Add Phase 8 — Multiplayer (Human vs Human + AI)

- run_n() accepts 2-4 decks; run() wraps for backward compat
- _select_attack_target() heuristic for multiplayer combat targeting
- N-player elimination order tracking with finish positions
- Removal scans all opponents for scariest creature
- ML snapshot captures all N players
- Log entries use per-seat boards dict with legacy boardA/boardB for 2-player

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

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

@@ -6,6 +6,10 @@ Fetches template decklists from Moxfield 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.moxfield")
+
 
 
 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 Moxfield for the given commander.
     Experimental — not yet implemented.
     """
-    print(f"    [MOXFIELD] Stub: Not yet implemented for '{commander_name}'")
+    log.info(f"    Stub: Not yet implemented for '{commander_name}'")
     return []

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 []

lab_api.py

@@ -19,6 +19,8 @@ import asyncio
 import csv
 import io
 import json
+import logging
+import logging.handlers
 import os
 import re
 import sqlite3
@@ -62,6 +64,61 @@ class Config:
 CFG = Config()
 
 
+# ══════════════════════════════════════════════════════════════
+# Logging Setup
+# ══════════════════════════════════════════════════════════════
+
+_LOG_FORMAT = "%(asctime)s %(levelname)-5s [%(name)s] %(message)s"
+_LOG_DATE_FMT = "%Y-%m-%d %H:%M:%S"
+_LOG_DIR = Path(os.environ.get("CAL_LOG_DIR", "logs"))
+_LOG_MAX_BYTES = int(os.environ.get("CAL_LOG_MAX_MB", "25")) * 1024 * 1024
+_LOG_BACKUP_COUNT = int(os.environ.get("CAL_LOG_BACKUP_COUNT", "5"))
+
+
+def setup_logging(level: int = logging.INFO) -> None:
+    """Configure the root 'commander_ai_lab' logger with console + rotating file."""
+    _LOG_DIR.mkdir(parents=True, exist_ok=True)
+
+    root_logger = logging.getLogger("commander_ai_lab")
+    root_logger.setLevel(level)
+
+    # Avoid duplicate handlers if called again
+    if root_logger.handlers:
+        return
+
+    formatter = logging.Formatter(_LOG_FORMAT, datefmt=_LOG_DATE_FMT)
+
+    # Console handler — matches previous print() behaviour
+    console = logging.StreamHandler(sys.stdout)
+    console.setLevel(level)
+    console.setFormatter(formatter)
+    root_logger.addHandler(console)
+
+    # Rotating file handler — 25 MB × 5 backups = 125 MB max
+    file_handler = logging.handlers.RotatingFileHandler(
+        _LOG_DIR / "commander-ai-lab.log",
+        maxBytes=_LOG_MAX_BYTES,
+        backupCount=_LOG_BACKUP_COUNT,
+        encoding="utf-8",
+    )
+    file_handler.setLevel(level)
+    file_handler.setFormatter(formatter)
+    root_logger.addHandler(file_handler)
+
+
+# Named loggers — import these where needed
+log = logging.getLogger("commander_ai_lab.api")          # API endpoints, server lifecycle
+log_batch = logging.getLogger("commander_ai_lab.batch")   # Batch sim (Java + DeepSeek)
+log_sim = logging.getLogger("commander_ai_lab.sim")       # DeepSeek game simulation
+log_coach = logging.getLogger("commander_ai_lab.coach")   # Coach + report generation
+log_deckgen = logging.getLogger("commander_ai_lab.deckgen")  # Deck generation (all versions)
+log_collect = logging.getLogger("commander_ai_lab.collection")  # Collection management
+log_scan = logging.getLogger("commander_ai_lab.scanner")  # Card scanner
+log_ml = logging.getLogger("commander_ai_lab.ml")         # ML training + inference
+log_cache = logging.getLogger("commander_ai_lab.cache")   # Scryfall cache
+log_pplx = logging.getLogger("commander_ai_lab.pplx")     # Perplexity API
+
+
 # ══════════════════════════════════════════════════════════════
 # App Setup
 # ══════════════════════════════════════════════════════════════
@@ -369,10 +426,10 @@ async def fetch_meta_deck(req: MetaFetchRequest):
 @app.post("/api/lab/start", response_model=StartResponse)
 async def start_batch(req: StartRequest, background_tasks: BackgroundTasks):
     """Start a new batch simulation run."""
-    if len(req.decks) != 3:
-        raise HTTPException(400, "Exactly 3 decks required")
+    if len(req.decks) < 2 or len(req.decks) > 4:
+        raise HTTPException(400, "2-4 decks required")
     if not all(req.decks):
-        raise HTTPException(400, "All 3 deck slots must be filled")
+        raise HTTPException(400, f"All {len(req.decks)} deck slots must be filled")
     if req.numGames < 1 or req.numGames > 10000:
         raise HTTPException(400, "numGames must be 1-10000")
     if req.threads < 1 or req.threads > 16:
@@ -512,7 +569,7 @@ async def list_decks():
                 })
                 seen_names.add(db_name.lower())
     except Exception as e:
-        print(f"  WARNING: Failed to load DB decks for /api/lab/decks: {e}")
+        log.warning(f"  WARNING: Failed to load DB decks for /api/lab/decks: {e}")
 
     return {"decks": decks}
 
@@ -633,7 +690,11 @@ def parse_dck_file(deck_path: str) -> dict:
                 qty = int(m.group(1))
                 name = m.group(2).strip()
                 set_code = m.group(3).strip() if m.group(3) else ""
-                cards.append({"quantity": qty, "name": name, "set": set_code, "section": section})
+                cards.append({
+                    "quantity": qty, "name": name, "set": set_code,
+                    "section": section,
+                    "is_commander": 1 if section == "Commander" else 0,
+                })
                 if section == "Commander":
                     commander = name
     total = sum(c["quantity"] for c in cards)
@@ -719,10 +780,10 @@ def load_precon_index():
     if idx_path.exists():
         with open(idx_path, "r", encoding="utf-8") as f:
             PRECON_INDEX = json.load(f)
-        print(f"  Precons:      {len(PRECON_INDEX)} precon decks loaded")
+        log.info(f"  Precons:      {len(PRECON_INDEX)} precon decks loaded")
     else:
         PRECON_INDEX = []
-        print(f"  Precons:      index not found at {idx_path}")
+        log.info(f"  Precons:      index not found at {idx_path}")
 
 
 @app.get("/api/lab/precons")
@@ -866,7 +927,7 @@ def download_precon_database(force: bool = False) -> dict:
             if len(existing) > 50:
                 age_hours = (time.time() - idx_path.stat().st_mtime) / 3600
                 if age_hours < PRECON_CACHE_HOURS:
-                    print(f"  Precons:      {len(existing)} decks cached "
+                    log.info(f"  Precons:      {len(existing)} decks cached "
                           f"({age_hours:.0f}h old, refresh after {PRECON_CACHE_HOURS}h)")
                     PRECON_INDEX = existing
                     return {"downloaded": 0, "skipped": True, "total": len(existing),
@@ -875,7 +936,7 @@ def download_precon_database(force: bool = False) -> dict:
             pass  # Fall through to download
 
     # ── Download ─────────────────────────────────────────────
-    print("  Precons:      Downloading full precon database from GitHub...")
+    log.info("  Precons:      Downloading full precon database from GitHub...")
     try:
         req = Request(GITHUB_PRECON_URL, headers={"User-Agent": "CommanderAILab/3.0"})
         with urlopen(req, timeout=120) as resp:
@@ -883,7 +944,7 @@ def download_precon_database(force: bool = False) -> dict:
         all_decks = json.loads(raw)
     except Exception as e:
         msg = f"Failed to download precon database: {e}"
-        print(f"  Precons:      ERROR — {msg}")
+        log.error(f"  Precons:      ERROR — {msg}")
         # Fall back to whatever we have locally
         if idx_path.exists():
             load_precon_index()
@@ -895,7 +956,7 @@ def download_precon_database(force: bool = False) -> dict:
         if d.get('type') == 'Commander Deck'
         and (d.get('format') or '').lower() == 'commander'
     ]
-    print(f"  Precons:      Found {len(commander_decks)} Commander precon decks")
+    log.info(f"  Precons:      Found {len(commander_decks)} Commander precon decks")
 
