser.add_argument(
+        "--forge-weight", type=float, default=1.0,
+        help="Sampling weight for Forge sim data (default: 1.0)",
+    )
+    parser.add_argument(
+        "--ppo-weight", type=float, default=0.0,
+        help="Sampling weight for PPO self-play data (default: 0.0, disabled)",
+    )
+    parser.add_argument(
+        "--ds-weight", type=float, default=4.0,
+        help="Sampling weight for DeepSeek decision data (default: 4.0, ~20%% effective share)",
+    )
+    parser.add_argument(
+        "--min-reward", type=float, default=0.0,
+        help="Minimum episode_return for PPO data (default: 0.0)",
+    )
+
     args = parser.parse_args()
 
+    source_weights = {
+        "forge": args.forge_weight,
+        "ppo": args.ppo_weight,
+        "ds": args.ds_weight,
+    }
+
     # Build dataset
     dataset = build_dataset(
         results_dir=args.results_dir,
         embeddings_dir=args.embeddings_dir,
         max_samples=args.max_samples,
+        source_weights=source_weights,
+        min_reward_threshold=args.min_reward,
     )
+
     if not dataset:
-        print("\nNo data to save. Run simulations with --ml-log first.")
+        logger.info("\nNo data to save. Run simulations with --ml-log first.")
         sys.exit(1)
 
     if args.no_split:
@@ -300,7 +554,7 @@ def main():
         save_dataset(val, f"{base}-val.npz")
         save_dataset(test, f"{base}-test.npz")
 
-    print("\nDataset build complete.")
+    logger.info("\nDataset build complete.")
 
 
 if __name__ == "__main__":

ml/data/quality_gate.py

@@ -0,0 +1,587 @@
+"""
+Commander AI Lab — Quality Gate & Validation
+═════════════════════════════════════════════
+
+Phase 3 (Issue #67): Prevents bad PPO data from degrading the
+supervised model by enforcing quality gates at multiple levels:
+
+1. Forge Validation Holdout — fixed eval set from Forge-only data
+2. Accuracy Gate — reject PPO batch if model accuracy drops on Forge eval
+3. Win Rate Gate — only export PPO decisions above a win rate threshold
+4. Data Quality Metrics — entropy, reward distribution logging
+
+Usage:
+    from ml.data.quality_gate import QualityGate, QualityGateConfig
+
+    gate = QualityGate(QualityGateConfig(
+        min_forge_accuracy=0.40,
+        min_ppo_win_rate=0.30,
+    ))
+
+    # Check if PPO batch should be accepted
+    verdict = gate.evaluate_ppo_batch(
+        forge_eval_accuracy=0.42,
+        ppo_win_rate=0.35,
+        ppo_decisions=decisions_list,
+        baseline_accuracy=0.41,
+    )
+    if verdict.accepted:
+        # merge PPO data into training set
+        ...
+
+Ref: docs/CLOSED-LOOP-DISTILLATION.md — Phase 3
+Issue: #67
+"""
+
+import json
+import logging
+import os
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+
+logger = logging.getLogger("ml.quality_gate")
+
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+@dataclass
+class QualityGateConfig:
+    """Configuration for the quality gate system."""
+
+    # --- Forge validation holdout (Task 1) ---
+    forge_holdout_ratio: float = 0.12  # 12% of Forge data held out
+    forge_holdout_seed: int = 99       # Fixed seed for reproducibility
+
+    # --- Accuracy gate (Task 2) ---
+    # If model accuracy on Forge eval drops below this absolute floor,
+    # the PPO batch is rejected regardless.
+    min_forge_accuracy: float = 0.35
+
+    # If model accuracy drops by more than this relative amount compared
+    # to the baseline (pre-PPO model), reject the batch.
+    max_accuracy_drop: float = 0.03  # e.g., 41% → 38% = 3% drop → reject
+
+    # --- Win rate gate (Task 3) ---
+    # PPO agent must exceed this win rate before its decisions are exported.
+    # In a 4-player pod, random baseline is 25%.
+    min_ppo_win_rate: float = 0.30
+
+    # --- Quality metrics (Task 4) ---
+    # Directory to write quality reports
+    reports_dir: str = "data/results/quality-reports"
+
+
+# ---------------------------------------------------------------------------
+# Verdict dataclass
+# ---------------------------------------------------------------------------
+
+@dataclass
+class GateVerdict:
+    """Result of a quality gate evaluation."""
+    accepted: bool
+    reason: str
+    metrics: Dict = field(default_factory=dict)
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+
+    def to_dict(self) -> dict:
+        return {
+            "accepted": self.accepted,
+            "reason": self.reason,
+            "metrics": self.metrics,
+            "timestamp": self.timestamp,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Forge Validation Holdout (Task 1)
+# ---------------------------------------------------------------------------
+
+def create_forge_holdout(
+    states: np.ndarray,
+    labels: np.ndarray,
+    sources: np.ndarray,
+    game_ids: np.ndarray,
+    holdout_ratio: float = 0.12,
+    seed: int = 99,
+) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray]]:
+    """
+    Split Forge-only data into training and holdout validation sets.
+
+    The holdout set is a fixed eval set used to detect accuracy regression
+    when PPO data is mixed in. Split is done by game_id to prevent leakage.
+
+    Args:
+        states, labels, sources, game_ids: Dataset arrays
+        holdout_ratio: Fraction of Forge games to hold out
+        seed: Fixed random seed for reproducibility
+
+    Returns:
+        (train_data, holdout_data) — each a dict with states, labels, sources, game_ids
+    """
+    # Identify Forge-only samples
+    forge_mask = np.array([s == "forge" for s in sources])
+    ppo_mask = ~forge_mask
+
+    forge_game_ids = game_ids[forge_mask]
+    unique_forge_games = np.unique(forge_game_ids)
+
+    # Deterministic split by game_id
+    rng = np.random.default_rng(seed)
+    rng.shuffle(unique_forge_games)
+
+    n_holdout = max(1, int(len(unique_forge_games) * holdout_ratio))
+    holdout_games = set(unique_forge_games[:n_holdout])
+    train_games = set(unique_forge_games[n_holdout:])
+
+    # Build masks
+    forge_holdout_mask = np.array([
+        (s == "forge" and gid in holdout_games)
+        for s, gid in zip(sources, game_ids)
+    ])
+    forge_train_mask = np.array([
+        (s == "forge" and gid in train_games)
+        for s, gid in zip(sources, game_ids)
+    ])
+
+    # Training set = Forge train + all PPO data
+    train_mask = forge_train_mask | ppo_mask
+
+    def _subset(mask):
+        return {
+            "states": states[mask],
+            "labels": labels[mask],
+            "sources": sources[mask],
+            "game_ids": game_ids[mask],
+        }
+
+    holdout = _subset(forge_holdout_mask)
+    train = _subset(train_mask)
+
+    logger.info(
+        "Forge holdout: %d samples (%d games) held out, %d train samples remaining",
+        forge_holdout_mask.sum(), n_holdout, train_mask.sum(),
+    )
+
+    return train, holdout
+
+
+# ---------------------------------------------------------------------------
+# Accuracy Gate (Task 2)
+# ---------------------------------------------------------------------------
+
+def check_accuracy_gate(
+    current_accuracy: float,
+    baseline_accuracy: float,
+    config: QualityGateConfig,
+) -> GateVerdict:
+    """
+    Check if the current model accuracy on the Forge holdout set
+    is acceptable after training with PPO data mixed in.
+
+    Args:
+        current_accuracy: Accuracy on Forge holdout after training with PPO data
+        baseline_accuracy: Accuracy on Forge holdout from previous model (no PPO)
+        config: Quality gate configuration
+
+    Returns:
+        GateVerdict — accepted=True if accuracy is acceptable
+    """
+    metrics = {
+        "current_accuracy": round(current_accuracy, 4),
+        "baseline_accuracy": round(baseline_accuracy, 4),
+        "accuracy_drop": round(baseline_accuracy - current_accuracy, 4),
+        "min_forge_accuracy": config.min_forge_accuracy,
+        "max_accuracy_drop": config.max_accuracy_drop,
+    }
+
+    # Check absolute floor
+    if current_accuracy < config.min_forge_accuracy:
+        return GateVerdict(
+            accepted=False,
+            reason=(
+                f"Forge eval accuracy ({current_accuracy:.1%}) below absolute "
+                f"minimum ({config.min_forge_accuracy:.1%}). PPO batch rejected."
+            ),
+            metrics=metrics,
+        )
+
+    # Check relative drop
+    drop = baseline_accuracy - current_accuracy
+    if drop > config.max_accuracy_drop:
+        return GateVerdict(
+            accepted=False,
+            reason=(
+                f"Forge eval accuracy dropped {drop:.1%} "
+                f"(from {baseline_accuracy:.1%} to {current_accuracy:.1%}), "
+                f"exceeding max allowed drop of {config.max_accuracy_drop:.1%}. "
+                f"PPO batch rejected."
+            ),
+            metrics=metrics,
+        )
+
+    return GateVerdict(
+        accepted=True,
+        reason=(
+            f"Forge eval accuracy {current_accuracy:.1%} is acceptable "
+            f"(baseline={baseline_accuracy:.1%}, drop={drop:.1%}). "
+            f"PPO batch accepted."
+        ),
+        metrics=metrics,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Win Rate Gate (Task 3)
+# ---------------------------------------------------------------------------
+
+def check_win_rate_gate(
+    ppo_win_rate: float,
+    config: QualityGateConfig,
+    total_episodes: int = 0,
+    min_episodes: int = 20,
+) -> GateVerdict:
+    """
+    Check if the PPO agent's win rate exceeds the minimum threshold
+    before allowing its decisions to be exported.
+
+    Args:
+        ppo_win_rate: Win rate of PPO agent (0.0 to 1.0)
+        config: Quality gate configuration
+        total_episodes: Number of episodes played
+        min_episodes: Minimum episodes required for statistical significance
+
+    Returns:
+        GateVerdict — accepted=True if win rate is sufficient
+    """
+    metrics = {
+        "ppo_win_rate": round(ppo_win_rate, 4),
+        "min_ppo_win_rate": config.min_ppo_win_rate,
+        "total_episodes": total_episodes,
+        "min_episodes": min_episodes,
+    }
+
+    # Check minimum episode count
+    if total_episodes < min_episodes:
+        return GateVerdict(
+            accepted=False,
+            reason=(
+                f"Insufficient episodes ({total_episodes}/{min_episodes}). "
+                f"Need at least {min_episodes} episodes for reliable win rate."
+            ),
+            metrics=metrics,
+        )
+
+    # Check win rate threshold
+    if ppo_win_rate < config.min_ppo_win_rate:
+        return GateVerdict(
+            accepted=False,
+            reason=(
+                f"PPO win rate ({ppo_win_rate:.1%}) below minimum "
+                f"threshold ({config.min_ppo_win_rate:.1%}). "
+                f"Agent not strong enough — decisions not exported."
+            ),
+            metrics=metrics,
+        )
+
+    return GateVerdict(
+        accepted=True,
+        reason=(
+            f"PPO win rate ({ppo_win_rate:.1%}) exceeds minimum "
+            f"({config.min_ppo_win_rate:.1%}) over {total_episodes} episodes. "
+            f"Decisions approved for export."
+        ),
+        metrics=metrics,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Data Quality Metrics (Task 4)
+# ---------------------------------------------------------------------------
+
+def compute_action_entropy(action_indices: np.ndarray, num_actions: int = 8) -> float:
+    """
+    Compute Shannon entropy of the action distribution.
+
+    High entropy = diverse actions (good).
+    Low entropy = collapsed to few actions (bad — possible mode collapse).
+    Max entropy for 8 actions = log2(8) = 3.0 bits.
+    """
+    counts = np.bincount(action_indices, minlength=num_actions).astype(np.float64)
+    probs = counts / max(counts.sum(), 1)
+    # Filter zero probabilities for log
+    probs = probs[probs > 0]
+    entropy = -np.sum(probs * np.log2(probs))
+    return float(entropy)
+
+
+def compute_reward_stats(rewards: np.ndarray) -> Dict[str, float]:
+    """Compute summary statistics for reward/episode_return distribution."""
+    if len(rewards) == 0:
+        return {"mean": 0.0, "std": 0.0, "min": 0.0, "max": 0.0,
+                "median": 0.0, "p25": 0.0, "p75": 0.0}
+    return {
+        "mean": float(np.mean(rewards)),
+        "std": float(np.std(rewards)),
+        "min": float(np.min(rewards)),
+        "max": float(np.max(rewards)),
+        "median": float(np.median(rewards)),
+        "p25": float(np.percentile(rewards, 25)),
+        "p75": float(np.percentile(rewards, 75)),
+    }
+
+
+def compute_quality_metrics(
+    decisions: List[dict],
+    num_actions: int = 8,
+) -> Dict:
+    """
+    Compute comprehensive data quality metrics for a batch of decisions.
+
+    Metrics:
+      - action_entropy: Shannon entropy of action distribution
+      - action_distribution: count per action
+      - reward_stats: mean, std, min, max, median, p25, p75
+      - source_breakdown: count per source
+      - total_decisions: number of decisions
+      - unique_games: number of unique game IDs
+
+    Args:
+        decisions: List of decision dicts (from JSONL records)
+        num_actions: Total number of possible actions
+
+    Returns:
+        Dict of quality metrics
+    """
+    if not decisions:
+        return {"total_decisions": 0, "error": "empty batch"}
+
+    # Extract action indices
+    action_indices = np.array([
+        d.get("action", {}).get("action_idx", d.get("action_idx", 0))
+        for d in decisions
+    ])
+
+    # Extract rewards/episode returns
+    rewards = np.array([
+        d.get("episode_return", d.get("reward", 0.0))
+        for d in decisions
+    ])
+
+    # Action distribution
+    action_counts = np.bincount(action_indices, minlength=num_actions)
+    action_distribution = {
+        str(i): int(c) for i, c in enumerate(action_counts)
+    }
+
+    # Source breakdown
+    sources = [d.get("source", d.get("_source", "unknown")) for d in decisions]
+    source_counts = {}
+    for s in sources:
+        source_counts[s] = source_counts.get(s, 0) + 1
+
+    # Unique games
+    game_ids = set(d.get("game_id", "?") for d in decisions)
+
+    # Entropy
+    entropy = compute_action_entropy(action_indices, num_actions)
+    max_entropy = np.log2(num_actions)
+
+    return {
+        "total_decisions": len(decisions),
+        "unique_games": len(game_ids),
+        "action_entropy": round(entropy, 4),
+        "max_entropy": round(max_entropy, 4),
+        "entropy_ratio": round(entropy / max(max_entropy, 1e-8), 4),
+        "action_distribution": action_distribution,
+        "reward_stats": compute_reward_stats(rewards),
+        "source_breakdown": source_counts,
+    }
+
+
+def log_quality_metrics(metrics: Dict):
+    """Pretty-print quality metrics to the logger."""
+    logger.info("")
+    logger.info("=" * 55)
+    logger.info("  Data Quality Report")
+    logger.info("=" * 55)
+    logger.info("  Total decisions:   %d", metrics.get("total_decisions", 0))
+    logger.info("  Unique games:      %d", metrics.get("unique_games", 0))
+    logger.info("  Action entropy:    %.3f / %.3f (%.0f%%)",
+                metrics.get("action_entropy", 0),
+                metrics.get("max_entropy", 0),
+                metrics.get("entropy_ratio", 0) * 100)
+    logger.info("")
+
+    # Action distribution
+    dist = metrics.get("action_distribution", {})
+    if dist:
+        total = sum(dist.values())
+        logger.info("  %-20s %8s %7s", "Action", "Count", "%")
+        logger.info("  " + "-" * 37)
+        for action_idx, count in sorted(dist.items(), key=lambda x: -x[1]):
+            pct = 100.0 * count / max(total, 1)
+            logger.info("  %-20s %8d %6.1f%%", f"action_{action_idx}", count, pct)
+
+    # Reward stats
+    rstats = metrics.get("reward_stats", {})
+    if rstats:
+        logger.info("")
+        logger.info("  Reward distribution:")
+        logger.info("    mean=%.3f  std=%.3f  min=%.3f  max=%.3f",
+                    rstats.get("mean", 0), rstats.get("std", 0),
+                    rstats.get("min", 0), rstats.get("max", 0))
+        logger.info("    p25=%.3f  median=%.3f  p75=%.3f",
+                    rstats.get("p25", 0), rstats.get("median", 0),
+                    rstats.get("p75", 0))
+
+    # Source breakdown
+    sources = metrics.get("source_breakdown", {})
+    if sources:
+        logger.info("")
+        logger.info("  Source breakdown:")
+        for src, cnt in sorted(sources.items(), key=lambda x: -x[1]):
+            logger.info("    %-12s %d", src, cnt)
+
+    logger.info("=" * 55)
+
+
+# ---------------------------------------------------------------------------
+# Orchestrator — QualityGate class
+# ---------------------------------------------------------------------------
+
+class QualityGate:
+    """
+    Orchestrates all quality checks for the closed-loop distillation pipeline.
+
+    Usage:
+        gate = QualityGate(config)
+
+        # Before exporting PPO decisions:
+        win_verdict = gate.check_win_rate(ppo_win_rate=0.35, total_episodes=100)
+
+        # After training with mixed data:
+        acc_verdict = gate.check_accuracy(
+            current_accuracy=0.42,
+            baseline_accuracy=0.41,
+        )
+
+        # Log quality metrics for a batch:
+        gate.report_batch_quality(decisions)
+    """
+
+    def __init__(self, config: QualityGateConfig = None):
+        self.config = config or QualityGateConfig()
+        self.verdicts: List[GateVerdict] = []
+
+    def check_win_rate(
+        self,
+        ppo_win_rate: float,
+        total_episodes: int = 0,
+        min_episodes: int = 20,
+    ) -> GateVerdict:
+        """Check if PPO agent meets win rate threshold for export."""
+        verdict = check_win_rate_gate(
+            ppo_win_rate, self.config, total_episodes, min_episodes
+        )
+        self.verdicts.append(verdict)
+        if verdict.accepted:
+            logger.info("WIN RATE GATE: PASS — %s", verdict.reason)
+        else:
+            logger.warning("WIN RATE GATE: FAIL — %s", verdict.reason)
+        return verdict
+
+    def check_accuracy(
+        self,
+        current_accuracy: float,
+        baseline_accuracy: float,
+    ) -> GateVerdict:
+        """Check if model accuracy on Forge holdout is acceptable."""
+        verdict = check_accuracy_gate(
+            current_accuracy, baseline_accuracy, self.config
+        )
+        self.verdicts.append(verdict)
+        if verdict.accepted:
+            logger.info("ACCURACY GATE: PASS — %s", verdict.reason)
+        else:
+            logger.warning("ACCURACY GATE: FAIL — %s", verdict.reason)
+        return verdict
+
+    def report_batch_quality(self, decisions: List[dict]) -> Dict:
+        """Compute and log quality metrics for a decision batch."""
+        metrics = compute_quality_metrics(decisions)
+        log_quality_metrics(metrics)
+
+        # Save report to disk
+        os.makedirs(self.config.reports_dir, exist_ok=True)
+        timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+        report_path = os.path.join(
+            self.config.reports_dir, f"quality_{timestamp}.json"
+        )
+        with open(report_path, "w") as f:
+            json.dump(metrics, f, indent=2)
+        logger.info("Quality report saved: %s", report_path)
+
+        return metrics
+
+    def evaluate_ppo_batch(
+        self,
+        forge_eval_accuracy: float,
+        ppo_win_rate: float,
+        ppo_decisions: List[dict],
+        baseline_accuracy: float,
+        total_episodes: int = 0,
+    ) -> GateVerdict:
+        """
+        Run all quality gates on a PPO batch. Returns a combined verdict.
+
+        This is the main entry point for the distillation pipeline to decide
+        whether to accept or reject a batch of PPO-generated training data.
+        """
+        # 1. Win rate gate
+        win_verdict = self.check_win_rate(ppo_win_rate, total_episodes)
+        if not win_verdict.accepted:
+            return win_verdict
+
+        # 2. Accuracy gate
+        acc_verdict = self.check_accuracy(forge_eval_accuracy, baseline_accuracy)
+        if not acc_verdict.accepted:
+            return acc_verdict
+
+        # 3. Log quality metrics (informational, doesn't block)
+        quality_metrics = self.report_batch_quality(ppo_decisions)
+
+        # 4. Check for extreme entropy collapse (soft warning)
+        entropy_ratio = quality_metrics.get("entropy_ratio", 1.0)
+        if entropy_ratio < 0.3:
+            logger.warning(
+                "LOW ENTROPY WARNING: action entropy ratio %.2f — "
+                "possible mode collapse in PPO agent",
+                entropy_ratio,
+            )
+
+        return GateVerdict(
+            accepted=True,
+            reason="All quality gates passed.",
+            metrics={
+                "win_rate": win_verdict.metrics,
+                "accuracy": acc_verdict.metrics,
+                "quality": quality_metrics,
+            },
+        )
+
+    def save_verdict_history(self, path: str = None):
+        """Save all verdict history to a JSON file."""
+        if path is None:
+            os.makedirs(self.config.reports_dir, exist_ok=True)
+            path = os.path.join(self.config.reports_dir, "verdict_history.json")
+
+        history = [v.to_dict() for v in self.verdicts]
+        with open(path, "w") as f:
+            json.dump(history, f, indent=2)
+        logger.info("Verdict history saved: %s (%d entries)", path, len(history))

ml/encoder/state_encoder.py

@@ -167,6 +167,24 @@ class CardEmbeddingIndex:
         return self._loaded
 
 
+def _resolve_cmdr_dmg(raw) -> int:
+    """Collapse cmdr_dmg to a scalar integer regardless of source format.
+
+    Forge sends cmdr_dmg as a dict keyed by opponent seat string, e.g.
+    {"0": 14, "2": 7}.  _build_encoder_snapshot already sums this before
+    passing to predict(), but the live Forge IPC path (and legacy JSONL)
+    may send the raw dict.  Summing all values is correct for Commander:
+    the total commander damage received from all opponents is what matters
+    for the life-loss threshold (21).
+    """
+    if isinstance(raw, dict):
+        return sum(int(v) for v in raw.values())
+    try:
+        return int(raw)
+    except (TypeError, ValueError):
+        return 0
+
+
 class StateEncoder:
     """
     Encodes a decision snapshot JSON dict into a fixed-size numpy vector.
@@ -178,6 +196,23 @@ class StateEncoder:
       [0:29]     - Global features (life, mana, turn, phase, etc.)
       [29:6173]  - Zone embeddings (4 zones × 2 players × 768)
       [6173:6177] - Playstyle one-hot (4 dims)
+
+    Global feature layout per player block (14 features × 2 players + 1 turn = 29):
+      idx 0  life           (normalised /40)
+      idx 1  cmdr_dmg       (normalised /21)
+      idx 2  mana           (normalised /20, clamped)
+      idx 3  cmdr_tax       (normalised /10, clamped)
+      idx 4  hand_size      (normalised /15, clamped)
+      idx 5  graveyard_size (normalised /100, clamped)
+      idx 6  creatures      (normalised /30, clamped)
+      idx 7  total_power    (normalised /100, clamped)
+                            └─ real Forge value when present;
+                               falls back to creatures*3 heuristic
+      idx 8  is_active      (binary 0/1)
+      idx 9-12 phase one-hot (4 dims: main_1/combat/main_2/end)
+      idx 13  lands          (normalised /15, clamped)
+      --- repeat for player 1 at idx 14-27 ---
+      idx 28 turn           (normalised /max_turns)
     """
 
     def __init__(self, card_index: CardEmbeddingIndex):
@@ -197,7 +232,6 @@ class StateEncoder:
         """
         players = decision.get("players", [])
         if len(players) < 2:
-            # Pad to 2 players for 1v1
             while len(players) < 2:
                 players.append(self._empty_player(len(players)))
 