     # ── Ensure precon-decks dir exists ───────────────────────
     PRECON_DIR.mkdir(parents=True, exist_ok=True)
@@ -953,7 +1014,7 @@ def download_precon_database(force: bool = False) -> dict:
         json.dump(index, f, indent=2, ensure_ascii=False)
 
     PRECON_INDEX = index
-    print(f"  Precons:      {written} .dck files written, index saved")
+    log.info(f"  Precons:      {written} .dck files written, index saved")
     return {"downloaded": written, "skipped": False, "total": written, "error": None}
 
 
@@ -1195,7 +1256,7 @@ def _save_profile_to_dck(profile: dict) -> Path:
 
     out_path = Path(save_dir) / f"{safe_name}.dck"
     out_path.write_text(content, encoding="utf-8")
-    print(f"  [Import] Saved .dck: {out_path}")
+    log_collect.info(f"  Saved .dck: {out_path}")
     return out_path
 
 
@@ -1239,7 +1300,7 @@ async def run_batch_subprocess(
             ai_think_time_ms=ai_think_time_ms,
             max_queue_depth=max_queue_depth,
         )
-        print(f"[Lab API] Starting batch {state.batch_id}: {' '.join(cmd[:6])}...")
+        log_batch.info(f"Starting batch {state.batch_id}: {' '.join(cmd[:6])}...")
         loop = asyncio.get_event_loop()
         await loop.run_in_executor(
             None,
@@ -1248,7 +1309,7 @@ async def run_batch_subprocess(
     except Exception as e:
         state.error = str(e)
         state.running = False
-        print(f"[Lab API] Batch {state.batch_id} ERROR: {e}")
+        log_batch.error(f"Batch {state.batch_id} ERROR: {e}")
 
 
 def _run_process_blocking(state: BatchState, cmd: list[str]):
@@ -1266,9 +1327,9 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
             dbg.write(f"Forge JAR exists: {os.path.exists(CFG.forge_jar)}\n")
             dbg.write(f"Forge Dir exists: {os.path.isdir(CFG.forge_dir)}\n")
             dbg.write(f"{'-'*60}\n")
-        print(f"[Lab API] Debug log: {debug_log_path}")
+        log_batch.warning(f"Debug log: {debug_log_path}")
     except Exception as e:
-        print(f"[Lab API] Could not write debug log header: {e}")
+        log_batch.warning(f"Could not write debug log header: {e}")
 
     # Ensure Forge subprocesses use Java 17 (Forge crashes on Java 25+)
     env = os.environ.copy()
@@ -1278,7 +1339,7 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
         java17_home = os.path.dirname(java17_bin)
         env['JAVA_HOME'] = java17_home
         env['PATH'] = java17_bin + os.pathsep + env.get('PATH', '')
-        print(f'[Lab API] Using Java 17: {java17}')
+        log_batch.info(f'Using Java 17: {java17}')
 
     # NOTE: Do NOT set _JAVA_OPTIONS=-Djava.awt.headless=true here.
     # Forge sim mode needs AWT classes for card data initialization;
@@ -1309,7 +1370,7 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
                 time.sleep(30)
                 elapsed_since_activity = time.time() - last_activity[0]
                 if elapsed_since_activity > stall_limit:
-                    print(f'[Lab API] WATCHDOG: No output for {int(elapsed_since_activity)}s. Killing process.')
+                    log_batch.warning(f'WATCHDOG: No output for {int(elapsed_since_activity)}s. Killing process.')
                     state.log_lines.append(f'[WATCHDOG] Process stalled ({int(elapsed_since_activity)}s with no output). Killed.')
                     proc.kill()
                     return
@@ -1345,7 +1406,7 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
                     except ValueError:
                         pass
 
-            print(f"  [{state.batch_id}] {line}")
+            log_batch.info(f"  [{state.batch_id}] {line}")
 
         proc.wait()
 
@@ -1367,7 +1428,7 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
                     dbg.write("<< NO OUTPUT >>\n")
                 dbg.write(f"-- END OUTPUT --\n\n")
         except Exception as e:
-            print(f"[Lab API] Could not write debug log output: {e}")
+            log_batch.warning(f"Could not write debug log output: {e}")
 
         if proc.returncode != 0:
             if proc.returncode == -9 or proc.returncode == 137:
@@ -1376,7 +1437,7 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
                 state.error = f'Java process exited with code {proc.returncode}. Check forge-sim-debug.log for details.'
         else:
             state.completed_games = state.total_games
-            print(f"[Lab API] Batch {state.batch_id} completed in {elapsed}ms")
+            log_batch.info(f"Batch {state.batch_id} completed in {elapsed}ms")
 
             # Auto-generate deck reports for the coach
             if state.result_path and os.path.exists(state.result_path):
@@ -1386,9 +1447,9 @@ def _run_process_blocking(state: BatchState, cmd: list[str]):
                     reports_dir = str(lab_root / "deck-reports")
                     updated = generate_single_deck_report(state.result_path, reports_dir)
                     if updated:
-                        print(f"[Lab API] Deck reports updated: {', '.join(updated)}")
+                        log_batch.info(f"Deck reports updated: {', '.join(updated)}")
                 except Exception as rpt_err:
-                    print(f"[Lab API] Deck report generation failed (non-fatal): {rpt_err}")
+                    log_batch.info(f"Deck report generation failed (non-fatal): {rpt_err}")
 
     except Exception as e:
         state.error = str(e)
@@ -1451,32 +1512,41 @@ def _load_deck_cards_by_name(deck_name: str) -> list[dict]:
             if result:
                 return result
     except Exception as e:
-        print(f"[DeepSeek Batch] DB lookup failed for '{deck_name}': {e}")
+        log_sim.error(f"DB lookup failed for '{deck_name}': {e}")
 
     # 2. Try .dck file
     if CFG.forge_decks_dir and os.path.isdir(CFG.forge_decks_dir):
         dck_path = Path(CFG.forge_decks_dir) / f"{deck_name}.dck"
         if dck_path.exists():
             cards = []
-            in_cards = False  # True when inside [Main], [Deck], or [Commander]
+            current_section = None
             with open(dck_path, 'r', encoding='utf-8') as f:
                 for line in f:
                     line = line.strip()
                     low = line.lower()
-                    if low in ('[main]', '[deck]', '[commander]'):
-                        in_cards = True
+                    if low == '[commander]':
+                        current_section = 'commander'
                         continue
-                    if line.startswith('['):
-                        in_cards = False
+                    elif low in ('[main]', '[deck]'):
+                        current_section = 'main'
                         continue
-                    if in_cards and line:
+                    elif line.startswith('['):
+                        current_section = None
+                        continue
+                    if current_section and line:
                         parts = line.split(' ', 1)
                         if len(parts) == 2:
                             try:
                                 qty = int(parts[0])
                                 name = parts[1].strip()
+                                is_cmdr = 1 if current_section == 'commander' else 0
                                 for _ in range(qty):
-                                    cards.append({'name': name, 'type_line': '', 'cmc': 0})
+                                    cards.append({
+                                        'name': name,
+                                        'type_line': '',
+                                        'cmc': 0,
+                                        'is_commander': is_cmdr,
+                                    })
                             except ValueError:
                                 pass
             if cards:
@@ -1752,7 +1822,7 @@ def _run_deepseek_batch_thread(
         for ds in all_deck_results:
             state.log_lines.append(f'  {ds["deckName"]}: {ds["wins"]}W-{ds["losses"]}L ({ds["winRate"]}% WR)')
 