@@ -218,55 +252,76 @@ class StateEncoder:
         return state.astype(np.float32)
 
     def _encode_global(self, decision: dict, players: list) -> np.ndarray:
-        """Encode global scalar features. Shape: (29,)"""
+        """Encode global scalar features. Shape: (29,)
+
+        Per-player block (14 features each, 2 players = 28) + 1 turn feature.
+
+        Index 7 priority for total_power:
+          1. p['total_power']  — real Forge value from Step 4 / _build_encoder_snapshot()
+          2. p['creatures'] * 3  — legacy heuristic for old JSONL snapshots
+
+        cmdr_dmg is resolved via _resolve_cmdr_dmg() which handles both the
+        live Forge format (dict keyed by opponent seat) and the pre-collapsed
+        int from _build_encoder_snapshot / JSONL training snapshots.
+        """
         features = []
 
-        for p in players[:2]:  # Exactly 2 players for 1v1
-            # Per-player scalars (10 each)
+        for p in players[:2]:
+            # idx 0: life
             life = p.get("life", 40)
-            features.append(life / 40.0)  # Normalized life
+            features.append(life / 40.0)
 
-            cmdr_dmg = p.get("cmdr_dmg", 0)
-            features.append(cmdr_dmg / 21.0)  # Normalized commander damage
+            # idx 1: commander damage taken
+            # _resolve_cmdr_dmg handles dict (live Forge IPC) and int (JSONL/pre-collapsed)
+            cmdr_dmg = _resolve_cmdr_dmg(p.get("cmdr_dmg", 0))
+            features.append(cmdr_dmg / 21.0)
 
+            # idx 2: mana available
             mana = p.get("mana", 0)
-            features.append(min(mana / 20.0, 1.0))  # Normalized mana
+            features.append(min(mana / 20.0, 1.0))
 
+            # idx 3: commander tax
             cmdr_tax = p.get("cmdr_tax", 0)
-            features.append(min(cmdr_tax / 10.0, 1.0))  # Normalized tax
+            features.append(min(cmdr_tax / 10.0, 1.0))
 
+            # idx 4: cards in hand
             hand_size = len(p.get("hand", []))
             features.append(min(hand_size / 15.0, 1.0))
 
+            # idx 5: graveyard size
             grave_size = len(p.get("graveyard", []))
             features.append(min(grave_size / 100.0, 1.0))
 
+            # idx 6: creatures on battlefield
             creatures = p.get("creatures", 0)
             features.append(min(creatures / 30.0, 1.0))
 
-            # Total power on board (not yet tracked accurately; use creatures as proxy)
-            total_power = creatures * 3  # rough heuristic: avg 3 power
+            # idx 7: total power on board
+            # Use real Forge value when available (Step 4); fall back to heuristic
+            # for legacy JSONL snapshots that predate Step 4.
+            raw_power = p.get("total_power", 0)
+            total_power = raw_power if raw_power > 0 else creatures * 3
             features.append(min(total_power / 100.0, 1.0))
 
-            # Is active player
+            # idx 8: is active player
             is_active = 1.0 if p.get("seat") == decision.get("active_seat") else 0.0
             features.append(is_active)
 
-            # Land count (extra feature — helps distinguish ramp strategies)
-            lands = p.get("lands", 0)
-            features.append(min(lands / 15.0, 1.0))
-
-            # Phase one-hot (4 dims per player)
+            # idx 9-12: phase one-hot (4 dims)
             phase_str = decision.get("phase", "main_1")
             phase_oh = [0.0] * 4
             try:
                 phase_enum = GamePhase(phase_str)
                 phase_oh[PHASE_TO_IDX[phase_enum]] = 1.0
             except (ValueError, KeyError):
-                phase_oh[0] = 1.0  # Default to main_1
+                phase_oh[0] = 1.0  # default to main_1
             features.extend(phase_oh)
 
-        # Turn number (1 feature, shared)
+            # idx 13: land count
+            lands = p.get("lands", 0)
+            features.append(min(lands / 15.0, 1.0))
+
+        # idx 28: turn number (shared, 1 feature)
         turn = decision.get("turn", 1)
         features.append(min(turn / GAME_SCOPE.max_turns, 1.0))
 
@@ -276,6 +331,13 @@ class StateEncoder:
         """
         Encode zone contents as mean-pooled card embeddings.
         Shape: (4 zones × 2 players × 768) = (6144,)
+
+        Zone embedding boundary: each zone slot is exactly ``zone_pool_dim``
+        (768) floats.  Empty zones produce a zero vector of that size.  This
+        fixed-size contract is relied upon by every downstream consumer
+        (PolicyNetwork, PPOTrainer, dataset builder).  See
+        ``StateDimensions`` in ``ml/config/scope.py`` for the canonical
+        definition.
         """
         zone_keys = ["hand", "battlefield", "graveyard", "command_zone"]
         vectors = []
@@ -295,24 +357,27 @@ class StateEncoder:
             style = Playstyle(playstyle_str.lower())
             vec[PLAYSTYLE_TO_IDX[style]] = 1.0
         except (ValueError, KeyError):
-            # Default: midrange
             vec[PLAYSTYLE_TO_IDX[Playstyle.MIDRANGE]] = 1.0
         return vec
 
     def _empty_player(self, seat: int) -> dict:
         """Create an empty player dict for padding."""
         return {
-            "seat": seat,
-            "life": 40,
-            "cmdr_dmg": 0,
-            "mana": 0,
-            "cmdr_tax": 0,
-            "creatures": 0,
-            "lands": 0,
-            "hand": [],
-            "battlefield": [],
-            "graveyard": [],
-            "command_zone": [],
+            "seat":            seat,
+            "life":            40,
+            "cmdr_dmg":        0,
+            "mana":            0,
+            "cmdr_tax":        0,
+            "creatures":       0,
+            "total_power":     0,
+            "total_toughness": 0,
+            "artifacts":       0,
+            "enchantments":    0,
+            "lands":           0,
+            "hand":            [],
+            "battlefield":     [],
+            "graveyard":       [],
+            "command_zone":    [],
         }
 
 

ml/eval/elo_tracker.py

@@ -0,0 +1,431 @@
+"""
+Commander AI Lab — ELO Tracker & Cross-Generation Tournament
+═══════════════════════════════════════════════════════════════
+Phase 6 (Issue #70): Quantify improvement across distillation
+generations using ELO ratings.
+
+- Runs round-robin tournaments between generation checkpoints
+  and baselines (heuristic, random)
+- Computes ELO ratings using the standard formula
+- Persists ELO history to `data/elo_history.json`
+- Provides ELO delta for convergence detection
+
+Reuses `run_match`, `LearnedPolicy`, `HeuristicPolicy`,
+`RandomPolicy` from `ml/eval/tournament.py`.
+
+Usage:
+    python -m ml.eval.elo_tracker
+    python -m ml.eval.elo_tracker --episodes 50 --output data/elo_history.json
+    python -m ml.eval.elo_tracker --top-n 10
+"""
+
+import json
+import logging
+import math
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from itertools import combinations
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+project_root = str(Path(__file__).parent.parent.parent)
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+from ml.eval.tournament import (
+    run_match, LearnedPolicy, HeuristicPolicy, RandomPolicy,
+)
+
+logger = logging.getLogger("ml.eval.elo")
+
+
+# ═══════════════════════════════════════════════════════════
+# ELO Rating Engine
+# ═══════════════════════════════════════════════════════════
+
+DEFAULT_K = 32
+DEFAULT_START_ELO = 1200.0
+
+
+class EloTracker:
+    """Standard ELO rating tracker."""
+
+    def __init__(self, k_factor: float = DEFAULT_K, start_elo: float = DEFAULT_START_ELO):
+        self.k = k_factor
+        self.start_elo = start_elo
+        self.ratings: Dict[str, float] = {}
+
+    def ensure_player(self, name: str):
+        if name not in self.ratings:
+            self.ratings[name] = self.start_elo
+
+    def expected_score(self, rating_a: float, rating_b: float) -> float:
+        return 1.0 / (1.0 + math.pow(10, (rating_b - rating_a) / 400.0))
+
+    def update(self, winner: str, loser: str):
+        """Update ratings after a decisive match."""
+        self.ensure_player(winner)
+        self.ensure_player(loser)
+
+        e_win = self.expected_score(self.ratings[winner], self.ratings[loser])
+        e_lose = self.expected_score(self.ratings[loser], self.ratings[winner])
+
+        self.ratings[winner] += self.k * (1.0 - e_win)
+        self.ratings[loser] += self.k * (0.0 - e_lose)
+
+    def update_draw(self, player_a: str, player_b: str):
+        """Update ratings after a draw."""
+        self.ensure_player(player_a)
+        self.ensure_player(player_b)
+
+        e_a = self.expected_score(self.ratings[player_a], self.ratings[player_b])
+        e_b = self.expected_score(self.ratings[player_b], self.ratings[player_a])
+
+        self.ratings[player_a] += self.k * (0.5 - e_a)
+        self.ratings[player_b] += self.k * (0.5 - e_b)
+
+    def get_ratings(self) -> Dict[str, float]:
+        """Return a copy of current ratings, rounded to 1 decimal."""
+        return {k: round(v, 1) for k, v in self.ratings.items()}
+
+
+# ═══════════════════════════════════════════════════════════
+# ELO Result
+# ═══════════════════════════════════════════════════════════
+
+@dataclass
+class EloResult:
+    """Result of an ELO-rated tournament."""
+    ratings: Dict[str, float] = field(default_factory=dict)
+    match_count: int = 0
+    total_time_s: float = 0.0
+    policies: List[str] = field(default_factory=list)
+    rating_progression: List[Dict[str, float]] = field(default_factory=list)
+
+    def to_dict(self) -> dict:
+        return {
+            "ratings": self.ratings,
+            "match_count": self.match_count,
+            "total_time_s": round(self.total_time_s, 1),
+            "policies": self.policies,
+            "rating_progression": self.rating_progression,
+        }
+
+
+# ═══════════════════════════════════════════════════════════
+# ELO History (persistence)
+# ═══════════════════════════════════════════════════════════
+
+class EloHistory:
+    """Persists ELO ratings across tournament runs."""
+
+    def __init__(self, path: str = "data/elo_history.json"):
+        self.path = path
+        self.entries: List[dict] = []
+        self._load()
+
+    def _load(self):
+        if os.path.exists(self.path):
+            try:
+                with open(self.path, "r") as f:
+                    data = json.load(f)
+                self.entries = data.get("entries", [])
+            except Exception:
+                self.entries = []
+
+    def save(self):
+        os.makedirs(os.path.dirname(self.path) or ".", exist_ok=True)
+        with open(self.path, "w") as f:
+            json.dump({"entries": self.entries}, f, indent=2)
+
+    def append(self, generation: int, ratings: Dict[str, float]):
+        """Add a new ELO snapshot after a tournament."""
+        self.entries.append({
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+            "generation": generation,
+            "ratings": {k: round(v, 1) for k, v in ratings.items()},
+        })
+        self.save()
+
+    def get_elo_delta(self, window: int = 2) -> Optional[float]:
+        """
+        Compute average ELO gain of the latest generation model
+        over the last `window` entries.
+
+        Returns None if insufficient data.
+        """
+        if len(self.entries) < window:
+            return None
+
+        recent = self.entries[-window:]
+        deltas = []
+
+        for i in range(1, len(recent)):
+            prev_ratings = recent[i - 1]["ratings"]
+            curr_ratings = recent[i]["ratings"]
+
+            # Find the latest generation model name in current entry
+            gen_models = [k for k in curr_ratings if k.startswith("gen-")]
+            if not gen_models:
+                continue
+
+            latest_gen = sorted(gen_models)[-1]
+            curr_elo = curr_ratings.get(latest_gen, DEFAULT_START_ELO)
+
+            # Compare to previous entry's latest gen model, or heuristic
+            prev_gen_models = [k for k in prev_ratings if k.startswith("gen-")]
+            if prev_gen_models:
+                prev_latest = sorted(prev_gen_models)[-1]
+                prev_elo = prev_ratings.get(prev_latest, DEFAULT_START_ELO)
+            else:
+                prev_elo = prev_ratings.get("heuristic", DEFAULT_START_ELO)
+
+            deltas.append(curr_elo - prev_elo)
+
+        if not deltas:
+            return None
+
+        return sum(deltas) / len(deltas)
+
+    def to_dict(self) -> dict:
+        return {"entries": self.entries}
+
+    @classmethod
+    def from_dict(cls, data: dict, path: str = "data/elo_history.json") -> "EloHistory":
+        h = cls.__new__(cls)
+        h.path = path
+        h.entries = data.get("entries", [])
+        return h
+
+
+# ═══════════════════════════════════════════════════════════
+# Tournament Functions
+# ═══════════════════════════════════════════════════════════
+
+def run_elo_tournament(
+    policies: Dict[str, object],
+    episodes_per_matchup: int = 50,
+    playstyle: str = "midrange",
+    k_factor: float = DEFAULT_K,
+) -> EloResult:
+    """
+    Run a round-robin ELO-rated tournament.
+
+    Uses `run_match` from tournament.py for match execution.
+
+    Args:
+        policies: Dict mapping name → policy object (with select_action method)
+        episodes_per_matchup: Games per pair (played both sides)
+        playstyle: Game playstyle
+        k_factor: ELO K-factor
+
+    Returns:
+        EloResult with final ratings, match count, and progression
+    """
+    t_start = time.time()
+    names = list(policies.keys())
+    tracker = EloTracker(k_factor=k_factor)
+
+    # Initialize all players
+    for name in names:
+        tracker.ensure_player(name)
+
+    progression = [tracker.get_ratings().copy()]
+    total_matches = 0
+
+    logger.info("ELO Tournament: %d policies, %d games/matchup", len(names), episodes_per_matchup)
+
+    for name_a, name_b in combinations(names, 2):
+        policy_a = policies[name_a]
+        policy_b = policies[name_b]
+
+        for ep in range(episodes_per_matchup):
+            # Game 1: A as seat 0
+            m1 = run_match(policy_a, name_a, policy_b, name_b, playstyle=playstyle)
+            total_matches += 1
+            if m1.winner == name_a:
+                tracker.update(name_a, name_b)
+            elif m1.winner == name_b:
+                tracker.update(name_b, name_a)
+            else:
+                tracker.update_draw(name_a, name_b)
+
+            # Game 2: B as seat 0
+            m2 = run_match(policy_b, name_b, policy_a, name_a, playstyle=playstyle)
+            total_matches += 1
+            if m2.winner == name_a:
+                tracker.update(name_a, name_b)
+            elif m2.winner == name_b:
+                tracker.update(name_b, name_a)
+            else:
+                tracker.update_draw(name_a, name_b)
+
+        # Record progression after each matchup
+        progression.append(tracker.get_ratings().copy())
+
+        logger.info(
+            "  %s (%.0f) vs %s (%.0f)",
+            name_a, tracker.ratings[name_a],
+            name_b, tracker.ratings[name_b],
+        )
+
+    return EloResult(
+        ratings=tracker.get_ratings(),
+        match_count=total_matches,
+        total_time_s=time.time() - t_start,
+        policies=names,
+        rating_progression=progression,
+    )
+
+
+def run_generation_tournament(
+    checkpoint_dir: str = "ml/models/checkpoints",
+    episodes_per_matchup: int = 50,
+    playstyle: str = "midrange",
+    k_factor: float = DEFAULT_K,
+    top_n: int = 10,
+) -> EloResult:
+    """
+    Discover generation checkpoints and run an ELO tournament.
+
+    Looks for `gen-*/best_policy.pt` under checkpoint_dir.
+    Also includes heuristic and random baselines.
+
+    Args:
+        top_n: Only include the N most recent gen-* checkpoints.
+               0 means include all (unbounded). Default is 10.
+    """
+    policies: Dict[str, object] = {
+        "heuristic": HeuristicPolicy(),
+        "random": RandomPolicy(),
+    }
+
+    ckpt_path = Path(checkpoint_dir)
+    if ckpt_path.exists():
+        gen_dirs = sorted(ckpt_path.glob("gen-*"))
+
+        # Apply top_n cutoff — keep only the latest N generation dirs
+        if top_n > 0 and len(gen_dirs) > top_n:
+            skipped = len(gen_dirs) - top_n
+            logger.info(
+                "top_n=%d: skipping %d older generation(s), using %s … %s",
+                top_n, skipped,
+                gen_dirs[-top_n].name,
+                gen_dirs[-1].name,
+            )
+            gen_dirs = gen_dirs[-top_n:]
+
+        for gen_dir in gen_dirs:
+            model_path = gen_dir / "best_policy.pt"
+            if model_path.exists():
+                name = gen_dir.name  # e.g., "gen-001"
+                policies[name] = LearnedPolicy(str(model_path))
+                logger.info("Loaded %s: %s", name, model_path)
+
+        # Also check standard checkpoints
+        sup_path = ckpt_path / "best_policy.pt"
+        if sup_path.exists() and "supervised" not in policies:
+            policies["supervised"] = LearnedPolicy(str(sup_path))
+
+        ppo_path = ckpt_path / "best_ppo.pt"
+        if ppo_path.exists() and "ppo" not in policies:
+            policies["ppo"] = LearnedPolicy(str(ppo_path))
+
+    if len(policies) < 2:
+        logger.warning("Need at least 2 policies for ELO tournament")
+        return EloResult(
+            ratings={k: DEFAULT_START_ELO for k in policies},
+            policies=list(policies.keys()),
+        )
+
+    logger.info("Running ELO tournament with %d policies: %s", len(policies), list(policies.keys()))
+
+    return run_elo_tournament(
+        policies=policies,
+        episodes_per_matchup=episodes_per_matchup,
+        playstyle=playstyle,
+        k_factor=k_factor,
+    )
+
+
+# ═══════════════════════════════════════════════════════════
+# CLI
+# ═══════════════════════════════════════════════════════════
+
+def main():
+    """CLI entry point for ELO tournament."""
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Commander AI Lab — ELO Tournament"
+    )
+    parser.add_argument(
+        "--episodes", type=int, default=50,
+        help="Episodes per matchup (default: 50)",
+    )
+    parser.add_argument(
+        "--checkpoint-dir", default="ml/models/checkpoints",
+        help="Checkpoint directory (default: ml/models/checkpoints)",
+    )
+    parser.add_argument(
+        "--playstyle", default="midrange",
+        help="Playstyle (default: midrange)",
+    )
+    parser.add_argument(
+        "--output", default="data/elo_history.json",
+        help="Output path for ELO history (default: data/elo_history.json)",
+    )
+    parser.add_argument(
+        "--k-factor", type=float, default=32,
+        help="ELO K-factor (default: 32)",
+    )
+    parser.add_argument(
+        "--top-n", type=int, default=10,
+        help="Only include the N most recent gen checkpoints (0 = all, default: 10)",
+    )
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+    result = run_generation_tournament(
+        checkpoint_dir=args.checkpoint_dir,
+        episodes_per_matchup=args.episodes,
+        playstyle=args.playstyle,
+        k_factor=args.k_factor,
+        top_n=args.top_n,
+    )
+
+    # Print results
+    logger.info("")
+    logger.info("=" * 50)
+    logger.info("  ELO RATINGS")
+    logger.info("=" * 50)
+    sorted_ratings = sorted(result.ratings.items(), key=lambda x: x[1], reverse=True)
+    for name, elo in sorted_ratings:
+        logger.info("  %-20s %7.1f", name, elo)
+    logger.info("")
+    logger.info("  %d matches in %.1f s", result.match_count, result.total_time_s)
+    logger.info("=" * 50)
+
+    # Save to history
+    history = EloHistory(path=args.output)
+    # Use the latest generation number, or 0 if no gen models
+    gen_models = [k for k in result.ratings if k.startswith("gen-")]
+    gen_num = 0
+    if gen_models:
+        try:
+            gen_num = max(int(g.split("-")[1]) for g in gen_models)
+        except (ValueError, IndexError):
+            pass
+    history.append(gen_num, result.ratings)
+    logger.info("ELO history saved: %s", args.output)
+
+    print(json.dumps(result.to_dict(), indent=2))
+
+
+if __name__ == "__main__":
+    main()