-        print(f'[DeepSeek Batch] Batch {state.batch_id} complete: {completed} games in {elapsed:.1f}s')
+        log_sim.info(f'Batch {state.batch_id} complete: {completed} games in {elapsed:.1f}s')
 
     except Exception as e:
         import traceback
@@ -1855,9 +1925,11 @@ def build_java_command(
         java17, "-jar", CFG.lab_jar,
         "--forge-jar", CFG.forge_jar,
         "--forge-dir", CFG.forge_dir,
-        "--deck1", decks[0],
-        "--deck2", decks[1],
-        "--deck3", decks[2],
+    ]
+    # Add deck args dynamically (2-4 decks)
+    for i, deck in enumerate(decks):
+        cmd.extend([f"--deck{i+1}", deck])
+    cmd += [
         "--games", str(num_games),
         "--threads", str(threads),
         "--clock", str(clock),
@@ -2015,10 +2087,10 @@ def init_collection_db():
     for col_name, col_def in _migrate_columns:
         if col_name not in existing_cols:
             conn.execute(f"ALTER TABLE collection_entries ADD COLUMN {col_name} {col_def}")
-            print(f"  Migration: added column '{col_name}' to collection_entries")
+            log_collect.info(f"  Migration: added column '{col_name}' to collection_entries")
     conn.commit()
     conn.close()
-    print(f"  Collection DB: {COLLECTION_DB_PATH}")
+    log_collect.info(f"  Collection DB: {COLLECTION_DB_PATH}")
 
 
 # Fields stored as JSON strings in SQLite that should be returned as parsed arrays
@@ -2488,6 +2560,147 @@ async def update_collection_card(cardId: int, body: dict):
 # Collection Import Helpers
 # ══════════════════════════════════════════════════════════════
 
+# ── Scryfall Response Cache ─────────────────────────────────
+# SQLite-backed cache for Scryfall API responses.
+# Keyed by (name, set_code, collector_number). Default TTL: 7 days.
+# Scryfall data changes infrequently (set releases ~every 3 months).
+# Eliminates redundant HTTP calls across imports and re-enrichments.
+
+SCRYFALL_CACHE_DB_PATH = Path(__file__).parent / "scryfall_cache.db"
+SCRYFALL_CACHE_TTL_SECONDS = int(os.environ.get("SCRYFALL_CACHE_TTL", 7 * 24 * 3600))  # 7 days
+
+
+class _ScryfallCache:
+    """
+    Thread-safe SQLite cache for raw Scryfall JSON responses.
+
+    Schema:
+        cache_key  TEXT PRIMARY KEY  — "name|set|cn" normalised lowercase
+        json_blob  TEXT              — raw Scryfall API JSON response
+        fetched_at TEXT              — ISO-8601 UTC timestamp
+    """
+
+    def __init__(self, db_path: Path = None, ttl_seconds: int = None):
+        self._db_path = db_path or SCRYFALL_CACHE_DB_PATH
+        self._ttl = ttl_seconds or SCRYFALL_CACHE_TTL_SECONDS
+        self._lock = threading.Lock()
+        self._conn: Optional[sqlite3.Connection] = None
+        self._hits = 0
+        self._misses = 0
+        self._init_db()
+
+    # ── internal ────────────────────────────────────────────
+
+    def _init_db(self):
+        self._conn = sqlite3.connect(str(self._db_path), check_same_thread=False)
+        self._conn.execute("PRAGMA journal_mode=WAL")
+        self._conn.execute("""
+            CREATE TABLE IF NOT EXISTS scryfall_cache (
+                cache_key  TEXT PRIMARY KEY,
+                json_blob  TEXT    NOT NULL,
+                fetched_at TEXT    NOT NULL DEFAULT (datetime('now'))
+            )
+        """)
+        self._conn.commit()
+
+    @staticmethod
+    def _make_key(name: str, set_code: str, collector_number: str) -> str:
+        return f"{name.strip().lower()}|{(set_code or '').strip().lower()}|{(collector_number or '').strip()}"
+
+    # ── public API ──────────────────────────────────────────
+
+    def get(self, name: str, set_code: str = "", collector_number: str = "") -> Optional[dict]:
+        """Return cached Scryfall JSON dict, or None if missing / expired."""
+        key = self._make_key(name, set_code, collector_number)
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT json_blob, fetched_at FROM scryfall_cache WHERE cache_key = ?",
+                (key,),
+            ).fetchone()
+        if not row:
+            self._misses += 1
+            return None
+        # TTL check
+        try:
+            fetched = datetime.fromisoformat(row[1])
+            age = (datetime.utcnow() - fetched).total_seconds()
+            if age > self._ttl:
+                self._misses += 1
+                return None
+        except (ValueError, TypeError):
+            self._misses += 1
+            return None
+        self._hits += 1
+        try:
+            return json.loads(row[0])
+        except json.JSONDecodeError:
+            self._misses += 1
+            return None
+
+    def put(self, name: str, set_code: str, collector_number: str, card_data: dict):
+        """Store a Scryfall response in the cache."""
+        key = self._make_key(name, set_code, collector_number)
+        blob = json.dumps(card_data, separators=(",", ":"))
+        with self._lock:
+            self._conn.execute(
+                "INSERT OR REPLACE INTO scryfall_cache (cache_key, json_blob, fetched_at) "
+                "VALUES (?, ?, datetime('now'))",
+                (key, blob),
+            )
+            self._conn.commit()
+
+    def stats(self) -> dict:
+        """Return cache statistics."""
+        with self._lock:
+            total = self._conn.execute("SELECT COUNT(*) FROM scryfall_cache").fetchone()[0]
+            oldest_row = self._conn.execute(
+                "SELECT MIN(fetched_at) FROM scryfall_cache"
+            ).fetchone()
+            newest_row = self._conn.execute(
+                "SELECT MAX(fetched_at) FROM scryfall_cache"
+            ).fetchone()
+            db_size_bytes = os.path.getsize(str(self._db_path)) if self._db_path.exists() else 0
+        return {
+            "total_entries": total,
+            "hits": self._hits,
+            "misses": self._misses,
+            "hit_rate": round(self._hits / max(self._hits + self._misses, 1) * 100, 1),
+            "ttl_seconds": self._ttl,
+            "ttl_days": round(self._ttl / 86400, 1),
+            "oldest_entry": oldest_row[0] if oldest_row else None,
+            "newest_entry": newest_row[0] if newest_row else None,
+            "db_size_kb": round(db_size_bytes / 1024, 1),
+            "db_path": str(self._db_path),
+        }
+
+    def clear(self) -> int:
+        """Delete all cached entries. Returns count of deleted rows."""
+        with self._lock:
+            count = self._conn.execute("SELECT COUNT(*) FROM scryfall_cache").fetchone()[0]
+            self._conn.execute("DELETE FROM scryfall_cache")
+            self._conn.commit()
+            self._conn.execute("VACUUM")  # must run outside transaction
+            self._hits = 0
+            self._misses = 0
+        return count
+
+    def evict_expired(self) -> int:
+        """Remove entries older than TTL. Returns count of evicted rows."""
+        cutoff = datetime.utcnow().isoformat()
+        with self._lock:
+            self._conn.execute(
+                "DELETE FROM scryfall_cache WHERE "
+                "(julianday(?) - julianday(fetched_at)) * 86400 > ?",
+                (cutoff, self._ttl),
+            )
+            deleted = self._conn.total_changes
+            self._conn.commit()
+        return deleted
+
+
+_scryfall_cache = _ScryfallCache()
+
+
 # Scryfall rate-limit semaphore (max 10 req/sec)
 _scryfall_lock = threading.Lock()
 _scryfall_last_call = 0.0
@@ -2671,20 +2884,26 @@ def _parse_csv_content(content: str, source: str, mapping: Optional[dict]) -> li
     return rows
 
 
-def _enrich_from_scryfall(name: str, set_code: str = "", collector_number: str = "") -> dict:
+def _fetch_scryfall_api(name: str, set_code: str = "", collector_number: str = "") -> dict:
     """
-    Fetch card data from Scryfall and return enriched fields dict.
-    Tries set+collectorNumber first, then exact name lookup.
-    Returns empty dict on failure.
+    Fetch raw card data from Scryfall API (with caching).
+    Returns the raw Scryfall JSON dict, or a dict with '_error' on failure.
+    Cache is checked first; live HTTP call only on miss.
     """
     from urllib.parse import quote
-    _scryfall_rate_limit()
 
+    # ── Cache check ──────────────────────────────────────
+    cached = _scryfall_cache.get(name, set_code, collector_number)
+    if cached is not None:
+        return cached
+
+    # ── Live fetch ───────────────────────────────────────
     card_data = None
     last_error = ""
 
     # Try set + collector number first
     if set_code and collector_number:
+        _scryfall_rate_limit()
         try:
             url = f"https://api.scryfall.com/cards/{set_code.lower()}/{collector_number}"
             req = Request(url, headers=_API_HEADERS)
@@ -2711,6 +2930,27 @@ def _enrich_from_scryfall(name: str, set_code: str = "", collector_number: str =
         err_detail = card_data.get("details", "unknown error") if card_data else last_error
         return {"_error": f"Scryfall returned error for '{name}': {err_detail}"}
 
+    # ── Cache the successful response ────────────────────
+    _scryfall_cache.put(name, set_code, collector_number, card_data)
+    # Also cache under the Scryfall-resolved name (handles spelling normalisation)
+    resolved_name = card_data.get("name", "")
+    if resolved_name and resolved_name.lower() != name.strip().lower():
+        _scryfall_cache.put(resolved_name, set_code, collector_number, card_data)
+
+    return card_data
+
+
+def _enrich_from_scryfall(name: str, set_code: str = "", collector_number: str = "") -> dict:
+    """
+    Fetch card data from Scryfall (cached) and return enriched fields dict.
+    Tries set+collectorNumber first, then exact name lookup.
+    Returns empty dict on failure.
+    """
+    card_data = _fetch_scryfall_api(name, set_code, collector_number)
+
+    if not card_data or "_error" in card_data:
+        return card_data or {}
+
     # Extract fields
     type_line = card_data.get("type_line", "")
     color_identity = card_data.get("color_identity", [])
@@ -2927,6 +3167,31 @@ async def import_collection(body: dict):
     }
 
 
+# ══════════════════════════════════════════════════════════════
+# Scryfall Cache Management Endpoints
+# ══════════════════════════════════════════════════════════════
+
+
+@app.get("/api/cache/scryfall")
+async def scryfall_cache_stats():
+    """Return Scryfall response cache statistics."""
+    return _scryfall_cache.stats()
+
+
+@app.delete("/api/cache/scryfall")
+async def scryfall_cache_clear():
+    """Clear all cached Scryfall responses."""
+    deleted = _scryfall_cache.clear()
+    return {"cleared": deleted, "message": f"Deleted {deleted} cached entries"}
+
+
+@app.post("/api/cache/scryfall/evict")
+async def scryfall_cache_evict_expired():
+    """Remove only expired entries (older than TTL) from the cache."""
+    evicted = _scryfall_cache.evict_expired()
+    return {"evicted": evicted, "message": f"Evicted {evicted} expired entries"}
+
+
 # ══════════════════════════════════════════════════════════════
 # Deck Builder Helpers
 # ══════════════════════════════════════════════════════════════
@@ -3988,7 +4253,7 @@ async def export_deck_to_sim(deck_id: int):
 
     out_path = Path(save_dir) / f"{safe_name}.dck"
     out_path.write_text(content, encoding="utf-8")
-    print(f"  [DeckBuilder] Exported deck {deck_id} to {out_path}")
+    log_deckgen.info(f"  Exported deck {deck_id} to {out_path}")
 
     return {
         "success": True,
@@ -4314,7 +4579,7 @@ async def scan_card_image(request: FastAPIRequest):
     if len(image_bytes) > 20 * 1024 * 1024:
         raise HTTPException(400, "Image file too large (max 20 MB)")
 
-    print(f"  [SCAN] Processing {len(image_bytes)} bytes, mode={mode} (Ximilar AI)")
+    log_scan.info(f"  Processing {len(image_bytes)} bytes, mode={mode} (Ximilar AI)")
 
     if mode == "multi":
         results = scan_multi(image_bytes, _scryfall_fuzzy_lookup, ximilar_api_key=api_key)
@@ -4725,7 +4990,7 @@ async def get_card_edhrec(cardId: int):
                     })
 
     except Exception as e:
-        print(f"  [EDHREC] Error fetching data for '{name}': {e}")
+        log_deckgen.error(f"  Error fetching data for '{name}': {e}")
         # Graceful degradation — return empty results
 
     result = {
@@ -5152,7 +5417,7 @@ def _get_deepseek_brain():
                     cfg.log_dir = _os2.path.join(_os2.path.dirname(_os2.path.abspath(__file__)), 'logs', 'decisions')
                     _deepseek_brain = DeepSeekBrain(cfg)
                 except Exception as e:
-                    print(f'[DeepSeek] Failed to initialize brain: {e}')
+                    log_sim.error(f'Failed to initialize brain: {e}')
                     return None
     return _deepseek_brain
 
@@ -5651,11 +5916,11 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
         return {"error": "Commander not found. Please check the name or Scryfall ID."}
 
     color_identity = req.color_identity or commander.get("color_identity", [])
-    print(f"  [DECKGEN] Commander: {commander['name']}, CI: {color_identity}")
+    log_deckgen.info(f"  Commander: {commander['name']}, CI: {color_identity}")
 
     # 2. Load collection
     collection = _get_collection_for_colors(color_identity)
-    print(f"  [DECKGEN] {len(collection)} cards in collection match color identity")
+    log_deckgen.info(f"  {len(collection)} cards in collection match color identity")
 
     # 3. Call source adapters (currently stubbed)
     template_cards = []  # list of (name, weight, source_name)
@@ -5668,7 +5933,7 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
                 for tc in td.cards:
                     template_cards.append((tc.name, tc.quantity, "archidekt"))
     except Exception as e:
-        print(f"  [DECKGEN] Archidekt adapter error: {e}")
+        log_deckgen.error(f"  Archidekt adapter error: {e}")
 
     try:
         if sources.use_edhrec:
@@ -5677,7 +5942,7 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
                 for tc in td.cards:
                     template_cards.append((tc.name, tc.quantity, "edhrec"))
     except Exception as e:
-        print(f"  [DECKGEN] EDHREC adapter error: {e}")
+        log_deckgen.error(f"  EDHREC adapter error: {e}")
 
     try:
         if sources.use_moxfield:
@@ -5686,7 +5951,7 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
                 for tc in td.cards:
                     template_cards.append((tc.name, tc.quantity, "moxfield"))
     except Exception as e:
-        print(f"  [DECKGEN] Moxfield adapter error: {e}")
+        log_deckgen.error(f"  Moxfield adapter error: {e}")
 
     try:
         if sources.use_mtggoldfish:
@@ -5695,7 +5960,7 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
                 for tc in td.cards:
                     template_cards.append((tc.name, tc.quantity, "mtggoldfish"))
     except Exception as e:
-        print(f"  [DECKGEN] MTGGoldfish adapter error: {e}")
+        log_deckgen.error(f"  MTGGoldfish adapter error: {e}")
 