ml/eval/forge_evaluator.py

@@ -0,0 +1,548 @@
+"""
+Commander AI Lab — Forge Evaluator (Issue #153)
+═════════════════════════════════════════════════
+Reusable eval harness that:
+  1. Starts the policy inference server pointing at a given checkpoint
+  2. Launches Forge games via MultiThreadBatchRunner with the policy
+     server wired in as the AI decision-maker
+  3. Collects per-game metrics and writes a JSON results file
+
+Metrics collected per game:
+  - won          : bool
+  - turns        : int
+  - life_final   : int  (winning seat final life)
+  - life_delta   : int  (winner life - loser life at game end)
+  - cmd_damage   : int  (commander damage dealt by policy seat)
+  - illegal_acts : int  (actions rejected by Forge — should be 0)
+  - entropy_mean : float (mean policy entropy across decision steps)
+  - inference_ms : float (mean per-step inference latency)
+
+Usage:
+    from ml.eval.forge_evaluator import ForgeEvaluator, EvalConfig
+    cfg = EvalConfig(
+        checkpoint_path="ml/models/checkpoints/forge-trained/final.pt",
+        forge_jar="forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar",
+        deck_files=["decks/cedh_test_a.dck", "decks/cedh_test_b.dck"],
+        num_games=200,
+        num_threads=4,
+        run_id="forge-trained",
+        results_dir="results",
+    )
+    evaluator = ForgeEvaluator(cfg)
+    summary = evaluator.run()
+    print(summary)
+"""
+
+import json
+import logging
+import os
+import sys
+import time
+import uuid
+import subprocess
+from dataclasses import dataclass, field, asdict
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+
+project_root = str(Path(__file__).parent.parent.parent)
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+logger = logging.getLogger("ml.eval.forge_evaluator")
+
+
+# ─────────────────────────────────────────────
+# Configuration
+# ─────────────────────────────────────────────
+
+@dataclass
+class EvalConfig:
+    """All parameters needed for a single evaluation run."""
+
+    # Model
+    checkpoint_path: str = "ml/models/checkpoints/best_policy.pt"
+    embeddings_dir: str = "embeddings"
+    device: str = None                          # None = auto-detect
+
+    # Forge
+    forge_jar: str = "forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar"
+    forge_work_dir: str = "forge"
+    java_path: str = "java"
+    deck_files: List[str] = field(default_factory=list)
+    deck_names: List[str] = field(default_factory=list)
+    policy_server_url: str = "http://localhost:8091"
+    clock_seconds: int = 120
+
+    # Run
+    num_games: int = 200
+    num_threads: int = 4
+    seed: Optional[int] = None
+    run_id: str = ""
+    results_dir: str = "results"
+
+    # Inference
+    greedy: bool = True
+    temperature: float = 1.0
+
+    def __post_init__(self):
+        if not self.run_id:
+            self.run_id = str(uuid.uuid4())[:8]
+
+
+# ─────────────────────────────────────────────
+# Per-game result
+# ─────────────────────────────────────────────
+
+@dataclass
+class GameRecord:
+    game_index: int
+    won: bool
+    turns: int
+    life_final_policy: int      # life total of policy seat at game end
+    life_final_opponent: int
+    life_delta: int             # policy life - opponent life
+    commander_damage_dealt: int
+    illegal_actions: int
+    entropy_mean: float         # mean per-step policy entropy
+    inference_ms_mean: float    # mean per-step inference latency
+    decision_steps: int         # number of decision points in this game
+    error: str = ""
+
+
+# ─────────────────────────────────────────────
+# Eval summary
+# ─────────────────────────────────────────────
+
+@dataclass
+class EvalSummary:
+    run_id: str
+    checkpoint_path: str
+    num_games: int
+    wins: int
+    losses: int
+    win_rate: float
+    win_rate_ci95_low: float
+    win_rate_ci95_high: float
+    avg_turns: float
+    avg_life_delta: float
+    avg_commander_damage: float
+    avg_illegal_actions: float
+    avg_entropy: float
+    avg_inference_ms: float
+    total_decision_steps: int
+    elapsed_wall_s: float
+    games_per_sec: float
+    error_count: int
+    result_file: str
+
+    def __str__(self) -> str:
+        return (
+            f"EvalSummary run={self.run_id}\n"
+            f"  checkpoint : {self.checkpoint_path}\n"
+            f"  games      : {self.num_games} ({self.wins}W / {self.losses}L)\n"
+            f"  win rate   : {self.win_rate:.1%}  "
+            f"[{self.win_rate_ci95_low:.1%}, {self.win_rate_ci95_high:.1%}] 95% CI\n"
+            f"  avg turns  : {self.avg_turns:.1f}\n"
+            f"  avg life Δ : {self.avg_life_delta:+.1f}\n"
+            f"  avg cmd dmg: {self.avg_commander_damage:.1f}\n"
+            f"  illegal    : {self.avg_illegal_actions:.2f}/game\n"
+            f"  entropy    : {self.avg_entropy:.4f}\n"
+            f"  latency    : {self.avg_inference_ms:.1f} ms/step\n"
+            f"  throughput : {self.games_per_sec:.2f} games/sec\n"
+            f"  result file: {self.result_file}\n"
+        )
+
+
+# ─────────────────────────────────────────────
+# Main evaluator
+# ─────────────────────────────────────────────
+
+class ForgeEvaluator:
+    """
+    Orchestrates a full eval run:
+      1. Spin up PolicyInferenceService on cfg.policy_server_url port
+      2. Run cfg.num_games Forge games via MultiThreadBatchRunner
+      3. Parse Forge JSONL output for per-game metrics
+      4. Write results to {results_dir}/eval-{run_id}.json
+    """
+
+    def __init__(self, cfg: EvalConfig):
+        self.cfg = cfg
+        self._records: List[GameRecord] = []
+        self._result_path = os.path.join(
+            cfg.results_dir, f"eval-{cfg.run_id}.json"
+        )
+
+    # ── Public entry point ───────────────────
+
+    def run(self) -> EvalSummary:
+        """Run the full evaluation. Returns an EvalSummary."""
+        t0 = time.time()
+        os.makedirs(self.cfg.results_dir, exist_ok=True)
+
+        logger.info("[Eval %s] Starting %d-game eval run", self.cfg.run_id, self.cfg.num_games)
+        logger.info("[Eval %s] Checkpoint: %s", self.cfg.run_id, self.cfg.checkpoint_path)
+
+        # Load policy service
+        service = self._load_policy_service()
+
+        # Run games — either via real Forge or synthetic fallback
+        forge_available = (
+            self.cfg.forge_jar
+            and Path(self.cfg.forge_jar).exists()
+            and len(self.cfg.deck_files) >= 3
+        )
+
+        if forge_available:
+            self._records = self._run_forge_games(service)
+        else:
+            logger.warning(
+                "[Eval %s] Forge JAR or decks not found — running synthetic eval.",
+                self.cfg.run_id
+            )
+            self._records = self._run_synthetic_eval(service)
+
+        elapsed = time.time() - t0
+        summary = self._build_summary(elapsed)
+        self._write_results(summary)
+        logger.info("[Eval %s] Done. %s", self.cfg.run_id, summary)
+        return summary
+
+    # ── Policy service setup ─────────────────
+
+    def _load_policy_service(self):
+        try:
+            from ml.serving.policy_server import PolicyInferenceService
+            svc = PolicyInferenceService(
+                checkpoint_dir=str(Path(self.cfg.checkpoint_path).parent),
+                embeddings_dir=self.cfg.embeddings_dir,
+                device=self.cfg.device,
+            )
+            svc.load()
+            ok = svc.reload(self.cfg.checkpoint_path)
+            if not ok:
+                logger.warning("[Eval] Policy service load failed — entropy/latency metrics unavailable")
+            return svc
+        except Exception as e:
+            logger.error("[Eval] Could not load PolicyInferenceService: %s", e)
+            return None
+
+    # ── Real Forge game runner ───────────────
+
+    def _run_forge_games(self, service) -> List[GameRecord]:
+        """Launch LabCli Java subprocess with --ml-log, then parse JSONL output."""
+        logger.info("[Eval %s] Launching Forge CLI subprocess for %d games", self.cfg.run_id, self.cfg.num_games)
+        deck_args = []
+        for i, df in enumerate(self.cfg.deck_files[:4]):
+            deck_args.extend([f"--deck{i+1}", Path(df).stem])
+        # Resolve Java 17
+        java_exe = self.cfg.java_path
+        for c in [r"C:\Program Files\Eclipse Adoptium\jdk-17.0.18.8-hotspot\bin\java.exe",
+                   r"C:\Program Files\Java\jdk-17\bin\java.exe"]:
+            if Path(c).exists():
+                java_exe = c
+                break
+        lab_jar = os.path.join(project_root, "target", "commander-ai-lab-1.0.0-SNAPSHOT.jar")
+        if not Path(lab_jar).exists():
+            logger.error("[Eval] Lab JAR not found at %s -- run mvn package -DskipTests first", lab_jar)
+            return self._run_synthetic_eval(service)
+        cmd = [
+            java_exe, "-jar", lab_jar,
+            "--forge-jar", self.cfg.forge_jar,
+            "--forge-dir", self.cfg.forge_work_dir,
+            *deck_args,
+            "--games", str(self.cfg.num_games),
+            "--threads", str(self.cfg.num_threads),
+            "--clock", str(self.cfg.clock_seconds),
+            "--ml-log",
+            "--output", os.path.join(self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"),
+        ]
+        if self.cfg.seed is not None:
+            cmd.extend(["--seed", str(self.cfg.seed)])
+        logger.info("[Eval %s] CMD: %s", self.cfg.run_id, " ".join(cmd))
+        try:
+            result = subprocess.run(cmd, capture_output=True, text=True,
+                timeout=self.cfg.clock_seconds * self.cfg.num_games + 300)
+            if result.returncode != 0:
+                logger.warning("[Eval %s] Forge CLI exit %d: %s", self.cfg.run_id, result.returncode, result.stderr[:500])
+        except (subprocess.TimeoutExpired, FileNotFoundError) as e:
+            logger.error("[Eval %s] Forge CLI failed: %s -- falling back to synthetic", self.cfg.run_id, e)
+            return self._run_synthetic_eval(service)
+        return self._parse_batch_json(service)
+
+      def _parse_batch_json(self, service) -> List[GameRecord]:
+        """Parse the batch JSON output from LabCli Java subprocess."""
+        batch_path = os.path.join(
+            self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
+        )
+        if not Path(batch_path).exists():
+            logger.warning("[Eval] Batch JSON not found at %s — falling back to synthetic", batch_path)
+            return self._run_synthetic_eval(service)
+        with open(batch_path) as f:
+            batch = json.load(f)
+        games_list = batch.get("games", [])
+        if not games_list:
+            logger.warning("[Eval] No games in batch JSON — falling back to synthetic")
+            return self._run_synthetic_eval(service)
+        records = []
+        for idx, g in enumerate(games_list):
+            won = g.get("winningSeat", -1) == 0
+            turns = g.get("totalTurns", 0)
+            pr = g.get("playerResults", [])
+            lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
+            lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
+            records.append(GameRecord(
+                game_index=idx,
+                won=won,
+                turns=turns,
+                life_final_policy=lp,
+                life_final_opponent=lo,
+                life_delta=lp - lo,
+                commander_damage_dealt=0,
+                illegal_actions=0,
+                entropy_mean=0.0,
+                inference_ms_mean=0.0,
+                decision_steps=turns,
+            ))
+            if len(records) >= self.cfg.num_games:
+                break
+        logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
+        return records
+
+    def _run_forge_via_subprocess(self, service) -> List[GameRecord]:
+        """Parse JSONL files produced by a separately-launched Forge batch."""
+        records = []
+        results_path = Path(self.cfg.results_dir)
+        pattern = f"ml-decisions-forge-eval-{self.cfg.run_id}*.jsonl"
+
+        all_files = sorted(results_path.glob(pattern))
+        if not all_files:
+            logger.warning("[Eval] No JSONL files found matching %s — falling back to synthetic", pattern)
+            return self._run_synthetic_eval(service)
+
+        from ml.training.forge_episode_generator import parse_forge_jsonl, snapshots_to_episode
+        games_seen: Dict[str, list] = {}
+        for f in all_files:
+            for snap in parse_forge_jsonl(f):
+                gid = snap.get("game_id", "g0")
+                games_seen.setdefault(gid, []).append(snap)
+
+        for idx, (gid, snaps) in enumerate(games_seen.items()):
+            records.append(self._snaps_to_record(idx, gid, snaps, service))
+            if len(records) >= self.cfg.num_games:
+                break
+
+        return records
+
+    def _java_results_to_records(self, java_results, service) -> List[GameRecord]:
+        """Convert Java GameResult objects to GameRecord."""
+        records = []
+        for jr in java_results:
+            try:
+                won = getattr(jr, "winningSeat", 0) == 0  # seat 0 = policy
+                records.append(GameRecord(
+                    game_index=getattr(jr, "gameIndex", len(records)),
+                    won=won,
+                    turns=getattr(jr, "turns", 0),
+                    life_final_policy=getattr(jr, "lifeAtEnd", [40])[0] if hasattr(jr, "lifeAtEnd") else 40,
+                    life_final_opponent=getattr(jr, "lifeAtEnd", [40, 40])[1] if hasattr(jr, "lifeAtEnd") else 40,
+                    life_delta=0,
+                    commander_damage_dealt=0,
+                    illegal_actions=getattr(jr, "illegalActions", 0),
+                    entropy_mean=0.0,
+                    inference_ms_mean=0.0,
+                    decision_steps=0,
+                ))
+            except Exception as e:
+                logger.warning("[Eval] Skipping malformed GameResult: %s", e)
+        return records
+
+    # ── Synthetic fallback ───────────────────
+
+    def _run_synthetic_eval(self, service) -> List[GameRecord]:
+        """Run synthetic games using self_play engine (no Forge required).
+
+        Used when Forge JAR is missing or deck files aren't configured.
+        Metrics like illegal_actions and commander_damage are 0 in this mode.
+        """
+        import warnings
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+        records = []
+        try:
+            from ml.training.self_play import (
+                create_random_initial_state, apply_action, advance_turn,
+                encode_state_simple, HeuristicPolicy
+            )
+            from ml.config.scope import NUM_ACTIONS, IDX_TO_ACTION
+            import torch
+            import torch.nn.functional as F
+        except ImportError as e:
+            logger.error("[Eval] Cannot import self_play: %s", e)
+            return []
+
+        opponent = HeuristicPolicy()
+        agent_has_model = service is not None and service._loaded
+
+        for game_idx in range(self.cfg.num_games):
+            state = create_random_initial_state()
+            entropies, latencies = [], []
+            steps = 0
+
+            while not state.game_over and steps < 60:
+                sv = encode_state_simple(state, 0, "midrange")
+                t0 = time.time()
+
+                if agent_has_model:
+                    snap = state.to_snapshot()
+                    result = service.predict(
+                        snap, playstyle="midrange",
+                        greedy=self.cfg.greedy,
+                        temperature=self.cfg.temperature,
+                    )
+                    action_idx = result.get("action_index", 7)
+                    probs_map = result.get("probabilities", {})
+                    probs = np.array(list(probs_map.values())) if probs_map else np.ones(NUM_ACTIONS) / NUM_ACTIONS
+                else:
+                    action_idx = np.random.randint(NUM_ACTIONS)
+                    probs = np.ones(NUM_ACTIONS) / NUM_ACTIONS
+
+                latencies.append((time.time() - t0) * 1000)
+                p = np.clip(probs, 1e-9, 1.0)
+                p /= p.sum()
+                entropies.append(float(-np.sum(p * np.log(p))))
+
+                state = apply_action(state, action_idx, 0)
+                if not state.game_over:
+                    opp_sv = encode_state_simple(state, 1, "midrange")
+                    opp_a, _ = opponent.select_action(opp_sv)
+                    state = apply_action(state, opp_a, 1)
+                if not state.game_over:
+                    state = advance_turn(state)
+                steps += 1
+
+            won = state.winner == 0
+            lp = state.players[0].life_total
+            lo = state.players[1].life_total
+            records.append(GameRecord(
+                game_index=game_idx,
+                won=won,
+                turns=state.turn,
+                life_final_policy=lp,
+                life_final_opponent=lo,
+                life_delta=lp - lo,
+                commander_damage_dealt=0,
+                illegal_actions=0,
+                entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
+                inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
+                decision_steps=steps,
+            ))
+
+        return records
+
+    # ── JSONL snapshot → GameRecord ──────────
+
+    def _snaps_to_record(self, idx: int, gid: str, snaps: list, service) -> GameRecord:
+        entropies, latencies = [], []
+        won = False
+        turns = len(snaps)
+        cmd_dmg = 0
+        illegal = 0
+        lp, lo = 40, 40
+
+        for snap in snaps:
+            if "outcome" in snap and snap == snaps[-1]:
+                won = snap["outcome"].get("winner_seat", -1) == 0
+                lp = snap["outcome"].get("life_remaining", 40)
+                lo = snap["outcome"].get("opp_life_remaining", 0)
+                cmd_dmg = snap["outcome"].get("commander_damage_dealt", 0)
+
+            if service and service._loaded and "state_vector" in snap:
+                t0 = time.time()
+                result = service.predict(snap, playstyle="midrange", greedy=self.cfg.greedy)
+                latencies.append((time.time() - t0) * 1000)
+                probs_map = result.get("probabilities", {})
+                if probs_map:
+                    p = np.clip(list(probs_map.values()), 1e-9, 1.0)
+                    p /= p.sum()
+                    entropies.append(float(-np.sum(p * np.log(p))))
+
+        return GameRecord(
+            game_index=idx,
+            won=won,
+            turns=turns,
+            life_final_policy=lp,
+            life_final_opponent=lo,
+            life_delta=lp - lo,
+            commander_damage_dealt=cmd_dmg,
+            illegal_actions=illegal,
+            entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
+            inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
+            decision_steps=len(snaps),
+        )
+
+    # ── Summary builder ──────────────────────
+
+    def _build_summary(self, elapsed: float) -> EvalSummary:
+        n = len(self._records)
+        if n == 0:
+            return EvalSummary(
+                run_id=self.cfg.run_id,
+                checkpoint_path=self.cfg.checkpoint_path,
+                num_games=0, wins=0, losses=0,
+                win_rate=0.0, win_rate_ci95_low=0.0, win_rate_ci95_high=0.0,
+                avg_turns=0.0, avg_life_delta=0.0, avg_commander_damage=0.0,
+                avg_illegal_actions=0.0, avg_entropy=0.0, avg_inference_ms=0.0,
+                total_decision_steps=0, elapsed_wall_s=elapsed, games_per_sec=0.0,
+                error_count=0, result_file=self._result_path,
+            )
+
+        wins = sum(1 for r in self._records if r.won)
+        losses = n - wins
+        wr = wins / n
+
+        # Wilson score 95% CI
+        z = 1.96
+        denom = 1 + z**2 / n
+        centre = (wr + z**2 / (2 * n)) / denom
+        margin = (z * (wr * (1 - wr) / n + z**2 / (4 * n**2)) ** 0.5) / denom
+        ci_lo = max(0.0, centre - margin)
+        ci_hi = min(1.0, centre + margin)
+
+        errors = sum(1 for r in self._records if r.error)
+
+        return EvalSummary(
+            run_id=self.cfg.run_id,
+            checkpoint_path=self.cfg.checkpoint_path,
+            num_games=n,
+            wins=wins,
+            losses=losses,
+            win_rate=wr,
+            win_rate_ci95_low=ci_lo,
+            win_rate_ci95_high=ci_hi,
+            avg_turns=float(np.mean([r.turns for r in self._records])),
+            avg_life_delta=float(np.mean([r.life_delta for r in self._records])),
+            avg_commander_damage=float(np.mean([r.commander_damage_dealt for r in self._records])),
+            avg_illegal_actions=float(np.mean([r.illegal_actions for r in self._records])),
+            avg_entropy=float(np.mean([r.entropy_mean for r in self._records])),
+            avg_inference_ms=float(np.mean([r.inference_ms_mean for r in self._records])),
+            total_decision_steps=sum(r.decision_steps for r in self._records),
+            elapsed_wall_s=elapsed,
+            games_per_sec=n / max(elapsed, 0.001),
+            error_count=errors,
+            result_file=self._result_path,
+        )
+
+    # ── Result writer ────────────────────────
+
+    def _write_results(self, summary: EvalSummary) -> None:
+        payload = {
+            "summary": asdict(summary),
+            "games": [asdict(r) for r in self._records],
+        }
+        os.makedirs(self.cfg.results_dir, exist_ok=True)
+        with open(self._result_path, "w") as f:
+            json.dump(payload, f, indent=2)
+        logger.info("[Eval %s] Results written to %s", self.cfg.run_id, self._result_path)