     # 4. Build candidate pool from collection, scored by type need
     #    Build a map: name_lower -> card dict (deduped)
@@ -5883,8 +6148,8 @@ def _generate_deck(req: DeckGenerationRequest) -> dict:
             "is_proxy": card.get("is_proxy", False),
         })
 
-    print(f"  [DECKGEN] Generated deck: {len(clean_cards)} cards + commander")
-    print(f"  [DECKGEN] Stats: {stats}")
+    log_deckgen.info(f"  Generated deck: {len(clean_cards)} cards + commander")
+    log_deckgen.info(f"  Stats: {stats}")
 
     return {
         "commander": commander,
@@ -6015,11 +6280,11 @@ async def deck_generator_commit(req: DeckGenerationRequest):
                     dck_lines.append(f"{card.get('quantity', 1)} {card_name}")
             with open(dck_path, "w", encoding="utf-8") as f:
                 f.write("\n".join(dck_lines))
-            print(f"  [DECKGEN] Exported .dck file: {dck_path}")
+            log_deckgen.info(f"  Exported .dck file: {dck_path}")
     except Exception as e:
-        print(f"  [DECKGEN] Warning: Failed to export .dck file: {e}")
+        log_deckgen.warning(f"  Warning: Failed to export .dck file: {e}")
 
-    print(f"  [DECKGEN] Saved deck '{deck_name}' (ID: {deck_id}) with {len(cards)} cards + commander")
+    log_deckgen.info(f"  Saved deck '{deck_name}' (ID: {deck_id}) with {len(cards)} cards + commander")
 
     result["deck_id"] = deck_id
     result["deck_name"] = deck_name
@@ -6098,7 +6363,7 @@ async def deck_generator_commander_search(q: str = ""):
                     "image_url": image_uris.get("normal", image_uris.get("small", "")),
                 })
         except Exception as e:
-            print(f"  [DECKGEN] Scryfall search error: {e}")
+            log_deckgen.error(f"  Scryfall search error: {e}")
 
     return {"results": results[:20]}
 
@@ -6268,7 +6533,7 @@ def _call_pplx_api(messages: list[dict], max_tokens: int = 4096, temperature: fl
             raise ValueError('Empty response from Perplexity API')
         content = choices[0].get('message', {}).get('content', '')
         usage = data.get('usage', {})
-        print(f'[pplx-api] tokens: prompt={usage.get("prompt_tokens","?")}, '
+        log_pplx.debug(f'tokens: prompt={usage.get("prompt_tokens","?")}, '
               f'completion={usage.get("completion_tokens","?")}, '
               f'model={data.get("model","?")}')
         return content
@@ -6583,7 +6848,7 @@ async def deck_generate_ai(req: DeckGenerateAIRequest):
         commander = scry_data.get('name', commander)  # Use canonical name
         commander_type = scry_data.get('type_line', '')
     except Exception as e:
-        print(f'[pplx-gen] Scryfall lookup failed for "{commander}": {e}')
+        log_pplx.error(f'Scryfall lookup failed for "{commander}": {e}')
 
     # Build collection summary
     collection_block = ''
@@ -6758,7 +7023,7 @@ async def deck_gen_v3_generate(req: DeckGenV3Request):
     except ValueError as e:
         return JSONResponse({'error': str(e)}, status_code=422)
     except Exception as e:
-        print(f'[deck-gen-v3] Error: {e}')
+        log_deckgen.error(f'Error: {e}')
         import traceback
         traceback.print_exc()
         raise HTTPException(500, f'Deck generation failed: {str(e)}')
@@ -6865,19 +7130,19 @@ async def deck_gen_v3_commit(req: DeckGenV3Request):
                         dck_lines.append(f"{card.get('count', 1)} {cname}")
                 with open(dck_path, 'w', encoding='utf-8') as f:
                     f.write('\n'.join(dck_lines))
-                print(f"  [DeckGenV3] Exported .dck: {dck_path}")
+                log_deckgen.info(f"  Exported .dck: {dck_path}")
         except Exception as e:
-            print(f"  [DeckGenV3] .dck export failed: {e}")
+            log_deckgen.error(f"  .dck export failed: {e}")
 
         result['deck_id'] = deck_id
         result['deck_name'] = deck_name
-        print(f"  [DeckGenV3] Committed deck '{deck_name}' (ID: {deck_id})")
+        log_deckgen.info(f"  Committed deck '{deck_name}' (ID: {deck_id})")
         return result
 
     except ValueError as e:
         return JSONResponse({'error': str(e)}, status_code=422)
     except Exception as e:
-        print(f'[deck-gen-v3] Commit error: {e}')
+        log_deckgen.error(f'Commit error: {e}')
         import traceback
         traceback.print_exc()
         raise HTTPException(500, f'Deck generation/commit failed: {str(e)}')
@@ -7083,22 +7348,22 @@ def init_coach_service():
             from coach.config import EMBEDDINGS_NPZ
             if EMBEDDINGS_NPZ.exists():
                 _coach_embeddings.load()
-                print(f"  Coach:        Embeddings loaded ({_coach_embeddings.card_count} cards)")
+                log_coach.info(f"  Coach:        Embeddings loaded ({_coach_embeddings.card_count} cards)")
             else:
-                print("  Coach:        Embeddings not yet downloaded (will download on first use)")
+                log_coach.warning("  Coach:        Embeddings not yet downloaded (will download on first use)")
         except Exception as e:
-            print(f"  Coach:        Embeddings load failed: {e}")
+            log_coach.error(f"  Coach:        Embeddings load failed: {e}")
 
         _coach_service = CoachService(_coach_llm, _coach_embeddings)
 
         # Check LLM connection
         llm_status = _coach_llm.check_connection()
         if llm_status.get("connected"):
-            print(f"  Coach LLM:    Connected ({llm_status.get('active_model', 'unknown')})")
+            log_coach.info(f"  Coach LLM:    Connected ({llm_status.get('active_model', 'unknown')})")
         else:
-            print(f"  Coach LLM:    Not connected (start LM Studio on 192.168.0.122:1234)")
+            log_coach.warning(f"  Coach LLM:    Not connected (start LM Studio on 192.168.0.122:1234)")
 
-        print("  Coach:        Service initialized")
+        log_coach.info("  Coach:        Service initialized")
 
         # Initialize V3 Deck Generator (Perplexity)
         _deck_gen_v3_error = None
@@ -7117,21 +7382,21 @@ def init_coach_service():
                     db_conn_factory=_get_db_conn,
                     embedding_index=_coach_embeddings,
                 )
-                print(f"  Deck Gen V3:  Initialized (model: {DECK_GEN_MODEL})")
+                log_deckgen.info(f"  Deck Gen V3:  Initialized (model: {DECK_GEN_MODEL})")
             except ImportError as e:
                 _deck_gen_v3_error = f"Missing dependency: {e}. Run: pip install openai"
-                print(f"  Deck Gen V3:  {_deck_gen_v3_error}")
+                log_deckgen.error(f"  Deck Gen V3:  {_deck_gen_v3_error}")
                 _deck_gen_v3 = None
             except Exception as e:
                 _deck_gen_v3_error = str(e)
-                print(f"  Deck Gen V3:  Failed to initialize: {e}")
+                log_deckgen.error(f"  Deck Gen V3:  Failed to initialize: {e}")
                 _deck_gen_v3 = None
         else:
             _deck_gen_v3_error = "PPLX_API_KEY not set"
-            print("  Deck Gen V3:  Skipped (no PPLX_API_KEY)")
+            log_deckgen.info("  Deck Gen V3:  Skipped (no PPLX_API_KEY)")
 
     except Exception as e:
-        print(f"  Coach:        Failed to initialize: {e}")
+        log_coach.error(f"  Coach:        Failed to initialize: {e}")
         _coach_service = None
 
 
@@ -7234,7 +7499,7 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
         try:
             _coach_embeddings.load(force_download=True)
         except Exception as e:
-            print(f"  Coach: Embeddings download failed: {e}")
+            log_coach.error(f"  Coach: Embeddings download failed: {e}")
 
     goals = None
     if body and body.goals:
@@ -7249,7 +7514,7 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
     try:
         fallback_report = _build_deck_report_from_db(deck_id)
     except Exception as e:
-        print(f"  Coach: Fallback report build failed for '{deck_id}': {e}")
+        log_coach.error(f"  Coach: Fallback report build failed for '{deck_id}': {e}")
 
     try:
         session = await _coach_service.run_coaching_session(deck_id, goals, fallback_report=fallback_report)
@@ -7805,11 +8070,11 @@ def _get_policy_service():
             from ml.serving.policy_server import PolicyInferenceService
             _policy_service = PolicyInferenceService()
             if _policy_service.load():
-                print(f"[ML] Policy model loaded on {_policy_service.device}")
+                log_ml.info(f"Policy model loaded on {_policy_service.device}")
             else:
-                print(f"[ML] Policy model not available: {_policy_service._load_error}")
+                log_ml.error(f"Policy model not available: {_policy_service._load_error}")
         except Exception as e:
-            print(f"[ML] Policy service init failed: {e}")
+            log_ml.error(f"Policy service init failed: {e}")
     return _policy_service
 
 
@@ -7969,7 +8234,7 @@ def _run_training_pipeline(
         if rebuild_dataset or not os.path.exists(train_path):
             _training_state["phase"] = "building"
             _training_state["message"] = "Loading card embeddings & building dataset..."
-            print("[ML Train] Building dataset (loading embeddings, may auto-download)...")
+            log_ml.info("Building dataset (loading embeddings, may auto-download)...")
 
             from ml.data.dataset_builder import build_dataset, split_dataset, save_dataset
             dataset = build_dataset(results_dir=results_dir)
@@ -7981,14 +8246,14 @@ def _run_training_pipeline(
             save_dataset(test_ds, os.path.join(data_dir, "test.npz"))
             total_samples = len(dataset["states"])
             _training_state["message"] = f"Dataset built: {total_samples} samples"
-            print(f"[ML Train] Dataset built: {total_samples} samples")
+            log_ml.info(f"Dataset built: {total_samples} samples")
 
         # --- Phase 2: Train ---
         _training_state["phase"] = "training"
         _training_state["total_epochs"] = epochs
         _training_state["current_epoch"] = 0
         _training_state["message"] = f"Training policy network ({epochs} epochs)..."
-        print(f"[ML Train] Starting training: {epochs} epochs, lr={lr}, bs={batch_size}")
+        log_ml.info(f"Starting training: {epochs} epochs, lr={lr}, bs={batch_size}")
 
         import numpy as np
         import torch
@@ -8060,13 +8325,13 @@ def _run_training_pipeline(
             "device": device,
         }
         _training_state["message"] = f"Training complete! Best val acc: {summary.get('best_val_accuracy', 0):.1%}"
-        print(f"[ML Train] Complete: {summary.get('best_val_accuracy', 0):.1%} val accuracy")
+        log_ml.info(f"Complete: {summary.get('best_val_accuracy', 0):.1%} val accuracy")
 
         # Auto-reload policy server with new checkpoint
         global _policy_service, _policy_service_init_attempted
         if _policy_service and _policy_service._loaded:
             _policy_service.reload(summary.get("checkpoint_path"))
-            print("[ML Train] Policy server reloaded with new checkpoint")
+            log_ml.info("Policy server reloaded with new checkpoint")
         elif not _policy_service_init_attempted:
             _policy_service_init_attempted = False  # Allow re-init with new model
 
@@ -8076,7 +8341,7 @@ def _run_training_pipeline(
         _training_state["running"] = False
         _training_state["error"] = str(e)
         _training_state["message"] = f"Training failed: {e}"
-        print(f"[ML Train] ERROR: {e}")
+        log_ml.error(f"ERROR: {e}")
         traceback.print_exc()
 
 
@@ -8485,6 +8750,8 @@ def parse_args():
                         help="Ximilar API key for card scanner (visual AI recognition)")
     parser.add_argument("--pplx-key", default=os.environ.get("PPLX_API_KEY", "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"),
                         help="Perplexity API key for AI deck research/generation (env: PPLX_API_KEY)")
+    parser.add_argument("--verbose", "-v", action="store_true",
+                        help="Enable DEBUG-level logging (default: INFO)")
     return parser.parse_args()
 
 
@@ -8528,17 +8795,18 @@ def load_commander_meta():
         try:
             with open(meta_path, "r", encoding="utf-8") as f:
                 COMMANDER_META = json.load(f)
-            print(f"  Meta:         Loaded {len(COMMANDER_META)} commanders from {meta_path}")
+            log.info(f"  Meta:         Loaded {len(COMMANDER_META)} commanders from {meta_path}")
             return
         except Exception as e:
-            print(f"  WARNING: Failed to load commander-meta.json: {e}")
+            log.warning(f"  WARNING: Failed to load commander-meta.json: {e}")
 
     COMMANDER_META = BUILTIN_COMMANDERS
-    print(f"  Meta:         {len(COMMANDER_META)} built-in commanders")
+    log.info(f"  Meta:         {len(COMMANDER_META)} built-in commanders")
 
 
 def main():
     args = parse_args()
+    setup_logging(logging.DEBUG if getattr(args, 'verbose', False) else logging.INFO)
 
     CFG.forge_jar = args.forge_jar
     CFG.forge_dir = args.forge_dir
@@ -8548,21 +8816,21 @@ def main():
     CFG.ximilar_api_key = args.ximilar_key
     CFG.pplx_api_key = args.pplx_key
 
-    print("╔══════════════════════════════════════════════════╗")
-    print("║      Commander AI Lab — API Server  v3.0.0      ║")
-    print("╚══════════════════════════════════════════════════╝")
-    print()
-    print(f"  Forge JAR:    {CFG.forge_jar}")
-    print(f"  Forge Dir:    {CFG.forge_dir}")
-    print(f"  Decks Dir:    {CFG.forge_decks_dir}")
-    print(f"  Lab JAR:      {CFG.lab_jar}")
-    print(f"  Results Dir:  {CFG.results_dir}")
-    print(f"  Port:         {CFG.port}")
-    print(f"  Ximilar:      {'configured' if CFG.ximilar_api_key else 'NOT SET (scanner will fail)'}")
-    print(f"  Perplexity:   {'configured' if CFG.pplx_api_key else 'NOT SET (AI research/gen disabled)'}")
+    log.info("╔══════════════════════════════════════════════════╗")
+    log.info("║      Commander AI Lab — API Server  v3.0.0      ║")
+    log.info("╚══════════════════════════════════════════════════╝")
+    log.info("")
+    log.info(f"  Forge JAR:    {CFG.forge_jar}")
+    log.info(f"  Forge Dir:    {CFG.forge_dir}")
+    log.info(f"  Decks Dir:    {CFG.forge_decks_dir}")
+    log.info(f"  Lab JAR:      {CFG.lab_jar}")
+    log.info(f"  Results Dir:  {CFG.results_dir}")
+    log.info(f"  Port:         {CFG.port}")
+    log.info(f"  Ximilar:      {'configured' if CFG.ximilar_api_key else 'NOT SET (scanner will fail)'}")
+    log.info(f"  Perplexity:   {'configured' if CFG.pplx_api_key else 'NOT SET (AI research/gen disabled)'}")
     j17 = get_java17()
-    print(f"  Java 17:      {j17 if j17 != 'java' else 'NOT FOUND (batch sim may fail on Java 25+)'}")
-    print(f"  LM Studio:    http://192.168.0.122:1234")
+    log.info(f"  Java 17:      {j17 if j17 != 'java' else 'NOT FOUND (batch sim may fail on Java 25+)'}")
+    log.info(f"  LM Studio:    http://192.168.0.122:1234")
 
     load_commander_meta()
     download_precon_database()  # Auto-downloads all 163+ Commander precons on first run
@@ -8571,27 +8839,27 @@ def main():
 