ml/scripts/update_weights.py

@@ -0,0 +1,344 @@
+"""
+update_weights.py
+=================
+Outcome-weighted reinforcement learning for Commander AI card weights.
+
+Reads all decisions_*.jsonl files produced by DeepSeekBrain.flush_log(),
+applies a gradient-free reward nudge to each weight key based on the
+normalised reward signal (-1.0 to +1.0) stamped on every decision entry,
+then saves the updated weights to learned_weights.json via save_weights().
+
+Usage
+-----
+    # Basic run (reads logs/decisions/, updates learned_weights.json)
+    python -m ml.scripts.update_weights
+
+    # Custom paths
+    python -m ml.scripts.update_weights \\
+        --log-dir logs/decisions \\
+        --weights-out src/commander_ai_lab/sim/learned_weights.json \\
+        --lr 0.05 \\
+        --min-games 10
+
+    # Dry-run (print proposed changes, don't write)
+    python -m ml.scripts.update_weights --dry-run
+
+    # Reset to defaults then exit
+    python -m ml.scripts.update_weights --reset
+
+Algorithm
+---------
+For each decision entry that has a non-null game_result:
+
+    delta = learning_rate * reward * action_weight_for(action)
+
+Each weight key is nudged by the mean delta across all decisions that
+touched that key.  Keys with no decisions are left unchanged.
+All values are clamped to [-20.0, 20.0] by save_weights().
+
+Reward is the normalised life-delta from flush_log():
+    reward = (my_final_life - avg_opp_final_life) / 40.0  in [-1.0, 1.0]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import sys
+from collections import defaultdict
+from pathlib import Path
+from typing import Iterator
+
+# ---------------------------------------------------------------------------
+# Allow running as a script without installing the package
+# ---------------------------------------------------------------------------
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(_REPO_ROOT / "src") not in sys.path:
+    sys.path.insert(0, str(_REPO_ROOT / "src"))
+
+from commander_ai_lab.sim.rules import (
+    AI_DEFAULT_WEIGHTS,
+    load_weights,
+    reset_weights,
+    save_weights,
+)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(message)s",
+    datefmt="%H:%M:%S",
+)
+logger = logging.getLogger("update_weights")
+
+# ---------------------------------------------------------------------------
+# Action → weight key mapping
+# Maps each VALID_ACTION to the weight(s) it most directly influences.
+# A single action may touch multiple keys (listed as a tuple).
+# ---------------------------------------------------------------------------
+ACTION_WEIGHT_MAP: dict[str, tuple[str, ...]] = {
+    "play_land":       (),                                    # no weight key — always correct
+    "cast_ramp":       ("spell_ramp",),
+    "cast_removal":    ("spell_destroy",),
+    "cast_board_wipe": ("spell_counter",),                   # reuse counter as board-control proxy
+    "cast_creature":   ("card_ptBonus",),
+    "cast_spell":      ("spell_enchantment", "spell_draw"),
+    "attack_all":      ("kw_haste", "kw_flying"),            # aggression / evasion proxy
+    "attack_safe":     ("kw_flying", "kw_trample"),
+    "hold":            (),                                    # no weight key — passing is neutral
+}
+
+
+# ---------------------------------------------------------------------------
+# JSONL helpers
+# ---------------------------------------------------------------------------
+
+def _iter_log_entries(log_dir: str) -> Iterator[dict]:
+    """Yield every entry from all decisions_*.jsonl files in log_dir."""
+    log_path = Path(log_dir)
+    if not log_path.exists():
+        logger.warning("Log directory not found: %s", log_dir)
+        return
+
+    files = sorted(log_path.glob("decisions_*.jsonl"))
+    if not files:
+        logger.warning("No decisions_*.jsonl files found in %s", log_dir)
+        return
+
+    logger.info("Found %d log file(s) in %s", len(files), log_dir)
+    for fpath in files:
+        with open(fpath, encoding="utf-8") as f:
+            for lineno, line in enumerate(f, 1):
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    yield json.loads(line)
+                except json.JSONDecodeError as exc:
+                    logger.warning("Skipping malformed line %d in %s: %s", lineno, fpath.name, exc)
+
+
+# ---------------------------------------------------------------------------
+# Core learning loop
+# ---------------------------------------------------------------------------
+
+def compute_weight_updates(
+    log_dir: str,
+    current_weights: dict[str, float],
+    learning_rate: float = 0.05,
+    min_games: int = 1,
+) -> tuple[dict[str, float], dict]:
+    """
+    Read decision logs and compute updated weights.
+
+    Returns
+    -------
+    updated_weights : dict[str, float]
+        New weight dict after applying mean reward nudges.
+    stats : dict
+        Diagnostic info (games seen, decisions processed, keys changed, etc.)
+    """
+    # Accumulators: key → list of (reward * lr) deltas
+    key_deltas: dict[str, list[float]] = defaultdict(list)
+
+    total_entries = 0
+    labelled_entries = 0  # entries with non-null game_result
+    game_ids_seen: set[str] = set()
+    action_counts: dict[str, int] = defaultdict(int)
+
+    for entry in _iter_log_entries(log_dir):
+        total_entries += 1
+
+        game_result = entry.get("game_result")
+        if not game_result:
+            continue  # no outcome stamped — skip (pre-flush or incomplete game)
+
+        labelled_entries += 1
+        game_ids_seen.add(entry.get("game_id", "unknown"))
+
+        reward: float = float(game_result.get("reward", 0.0))
+        action: str = entry.get("decision", {}).get("action", "hold")
+        action_counts[action] += 1
+
+        weight_keys = ACTION_WEIGHT_MAP.get(action, ())
+        for key in weight_keys:
+            if key in current_weights:
+                key_deltas[key].append(learning_rate * reward)
+
+    num_games = len(game_ids_seen)
+    logger.info(
+        "Processed %d entries | %d labelled | %d unique games",
+        total_entries, labelled_entries, num_games,
+    )
+
+    if num_games < min_games:
+        logger.warning(
+            "Only %d game(s) found — minimum is %d.  Skipping update.",
+            num_games, min_games,
+        )
+        return dict(current_weights), {
+            "skipped": True,
+            "reason": f"need {min_games} games, have {num_games}",
+            "total_entries": total_entries,
+            "labelled_entries": labelled_entries,
+            "games": num_games,
+        }
+
+    # Apply mean delta per key
+    updated = dict(current_weights)
+    changed_keys: dict[str, tuple[float, float]] = {}  # key → (old, new)
+
+    for key, deltas in key_deltas.items():
+        if not deltas:
+            continue
+        mean_delta = sum(deltas) / len(deltas)
+        old_val = updated[key]
+        new_val = round(old_val + mean_delta, 6)
+        updated[key] = new_val
+        if abs(new_val - old_val) >= 0.0001:
+            changed_keys[key] = (old_val, new_val)
+
+    stats = {
+        "skipped": False,
+        "total_entries": total_entries,
+        "labelled_entries": labelled_entries,
+        "games": num_games,
+        "keys_changed": len(changed_keys),
+        "changes": changed_keys,
+        "action_counts": dict(action_counts),
+    }
+    return updated, stats
+
+
+def print_diff(stats: dict, current: dict, updated: dict) -> None:
+    """Pretty-print weight changes to stdout."""
+    changes = stats.get("changes", {})
+    if not changes:
+        print("  (no weight changes above threshold)")
+        return
+
+    col_w = max(len(k) for k in changes) + 2
+    print(f"  {'Key':<{col_w}}  {'Before':>10}  {'After':>10}  {'Delta':>10}")
+    print("  " + "-" * (col_w + 36))
+    for key, (old, new) in sorted(changes.items(), key=lambda x: abs(x[1][1] - x[1][0]), reverse=True):
+        delta = new - old
+        arrow = "▲" if delta > 0 else "▼"
+        print(f"  {key:<{col_w}}  {old:>10.4f}  {new:>10.4f}  {arrow} {abs(delta):>8.4f}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+def _default_log_dir() -> str:
+    return str(_REPO_ROOT / "logs" / "decisions")
+
+
+def _default_weights_out() -> str:
+    return str(_REPO_ROOT / "src" / "commander_ai_lab" / "sim" / "learned_weights.json")
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Update AI card weights from simulation decision logs."
+    )
+    parser.add_argument(
+        "--log-dir",
+        default=_default_log_dir(),
+        help="Directory containing decisions_*.jsonl files (default: logs/decisions/)",
+    )
+    parser.add_argument(
+        "--weights-out",
+        default=_default_weights_out(),
+        help="Path to write learned_weights.json (default: src/.../sim/learned_weights.json)",
+    )
+    parser.add_argument(
+        "--lr",
+        type=float,
+        default=0.05,
+        help="Learning rate — how much each game nudges a weight (default: 0.05)",
+    )
+    parser.add_argument(
+        "--min-games",
+        type=int,
+        default=10,
+        help="Minimum number of completed games required before updating (default: 10)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print proposed changes without writing learned_weights.json",
+    )
+    parser.add_argument(
+        "--reset",
+        action="store_true",
+        help="Delete learned_weights.json and restore AI_DEFAULT_WEIGHTS, then exit",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Show action distribution and per-key delta counts",
+    )
+    args = parser.parse_args(argv)
+
+    # ── Reset mode ──────────────────────────────────────────────
+    if args.reset:
+        reset_weights(args.weights_out)
+        print(f"Reset: deleted {args.weights_out} (if it existed). Defaults restored.")
+        return 0
+
+    # ── Load current weights ─────────────────────────────────────
+    current = load_weights(args.weights_out)
+    logger.info("Loaded %d weight keys from %s", len(current), args.weights_out)
+
+    # ── Compute updates ──────────────────────────────────────────
+    updated, stats = compute_weight_updates(
+        log_dir=args.log_dir,
+        current_weights=current,
+        learning_rate=args.lr,
+        min_games=args.min_games,
+    )
+
+    # ── Report ───────────────────────────────────────────────────
+    print()
+    print("=" * 60)
+    print("  Commander AI — Weight Update Report")
+    print("=" * 60)
+    print(f"  Log dir    : {args.log_dir}")
+    print(f"  Weights out: {args.weights_out}")
+    print(f"  Games      : {stats['games']}")
+    print(f"  Entries    : {stats['total_entries']}  ({stats['labelled_entries']} labelled)")
+    print(f"  LR         : {args.lr}")
+    print()
+
+    if stats.get("skipped"):
+        print(f"  SKIPPED — {stats['reason']}")
+        print()
+        return 0
+
+    print(f"  Keys changed: {stats['keys_changed']}")
+    print()
+    print_diff(stats, current, updated)
+    print()
+
+    if args.verbose:
+        print("  Action distribution:")
+        for action, count in sorted(stats["action_counts"].items(), key=lambda x: -x[1]):
+            print(f"    {action:<20} {count:>6} decisions")
+        print()
+
+    # ── Write ────────────────────────────────────────────────────
+    if args.dry_run:
+        print("  DRY RUN — no file written.")
+    else:
+        out_path = save_weights(updated, args.weights_out)
+        print(f"  Saved updated weights to: {out_path}")
+
+    print("=" * 60)
+    print()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

ml/serving/online_learning_store.py

@@ -0,0 +1,210 @@
+"""
+Commander AI Lab — Online Learning Store (SQLite WAL)
+═════════════════════════════════════════════════════
+
+Single-machine backing store for online learning data collected during
+policy inference (``/api/ml/predict`` and ``/api/policy/decide``).
+
+Each prediction request can optionally log the state snapshot and the
+chosen action so that future training runs can incorporate online
+decision data alongside Forge and PPO batches.
+
+Design decisions (Phase 5 architecture follow-up):
+  - SQLite in WAL mode for concurrent readers + single writer, which is
+    the right fit for a single-machine baseline (one API server process).
+  - Separate DB file (``online_learning.db``) to avoid contention with
+    the collection database.
+  - Append-only writes; reads are batched by the dataset builder.
+  - Thread-safe via the Python sqlite3 module's ``check_same_thread=False``
+    and WAL's built-in reader/writer concurrency.
+
+Usage::
+
+    store = OnlineLearningStore()   # uses default path
+    store.init_db()
+
+    # At prediction time:
+    store.record_decision(snapshot_json, action_index, confidence, playstyle)
+
+    # At training time:
+    decisions = store.fetch_decisions(since_rowid=last_seen, limit=10000)
+    store.mark_exported(max_rowid)
+"""
+
+import json
+import logging
+import os
+import sqlite3
+import threading
+from pathlib import Path
+from typing import Dict, List, Optional
+
+logger = logging.getLogger("ml.serving.online_store")
+
+_DEFAULT_DB_PATH = str(
+    Path(__file__).parent.parent.parent / "online_learning.db"
+)
+
+
+class OnlineLearningStore:
+    """SQLite WAL-backed store for online policy decision collection."""
+
+    def __init__(self, db_path: str = None):
+        self.db_path = db_path or _DEFAULT_DB_PATH
+        self._local = threading.local()
+
+    # ------------------------------------------------------------------
+    # Connection management
+    # ------------------------------------------------------------------
+
+    def _get_conn(self) -> sqlite3.Connection:
+        """Return a thread-local connection with WAL mode enabled."""
+        if not hasattr(self._local, "conn") or self._local.conn is None:
+            conn = sqlite3.connect(self.db_path, check_same_thread=False)
+            conn.row_factory = sqlite3.Row
+            # Enable WAL for concurrent read/write on single machine.
+            conn.execute("PRAGMA journal_mode=WAL")
+            conn.execute("PRAGMA synchronous=NORMAL")
+            self._local.conn = conn
+        return self._local.conn
+
+    def close(self) -> None:
+        """Close the thread-local connection."""
+        conn = getattr(self._local, "conn", None)
+        if conn is not None:
+            try:
+                conn.close()
+            except Exception:
+                pass
+            self._local.conn = None
+
+    # ------------------------------------------------------------------
+    # Schema
+    # ------------------------------------------------------------------
+
+    def init_db(self) -> None:
+        """Create the decisions table if it doesn't exist."""
+        conn = self._get_conn()
+        conn.executescript("""
+            CREATE TABLE IF NOT EXISTS online_decisions (
+                id          INTEGER PRIMARY KEY AUTOINCREMENT,
+                snapshot    TEXT    NOT NULL,
+                action_idx  INTEGER NOT NULL,
+                confidence  REAL    NOT NULL DEFAULT 0.0,
+                playstyle   TEXT    NOT NULL DEFAULT 'midrange',
+                temperature REAL    NOT NULL DEFAULT 1.0,
+                greedy      INTEGER NOT NULL DEFAULT 0,
+                exported    INTEGER NOT NULL DEFAULT 0,
+                created_at  TEXT    NOT NULL DEFAULT (datetime('now'))
+            );
+
+            CREATE INDEX IF NOT EXISTS idx_od_exported
+                ON online_decisions(exported);
+            CREATE INDEX IF NOT EXISTS idx_od_created
+                ON online_decisions(created_at);
+        """)
+        conn.commit()
+        logger.info("Online learning store ready: %s (WAL mode)", self.db_path)
+
+    # ------------------------------------------------------------------
+    # Write path (called during prediction)
+    # ------------------------------------------------------------------
+
+    def record_decision(
+        self,
+        snapshot: Dict,
+        action_idx: int,
+        confidence: float,
+        playstyle: str = "midrange",
+        temperature: float = 1.0,
+        greedy: bool = False,
+    ) -> int:
+        """Append a policy decision for future training.
+
+        Returns the row id of the inserted record.
+        """
+        conn = self._get_conn()
+        cur = conn.execute(
+            """
+            INSERT INTO online_decisions
+                (snapshot, action_idx, confidence, playstyle, temperature, greedy)
+            VALUES (?, ?, ?, ?, ?, ?)
+            """,
+            (
+                json.dumps(snapshot, separators=(",", ":")),
+                action_idx,
+                confidence,
+                playstyle,
+                temperature,
+                1 if greedy else 0,
+            ),
+        )
+        conn.commit()
+        return cur.lastrowid
+
+    # ------------------------------------------------------------------
+    # Read path (called by dataset builder / training pipeline)
+    # ------------------------------------------------------------------
+
+    def fetch_decisions(
+        self,
+        since_rowid: int = 0,
+        limit: int = 50000,
+        only_unexported: bool = True,
+    ) -> List[Dict]:
+        """Fetch collected decisions for training.
+
+        Args:
+            since_rowid: Only return rows with id > this value.
+            limit: Max rows to return.
+            only_unexported: If True, skip rows already marked exported.
+
+        Returns:
+            List of dicts with keys: id, snapshot (parsed), action_idx,
+            confidence, playstyle, temperature, greedy, created_at.
+        """
+        conn = self._get_conn()
+        clauses = ["id > ?"]
+        params: list = [since_rowid]
+        if only_unexported:
+            clauses.append("exported = 0")
+        where = " AND ".join(clauses)
+        rows = conn.execute(
+            f"SELECT * FROM online_decisions WHERE {where} ORDER BY id LIMIT ?",
+            (*params, limit),
+        ).fetchall()
+        results = []
+        for row in rows:
+            d = dict(row)
+            try:
+                d["snapshot"] = json.loads(d["snapshot"])
+            except (json.JSONDecodeError, TypeError):
+                pass
+            results.append(d)
+        return results
+
+    def mark_exported(self, up_to_rowid: int) -> int:
+        """Mark decisions as exported so they aren't fetched again.
+
+        Returns the number of rows updated.
+        """
+        conn = self._get_conn()
+        cur = conn.execute(
+            "UPDATE online_decisions SET exported = 1 WHERE id <= ? AND exported = 0",
+            (up_to_rowid,),
+        )
+        conn.commit()
+        return cur.rowcount
+
+    def count(self, only_unexported: bool = False) -> int:
+        """Return the total number of stored decisions."""
+        conn = self._get_conn()
+        if only_unexported:
+            row = conn.execute(
+                "SELECT COUNT(*) FROM online_decisions WHERE exported = 0"
+            ).fetchone()
+        else:
+            row = conn.execute(
+                "SELECT COUNT(*) FROM online_decisions"
+            ).fetchone()
+        return row[0]

ml/serving/policy_server.py

@@ -52,14 +52,44 @@ from ml.config.scope import (
 )
 from ml.encoder.state_encoder import CardEmbeddingIndex, StateEncoder
 
-logger = logging.getLogger("ml.serving")
+# Unique logger name so log lines are attributable to this module specifically
+logger = logging.getLogger("ml.serving.policy_server")
+
+
+def _unwrap_logits(raw):
+    """
+    Normalise whatever self.model() returns to a plain logits tensor.
+
+    PolicyNetwork.forward() returns a raw tensor, but the model may be
+    wrapped (DataParallel, torch.compile) or accidentally swapped for
+    PolicyValueNetwork whose forward_with_value() returns (logits, value).
+    A dict return would cause 'dict / float' TypeError on the temperature
+    division -- guard against all three cases here.
+    """
+    if isinstance(raw, (tuple, list)):
+        # (logits, value) from PolicyValueNetwork or similar
+        return raw[0]
+    if isinstance(raw, dict):
+        # e.g. {"logits": tensor, "value": tensor}
+        for key in ("logits", "policy", "action_logits"):
+            if key in raw:
+                return raw[key]
+        # Fallback: grab the first tensor value in the dict
+        for v in raw.values():
+            if isinstance(v, torch.Tensor):
+                return v
+        raise TypeError(
+            f"model() returned a dict with no recognisable logits key: {list(raw.keys())}"
+        )
+    # Plain tensor -- the normal case
+    return raw
 
 
 class PolicyInferenceService:
     """
     Loads a trained PolicyNetwork and provides action predictions.
 
-    Thread-safe — uses torch.no_grad() for inference.
+    Thread-safe -- uses torch.no_grad() for inference.
     Designed to be embedded in lab_api.py or run standalone.
     """
 
@@ -100,7 +130,7 @@ class PolicyInferenceService:
         """
         if not TORCH_AVAILABLE:
             self._load_error = "PyTorch not installed"
-            logger.error("PyTorch not installed — cannot serve policy")
+            logger.error("PyTorch not installed -- cannot serve policy")
             return False
 
         # 1. Load card embeddings
@@ -146,7 +176,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             self._load_error = str(e)
-            logger.error("Failed to load checkpoint: %s", e)
+            logger.error("Failed to load checkpoint: %s", e, exc_info=True)
             return False
 
     def reload(self, checkpoint_path: str = None) -> bool:
@@ -178,7 +208,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             self._load_error = str(e)
-            logger.error("Hot-reload failed: %s", e)
+            logger.error("Hot-reload failed: %s", e, exc_info=True)
             return False
 
     def predict(
@@ -224,7 +254,10 @@ class PolicyInferenceService:
 
             # Run inference
             with torch.no_grad():
-                logits = self.model(state_tensor)
+                raw_output = self.model(state_tensor)
+                # Guard: unwrap tuple/dict returns (DataParallel, torch.compile,
+                # or accidental PolicyValueNetwork swap) before any arithmetic.
+                logits = _unwrap_logits(raw_output)
 
                 if greedy:
                     action_idx = logits.argmax(dim=-1).item()
@@ -254,7 +287,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             elapsed_ms = (time.time() - t_start) * 1000
-            logger.error("Inference error: %s", e)
+            logger.error("Inference error: %s", e, exc_info=True)
             return {
                 "error": str(e),
                 "inference_ms": round(elapsed_ms, 2),
@@ -286,7 +319,9 @@ class PolicyInferenceService:
 
             # Batch inference
             with torch.no_grad():
-                logits = self.model(state_tensor)
+                raw_output = self.model(state_tensor)
+                # Same guard as predict() -- unwrap before softmax/argmax.
+                logits = _unwrap_logits(raw_output)
                 probs = torch.softmax(logits, dim=-1).cpu().numpy()
 
                 if greedy:
@@ -312,7 +347,7 @@ class PolicyInferenceService:
             )
 
         except Exception as e:
-            logger.error("Batch inference error: %s", e)
+            logger.error("Batch inference error: %s", e, exc_info=True)
             results = [{"error": str(e)}] * len(snapshots)
 
         return results
@@ -331,9 +366,9 @@ class PolicyInferenceService:
         }
 
 
-# ══════════════════════════════════════════════════════════
+# ==========================================================
 # Standalone FastAPI Server
-# ══════════════════════════════════════════════════════════
+# ==========================================================
 
 def create_standalone_app():
     """Create a standalone FastAPI app for the policy server."""
@@ -341,7 +376,7 @@ def create_standalone_app():
     from pydantic import BaseModel
 
     app = FastAPI(
-        title="Commander AI Lab — Policy Server",
+        title="Commander AI Lab -- Policy Server",
         description="Serves macro-action predictions from a trained policy network",
         version="1.0.0",
     )
@@ -424,7 +459,7 @@ def main():
     import uvicorn
 
     parser = argparse.ArgumentParser(
-        description="Commander AI Lab — Policy Inference Server"
+        description="Commander AI Lab -- Policy Inference Server"
     )
     parser.add_argument("--port", type=int, default=8090)
     parser.add_argument("--host", default="127.0.0.1")

ml/training/__init__.py

@@ -0,0 +1,40 @@
+"""
+Commander AI Lab - Training Subpackage
+==========================================
+Provides RL training infrastructure including PPO, rollout buffers,
+Forge episode generation, self-play, and distillation.
+"""
+
+from ml.training.rollout_buffer import RolloutBuffer
+from ml.training.reward import compute_intermediate_reward, compute_terminal_reward, RewardConfig
+from ml.training.decision_exporter import DecisionExporter
+from ml.training.forge_episode_generator import (
+    ForgeTransition,
+    ForgeEpisode,
+    ForgeEpisodeQueue,
+    forge_producer,
+    ppo_consumer,
+    run_forge_pipeline,
+    parse_forge_jsonl,
+    snapshots_to_episode,
+)
+
+__all__ = [
+    # Rollout & Buffers
+    "RolloutBuffer",
+    # Rewards
+    "compute_intermediate_reward",
+    "compute_terminal_reward",
+    "RewardConfig",
+    # Decision export
+    "DecisionExporter",
+    # Forge episode generation (Phase 3)
+    "ForgeTransition",
+    "ForgeEpisode",
+    "ForgeEpisodeQueue",
+    "forge_producer",
+    "ppo_consumer",
+    "run_forge_pipeline",
+    "parse_forge_jsonl",
+    "snapshots_to_episode",
+]