     # Ximilar API key check
     if not CFG.ximilar_api_key:
-        print("  WARNING: --ximilar-key not set. Card scanner will not work.")
-        print("           Set via CLI: --ximilar-key YOUR_KEY")
-        print("           Or env var:  XIMILAR_API_KEY=YOUR_KEY")
+        log_scan.warning("  WARNING: --ximilar-key not set. Card scanner will not work.")
+        log.info("           Set via CLI: --ximilar-key YOUR_KEY")
+        log.info("           Or env var:  XIMILAR_API_KEY=YOUR_KEY")
 
     if not CFG.forge_jar:
-        print("WARNING: --forge-jar not set. /api/lab/start will fail.")
+        log.warning("WARNING: --forge-jar not set. /api/lab/start will fail.")
     if not CFG.lab_jar:
-        print("WARNING: Lab JAR not found. Build with: mvn package -DskipTests")
+        log.warning("WARNING: Lab JAR not found. Build with: mvn package -DskipTests")
     if not CFG.forge_decks_dir:
-        print("WARNING: Commander decks dir not found. /api/lab/decks will return empty.")
+        log.warning("WARNING: Commander decks dir not found. /api/lab/decks will return empty.")
 
-    print()
-    print(f"  Starting server on http://localhost:{CFG.port}")
+    log.info("")
+    log.info(f"  Starting server on http://localhost:{CFG.port}")
     if _spa_dir.exists():
-        print(f"  Web UI:       http://localhost:{CFG.port}/  (React SPA)")
-        print(f"  Routes:       / (Batch Sim), /collection, /decks, /autogen, /simulator, /coach, /training")
+        log.info(f"  Web UI:       http://localhost:{CFG.port}/  (React SPA)")
+        log.info(f"  Routes:       / (Batch Sim), /collection, /decks, /autogen, /simulator, /coach, /training")
     else:
-        print(f"  Web UI:       http://localhost:{CFG.port}/index.html  (legacy HTML)")
-        print(f"  NOTE: React SPA not built. Run: cd frontend/commander-ai-lab-ui && npm install && npm run build")
-    print(f"  API docs:     http://localhost:{CFG.port}/docs")
-    print()
+        log.info(f"  Web UI:       http://localhost:{CFG.port}/index.html  (legacy HTML)")
+        log.info(f"  NOTE: React SPA not built. Run: cd frontend/commander-ai-lab-ui && npm install && npm run build")
+    log.info(f"  API docs:     http://localhost:{CFG.port}/docs")
+    log.info("")
 
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=CFG.port, log_level="info")

ml/actions/labeler.py

@@ -240,12 +240,12 @@ 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)
+    log.info(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
+    log.info("─" * 43)
     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}")
+        log.info(f"  {action_name:<23} {count:>8} {pct:>7.1f}%")
+    log.info(f"{'─' * 43}")
+    log.info(f"  {'TOTAL':<23} {total:>8}")
 
     return dist

ml/data/dataset_builder.py

@@ -154,17 +154,17 @@ 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)
+    log.info(f"\n{'Outcome':<25} {'Count':>8}")
+    log.info("─" * 35)
     for o, c in sorted(zip(unique_outcomes, outcome_counts), key=lambda x: -x[1]):
-        print(f"  {str(o):<23} {c:>8}")
+        log.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)
+    log.info(f"\n{'Playstyle':<25} {'Count':>8}")
+    log.info("─" * 35)
     for s, c in sorted(zip(unique_styles, style_counts), key=lambda x: -x[1]):
-        print(f"  {str(s):<23} {c:>8}")
+        log.info(f"  {str(s):<23} {c:>8}")
 
     return {
         "states": states,
@@ -288,7 +288,7 @@ def main():
         max_samples=args.max_samples,
     )
     if not dataset:
-        print("\nNo data to save. Run simulations with --ml-log first.")
+        log.info("\nNo data to save. Run simulations with --ml-log first.")
         sys.exit(1)
 
     if args.no_split:
@@ -300,7 +300,7 @@ def main():
         save_dataset(val, f"{base}-val.npz")
         save_dataset(test, f"{base}-test.npz")
 
-    print("\nDataset build complete.")
+    log.info("\nDataset build complete.")
 
 
 if __name__ == "__main__":

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"):

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)

overnight-run.py

@@ -22,11 +22,15 @@ Configuration:
 
 import argparse
 import json
+import logging
+import logging.handlers
+import os
 import sys
 import time
 import urllib.request
 import urllib.error
 from datetime import datetime, timedelta
+from pathlib import Path
 
 
 # ── Default Config ──────────────────────────────────────────────
@@ -51,10 +55,48 @@ DEFAULT_CONFIG = {
 }
 
 
-def log(msg):
-    ts = datetime.now().strftime("%H:%M:%S")
-    print(f"[{ts}] {msg}")
-    sys.stdout.flush()
+# ── Logging ──────────────────────────────────────────────────
+_LOG_DIR = Path(os.environ.get("CAL_LOG_DIR", "logs"))
+
+
+def setup_overnight_logging() -> logging.Logger:
+    """Configure logging for the overnight runner.
+
+    Outputs to both console and a rotating log file so that
+    unattended runs always have a persistent record.
+    """
+    _LOG_DIR.mkdir(parents=True, exist_ok=True)
+
+    logger = logging.getLogger("commander_ai_lab.overnight")
+    logger.setLevel(logging.INFO)
+
+    if logger.handlers:
+        return logger
+
+    fmt = logging.Formatter(
+        "%(asctime)s %(levelname)-5s [%(name)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+    console = logging.StreamHandler(sys.stdout)
+    console.setLevel(logging.INFO)
+    console.setFormatter(fmt)
+    logger.addHandler(console)
+
+    file_handler = logging.handlers.RotatingFileHandler(
+        _LOG_DIR / "overnight-run.log",
+        maxBytes=10 * 1024 * 1024,  # 10 MB
+        backupCount=5,
+        encoding="utf-8",
+    )
+    file_handler.setLevel(logging.INFO)
+    file_handler.setFormatter(fmt)
+    logger.addHandler(file_handler)
+
+    return logger
+
+
+log = setup_overnight_logging()
 
 
 def api_get(base_url, path):
@@ -129,8 +171,8 @@ def wait_for_batch(base_url, batch_id, engine="deepseek", deadline=None):
     while True:
         # Hard deadline check — stop waiting if we've run out of time
         if deadline and time.time() >= deadline:
-            log(f"  Time limit reached while batch {batch_id} was running.")
-            log(f"  The batch will continue on the server but we're moving on.")
+            log.info(f"  Time limit reached while batch {batch_id} was running.")
+            log.info(f"  The batch will continue on the server but we're moving on.")
             return {"completed": 0, "timed_out": True}
 
         try:
@@ -144,12 +186,12 @@ def wait_for_batch(base_url, batch_id, engine="deepseek", deadline=None):
             elapsed = status.get("elapsedMs", 0)
 
             if error:
-                log(f"  Batch {batch_id} ERROR: {error}")
+                log.error(f"  Batch {batch_id} ERROR: {error}")
                 return None
 
             if not running and completed > 0:
                 elapsed_s = elapsed / 1000 if elapsed else 0
-                log(f"  Batch {batch_id} complete: {completed} games in {elapsed_s:.0f}s")
+                log.info(f"  Batch {batch_id} complete: {completed} games in {elapsed_s:.0f}s")
                 try:
                     result = api_get(base_url, f"/api/lab/result?batchId={batch_id}")
                     return result
@@ -160,15 +202,15 @@ def wait_for_batch(base_url, batch_id, engine="deepseek", deadline=None):
             if not running and completed == 0:
                 stall_checks += 1
                 if stall_checks > 20:  # 5 min with no progress
-                    log(f"  Batch {batch_id} never started. Aborting.")
+                    log.info(f"  Batch {batch_id} never started. Aborting.")
                     return None
 