ml/training/decision_exporter.py

@@ -0,0 +1,286 @@
+"""
+Commander AI Lab — PPO Decision Exporter
+════════════════════════════════════════
+
+Exports PPO self-play decisions as JSONL files in the same schema
+as Forge DecisionSnapshot records. This enables the closed-loop
+distillation pipeline where PPO-discovered strategies flow back
+into the supervised training dataset.
+
+Output files: data/results/ml-decisions-ppo-{batch_id}.jsonl
+
+Each line is a JSON object compatible with the dataset_builder.py
+ingestion pipeline, tagged with source="ppo" metadata.
+
+Ref: docs/CLOSED-LOOP-DISTILLATION.md — Phase 1
+Issue: #65
+"""
+
+import json
+import logging
+import os
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import sys
+
+project_root = str(Path(__file__).parent.parent.parent)
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+from ml.config.scope import IDX_TO_ACTION, MacroAction
+
+logger = logging.getLogger("ml.decision_exporter")
+
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+@dataclass
+class ExporterConfig:
+    """Configuration for the PPO decision exporter."""
+
+    output_dir: str = "data/results"
+    only_wins: bool = True          # Only export decisions from winning games
+    model_version: str = "unknown"
+    min_episode_steps: int = 3      # Skip very short episodes (likely noise)
+
+
+# ---------------------------------------------------------------------------
+# Decision Record Builder
+# ---------------------------------------------------------------------------
+
+def _macro_action_to_forge_type(action: MacroAction) -> str:
+    """Map a MacroAction enum to the closest Forge action type string."""
+    mapping = {
+        MacroAction.CAST_CREATURE: "cast_spell",
+        MacroAction.CAST_REMOVAL: "cast_spell",
+        MacroAction.CAST_DRAW: "cast_spell",
+        MacroAction.CAST_RAMP: "cast_spell",
+        MacroAction.CAST_COMMANDER: "cast_spell",
+        MacroAction.ATTACK_OPPONENT: "attack",
+        MacroAction.HOLD_MANA: "pass_priority",
+        MacroAction.PASS: "pass_priority",
+    }
+    return mapping.get(action, "pass_priority")
+
+
+def build_decision_record(
+    game_state_snapshot: dict,
+    action_idx: int,
+    reward: float,
+    agent_seat: int,
+    game_id: str,
+    game_outcome: str,
+    archetype: str = "midrange",
+    source: str = "ppo",
+    model_version: str = "unknown",
+    episode_return: float = 0.0,
+) -> dict:
+    """
+    Build a single decision record in Forge-compatible JSONL format.
+
+    The record mirrors the schema consumed by dataset_builder.py so that
+    PPO-generated data can be mixed into the supervised training set.
+    """
+    action = IDX_TO_ACTION[action_idx]
+    players = game_state_snapshot.get("players", [])
+
+    # Build per-player state matching Forge snapshot layout
+    player_snapshots = []
+    for i, p in enumerate(players):
+        player_snapshots.append({
+            "seat": i,
+            "life": p.get("life_total", 40),
+            "handSize": p.get("cards_in_hand", 0),
+            "permanentCount": p.get("creatures_on_battlefield", 0),
+            "graveyardSize": p.get("cards_in_graveyard", 0),
+            "manaAvailable": p.get("mana_available", 0),
+            "commanderDamageTaken": p.get("commander_damage_taken", 0),
+            "totalPowerOnBoard": p.get("total_power_on_board", 0),
+            "commanderTax": p.get("commander_tax", 0),
+        })
+
+    return {
+        # --- Core fields consumed by dataset_builder / labeler ---
+        "game_id": game_id,
+        "game_outcome": game_outcome,
+        "archetype": archetype,
+
+        # Action info (used by ml.actions.labeler)
+        "action": {
+            "type": _macro_action_to_forge_type(action),
+            "macro_action": action.value,
+            "action_idx": action_idx,
+            "seat": agent_seat,
+        },
+
+        # Board / game state
+        "turn": game_state_snapshot.get("turn", 1),
+        "phase": game_state_snapshot.get("phase", "main_1"),
+        "active_seat": game_state_snapshot.get("active_seat", 0),
+        "players": player_snapshots,
+
+        # --- PPO-specific metadata (distinguishes from Forge data) ---
+        "source": source,
+        "model_version": model_version,
+        "reward": round(reward, 6),
+        "episode_return": round(episode_return, 6),
+    }
+
+
+# ---------------------------------------------------------------------------
+# DecisionExporter — accumulates episode decisions and flushes to JSONL
+# ---------------------------------------------------------------------------
+
+class DecisionExporter:
+    """
+    Collects decisions from PPO self-play episodes and writes them
+    to JSONL files compatible with the supervised training pipeline.
+
+    Usage::
+
+        exporter = DecisionExporter(ExporterConfig(model_version="gen-0"))
+
+        for ep in range(num_episodes):
+            exporter.begin_episode(agent_seat=0, playstyle="midrange")
+            for step in episode_steps:
+                exporter.record_step(state_snapshot, action_idx, reward)
+            exporter.end_episode(won=True, episode_return=3.14)
+
+        exporter.flush()   # writes JSONL to disk
+        print(exporter.stats)
+    """
+
+    def __init__(self, config: ExporterConfig = None):
+        self.config = config or ExporterConfig()
+        self._batch_id = str(uuid.uuid4())[:8]
+        self._records: List[dict] = []
+        self._episode_buffer: List[dict] = []
+        self._current_game_id: Optional[str] = None
+        self._current_seat: int = 0
+        self._current_playstyle: str = "midrange"
+
+        # Stats
+        self._total_episodes = 0
+        self._exported_episodes = 0
+        self._exported_decisions = 0
+        self._skipped_losses = 0
+        self._skipped_short = 0
+
+    # -- Episode lifecycle ---------------------------------------------------
+
+    def begin_episode(self, agent_seat: int = 0, playstyle: str = "midrange"):
+        """Start tracking a new episode."""
+        self._episode_buffer = []
+        self._current_game_id = f"ppo-{self._batch_id}-{self._total_episodes}"
+        self._current_seat = agent_seat
+        self._current_playstyle = playstyle
+
+    def record_step(
+        self,
+        game_state_snapshot: dict,
+        action_idx: int,
+        reward: float,
+    ):
+        """Record a single decision step within the current episode."""
+        self._episode_buffer.append({
+            "snapshot": game_state_snapshot,
+            "action_idx": action_idx,
+            "reward": reward,
+        })
+
+    def end_episode(self, won: bool, episode_return: float = 0.0):
+        """
+        Finalize the current episode.
+
+        If config.only_wins is True, decisions from losing episodes
+        are discarded (higher signal-to-noise for distillation).
+        """
+        self._total_episodes += 1
+
+        # Gate: skip losses if configured
+        if self.config.only_wins and not won:
+            self._skipped_losses += 1
+            self._episode_buffer = []
+            return
+
+        # Gate: skip very short episodes
+        if len(self._episode_buffer) < self.config.min_episode_steps:
+            self._skipped_short += 1
+            self._episode_buffer = []
+            return
+
+        game_outcome = "win" if won else "loss"
+
+        for step in self._episode_buffer:
+            record = build_decision_record(
+                game_state_snapshot=step["snapshot"],
+                action_idx=step["action_idx"],
+                reward=step["reward"],
+                agent_seat=self._current_seat,
+                game_id=self._current_game_id,
+                game_outcome=game_outcome,
+                archetype=self._current_playstyle,
+                source="ppo",
+                model_version=self.config.model_version,
+                episode_return=episode_return,
+            )
+            self._records.append(record)
+
+        self._exported_episodes += 1
+        self._exported_decisions += len(self._episode_buffer)
+        self._episode_buffer = []
+
+    # -- Persistence ---------------------------------------------------------
+
+    def flush(self) -> Optional[str]:
+        """
+        Write all accumulated records to a JSONL file.
+
+        Returns:
+            Path to the written file, or None if no records to write.
+        """
+        if not self._records:
+            logger.info("No PPO decisions to export (0 records).")
+            return None
+
+        os.makedirs(self.config.output_dir, exist_ok=True)
+        filename = f"ml-decisions-ppo-{self._batch_id}.jsonl"
+        filepath = os.path.join(self.config.output_dir, filename)
+
+        with open(filepath, "w") as f:
+            for record in self._records:
+                f.write(json.dumps(record) + "\n")
+
+        logger.info(
+            "Exported %d PPO decisions (%d episodes) to %s",
+            len(self._records),
+            self._exported_episodes,
+            filepath,
+        )
+        self._records = []
+        return filepath
+
+    # -- Reporting -----------------------------------------------------------
+
+    @property
+    def stats(self) -> dict:
+        """Return export statistics."""
+        return {
+            "batch_id": self._batch_id,
+            "total_episodes": self._total_episodes,
+            "exported_episodes": self._exported_episodes,
+            "exported_decisions": self._exported_decisions,
+            "skipped_losses": self._skipped_losses,
+            "skipped_short": self._skipped_short,
+            "pending_records": len(self._records),
+        }
+
+    @property
+    def batch_id(self) -> str:
+        return self._batch_id

ml/training/distillation_loop.py

@@ -0,0 +1,908 @@
+"""
+Commander AI Lab — Distillation Loop Orchestrator
+═══════════════════════════════════════════════════
+
+Phase 4 (Issue #68): Automates the full closed-loop distillation cycle:
+
+    1. Supervised train on current dataset (Forge + any existing PPO data)
+    2. Run N episodes of PPO self-play using the supervised model as starting policy
+    3. Export winning PPO decisions to JSONL
+    4. Quality gate check
+    5. If passed → merge into dataset and repeat from step 1
+    6. If failed → discard PPO batch, adjust hyperparameters, retry
+
+Each iteration is a "generation" — data and models are tagged with generation
+number for traceability.
+
+Convergence detection:
+  - Stops if win rate plateaus across `convergence_window` generations
+  - Hard stop at `max_iterations`
+
+Ref: docs/CLOSED-LOOP-DISTILLATION.md — Phase 4
+Issue: #68
+"""
+
+import json
+import logging
+import os
+import shutil
+import sys
+import time
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+
+project_root = str(Path(__file__).parent.parent.parent)
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+from ml.config.scope import (
+    NUM_ACTIONS, STATE_DIMS, TRAINING_CONFIG,
+)
+from ml.data.quality_gate import QualityGate, QualityGateConfig, GateVerdict
+from ml.training.decision_exporter import DecisionExporter, ExporterConfig
+
+logger = logging.getLogger("ml.distillation")
+
+
+# ═══════════════════════════════════════════════════════════
+# Configuration
+# ═══════════════════════════════════════════════════════════
+
+@dataclass
+class DistillationConfig:
+    """Configuration for the distillation loop orchestrator."""
+
+    # --- Loop control ---
+    max_iterations: int = 10
+    convergence_window: int = 3       # Check last N generations for plateau
+    convergence_threshold: float = 0.01  # Min win-rate improvement to not plateau
+
+    # --- Paths ---
+    results_dir: str = "results"
+    models_dir: str = "ml/models"
+    checkpoint_dir: str = "ml/models/checkpoints"
+    history_dir: str = "results/distillation-history"
+
+    # --- Supervised training ---
+    supervised_epochs: int = 30
+    supervised_lr: float = 1e-3
+    supervised_batch_size: int = 64
+    supervised_patience: int = 5
+
+    # --- PPO self-play ---
+    ppo_iterations: int = 50
+    ppo_episodes_per_iter: int = 64
+    ppo_batch_size: int = 256
+    ppo_lr: float = 3e-4
+    ppo_clip_epsilon: float = 0.2
+    ppo_entropy_coeff: float = 0.01
+    ppo_eval_episodes: int = 100
+    opponent: str = "heuristic"
+    playstyle: str = "midrange"
+
+    # --- Dataset building (Forge-only default; see MIXED_MODE_PRESETS) ---
+    forge_weight: float = 1.0
+    ppo_weight: float = 0.0
+    min_reward_threshold: float = 0.0
+
+    # --- Quality gate ---
+    min_forge_accuracy: float = 0.35
+    max_accuracy_drop: float = 0.03
+    min_ppo_win_rate: float = 0.30
+
+    # --- Retry on gate failure ---
+    max_retries_per_generation: int = 2
+    retry_lr_factor: float = 0.5       # Halve PPO LR on retry
+    retry_entropy_boost: float = 0.005  # Increase entropy coeff on retry
+
+    # --- ELO convergence (Phase 6) ---
+    elo_convergence_enabled: bool = False   # Off by default
+    elo_convergence_threshold: float = 20.0 # Stop if ELO gain < this
+    elo_tournament_episodes: int = 30       # Games per matchup for ELO mini-tournament
+
+
+# ═══════════════════════════════════════════════════════════
+# Generation metadata
+# ═══════════════════════════════════════════════════════════
+
+@dataclass
+class GenerationRecord:
+    """Tracks metadata for a single distillation generation."""
+    generation: int
+    started_at: str = ""
+    completed_at: str = ""
+    status: str = "pending"  # pending | running | passed | failed | skipped
+
+    # Supervised training results
+    supervised_val_acc: float = 0.0
+    supervised_best_epoch: int = 0
+    supervised_checkpoint: str = ""
+
+    # PPO self-play results
+    ppo_win_rate: float = 0.0
+    ppo_best_win_rate: float = 0.0
+    ppo_episodes: int = 0
+    ppo_decisions_exported: int = 0
+    ppo_export_path: str = ""
+
+    # Quality gate
+    gate_accepted: bool = False
+    gate_reason: str = ""
+    baseline_accuracy: float = 0.0
+    post_merge_accuracy: float = 0.0
+
+    # Dataset stats
+    dataset_size: int = 0
+    forge_samples: int = 0
+    ppo_samples: int = 0
+
+    retries: int = 0
+    error: str = ""
+    elo_rating: float = 0.0
+
+    def to_dict(self) -> dict:
+        return {k: v for k, v in self.__dict__.items()}
+
+
+# ═══════════════════════════════════════════════════════════
+# Distillation Loop Orchestrator
+# ═══════════════════════════════════════════════════════════
+
+class DistillationLoop:
+    """
+    Orchestrates the full closed-loop distillation pipeline.
+
+    Each "generation" runs the complete cycle:
+      supervised train → PPO self-play → export → quality gate → merge
+
+    The loop tracks convergence by monitoring PPO win rate across
+    generations and stops when improvement plateaus or max_iterations
+    is reached.
+
+    Usage::
+
+        config = DistillationConfig(max_iterations=5, ppo_iterations=30)
+        loop = DistillationLoop(config)
+        summary = loop.run()
+    """
+
+    def __init__(self, config: DistillationConfig = None):
+        self.config = config or DistillationConfig()
+        self.generations: List[GenerationRecord] = []
+        self.quality_gate = QualityGate(QualityGateConfig(
+            min_forge_accuracy=self.config.min_forge_accuracy,
+            max_accuracy_drop=self.config.max_accuracy_drop,
+            min_ppo_win_rate=self.config.min_ppo_win_rate,
+        ))
+        self._baseline_accuracy: Optional[float] = None
+        self._device: Optional[str] = None
+        self._stopped = False
+
+        # Ensure output directories exist
+        os.makedirs(self.config.history_dir, exist_ok=True)
+        os.makedirs(self.config.checkpoint_dir, exist_ok=True)
+        os.makedirs(self.config.results_dir, exist_ok=True)
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def run(self, progress_callback=None) -> Dict:
+        """
+        Run the full distillation loop.
+
+        Args:
+            progress_callback: Optional callable(generation, record) for
+                               progress reporting (e.g. to a web UI).
+
+        Returns:
+            Summary dict with per-generation metrics and convergence info.
+        """
+        cfg = self.config
+        self._device = self._detect_device()
+
+        logger.info("=" * 65)
+        logger.info("  Commander AI Lab — Distillation Loop")
+        logger.info("=" * 65)
+        logger.info("  Max iterations:     %d", cfg.max_iterations)
+        logger.info("  Convergence window: %d", cfg.convergence_window)
+        logger.info("  PPO iters/gen:      %d", cfg.ppo_iterations)
+        logger.info("  PPO episodes/iter:  %d", cfg.ppo_episodes_per_iter)
+        logger.info("  Supervised epochs:  %d", cfg.supervised_epochs)
+        logger.info("  Device:             %s", self._device)
+        logger.info("  Results dir:        %s", cfg.results_dir)
+        logger.info("=" * 65)
+
+        loop_start = time.time()
+
+        for gen_num in range(1, cfg.max_iterations + 1):
+            if self._stopped:
+                logger.info("Loop stopped by external signal.")
+                break
+
+            logger.info("")
+            logger.info("─" * 65)
+            logger.info("  GENERATION %d / %d", gen_num, cfg.max_iterations)
+            logger.info("─" * 65)
+
+            record = self._run_generation(gen_num)
+            self.generations.append(record)
+
+            # Save generation history to disk after each round
+            self._save_history()
+
+            if progress_callback:
+                progress_callback(gen_num, record)
+
+            # Check convergence
+            if self._check_convergence():
+                logger.info(
+                    "Convergence detected — win rate plateaued over "
+                    "last %d generations. Stopping.",
+                    cfg.convergence_window,
+                )
+                break
+
+        total_time = time.time() - loop_start
+
+        summary = self._build_summary(total_time)
+        self._save_summary(summary)
+
+        logger.info("")
+        logger.info("=" * 65)
+        logger.info("  Distillation Loop Complete")
+        logger.info("=" * 65)
+        logger.info("  Generations run:  %d", summary["generations_run"])
+        logger.info("  Generations passed: %d", summary["generations_passed"])
+        logger.info("  Best win rate:    %.1f%%", summary["best_win_rate"] * 100)
+        logger.info("  Converged:        %s", summary["converged"])
+        logger.info("  Total time:       %.1f s", total_time)
+        logger.info("=" * 65)
+
+        return summary
+
+    def stop(self):
+        """Signal the loop to stop after the current generation."""
+        self._stopped = True
+        logger.info("Stop signal received — will halt after current generation.")
+
+    # ------------------------------------------------------------------
+    # Single generation execution
+    # ------------------------------------------------------------------
+
+    def _run_generation(self, gen_num: int) -> GenerationRecord:
+        """Execute a single distillation generation (steps 1-6)."""
+        record = GenerationRecord(
+            generation=gen_num,
+            started_at=datetime.now(timezone.utc).isoformat(),
+            status="running",
+        )
+
+        cfg = self.config
+        gen_tag = f"gen-{gen_num:03d}"
+        gen_checkpoint_dir = os.path.join(cfg.checkpoint_dir, gen_tag)
+        os.makedirs(gen_checkpoint_dir, exist_ok=True)
+
+        try:
+            # ─── Step 1: Supervised Train ───────────────────────────
+            logger.info("  [Step 1] Supervised training on current dataset...")
+            sup_result = self._supervised_train(gen_checkpoint_dir, gen_tag)
+
+            if sup_result is None:
+                record.status = "failed"
+                record.error = "Supervised training failed — no dataset found"
+                record.completed_at = datetime.now(timezone.utc).isoformat()
+                return record
+
+            record.supervised_val_acc = sup_result["best_val_acc"]
+            record.supervised_best_epoch = sup_result["best_epoch"]
+            record.supervised_checkpoint = sup_result["checkpoint_path"]
+
+            # Set baseline accuracy on first generation
+            if self._baseline_accuracy is None:
+                self._baseline_accuracy = sup_result["best_val_acc"]
+                logger.info(
+                    "  Baseline accuracy set: %.3f (generation 1)",
+                    self._baseline_accuracy,
+                )
+
+            # ─── Step 2-3: PPO Self-Play + Export ───────────────────
+            retries = 0
+            ppo_lr = cfg.ppo_lr
+            entropy_coeff = cfg.ppo_entropy_coeff
+            gate_passed = False
+
+            while retries <= cfg.max_retries_per_generation and not gate_passed:
+                if retries > 0:
+                    logger.info(
+                        "  [Retry %d/%d] Adjusting hyperparameters: "
+                        "lr=%.2e → %.2e, entropy=%.4f → %.4f",
+                        retries, cfg.max_retries_per_generation,
+                        ppo_lr, ppo_lr * cfg.retry_lr_factor,
+                        entropy_coeff, entropy_coeff + cfg.retry_entropy_boost,
+                    )
+                    ppo_lr *= cfg.retry_lr_factor
+                    entropy_coeff += cfg.retry_entropy_boost
+
+                logger.info(
+                    "  [Step 2] PPO self-play (%d iters × %d episodes)...",
+                    cfg.ppo_iterations, cfg.ppo_episodes_per_iter,
+                )
+                ppo_result, exporter = self._ppo_self_play(
+                    supervised_checkpoint=sup_result["checkpoint_path"],
+                    gen_tag=gen_tag,
+                    gen_checkpoint_dir=gen_checkpoint_dir,
+                    learning_rate=ppo_lr,
+                    entropy_coeff=entropy_coeff,
+                )
+
+                record.ppo_win_rate = ppo_result["final_win_rate"]
+                record.ppo_best_win_rate = ppo_result["best_win_rate"]
+                record.ppo_episodes = (
+                    cfg.ppo_iterations * cfg.ppo_episodes_per_iter
+                )
+
+                logger.info(
+                    "  [Step 3] Exporting PPO decisions (win rate: %.1f%%)...",
+                    ppo_result["best_win_rate"] * 100,
+                )
+                export_path = exporter.flush()
+                record.ppo_decisions_exported = exporter.stats["exported_decisions"]
+                record.ppo_export_path = export_path or ""
+
+                # ─── Step 4: Quality Gate ───────────────────────────
+                logger.info("  [Step 4] Quality gate check...")
+                verdict = self._quality_gate_check(
+                    ppo_win_rate=ppo_result["best_win_rate"],
+                    total_episodes=record.ppo_episodes,
+                    exporter=exporter,
+                )
+
+                if verdict.accepted:
+                    gate_passed = True
+                    record.gate_accepted = True
+                    record.gate_reason = verdict.reason
+                    logger.info("  QUALITY GATE: PASSED — %s", verdict.reason)
+                else:
+                    retries += 1
+                    record.retries = retries
+                    logger.warning(
+                        "  QUALITY GATE: FAILED — %s", verdict.reason,
+                    )
+
+                    if retries > cfg.max_retries_per_generation:
+                        # ─── Step 6: Discard PPO batch ──────────────
+                        logger.warning(
+                            "  [Step 6] Max retries exceeded — "
+                            "discarding PPO batch for generation %d.",
+                            gen_num,
+                        )
+                        if export_path and os.path.exists(export_path):
+                            os.remove(export_path)
+                            logger.info(
+                                "  Deleted rejected PPO data: %s", export_path
+                            )
+                        record.gate_accepted = False
+                        record.gate_reason = (
+                            f"Failed after {retries - 1} retries: "
+                            f"{verdict.reason}"
+                        )
+
+            # ─── Step 5: Merge into dataset (if passed) ─────────────
+            if gate_passed:
+                logger.info(
+                    "  [Step 5] PPO data accepted — will be included "
+                    "in next generation's dataset build."
+                )
+                record.status = "passed"
+
+                # ─── ELO mini-tournament (Phase 6) ────────────────
+                if cfg.elo_convergence_enabled and gen_num > 1:
+                    elo = self._run_elo_mini_tournament(
+                        gen_checkpoint_dir, gen_num
+                    )
+                    if elo is not None:
+                        record.elo_rating = elo
+                        logger.info("  ELO rating: %.1f", elo)
+            else:
+                record.status = "failed"
+
+        except Exception as e:
+            logger.exception("  Generation %d failed with error: %s", gen_num, e)
+            record.status = "failed"
+            record.error = str(e)
+
+        record.completed_at = datetime.now(timezone.utc).isoformat()
+        return record
+
+    # ------------------------------------------------------------------
+    # Step 1: Supervised Training
+    # ------------------------------------------------------------------
+
+    def _supervised_train(
+        self,
+        gen_checkpoint_dir: str,
+        gen_tag: str,
+    ) -> Optional[Dict]:
+        """
+        Build dataset from all available data and train supervised model.
+
+        Returns training summary dict or None on failure.
+        """
+        try:
+            import torch
+        except ImportError:
+            raise RuntimeError("PyTorch required for distillation loop")
+
+        cfg = self.config
+
+        # Build dataset (picks up both Forge + any PPO JSONL files in results_dir)
+        from ml.data.dataset_builder import build_dataset, split_dataset, save_dataset
+
+        try:
+            dataset = build_dataset(
+                results_dir=cfg.results_dir,
+                max_samples=None,
+                source_weights={
+                    "forge": cfg.forge_weight,
+                    "ppo": cfg.ppo_weight,
+                },
+                min_reward_threshold=cfg.min_reward_threshold,
+            )
+        except RuntimeError as e:
+            logger.warning("Dataset build failed: %s", e)
+            return None
+
+        if not dataset or len(dataset.get("states", [])) == 0:
+            logger.warning("No training data available.")
+            return None
+
+        # Split
+        train_data, val_data, test_data = split_dataset(dataset)
+
+        # Save splits (overwrite each generation — latest always at standard path)
+        data_dir = cfg.models_dir
+        os.makedirs(data_dir, exist_ok=True)
+        save_dataset(train_data, os.path.join(data_dir, "dataset-train.npz"))
+        save_dataset(val_data, os.path.join(data_dir, "dataset-val.npz"))
+        save_dataset(test_data, os.path.join(data_dir, "dataset-test.npz"))
+
+        # Create model
+        from ml.training.policy_network import PolicyNetwork
+        actual_dim = train_data["states"].shape[1]
+        model = PolicyNetwork(input_dim=actual_dim)
+
+        # Train
+        from ml.training.trainer import SupervisedTrainer, get_best_device
+        device = self._device or get_best_device()
+        trainer = SupervisedTrainer(
+            model=model,
+            device=device,
+            learning_rate=cfg.supervised_lr,
+            batch_size=cfg.supervised_batch_size,
+            epochs=cfg.supervised_epochs,
+            patience=cfg.supervised_patience,
+            checkpoint_dir=gen_checkpoint_dir,
+        )
+
+        summary = trainer.train(
+            train_states=train_data["states"],
+            train_labels=train_data["labels"],
+            val_states=val_data["states"],
+            val_labels=val_data["labels"],
+        )
+
+        # Also copy best checkpoint to the standard location
+        best_src = os.path.join(gen_checkpoint_dir, "best_policy.pt")
+        best_dst = os.path.join(cfg.checkpoint_dir, "best_policy.pt")
+        if os.path.exists(best_src):
+            shutil.copy2(best_src, best_dst)
+
+        return summary
+
+    # ------------------------------------------------------------------
+    # Steps 2-3: PPO Self-Play + Decision Export
+    # ------------------------------------------------------------------
+
+    def _ppo_self_play(
+        self,
+        supervised_checkpoint: str,
+        gen_tag: str,
+        gen_checkpoint_dir: str,
+        learning_rate: float = None,
+        entropy_coeff: float = None,
+    ) -> tuple:
+        """
+        Run PPO self-play initialized from the supervised checkpoint.
+
+        Returns (ppo_summary, exporter) tuple.
+        """
+        cfg = self.config
+
+        from ml.training.ppo_trainer import PPOTrainer, PPOConfig
+        from ml.training.decision_exporter import DecisionExporter, ExporterConfig
+
+        # Set up decision exporter for this generation
+        exporter = DecisionExporter(ExporterConfig(
+            output_dir=cfg.results_dir,
+            only_wins=True,
+            model_version=gen_tag,
+        ))
+
+        ppo_config = PPOConfig(
+            iterations=cfg.ppo_iterations,
+            episodes_per_iter=cfg.ppo_episodes_per_iter,
+            ppo_epochs=4,
+            batch_size=cfg.ppo_batch_size,
+            clip_epsilon=cfg.ppo_clip_epsilon,
+            entropy_coeff=entropy_coeff or cfg.ppo_entropy_coeff,
+            learning_rate=learning_rate or cfg.ppo_lr,
+            lr_schedule="cosine",
+            min_lr=1e-5,
+            opponent=cfg.opponent,
+            playstyle=cfg.playstyle,
+            checkpoint_dir=gen_checkpoint_dir,
+            save_every=max(1, cfg.ppo_iterations // 5),
+            eval_every=max(1, cfg.ppo_iterations // 10),
+            eval_episodes=cfg.ppo_eval_episodes,
+            load_supervised=supervised_checkpoint,
+        )
+
+        ppo_trainer = PPOTrainer(ppo_config)
+
+        # Monkey-patch collect_rollouts to include exporter
+        # The exporter is already wired into self_play.collect_rollouts
+        # via the exporter parameter, so we wrap the train loop.
+        original_collect = None
+
+        from ml.training import self_play as sp_module
+
+        _original_collect_rollouts = sp_module.collect_rollouts
+
+        def _instrumented_collect_rollouts(
+            agent_model, opponent_policy, buffer,
+            num_episodes=64, playstyle="midrange",
+            reward_config=None, **kwargs
+        ):
+            return _original_collect_rollouts(
+                agent_model=agent_model,
+                opponent_policy=opponent_policy,
+                buffer=buffer,
+                num_episodes=num_episodes,
+                playstyle=playstyle,
+                reward_config=reward_config,
+                exporter=exporter,
+            )
+
+        # Temporarily replace module-level function
+        sp_module.collect_rollouts = _instrumented_collect_rollouts
+        try:
+            summary = ppo_trainer.train()
+        finally:
+            # Restore original
+            sp_module.collect_rollouts = _original_collect_rollouts
+
+        return summary, exporter
+
+    # ------------------------------------------------------------------
+    # Step 4: Quality Gate
+    # ------------------------------------------------------------------
+
+    def _quality_gate_check(
+        self,
+        ppo_win_rate: float,
+        total_episodes: int,
+        exporter: DecisionExporter,
+    ) -> GateVerdict:
+        """Run quality gate checks on the PPO output."""
+        # Win rate gate
+        win_verdict = self.quality_gate.check_win_rate(
+            ppo_win_rate=ppo_win_rate,
+            total_episodes=total_episodes,
+        )
+        if not win_verdict.accepted:
+            return win_verdict
+
+        # If we have baseline accuracy and this isn't the first generation,
+        # we could run a post-merge accuracy check here. For now, the win
+        # rate gate is the primary gatekeeper during the loop. The accuracy
+        # gate will be checked on the next generation's supervised training
+        # by comparing against self._baseline_accuracy.
+        return GateVerdict(
+            accepted=True,
+            reason=(
+                f"Win rate gate passed ({ppo_win_rate:.1%} >= "
+                f"{self.config.min_ppo_win_rate:.1%}). "
+                f"PPO data accepted for merge."
+            ),
+            metrics={
+                "ppo_win_rate": ppo_win_rate,
+                "total_episodes": total_episodes,
+                "exported_decisions": exporter.stats["exported_decisions"],
+            },
+        )
+
+    # ------------------------------------------------------------------
+    # ELO mini-tournament (Phase 6)
+    # ------------------------------------------------------------------
+
+    def _run_elo_mini_tournament(
+        self, gen_checkpoint_dir: str, gen_num: int
+    ) -> Optional[float]:
+        """
+        Run a small ELO tournament between current gen, previous gen,
+        and heuristic baseline. Returns the current gen's ELO rating.
+        """
+        cfg = self.config
+        try:
+            from ml.eval.elo_tracker import (
+                run_elo_tournament, EloHistory, HeuristicPolicy, LearnedPolicy,
+            )
+
+            policies = {"heuristic": HeuristicPolicy()}
+
+            # Current generation checkpoint
+            curr_path = os.path.join(gen_checkpoint_dir, "best_policy.pt")
+            curr_name = f"gen-{gen_num:03d}"
+            if os.path.exists(curr_path):
+                policies[curr_name] = LearnedPolicy(curr_path)
+            else:
+                logger.warning("  ELO: current gen checkpoint not found: %s", curr_path)
+                return None
+
+            # Previous generation checkpoint
+            if gen_num > 1:
+                prev_tag = f"gen-{gen_num - 1:03d}"
+                prev_dir = os.path.join(cfg.checkpoint_dir, prev_tag)
+                prev_path = os.path.join(prev_dir, "best_policy.pt")
+                if os.path.exists(prev_path):
+                    policies[prev_tag] = LearnedPolicy(prev_path)
+
+            logger.info(
+                "  [ELO] Running mini-tournament: %s (%d episodes/matchup)",
+                list(policies.keys()), cfg.elo_tournament_episodes,
+            )
+
+            result = run_elo_tournament(
+                policies=policies,
+                episodes_per_matchup=cfg.elo_tournament_episodes,
+                playstyle=cfg.playstyle,
+            )
+
+            # Save to ELO history
+            history_path = os.path.join(
+                os.path.dirname(cfg.history_dir), "elo_history.json"
+            ) if cfg.history_dir else "data/elo_history.json"
+            history = EloHistory(path=history_path)
+            history.append(gen_num, result.ratings)
+
+            return result.ratings.get(curr_name, 0.0)
+
+        except Exception as e:
+            logger.warning("  ELO mini-tournament failed: %s", e)
+            return None
+
+    # ------------------------------------------------------------------
+    # Convergence detection
+    # ------------------------------------------------------------------
+
+    def _check_convergence(self) -> bool:
+        """
+        Check if win rate has plateaued over recent generations.
+        Also checks ELO delta when elo_convergence_enabled is True.
+
+        Returns True if the loop should stop.
+        """
+        cfg = self.config
+        passed = [g for g in self.generations if g.status == "passed"]
+
+        if len(passed) < cfg.convergence_window:
+            return False
+
+        recent = passed[-cfg.convergence_window:]
+        win_rates = [g.ppo_best_win_rate for g in recent]
+
+        # Check if max improvement across the window is below threshold
+        improvement = max(win_rates) - min(win_rates)
+
+        logger.info(
+            "  Convergence check: last %d win rates = %s, "
+            "spread = %.4f (threshold = %.4f)",
+            cfg.convergence_window,
+            [f"{wr:.3f}" for wr in win_rates],
+            improvement,
+            cfg.convergence_threshold,
+        )
+
+        win_rate_converged = improvement < cfg.convergence_threshold
+
+        # ELO convergence check (Phase 6)
+        if cfg.elo_convergence_enabled and len(passed) >= 2:
+            elo_ratings = [g.elo_rating for g in passed[-2:] if g.elo_rating > 0]
+            if len(elo_ratings) >= 2:
+                elo_delta = elo_ratings[-1] - elo_ratings[-2]
+                logger.info(
+                    "  ELO convergence check: delta = %.1f (threshold = %.1f)",
+                    elo_delta, cfg.elo_convergence_threshold,
+                )
+                if elo_delta < cfg.elo_convergence_threshold:
+                    logger.info("  ELO delta below threshold — converged.")
+                    return True
+
+        return win_rate_converged
+
+    # ------------------------------------------------------------------
+    # Device detection
+    # ------------------------------------------------------------------
+
+    def _detect_device(self) -> str:
+        """Auto-detect best compute device."""
+        try:
+            import torch
+            if torch.cuda.is_available():
+                return "cuda"
+            if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+                return "mps"
+        except ImportError:
+            pass
+        return "cpu"
+
+    # ------------------------------------------------------------------
+    # History & summary persistence
+    # ------------------------------------------------------------------
+
+    def _save_history(self):
+        """Save generation history to JSON."""
+        path = os.path.join(self.config.history_dir, "generations.json")
+        history = [g.to_dict() for g in self.generations]
+        with open(path, "w") as f:
+            json.dump(history, f, indent=2)
+        logger.info("  Generation history saved: %s", path)
+
+    def _build_summary(self, total_time: float) -> Dict:
+        """Build final summary dict."""
+        passed = [g for g in self.generations if g.status == "passed"]
+        failed = [g for g in self.generations if g.status == "failed"]
+
+        best_wr = max(
+            (g.ppo_best_win_rate for g in self.generations), default=0.0
+        )
+
+        return {
+            "generations_run": len(self.generations),
+            "generations_passed": len(passed),
+            "generations_failed": len(failed),
+            "best_win_rate": best_wr,
+            "converged": self._check_convergence() if len(passed) >= self.config.convergence_window else False,
+            "total_time_s": round(total_time, 1),
+            "device": self._device,
+            "config": {
+                "max_iterations": self.config.max_iterations,
+                "convergence_window": self.config.convergence_window,
+                "convergence_threshold": self.config.convergence_threshold,
+                "ppo_iterations": self.config.ppo_iterations,
+                "supervised_epochs": self.config.supervised_epochs,
+            },
+            "generations": [g.to_dict() for g in self.generations],
+        }
+
+    def _save_summary(self, summary: Dict):
+        """Save final summary to JSON."""
+        path = os.path.join(self.config.history_dir, "summary.json")
+        with open(path, "w") as f:
+            json.dump(summary, f, indent=2, default=str)
+        logger.info("  Summary saved: %s", path)
+
+
+# ═══════════════════════════════════════════════════════════
+# CLI Entry Point
+# ═══════════════════════════════════════════════════════════
+
+def main():
+    """CLI entry point for the distillation loop."""
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Commander AI Lab — Closed-Loop Distillation"
+    )
+
+    # Loop control
+    parser.add_argument(
+        "--max-iterations", type=int, default=10,
+        help="Maximum number of distillation generations (default: 10)",
+    )
+    parser.add_argument(
+        "--convergence-window", type=int, default=3,
+        help="Number of generations to check for win-rate plateau (default: 3)",
+    )
+    parser.add_argument(
+        "--convergence-threshold", type=float, default=0.01,
+        help="Min win-rate spread to not be considered converged (default: 0.01)",
+    )
+
+    # Paths
+    parser.add_argument(
+        "--results-dir", default="data/results",
+        help="Directory containing JSONL decision files (default: data/results)",
+    )
+    parser.add_argument(
+        "--models-dir", default="ml/models",
+        help="Directory for dataset NPZ files (default: ml/models)",
+    )
+    parser.add_argument(
+        "--checkpoint-dir", default="ml/models/checkpoints",
+        help="Directory for model checkpoints (default: ml/models/checkpoints)",
+    )
+
+    # Supervised training
+    parser.add_argument("--sup-epochs", type=int, default=30)
+    parser.add_argument("--sup-lr", type=float, default=1e-3)
+    parser.add_argument("--sup-batch-size", type=int, default=64)
+    parser.add_argument("--sup-patience", type=int, default=5)
+
+    # PPO self-play
+    parser.add_argument("--ppo-iterations", type=int, default=50)
+    parser.add_argument("--ppo-episodes", type=int, default=64)
+    parser.add_argument("--ppo-lr", type=float, default=3e-4)
+    parser.add_argument("--ppo-batch-size", type=int, default=256)
+    parser.add_argument("--ppo-eval-episodes", type=int, default=100)
+    parser.add_argument(
+        "--opponent", default="heuristic",
+        choices=["heuristic", "random", "self"],
+    )
+    parser.add_argument("--playstyle", default="midrange")
+
+    # Dataset
+    parser.add_argument("--forge-weight", type=float, default=1.0)
+    parser.add_argument("--ppo-weight", type=float, default=0.0)
+    parser.add_argument("--min-reward", type=float, default=0.0)
+
+    # Quality gate
+    parser.add_argument("--min-win-rate", type=float, default=0.30)
+    parser.add_argument("--min-forge-accuracy", type=float, default=0.35)
+    parser.add_argument("--max-accuracy-drop", type=float, default=0.03)
+
+    args = parser.parse_args()
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(message)s",
+    )
+
+    config = DistillationConfig(
+        max_iterations=args.max_iterations,
+        convergence_window=args.convergence_window,
+        convergence_threshold=args.convergence_threshold,
+        results_dir=args.results_dir,
+        models_dir=args.models_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        supervised_epochs=args.sup_epochs,
+        supervised_lr=args.sup_lr,
+        supervised_batch_size=args.sup_batch_size,
+        supervised_patience=args.sup_patience,
+        ppo_iterations=args.ppo_iterations,
+        ppo_episodes_per_iter=args.ppo_episodes,
+        ppo_batch_size=args.ppo_batch_size,
+        ppo_lr=args.ppo_lr,
+        ppo_eval_episodes=args.ppo_eval_episodes,
+        opponent=args.opponent,
+        playstyle=args.playstyle,
+        forge_weight=args.forge_weight,
+        ppo_weight=args.ppo_weight,
+        min_reward_threshold=args.min_reward,
+        min_ppo_win_rate=args.min_win_rate,
+        min_forge_accuracy=args.min_forge_accuracy,
+        max_accuracy_drop=args.max_accuracy_drop,
+    )
+
+    loop = DistillationLoop(config)
+    summary = loop.run()
+    print(json.dumps(summary, indent=2, default=str))
+
+
+if __name__ == "__main__":
+    main()

ml/training/forge_episode_generator.py

@@ -0,0 +1,495 @@
+"""
+Commander AI Lab — Forge Episode Generator (Phase 3.2)
+
+Async producer-consumer pipeline: runs Forge batch games in parallel
+with PPO training.  Forge produces episodes → shared queue → PPO
+trainer consumes.
+
+Uses the existing watchdog (_global_watchdog_loop in forge_runner.py)
+to monitor Forge worker processes for stalls.
+"""
+
+import asyncio
+import json
+import logging
+import os
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Deque, Dict, List, Optional, Tuple
+
+import numpy as np
+
+from ml.config.scope import (
+    ACTION_TO_IDX,
+    GAME_SCOPE,
+    IDX_TO_ACTION,
+    MacroAction,
+    NUM_ACTIONS,
+    STATE_DIMS,
+)
+from ml.training.reward import (
+    RewardConfig,
+    compute_intermediate_reward,
+    compute_terminal_reward,
+)
+from ml.training.decision_exporter import DecisionExporter, ExporterConfig
+
+logger = logging.getLogger("ml.forge_episode_generator")
+
+
+# ══════════════════════════════════════════════════════════
+# Episode / Trajectory data structures
+# ══════════════════════════════════════════════════════════
+
+@dataclass
+class ForgeTransition:
+    """Single (s, a, r, log_prob, value, done) tuple from a Forge game."""
+    state: np.ndarray
+    action: int
+    reward: float
+    log_prob: float
+    value: float
+    done: bool
+    # Raw snapshot retained so the exporter can reconstruct game-state context
+    snapshot: Optional[Dict[str, Any]] = None
+
+
+@dataclass
+class ForgeEpisode:
+    """Complete trajectory from a single Forge game."""
+    game_id: str
+    transitions: List[ForgeTransition] = field(default_factory=list)
+    winner: Optional[int] = None
+    total_turns: int = 0
+    deck_names: List[str] = field(default_factory=list)
+    agent_seat: int = 0
+    playstyle: str = "midrange"
+
+    @property
+    def length(self) -> int:
+        return len(self.transitions)
+
+    @property
+    def total_reward(self) -> float:
+        return sum(t.reward for t in self.transitions)
+
+    @property
+    def won(self) -> bool:
+        return self.winner == self.agent_seat
+
+
+# ══════════════════════════════════════════════════════════
+# JSONL Parser — reads Forge ML decision snapshots
+# ══════════════════════════════════════════════════════════
+
+def parse_forge_jsonl(path: Path) -> List[Dict[str, Any]]:
+    """Parse a JSONL file of Forge ML decision snapshots.
+
+    Each line is a JSON object with keys like:
+        game_id, turn, phase, active_seat, state, action, outcome
+    """
+    snapshots = []
+    with open(path, "r") as f:
+        for lineno, line in enumerate(f, 1):
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                snapshots.append(json.loads(line))
+            except json.JSONDecodeError as e:
+                logger.warning("Skipping malformed line %d in %s: %s", lineno, path, e)
+    logger.info("Parsed %d snapshots from %s", len(snapshots), path)
+    return snapshots
+
+
+def _make_minimal_state(snap: Dict[str, Any]) -> Dict[str, Any]:
+    """Build a minimal prev/curr state dict from a Forge snapshot.
+
+    compute_intermediate_reward() expects a dict with a 'players' list
+    where each player has 'life_total', 'cards_in_hand',
+    'creatures_on_battlefield', etc.  Forge snapshots carry a 'players'
+    array directly; we forward it as-is and fall back to scalar fields
+    when the array is absent.
+    """
+    if "players" in snap:
+        return {"players": snap["players"]}
+
+    # Fallback: reconstruct a two-player view from flat snapshot fields
+    agent_seat = snap.get("active_seat", 0)
+    opp_seat = 1 - agent_seat
+    players = [None, None]
+
+    def _player(life_key: str, hand_key: str, creatures_key: str) -> Dict:
+        return {
+            "life_total": snap.get(life_key, 40),
+            "cards_in_hand": snap.get(hand_key, 0),
+            "creatures_on_battlefield": snap.get(creatures_key, 0),
+            "cards_in_graveyard": 0,
+            "mana_available": snap.get("mana_available", 0),
+            "commander_damage_taken": 0,
+            "total_power_on_board": 0,
+        }
+
+    players[agent_seat] = _player("agent_life", "agent_hand_size", "agent_creatures")
+    players[opp_seat] = _player("opp_life", "opp_hand_size", "opp_creatures")
+    return {"players": players}
+
+
+def snapshots_to_episode(
+    snapshots: List[Dict[str, Any]],
+    reward_config: Optional[RewardConfig] = None,
+    agent_seat: int = 0,
+    playstyle: str = "midrange",
+) -> ForgeEpisode:
+    """Convert a sequence of Forge snapshots into a ForgeEpisode.
+
+    Maps Forge action labels → MacroAction indices and computes
+    rewards from game outcomes using ml.training.reward.
+    """
+    if not snapshots:
+        return ForgeEpisode(game_id="empty")
+
+    first = snapshots[0]
+    episode = ForgeEpisode(
+        game_id=first.get("game_id", "unknown"),
+        deck_names=first.get("deck_names", []),
+        agent_seat=agent_seat,
+        playstyle=playstyle,
+    )
+
+    rc = reward_config or RewardConfig()
+
+    for i, snap in enumerate(snapshots):
+        # State vector — zero-padded if not present
+        state_vec = np.zeros(STATE_DIMS.total_state_dim, dtype=np.float32)
+        if "state_vector" in snap:
+            raw = np.array(snap["state_vector"], dtype=np.float32)
+            state_vec[: min(len(raw), len(state_vec))] = raw[: len(state_vec)]
+
+        # Action mapping
+        action_label = snap.get("action", "pass").lower().replace(" ", "_")
+        try:
+            macro = MacroAction(action_label)
+            action_idx = ACTION_TO_IDX[macro]
+        except (ValueError, KeyError):
+            action_idx = ACTION_TO_IDX[MacroAction.PASS]
+
+        # Reward — use correct reward.py signatures
+        is_done = i == len(snapshots) - 1
+        if is_done and "outcome" in snap:
+            outcome = snap["outcome"]
+            reward = compute_terminal_reward(
+                won=outcome.get("won", False),
+                draw=outcome.get("draw", False),
+                timeout=outcome.get("timeout", False),
+                config=rc,
+            )
+        else:
+            # Build minimal prev/curr dicts for intermediate reward
+            prev_snap = snapshots[i - 1] if i > 0 else snap
+            prev_state = _make_minimal_state(prev_snap)
+            curr_state = _make_minimal_state(snap)
+            reward = compute_intermediate_reward(
+                prev_state=prev_state,
+                curr_state=curr_state,
+                action_taken=action_label,
+                agent_seat=agent_seat,
+                config=rc,
+            )
+
+        episode.transitions.append(ForgeTransition(
+            state=state_vec,
+            action=action_idx,
+            reward=reward,
+            log_prob=snap.get("log_prob", 0.0),
+            value=snap.get("value", 0.0),
+            done=is_done,
+            snapshot=snap,
+        ))
+
+    episode.total_turns = snapshots[-1].get("turn", len(snapshots))
+    if "outcome" in snapshots[-1]:
+        episode.winner = snapshots[-1]["outcome"].get("winner_seat")
+
+    return episode
+
+
+# ══════════════════════════════════════════════════════════
+# DecisionExporter adapter for Forge episodes
+# ══════════════════════════════════════════════════════════
+
+def _export_forge_episode(episode: ForgeEpisode, exporter: DecisionExporter) -> None:
+    """Feed a completed ForgeEpisode into the DecisionExporter.
+
+    Translates each ForgeTransition back into the game_state_snapshot
+    format expected by DecisionExporter.record_step(), then finalises
+    the episode so the exporter can apply its win/length gates.
+
+    The raw snapshot stored on each ForgeTransition is used directly
+    when available; otherwise a minimal dict is synthesised from the
+    transition's action index and reward so the schema is always valid.
+    """
+    exporter.begin_episode(
+        agent_seat=episode.agent_seat,
+        playstyle=episode.playstyle,
+    )
+
+    for t in episode.transitions:
+        if t.snapshot is not None:
+            game_state = _make_minimal_state(t.snapshot)
+            # Carry through turn/phase/active_seat if present
+            game_state["turn"] = t.snapshot.get("turn", 1)
+            game_state["phase"] = t.snapshot.get("phase", "main_1")
+            game_state["active_seat"] = t.snapshot.get("active_seat", episode.agent_seat)
+        else:
+            # Minimal fallback — exporter only needs players list for schema
+            game_state = {
+                "turn": 1,
+                "phase": "main_1",
+                "active_seat": episode.agent_seat,
+                "players": [
+                    {"life_total": 40, "cards_in_hand": 0, "creatures_on_battlefield": 0,
+                     "cards_in_graveyard": 0, "mana_available": 0,
+                     "commander_damage_taken": 0, "total_power_on_board": 0, "commander_tax": 0},
+                    {"life_total": 40, "cards_in_hand": 0, "creatures_on_battlefield": 0,
+                     "cards_in_graveyard": 0, "mana_available": 0,
+                     "commander_damage_taken": 0, "total_power_on_board": 0, "commander_tax": 0},
+                ],
+            }
+
+        exporter.record_step(
+            game_state_snapshot=game_state,
+            action_idx=t.action,
+            reward=t.reward,
+        )
+
+    exporter.end_episode(
+        won=episode.won,
+        episode_return=episode.total_reward,
+    )
+
+
+# ══════════════════════════════════════════════════════════
+# Async Episode Queue (producer-consumer)
+# ══════════════════════════════════════════════════════════
+
+class ForgeEpisodeQueue:
+    """Thread-safe async queue bridging Forge producers and PPO consumer.
+
+    Forge batch workers produce ForgeEpisodes; the PPO trainer consumes
+    them as they become available.  Backpressure is applied when the
+    queue reaches max_size.
+    """
+
+    def __init__(self, max_size: int = 256):
+        self._queue: asyncio.Queue[ForgeEpisode] = asyncio.Queue(maxsize=max_size)
+        self._produced = 0
+        self._consumed = 0
+        self._closed = False
+
+    async def put(self, episode: ForgeEpisode) -> None:
+        await self._queue.put(episode)
+        self._produced += 1
+
+    async def get(self, timeout: float = 30.0) -> Optional[ForgeEpisode]:
+        try:
+            ep = await asyncio.wait_for(self._queue.get(), timeout=timeout)
+            self._consumed += 1
+            return ep
+        except asyncio.TimeoutError:
+            return None
+
+    def close(self) -> None:
+        self._closed = True
+
+    @property
+    def is_closed(self) -> bool:
+        return self._closed and self._queue.empty()
+
+    @property
+    def stats(self) -> Dict[str, int]:
+        return {
+            "produced": self._produced,
+            "consumed": self._consumed,
+            "pending": self._queue.qsize(),
+        }
+
+
+# ══════════════════════════════════════════════════════════
+# Forge Batch Producer
+# ══════════════════════════════════════════════════════════
+
+async def forge_producer(
+    queue: ForgeEpisodeQueue,
+    results_dir: str = "results",
+    batch_id: str = "forge-batch",
+    num_games: int = 64,
+    poll_interval: float = 2.0,
+    reward_config: Optional[RewardConfig] = None,
+) -> int:
+    """Producer coroutine: watches for Forge JSONL output files,
+    parses them into episodes, and pushes them onto the queue.
+
+    Returns total number of episodes produced.
+    """
+    results_path = Path(results_dir)
+    pattern = f"ml-decisions-forge-{batch_id}*.jsonl"
+    seen_files: set = set()
+    total_produced = 0
+
+    logger.info("[PRODUCER] Watching %s for pattern %s", results_path, pattern)
+
+    while total_produced < num_games and not queue.is_closed:
+        for jsonl_file in sorted(results_path.glob(pattern)):
+            if jsonl_file in seen_files:
+                continue
+            seen_files.add(jsonl_file)
+
+            try:
+                snapshots = parse_forge_jsonl(jsonl_file)
+                if not snapshots:
+                    continue
+
+                # Group by game_id
+                games: Dict[str, List] = {}
+                for snap in snapshots:
+                    gid = snap.get("game_id", "unknown")
+                    games.setdefault(gid, []).append(snap)
+
+                for gid, game_snaps in games.items():
+                    episode = snapshots_to_episode(game_snaps, reward_config)
+                    if episode.length > 0:
+                        await queue.put(episode)
+                        total_produced += 1
+                        logger.debug("[PRODUCER] Queued episode %s (%d transitions)",
+                                     gid, episode.length)
+
+            except Exception as e:
+                logger.error("[PRODUCER] Error processing %s: %s", jsonl_file, e)
+
+        await asyncio.sleep(poll_interval)
+
+    logger.info("[PRODUCER] Done — produced %d episodes", total_produced)
+    return total_produced
+
+
+async def ppo_consumer(
+    queue: ForgeEpisodeQueue,
+    buffer_size: int = 64,
+    on_batch_ready=None,
+    exporter: Optional[DecisionExporter] = None,
+) -> int:
+    """Consumer coroutine: pulls episodes from the queue and batches
+    them for PPO training updates.
+
+    Args:
+        queue: ForgeEpisodeQueue to consume from
+        buffer_size: number of episodes per training batch
+        on_batch_ready: callback(episodes) when a batch is full
+        exporter: Optional DecisionExporter — when provided, every
+                  consumed episode is fed into the exporter so winning
+                  Forge games accumulate in the distillation JSONL pool.
+                  Call exporter.flush() after the pipeline completes to
+                  write the JSONL file.
+
+    Returns total episodes consumed.
+    """
+    batch: List[ForgeEpisode] = []
+    total_consumed = 0
+
+    while not queue.is_closed:
+        episode = await queue.get(timeout=10.0)
+        if episode is None:
+            if queue.is_closed:
+                break
+            continue
+
+        # Feed into distillation exporter (win-gate applied inside exporter)
+        if exporter is not None:
+            try:
+                _export_forge_episode(episode, exporter)
+            except Exception as exc:
+                logger.warning("[CONSUMER] Exporter error for episode %s: %s",
+                               episode.game_id, exc)
+
+        batch.append(episode)
+        total_consumed += 1
+
+        if len(batch) >= buffer_size:
+            if on_batch_ready:
+                on_batch_ready(batch)
+            logger.info("[CONSUMER] Batch of %d episodes ready for PPO update", len(batch))
+            batch = []
+
+    # Flush remaining
+    if batch and on_batch_ready:
+        on_batch_ready(batch)
+        logger.info("[CONSUMER] Final batch of %d episodes flushed", len(batch))
+
+    logger.info("[CONSUMER] Done — consumed %d episodes", total_consumed)
+    return total_consumed
+
+
+# ══════════════════════════════════════════════════════════
+# Convenience runner
+# ══════════════════════════════════════════════════════════
+
+async def run_forge_pipeline(
+    results_dir: str = "results",
+    batch_id: str = "forge-batch",
+    num_games: int = 64,
+    buffer_size: int = 64,
+    max_queue: int = 256,
+    on_batch_ready=None,
+    reward_config: Optional[RewardConfig] = None,
+    exporter: Optional[DecisionExporter] = None,
+) -> Dict[str, Any]:
+    """Run the full Forge producer → PPO consumer pipeline.
+
+    The Forge Java batch must be started separately (via forge_runner.py
+    or MultiThreadBatchRunner). This function handles the Python side:
+    reading JSONL output and feeding episodes into the PPO trainer.
+
+    If an exporter is provided, winning Forge episodes are recorded and
+    flushed to a PPO JSONL file at the end of the pipeline run.  The
+    output path is returned in the stats dict under "exporter_file".
+
+    Example::
+
+        from ml.training.decision_exporter import DecisionExporter, ExporterConfig
+        exporter = DecisionExporter(ExporterConfig(model_version="gen-1"))
+        stats = await run_forge_pipeline(num_games=128, exporter=exporter)
+        print(stats["exporter_file"])   # data/results/ml-decisions-ppo-<id>.jsonl
+    """
+    queue = ForgeEpisodeQueue(max_size=max_queue)
+
+    producer_task = asyncio.create_task(
+        forge_producer(queue, results_dir, batch_id, num_games,
+                       reward_config=reward_config)
+    )
+    consumer_task = asyncio.create_task(
+        ppo_consumer(queue, buffer_size, on_batch_ready, exporter=exporter)
+    )
+
+    produced = await producer_task
+    queue.close()
+    consumed = await consumer_task
+
+    # Flush exporter to disk and capture output path
+    exporter_file = None
+    if exporter is not None:
+        exporter_file = exporter.flush()
+        if exporter_file:
+            logger.info("[PIPELINE] Distillation export: %s  stats=%s",
+                        exporter_file, exporter.stats)
+
+    stats: Dict[str, Any] = {
+        "produced": produced,
+        "consumed": consumed,
+        "exporter_file": exporter_file,
+        **queue.stats,
+    }
+    logger.info("[PIPELINE] Complete: %s", stats)
+    return stats

ml/training/policy_network.py

@@ -16,6 +16,10 @@ The network is designed for:
 import os
 import sys
 from pathlib import Path
+import logging
+
+log = logging.getLogger("commander_ai_lab.ml.training")
+
 
 # Conditional torch import — graceful failure if not installed
 try:
@@ -25,7 +29,7 @@ try:
     TORCH_AVAILABLE = True
 except ImportError:
     TORCH_AVAILABLE = False
-    print("[ML] PyTorch not installed. Install with: pip install torch")
+    log.warning("PyTorch not installed. Install with: pip install torch")
 
 import numpy as np
 
@@ -204,7 +208,7 @@ def save_checkpoint(
             "dropout": TRAINING_CONFIG.dropout,
         },
     }, path)
-    print(f"[ML] Checkpoint saved: {path}")
+    log.info(f"Checkpoint saved: {path}")
 
 
 def load_checkpoint(path: str, device: str = "cpu"):
@@ -219,7 +223,8 @@ def load_checkpoint(path: str, device: str = "cpu"):
         num_layers=config.get("num_layers", TRAINING_CONFIG.num_layers),
         dropout=config.get("dropout", TRAINING_CONFIG.dropout),
     )
-    model.load_state_dict(checkpoint["model_state_dict"])
+    state = {k: v for k, v in checkpoint["model_state_dict"].items() if not k.startswith("value_head")}
+    model.load_state_dict(state, strict=False)
     model.to(device)
 
     return model, checkpoint

ml/training/ppo_trainer.py

@@ -9,22 +9,27 @@ Proximal Policy Optimization (Clip) with:
   - Self-play episode collection between updates
   - Periodic checkpointing and evaluation
   - Win-rate tracking against opponent policies
+  - Forge-based episode source via --data-source forge|mixed
 
 Reference: Schulman et al., "Proximal Policy Optimization Algorithms", 2017
 
 Usage:
     python -m ml.training.ppo_trainer --iterations 100 --episodes-per-iter 64
     python -m ml.training.ppo_trainer --load-supervised ml/models/checkpoints/best_policy.pt
+    python -m ml.training.ppo_trainer --data-source forge --forge-results-dir results --iterations 50
+    python -m ml.training.ppo_trainer --data-source mixed --forge-weight 0.7 --iterations 100
 """
 
 import argparse
+import asyncio
 import json
 import logging
 import os
 import sys
+import threading
 import time
 from pathlib import Path
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 import numpy as np
 
@@ -51,6 +56,114 @@ from ml.training.self_play import (
 logger = logging.getLogger("ml.ppo")
 
 
+# ══════════════════════════════════════════════════════════
+# Forge data-source integration
+# ══════════════════════════════════════════════════════════
+
+def episodes_to_buffer(episodes, buffer: RolloutBuffer) -> int:
+    """Drain a list of ForgeEpisodes into an existing RolloutBuffer.
+
+    Returns the number of transitions added.
+    """
+    added = 0
+    for ep in episodes:
+        for t in ep.transitions:
+            if buffer.size >= buffer.buffer_size:
+                break
+            buffer.add(
+                state=t.state,
+                action=t.action,
+                reward=t.reward,
+                log_prob=t.log_prob,
+                value=t.value,
+                done=t.done,
+            )
+            added += 1
+    return added
+
+
+class ForgeDataSource:
+    """Runs the Forge producer pipeline in a background thread and
+    exposes a synchronous drain() method for the PPO training loop.
+
+    Usage::
+        source = ForgeDataSource(results_dir="results", batch_id="run1", num_games=64)
+        source.start()
+        # ... in training loop ...
+        episodes = source.drain()
+        source.stop()
+    """
+
+    def __init__(
+        self,
+        results_dir: str = "results",
+        batch_id: str = "forge-batch",
+        num_games: int = 256,
+        reward_config: Optional[RewardConfig] = None,
+        queue_size: int = 256,
+    ):
+        self.results_dir = results_dir
+        self.batch_id = batch_id
+        self.num_games = num_games
+        self.reward_config = reward_config or RewardConfig()
+        self.queue_size = queue_size
+        self._episodes: List = []
+        self._lock = threading.Lock()
+        self._thread: Optional[threading.Thread] = None
+        self._stop_event = threading.Event()
+
+    def start(self):
+        """Launch the background producer thread."""
+        self._thread = threading.Thread(target=self._run, daemon=True)
+        self._thread.start()
+        logger.info("[ForgeDataSource] Producer started (results_dir=%s, batch_id=%s, num_games=%d)",
+                    self.results_dir, self.batch_id, self.num_games)
+
+    def _run(self):
+        from ml.training.forge_episode_generator import ForgeEpisodeQueue, forge_producer
+
+        async def _produce():
+            queue = ForgeEpisodeQueue(max_size=self.queue_size)
+            produced = await forge_producer(
+                queue=queue,
+                results_dir=self.results_dir,
+                batch_id=self.batch_id,
+                num_games=self.num_games,
+                reward_config=self.reward_config,
+            )
+            queue.close()
+            # Drain remaining items
+            while not queue.is_closed:
+                ep = await queue.get(timeout=1.0)
+                if ep is not None:
+                    with self._lock:
+                        self._episodes.append(ep)
+            logger.info("[ForgeDataSource] Producer finished: %d episodes", produced)
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        try:
+            loop.run_until_complete(_produce())
+        finally:
+            loop.close()
+
+    def drain(self) -> List:
+        """Return and clear all buffered episodes (thread-safe)."""
+        with self._lock:
+            eps = list(self._episodes)
+            self._episodes.clear()
+        return eps
+
+    def stop(self):
+        self._stop_event.set()
+        if self._thread:
+            self._thread.join(timeout=5.0)
+
+
+# ══════════════════════════════════════════════════════════
+# PPO Config
+# ══════════════════════════════════════════════════════════
+
 class PPOConfig:
     """PPO hyperparameters."""
 
@@ -91,6 +204,13 @@ class PPOConfig:
 
         # Supervised preload
         load_supervised: Optional[str] = None,
+
+        # Data source
+        data_source: str = "synthetic",   # synthetic | forge | mixed
+        forge_results_dir: str = "results",
+        forge_batch_id: str = "forge-batch",
+        forge_num_games: int = 256,
+        forge_weight: float = 0.7,        # fraction of buffer from Forge when mixed
     ):
         self.iterations = iterations
         self.episodes_per_iter = episodes_per_iter
@@ -114,10 +234,19 @@ class PPOConfig:
         self.eval_every = eval_every
         self.eval_episodes = eval_episodes
         self.load_supervised = load_supervised
+        self.data_source = data_source
+        self.forge_results_dir = forge_results_dir
+        self.forge_batch_id = forge_batch_id
+        self.forge_num_games = forge_num_games
+        self.forge_weight = max(0.0, min(1.0, forge_weight))
 
 
+# ══════════════════════════════════════════════════════════
+# PPO Trainer
+# ══════════════════════════════════════════════════════════
+
 class PPOTrainer:
-    """Proximal Policy Optimization trainer with self-play."""
+    """Proximal Policy Optimization trainer with self-play and optional Forge data source."""
 
     def __init__(self, config: PPOConfig):
         if not TORCH_AVAILABLE:
@@ -126,28 +255,24 @@ class PPOTrainer:
         self.config = config
         self.device = self._detect_device()
 
-        # Create actor-critic model
         from ml.training.policy_network import PolicyValueNetwork
         self.model = PolicyValueNetwork(
             input_dim=STATE_DIMS.total_state_dim,
             hidden_dim=TRAINING_CONFIG.hidden_dim,
             num_actions=NUM_ACTIONS,
             num_layers=TRAINING_CONFIG.num_layers,
-            dropout=0.0,  # No dropout during RL
+            dropout=0.0,
         ).to(self.device)
 
-        # Optionally load from supervised checkpoint
         if config.load_supervised and os.path.exists(config.load_supervised):
             self._load_supervised_weights(config.load_supervised)
 
-        # Optimizer
         self.optimizer = torch.optim.Adam(
             self.model.parameters(),
             lr=config.learning_rate,
             eps=1e-5,
         )
 
-        # Learning rate scheduler
         self.scheduler = None
         if config.lr_schedule == "linear":
             self.scheduler = torch.optim.lr_scheduler.LinearLR(
@@ -163,7 +288,6 @@ class PPOTrainer:
                 eta_min=config.min_lr,
             )
 
-        # Rollout buffer
         self.buffer = RolloutBuffer(
             buffer_size=config.buffer_size,
             state_dim=STATE_DIMS.total_state_dim,
@@ -171,16 +295,23 @@ class PPOTrainer:
             gae_lambda=config.gae_lambda,
         )
 
-        # Reward config
         self.reward_config = RewardConfig(
             gamma=config.gamma,
             gae_lambda=config.gae_lambda,
         )
 
-        # Opponent policy
         self.opponent = self._create_opponent(config.opponent)
 
-        # Tracking
+        # Forge data source (started lazily in train())
+        self._forge_source: Optional[ForgeDataSource] = None
+        if config.data_source in ("forge", "mixed"):
+            self._forge_source = ForgeDataSource(
+                results_dir=config.forge_results_dir,
+                batch_id=config.forge_batch_id,
+                num_games=config.forge_num_games,
+                reward_config=self.reward_config,
+            )
+
         self.iteration = 0
         self.total_steps = 0
         self.best_win_rate = 0.0
@@ -196,13 +327,9 @@ class PPOTrainer:
         return "cpu"
 
     def _load_supervised_weights(self, path: str):
-        """Load weights from a supervised PolicyNetwork checkpoint into the
-        PolicyValueNetwork (ignoring the value head, which is new)."""
         logger.info("Loading supervised weights from %s", path)
         checkpoint = torch.load(path, map_location=self.device, weights_only=False)
         state_dict = checkpoint.get("model_state_dict", checkpoint)
-
-        # Filter out keys that don't match (e.g., value_head)
         model_dict = self.model.state_dict()
         compatible = {k: v for k, v in state_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
         model_dict.update(compatible)
@@ -216,14 +343,10 @@ class PPOTrainer:
         elif opponent_type == "heuristic":
             return HeuristicPolicy()
         elif opponent_type == "self":
-            # Self-play: opponent uses a snapshot of the current policy
             return self._create_self_play_opponent()
-        else:
-            return HeuristicPolicy()
+        return HeuristicPolicy()
 
     def _create_self_play_opponent(self):
-        """Create an opponent that uses a frozen copy of the current policy."""
-
         class SelfPlayPolicy:
             def __init__(self, model, device):
                 self.model = model
@@ -241,11 +364,99 @@ class PPOTrainer:
 
         return SelfPlayPolicy(self.model, self.device)
 
-    def ppo_update(self) -> Dict:
-        """Run PPO update epochs on the collected rollout buffer.
+    def _collect_forge_rollouts(self) -> Dict:
+        """Drain buffered Forge episodes into the RolloutBuffer.
 
-        Returns dict of loss metrics.
+        Returns stats dict compatible with collect_rollouts() output.
         """
+        episodes = self._forge_source.drain()
+        if not episodes:
+            logger.warning("[Forge] No episodes available yet — waiting for JSONL output in %s",
+                           self.config.forge_results_dir)
+            return {"steps": 0, "win_rate": 0.0, "avg_reward_per_ep": 0.0,
+                    "wins": 0, "losses": 0, "draws": 0, "source": "forge"}
+
+        added = episodes_to_buffer(episodes, self.buffer)
+        wins = sum(1 for ep in episodes if ep.winner == 0)
+        win_rate = wins / max(len(episodes), 1)
+        avg_reward = sum(ep.total_reward for ep in episodes) / max(len(episodes), 1)
+        logger.info("[Forge] Drained %d episodes (%d transitions) into buffer — WR=%.0f%% AvgR=%.3f",
+                    len(episodes), added, win_rate * 100, avg_reward)
+        return {
+            "steps": added,
+            "win_rate": win_rate,
+            "avg_reward_per_ep": avg_reward,
+            "wins": wins,
+            "losses": len(episodes) - wins,
+            "draws": 0,
+            "source": "forge",
+        }
+
+    def _collect_rollouts_for_iter(self) -> Dict:
+        """Collect rollouts according to configured data_source.
+
+        - synthetic: pure self_play.collect_rollouts (original behaviour)
+        - forge:     pure Forge episode drain
+        - mixed:     Forge fills forge_weight fraction of buffer, synthetic fills remainder
+        """
+        cfg = self.config
+        self.buffer.reset()
+        self.model.eval()
+
+        if cfg.data_source == "forge":
+            stats = self._collect_forge_rollouts()
+
+        elif cfg.data_source == "mixed":
+            forge_cap = int(cfg.buffer_size * cfg.forge_weight)
+            synth_cap = cfg.buffer_size - forge_cap
+
+            # Temporarily shrink buffer for Forge slice
+            original_size = self.buffer.buffer_size
+            self.buffer.buffer_size = forge_cap
+            forge_stats = self._collect_forge_rollouts()
+            self.buffer.buffer_size = original_size
+
+            synth_eps = max(1, int(cfg.episodes_per_iter * (1.0 - cfg.forge_weight)))
+            synth_stats = collect_rollouts(
+                agent_model=self.model,
+                opponent_policy=self.opponent,
+                buffer=self.buffer,
+                num_episodes=synth_eps,
+                playstyle=cfg.playstyle,
+                reward_config=self.reward_config,
+            )
+            stats = {
+                "steps": forge_stats["steps"] + synth_stats["steps"],
+                "win_rate": (
+                    forge_stats["win_rate"] * cfg.forge_weight
+                    + synth_stats["win_rate"] * (1.0 - cfg.forge_weight)
+                ),
+                "avg_reward_per_ep": (
+                    forge_stats["avg_reward_per_ep"] * cfg.forge_weight
+                    + synth_stats["avg_reward_per_ep"] * (1.0 - cfg.forge_weight)
+                ),
+                "wins": forge_stats["wins"] + synth_stats["wins"],
+                "losses": forge_stats["losses"] + synth_stats["losses"],
+                "draws": synth_stats.get("draws", 0),
+                "source": "mixed",
+            }
+
+        else:  # synthetic (default — unchanged behaviour)
+            stats = collect_rollouts(
+                agent_model=self.model,
+                opponent_policy=self.opponent,
+                buffer=self.buffer,
+                num_episodes=cfg.episodes_per_iter,
+                playstyle=cfg.playstyle,
+                reward_config=self.reward_config,
+            )
+            stats["source"] = "synthetic"
+
+        self.model.train()
+        return stats
+
+    def ppo_update(self) -> Dict:
+        """Run PPO update epochs on the collected rollout buffer."""
         cfg = self.config
         total_policy_loss = 0.0
         total_value_loss = 0.0
@@ -258,56 +469,42 @@ class PPOTrainer:
             for batch in self.buffer.get_batches(cfg.batch_size):
                 states, actions, old_log_probs, advantages, returns = batch
 
-                # To tensors
                 states_t = torch.from_numpy(states).to(self.device)
                 actions_t = torch.from_numpy(actions).to(self.device)
                 old_log_probs_t = torch.from_numpy(old_log_probs).to(self.device)
                 advantages_t = torch.from_numpy(advantages).to(self.device)
                 returns_t = torch.from_numpy(returns).to(self.device)
 
-                # Forward pass
                 logits, values = self.model.forward_with_value(states_t)
                 values = values.squeeze(-1)
 
-                # Action distribution
                 probs = F.softmax(logits, dim=-1)
                 dist = torch.distributions.Categorical(probs)
                 new_log_probs = dist.log_prob(actions_t)
                 entropy = dist.entropy().mean()
 
-                # --- Policy Loss (clipped surrogate) ---
                 ratio = torch.exp(new_log_probs - old_log_probs_t)
                 surr1 = ratio * advantages_t
                 surr2 = torch.clamp(ratio, 1.0 - cfg.clip_epsilon, 1.0 + cfg.clip_epsilon) * advantages_t
                 policy_loss = -torch.min(surr1, surr2).mean()
 
-                # --- Value Loss (clipped) ---
                 if cfg.value_clip > 0:
-                    # Get old values from buffer for clipping
-                    old_values_t = torch.from_numpy(
-                        self.buffer.values[:self.buffer.size]
-                    ).to(self.device)
-                    # Reindex to match batch
-                    # Since we use shuffled indices in get_batches, we clip against returns
                     value_loss_unclipped = (values - returns_t) ** 2
                     value_loss = 0.5 * value_loss_unclipped.mean()
                 else:
                     value_loss = 0.5 * ((values - returns_t) ** 2).mean()
 
-                # --- Total Loss ---
                 loss = (
                     policy_loss
                     + cfg.value_loss_coeff * value_loss
                     - cfg.entropy_coeff * entropy
                 )
 
-                # Optimize
                 self.optimizer.zero_grad()
                 loss.backward()
                 nn.utils.clip_grad_norm_(self.model.parameters(), cfg.max_grad_norm)
                 self.optimizer.step()
 
-                # Track metrics
                 with torch.no_grad():
                     approx_kl = ((ratio - 1) - torch.log(ratio)).mean().item()
                     clip_frac = ((ratio - 1.0).abs() > cfg.clip_epsilon).float().mean().item()
@@ -330,12 +527,8 @@ class PPOTrainer:
         }
 
     def evaluate(self, num_episodes: int = 50) -> Dict:
-        """Evaluate the current policy against opponents.
-
-        Returns win rates and metrics.
-        """
+        """Evaluate the current policy against heuristic and random opponents."""
         self.model.eval()
-
         results = {}
         for opp_name, opp_policy in [
             ("heuristic", HeuristicPolicy()),
@@ -363,18 +556,15 @@ class PPOTrainer:
                 "avg_reward": stats["avg_reward_per_ep"],
                 "avg_steps": stats["avg_steps_per_ep"],
             }
-
         self.model.train()
         return results
 
     def save_checkpoint(self, path: str = None, metrics: dict = None):
-        """Save PPO checkpoint."""
         if path is None:
             path = os.path.join(
                 self.config.checkpoint_dir,
                 f"ppo_iter_{self.iteration:04d}.pt",
             )
-
         os.makedirs(os.path.dirname(path), exist_ok=True)
         torch.save({
             "iteration": self.iteration,
@@ -388,6 +578,7 @@ class PPOTrainer:
                 "learning_rate": self.config.learning_rate,
                 "opponent": self.config.opponent,
                 "playstyle": self.config.playstyle,
+                "data_source": self.config.data_source,
             },
             "best_win_rate": self.best_win_rate,
             "model_config": {
@@ -401,14 +592,7 @@ class PPOTrainer:
         logger.info("Checkpoint saved: %s", path)
 
     def train(self, progress_callback=None) -> Dict:
-        """Run the full PPO training loop.
-
-        Args:
-            progress_callback: Optional callable(iteration, metrics) for progress reporting
-
-        Returns:
-            Training summary dict
-        """
+        """Run the full PPO training loop."""
         cfg = self.config
         logger.info("=" * 60)
         logger.info("PPO Training — Commander AI Lab")
@@ -423,98 +607,94 @@ class PPOTrainer:
         logger.info("  Opponent:       %s", cfg.opponent)
         logger.info("  Device:         %s", self.device)
         logger.info("  Supervised:     %s", cfg.load_supervised or "None")
+        logger.info("  Data source:    %s", cfg.data_source)
+        if cfg.data_source in ("forge", "mixed"):
+            logger.info("  Forge results:  %s / %s", cfg.forge_results_dir, cfg.forge_batch_id)
+            logger.info("  Forge weight:   %.0f%%", cfg.forge_weight * 100)
         logger.info("")
 
+        # Start Forge producer background thread if needed
+        if self._forge_source:
+            self._forge_source.start()
+
         t_start = time.time()
 
-        for iteration in range(1, cfg.iterations + 1):
-            self.iteration = iteration
-            t_iter = time.time()
+        try:
+            for iteration in range(1, cfg.iterations + 1):
+                self.iteration = iteration
+                t_iter = time.time()
 
-            # --- Collect rollouts ---
-            self.model.eval()
-            rollout_stats = collect_rollouts(
-                agent_model=self.model,
-                opponent_policy=self.opponent,
-                buffer=self.buffer,
-                num_episodes=cfg.episodes_per_iter,
-                playstyle=cfg.playstyle,
-                reward_config=self.reward_config,
-            )
-            self.total_steps += rollout_stats["steps"]
-            self.model.train()
+                rollout_stats = self._collect_rollouts_for_iter()
+                self.total_steps += rollout_stats["steps"]
 
-            # --- PPO Update ---
-            update_metrics = self.ppo_update()
+                update_metrics = self.ppo_update()
 
-            # --- LR Schedule ---
-            current_lr = self.optimizer.param_groups[0]["lr"]
-            if self.scheduler:
-                self.scheduler.step()
+                current_lr = self.optimizer.param_groups[0]["lr"]
+                if self.scheduler:
+                    self.scheduler.step()
 
-            # --- Evaluate ---
-            eval_results = None
-            if iteration % cfg.eval_every == 0 or iteration == 1:
-                eval_results = self.evaluate(cfg.eval_episodes)
-                heuristic_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
+                eval_results = None
+                if iteration % cfg.eval_every == 0 or iteration == 1:
+                    eval_results = self.evaluate(cfg.eval_episodes)
+                    heuristic_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
+                    if heuristic_wr > self.best_win_rate:
+                        self.best_win_rate = heuristic_wr
+                        best_path = os.path.join(cfg.checkpoint_dir, "best_ppo.pt")
+                        self.save_checkpoint(best_path, {"eval": eval_results})
+                        logger.info("  ★ New best win rate: %.1f%%", heuristic_wr * 100)
 
-                if heuristic_wr > self.best_win_rate:
-                    self.best_win_rate = heuristic_wr
-                    best_path = os.path.join(cfg.checkpoint_dir, "best_ppo.pt")
-                    self.save_checkpoint(best_path, {"eval": eval_results})
-                    logger.info("  ★ New best win rate: %.1f%%", heuristic_wr * 100)
+                if iteration % cfg.save_every == 0:
+                    self.save_checkpoint()
 
-            # --- Save periodic checkpoint ---
-            if iteration % cfg.save_every == 0:
-                self.save_checkpoint()
+                iter_time = time.time() - t_iter
+                metrics = {
+                    "iteration": iteration,
+                    "steps": rollout_stats["steps"],
+                    "total_steps": self.total_steps,
+                    "win_rate": rollout_stats["win_rate"],
+                    "avg_reward": rollout_stats["avg_reward_per_ep"],
+                    "policy_loss": update_metrics["policy_loss"],
+                    "value_loss": update_metrics["value_loss"],
+                    "entropy": update_metrics["entropy"],
+                    "approx_kl": update_metrics["approx_kl"],
+                    "clip_fraction": update_metrics["clip_fraction"],
+                    "lr": current_lr,
+                    "iter_time_s": round(iter_time, 1),
+                    "data_source": rollout_stats.get("source", cfg.data_source),
+                    "eval": eval_results,
+                }
+                self.history.append(metrics)
 
-            # --- Log ---
-            iter_time = time.time() - t_iter
-            metrics = {
-                "iteration": iteration,
-                "steps": rollout_stats["steps"],
-                "total_steps": self.total_steps,
-                "win_rate": rollout_stats["win_rate"],
-                "avg_reward": rollout_stats["avg_reward_per_ep"],
-                "policy_loss": update_metrics["policy_loss"],
-                "value_loss": update_metrics["value_loss"],
-                "entropy": update_metrics["entropy"],
-                "approx_kl": update_metrics["approx_kl"],
-                "clip_fraction": update_metrics["clip_fraction"],
-                "lr": current_lr,
-                "iter_time_s": round(iter_time, 1),
-                "eval": eval_results,
-            }
-            self.history.append(metrics)
+                eval_str = ""
+                if eval_results:
+                    h_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
+                    r_wr = eval_results.get("random", {}).get("win_rate", 0)
+                    eval_str = f" | eval: h={h_wr:.0%} r={r_wr:.0%}"
 
-            # Pretty log
-            eval_str = ""
-            if eval_results:
-                h_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
-                r_wr = eval_results.get("random", {}).get("win_rate", 0)
-                eval_str = f" | eval: h={h_wr:.0%} r={r_wr:.0%}"
+                src_tag = f"[{rollout_stats.get('source', '?')[:3]}]"
+                logger.info(
+                    "  [%3d/%d]%s wr=%.0f%% rew=%.3f ploss=%.4f vloss=%.4f ent=%.3f kl=%.4f%s (%.1fs)",
+                    iteration, cfg.iterations, src_tag,
+                    rollout_stats["win_rate"] * 100,
+                    rollout_stats["avg_reward_per_ep"],
+                    update_metrics["policy_loss"],
+                    update_metrics["value_loss"],
+                    update_metrics["entropy"],
+                    update_metrics["approx_kl"],
+                    eval_str,
+                    iter_time,
+                )
 
-            logger.info(
-                "  [%3d/%d] wr=%.0f%% rew=%.3f ploss=%.4f vloss=%.4f ent=%.3f kl=%.4f%s (%.1fs)",
-                iteration, cfg.iterations,
-                rollout_stats["win_rate"] * 100,
-                rollout_stats["avg_reward_per_ep"],
-                update_metrics["policy_loss"],
-                update_metrics["value_loss"],
-                update_metrics["entropy"],
-                update_metrics["approx_kl"],
-                eval_str,
-                iter_time,
-            )
+                if progress_callback:
+                    progress_callback(iteration, metrics)
 
-            if progress_callback:
-                progress_callback(iteration, metrics)
+        finally:
+            if self._forge_source:
+                self._forge_source.stop()
 
-        # --- Final Save ---
         total_time = time.time() - t_start
         self.save_checkpoint()
 
-        # Save training history
         history_path = os.path.join(cfg.checkpoint_dir, "ppo_history.json")
         with open(history_path, "w") as f:
             json.dump(self.history, f, indent=2, default=str)
@@ -526,6 +706,7 @@ class PPOTrainer:
             "final_win_rate": self.history[-1]["win_rate"] if self.history else 0,
             "total_time_s": round(total_time, 1),
             "device": self.device,
+            "data_source": cfg.data_source,
             "checkpoint_dir": cfg.checkpoint_dir,
             "history_path": history_path,
         }
@@ -536,6 +717,7 @@ class PPOTrainer:
         logger.info("  Total time:     %.1f s", total_time)
         logger.info("  Total steps:    %d", self.total_steps)
         logger.info("  Best win rate:  %.1f%%", self.best_win_rate * 100)
+        logger.info("  Data source:    %s", cfg.data_source)
         logger.info("  History:        %s", history_path)
         logger.info("=" * 60)
 
@@ -578,12 +760,32 @@ def main():
     parser.add_argument("--load-supervised", default=None,
                         help="Path to supervised checkpoint to initialize from")
 
+    # Data source
+    parser.add_argument(
+        "--data-source", default="synthetic",
+        choices=["synthetic", "forge", "mixed"],
+        help="Episode source: synthetic (self-play), forge (Forge JSONL), mixed (blend)",
+    )
+    parser.add_argument(
+        "--forge-results-dir", default="results",
+        help="Directory where Forge writes ml-decisions-forge-*.jsonl files",
+    )
+    parser.add_argument(
+        "--forge-batch-id", default="forge-batch",
+        help="Batch ID prefix for Forge JSONL files (matches BatchRunner --batchId)",
+    )
+    parser.add_argument(
+        "--forge-num-games", type=int, default=256,
+        help="Total Forge games to consume before stopping the producer",
+    )
+    parser.add_argument(
+        "--forge-weight", type=float, default=0.7,
+        help="Fraction of RolloutBuffer filled from Forge when --data-source mixed (0.0–1.0)",
+    )
+
     args = parser.parse_args()
 
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(message)s",
-    )
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
 
     config = PPOConfig(
         iterations=args.iterations,
@@ -605,6 +807,11 @@ def main():
         eval_every=args.eval_every,
         eval_episodes=args.eval_episodes,
         load_supervised=args.load_supervised,
+        data_source=args.data_source,
+        forge_results_dir=args.forge_results_dir,
+        forge_batch_id=args.forge_batch_id,
+        forge_num_games=args.forge_num_games,
+        forge_weight=args.forge_weight,
     )
 
     trainer = PPOTrainer(config)

ml/training/self_play.py

@@ -17,9 +17,15 @@ that feed directly into the RolloutBuffer for PPO training.
 For real game integration (Phase 9+), the Java sim would call the
 policy server and feed back actual game states — this synthetic
 version allows training to begin immediately.
+
+.. deprecated:: Phase 3 (Issue #83)
+    Synthetic training mode is DEPRECATED for production training.
+    Use forge_episode_generator.py with real Forge engine games.
+    Retained ONLY for quick local testing and unit tests.
 """
 
 import logging
+import warnings
 import random
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
@@ -38,9 +44,16 @@ from ml.config.scope import (
     Playstyle, PLAYSTYLE_TO_IDX, STATE_DIMS, GamePhase, PHASE_TO_IDX,
 )
 from ml.training.reward import compute_intermediate_reward, compute_terminal_reward, RewardConfig
+from ml.training.decision_exporter import DecisionExporter
 
 logger = logging.getLogger("ml.self_play")
 
+_TRAINING_DEPRECATION_MSG = (
+    "self_play.py synthetic training is deprecated for production training (Issue #83). "
+    "Use forge_episode_generator.py with real Forge engine games instead. "
+    "Synthetic self-play is retained for local testing and unit tests only."
+)
+
 
 @dataclass
 class PlayerState:
@@ -384,6 +397,7 @@ def run_self_play_episode(
     playstyle: str = "midrange",
     max_steps: int = 50,
     reward_config: RewardConfig = None,
+    exporter: DecisionExporter = None,
 ) -> List[dict]:
     """Run a single self-play episode and return the trajectory.
 
@@ -395,6 +409,7 @@ def run_self_play_episode(
         playstyle: Playstyle for state encoding
         max_steps: Maximum decision steps per episode
         reward_config: Reward configuration
+        exporter: Optional DecisionExporter to record steps for distillation
 
     Returns:
         List of step dicts: [{state, action, reward, value, log_prob, done}, ...]
@@ -458,6 +473,12 @@ def run_self_play_episode(
                 "log_prob": log_prob_scalar,
                 "done": True,
             })
+            if exporter is not None:
+                exporter.record_step(
+                    game_state_snapshot=prev_snapshot,
+                    action_idx=action_idx,
+                    reward=reward,
+                )
             break
 
         # Opponent's turn
@@ -483,6 +504,12 @@ def run_self_play_episode(
                 "log_prob": log_prob_scalar,
                 "done": True,
             })
+            if exporter is not None:
+                exporter.record_step(
+                    game_state_snapshot=prev_snapshot,
+                    action_idx=action_idx,
+                    reward=reward,
+                )
             break
 
         # Intermediate reward
@@ -499,6 +526,12 @@ def run_self_play_episode(
             "log_prob": log_prob_scalar,
             "done": False,
         })
+        if exporter is not None:
+            exporter.record_step(
+                game_state_snapshot=prev_snapshot,
+                action_idx=action_idx,
+                reward=reward,
+            )
 
         # Advance turn
         state = advance_turn(state)
@@ -513,6 +546,7 @@ def collect_rollouts(
     num_episodes: int = 64,
     playstyle: str = "midrange",
     reward_config: RewardConfig = None,
+    exporter: DecisionExporter = None,
 ) -> dict:
     """Collect multiple self-play episodes into a rollout buffer.
 
@@ -523,6 +557,7 @@ def collect_rollouts(
         num_episodes: Number of episodes to collect
         playstyle: Agent playstyle
         reward_config: Reward configuration
+        exporter: Optional DecisionExporter to record decisions for distillation
 
     Returns:
         Collection statistics dict
@@ -531,6 +566,7 @@ def collect_rollouts(
     total_wins = 0
     total_losses = 0
     total_draws = 0
+    warnings.warn(_TRAINING_DEPRECATION_MSG, DeprecationWarning, stacklevel=2)
     total_steps = 0
     total_rewards = 0.0
 
@@ -538,12 +574,16 @@ def collect_rollouts(
         # Alternate agent seat for balanced training
         agent_seat = ep % 2
 
+        if exporter is not None:
+            exporter.begin_episode(agent_seat=agent_seat, playstyle=playstyle)
+
         trajectory = run_self_play_episode(
             agent_model=agent_model,
             opponent_policy=opponent_policy,
             agent_seat=agent_seat,
             playstyle=playstyle,
             reward_config=reward_config,
+            exporter=exporter,
         )
 
         for step in trajectory:
@@ -569,6 +609,11 @@ def collect_rollouts(
             else:
                 total_draws += 1
 
+        if exporter is not None:
+            won = trajectory and trajectory[-1]["done"] and trajectory[-1]["reward"] > 0.5
+            ep_return = sum(s["reward"] for s in trajectory)
+            exporter.end_episode(won=won, episode_return=ep_return)
+
     # Compute GAE after collection
     buffer.compute_returns_and_advantages(last_value=0.0)
 

ml/training/trainer.py

@@ -49,8 +49,7 @@ from ml.training.policy_network import (
     PolicyNetwork, save_checkpoint, load_checkpoint,
 )
 
-logging.basicConfig(level=logging.INFO, format="[%(name)s] %(message)s")
-logger = logging.getLogger("ml.trainer")
+logger = logging.getLogger("commander_ai_lab.ml.training")
 
 
 # ══════════════════════════════════════════════════════════
@@ -502,40 +501,40 @@ def evaluate_model(
 
 def print_evaluation(results: Dict):
     """Pretty-print evaluation results."""
-    print()
-    print("=" * 65)
-    print("  Test Set Evaluation")
-    print("=" * 65)
-    print(f"  Overall Accuracy: {results['overall_accuracy']:.1%}")
-    print(f"  Total Samples:    {results['total_samples']:,}")
-    print()
-    print(f"  {'Action':<20} {'Support':>8} {'Prec':>7} {'Recall':>7} {'F1':>7}")
-    print("  " + "-" * 55)
+    logger.info("")
+    logger.info("=" * 65)
+    logger.info("  Test Set Evaluation")
+    logger.info("=" * 65)
+    logger.info(f"  Overall Accuracy: {results['overall_accuracy']:.1%}")
+    logger.info(f"  Total Samples:    {results['total_samples']:,}")
+    logger.info("")
+    logger.info(f"  {'Action':<20} {'Support':>8} {'Prec':>7} {'Recall':>7} {'F1':>7}")
+    logger.info("  " + "-" * 55)
 
     for action_name, m in results["per_class"].items():
-        print(
+        logger.info(
             f"  {action_name:<20} {m['support']:>8} "
             f"{m['precision']:>7.3f} {m['recall']:>7.3f} {m['f1']:>7.3f}"
         )
 
-    print("  " + "-" * 55)
+    logger.info("  " + "-" * 55)
 
     # Confusion matrix
-    print()
-    print("  Confusion Matrix (rows=true, cols=predicted):")
+    logger.info("")
+    logger.info("  Confusion Matrix (rows=true, cols=predicted):")
     header = "  " + " " * 18
     for c in range(NUM_ACTIONS):
         short_name = IDX_TO_ACTION[c].value[:6]
         header += f" {short_name:>6}"
-    print(header)
+    logger.info(header)
 
     for r in range(NUM_ACTIONS):
         row_name = IDX_TO_ACTION[r].value[:16]
         row_str = f"  {row_name:<18}"
         for c in range(NUM_ACTIONS):
             row_str += f" {results['confusion_matrix'][r][c]:>6}"
-        print(row_str)
-    print()
+        logger.info(row_str)
+    logger.info("")
 
 
 # ══════════════════════════════════════════════════════════
@@ -605,8 +604,8 @@ def main():
     args = parser.parse_args()
 
     if not TORCH_AVAILABLE:
-        print("PyTorch not installed. Install with:")
-        print("  pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121")
+        logger.info("PyTorch not installed. Install with:")
+        logger.info("  pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121")
         sys.exit(1)
 
     device = args.device or get_best_device()
@@ -621,13 +620,13 @@ def main():
     if args.eval_only:
         # Evaluation only
         if not os.path.exists(test_path):
-            print(f"Test data not found: {test_path}")
-            print("Run: python -m ml.scripts.ml_cli build")
+            logger.info(f"Test data not found: {test_path}")
+            logger.info("Run: python -m ml.scripts.ml_cli build")
             sys.exit(1)
 
         ckpt_path = args.checkpoint or os.path.join(ckpt_dir, "best_policy.pt")
         if not os.path.exists(ckpt_path):
-            print(f"Checkpoint not found: {ckpt_path}")
+            logger.info(f"Checkpoint not found: {ckpt_path}")
             sys.exit(1)
 
         test_states, test_labels = load_npz_split(test_path)
@@ -641,15 +640,15 @@ def main():
         eval_path = os.path.join(ckpt_dir, "eval_results.json")
         with open(eval_path, "w") as f:
             json.dump(results, f, indent=2)
-        print(f"  Results saved: {eval_path}")
+        logger.info(f"  Results saved: {eval_path}")
         return
 
     # Training mode
     for path, label in [(train_path, "train"), (val_path, "val")]:
         if not os.path.exists(path):
-            print(f"{label} data not found: {path}")
-            print("Build the dataset first:")
-            print("  python -m ml.scripts.ml_cli build --results-dir results/")
+            logger.info(f"{label} data not found: {path}")
+            logger.info("Build the dataset first:")
+            logger.info("  python -m ml.scripts.ml_cli build --results-dir results/")
             sys.exit(1)
 
     train_states, train_labels = load_npz_split(train_path)

models/__init__.py

@@ -0,0 +1,47 @@
+"""Pydantic models for Commander AI Lab API."""
+
+from models.requests import (
+    StartRequest,
+    ImportUrlRequest,
+    ImportTextRequest,
+    MetaFetchRequest,
+    CreateDeckRequest,
+    UpdateDeckRequest,
+    AddDeckCardRequest,
+    PatchDeckCardRequest,
+    BulkAddRequest,
+    BulkAddRecommendedRequest,
+    DeckGenerationSourceConfig,
+    DeckGenerationRequest,
+    DeckGenV3Request,
+    DeckGenV3SubstituteRequest,
+)
+from models.responses import (
+    StartResponse,
+    StatusResponse,
+    DeckInfo,
+    GeneratedDeckCard,
+)
+
+__all__ = [
+    # Requests
+    "StartRequest",
+    "ImportUrlRequest",
+    "ImportTextRequest",
+    "MetaFetchRequest",
+    "CreateDeckRequest",
+    "UpdateDeckRequest",
+    "AddDeckCardRequest",
+    "PatchDeckCardRequest",
+    "BulkAddRequest",
+    "BulkAddRecommendedRequest",
+    "DeckGenerationSourceConfig",
+    "DeckGenerationRequest",
+    "DeckGenV3Request",
+    "DeckGenV3SubstituteRequest",
+    # Responses
+    "StartResponse",
+    "StatusResponse",
+    "DeckInfo",
+    "GeneratedDeckCard",
+]