             # Progress log with ETA
             if deadline:
                 mins_left = (deadline - time.time()) / 60
-                log(f"  Progress: {completed}/{total} games ({elapsed / 1000:.0f}s) — {mins_left:.0f}min left in run")
+                log.info(f"  Progress: {completed}/{total} games ({elapsed / 1000:.0f}s) — {mins_left:.0f}min left in run")
             else:
-                log(f"  Progress: {completed}/{total} games ({elapsed / 1000:.0f}s)")
+                log.info(f"  Progress: {completed}/{total} games ({elapsed / 1000:.0f}s)")
 
             # Track stall
             if completed == last_completed:
@@ -180,9 +222,9 @@ def wait_for_batch(base_url, batch_id, engine="deepseek", deadline=None):
         except Exception as e:
             consecutive_errors += 1
             if consecutive_errors > 10:
-                log(f"  Lost connection to server after 10 retries. Aborting batch.")
+                log.info(f"  Lost connection to server after 10 retries. Aborting batch.")
                 return None
-            log(f"  Connection error (retry {consecutive_errors}/10): {e}")
+            log.info(f"  Connection error (retry {consecutive_errors}/10): {e}")
 
         time.sleep(poll_interval)
 
@@ -214,26 +256,26 @@ def wait_for_training(base_url):
             error = status.get("error")
 
             if error:
-                log(f"  Training ERROR: {error}")
+                log.error(f"  Training ERROR: {error}")
                 return False
 
             if not running and phase in ("done", "idle") and epoch > 0:
                 result = status.get("result", {})
-                log(f"  Training complete!")
+                log.info(f"  Training complete!")
                 if result:
-                    log(f"  Final accuracy: {result.get('best_val_accuracy', '?')}")
-                    log(f"  Model saved: {result.get('model_path', '?')}")
+                    log.info(f"  Final accuracy: {result.get('best_val_accuracy', '?')}")
+                    log.info(f"  Model saved: {result.get('model_path', '?')}")
                 return True
 
             if phase == "training" and total > 0:
                 metrics = status.get("metrics", {})
                 val_acc = metrics.get("val_accuracy", "?") if metrics else "?"
-                log(f"  Training: epoch {epoch}/{total} — {message} (val_acc: {val_acc})")
+                log.info(f"  Training: epoch {epoch}/{total} — {message} (val_acc: {val_acc})")
             else:
-                log(f"  [{phase}] {message}")
+                log.info(f"  [{phase}] {message}")
 
         except Exception as e:
-            log(f"  Connection error: {e}")
+            log.info(f"  Connection error: {e}")
 
         time.sleep(poll_interval)
 
@@ -253,46 +295,46 @@ def run_overnight(cfg):
     """Main overnight pipeline."""
     base_url = cfg["lab_url"]
 
-    log("=" * 60)
-    log("  Commander AI Lab — Overnight AFK Runner")
-    log("=" * 60)
+    log.info("=" * 60)
+    log.info("  Commander AI Lab — Overnight AFK Runner")
+    log.info("=" * 60)
 
     # ── Verify server ────────────────────────────────────────
-    log("Checking lab server...")
+    log.info("Checking lab server...")
     if not check_server(base_url):
-        log(f"ERROR: Cannot reach lab server at {base_url}")
-        log("Start the lab first: start-lab.bat")
+        log.error(f"ERROR: Cannot reach lab server at {base_url}")
+        log.info("Start the lab first: start-lab.bat")
         return False
 
-    log(f"Server OK at {base_url}")
+    log.info(f"Server OK at {base_url}")
 
     # ── Get decks ─────────────────────────────────────────────
     available_decks = get_available_decks(base_url)
-    log(f"Found {len(available_decks)} decks: {', '.join(available_decks[:5])}{'...' if len(available_decks) > 5 else ''}")
+    log.info(f"Found {len(available_decks)} decks: {', '.join(available_decks[:5])}{'...' if len(available_decks) > 5 else ''}")
 
     engine = cfg["engine"]
     if engine == "deepseek":
         decks = cfg["ds_decks"] if cfg["ds_decks"] else available_decks
         if not decks:
-            log("ERROR: No decks available. Create decks in the Deck Builder first.")
+            log.error("ERROR: No decks available. Create decks in the Deck Builder first.")
             return False
-        log(f"Using DeepSeek engine with {len(decks)} deck(s)")
+        log.info(f"Using DeepSeek engine with {len(decks)} deck(s)")
     else:
         decks = cfg["java_decks"]
         if len(decks) != 3:
-            log("ERROR: Java engine requires exactly 3 deck names.")
-            log("Set java_decks in the config or use --decks deck1 deck2 deck3")
+            log.error("ERROR: Java engine requires exactly 3 deck names.")
+            log.info("Set java_decks in the config or use --decks deck1 deck2 deck3")
             return False
-        log(f"Using Java/Forge engine with decks: {decks}")
+        log.info(f"Using Java/Forge engine with decks: {decks}")
 
     # ── ML logging ────────────────────────────────────────────
-    log("Enabling ML logging...")
+    log.info("Enabling ML logging...")
     try:
         api_post(base_url, "/api/ml/toggle?enable=true", {})
-        log("ML logging enabled")
+        log.info("ML logging enabled")
     except Exception as e:
-        log(f"WARNING: Could not enable ML logging: {e}")
-        log("(DeepSeek batches always log ML data regardless)")
+        log.warning(f"WARNING: Could not enable ML logging: {e}")
+        log.info("(DeepSeek batches always log ML data regardless)")
         # Try alternate form
         try:
             api_get(base_url, "/api/ml/toggle?enable=true")
@@ -301,7 +343,7 @@ def run_overnight(cfg):
 
     # ── Initial data stats ────────────────────────────────────
     decisions_before, files_before = get_ml_data_summary(base_url)
-    log(f"Current ML data: {decisions_before} decisions in {files_before} files")
+    log.info(f"Current ML data: {decisions_before} decisions in {files_before} files")
 
     # ── Run batches ───────────────────────────────────────────
     games_per_batch = cfg["games_per_batch"]
@@ -315,32 +357,32 @@ def run_overnight(cfg):
     total_games = 0
     total_decisions = 0
 
-    log("")
-    log(f"Starting batch loop:")
+    log.info("")
+    log.info(f"Starting batch loop:")
     if num_batches > 0:
-        log(f"  Mode: {num_batches} batches × {games_per_batch} games")
+        log.info(f"  Mode: {num_batches} batches × {games_per_batch} games")
     else:
-        log(f"  Mode: Run for {cfg['time_limit_hours']}h, {games_per_batch} games/batch")
-    log(f"  Engine: {engine}")
-    log(f"  Auto-train after: {'Yes' if cfg['auto_train'] else 'No'}")
-    log("")
+        log.info(f"  Mode: Run for {cfg['time_limit_hours']}h, {games_per_batch} games/batch")
+    log.info(f"  Engine: {engine}")
+    log.info(f"  Auto-train after: {'Yes' if cfg['auto_train'] else 'No'}")
+    log.info("")
 
     while True:
         # Check stopping conditions
         if num_batches > 0 and batch_num >= num_batches:
-            log(f"Reached target of {num_batches} batches. Stopping.")
+            log.info(f"Reached target of {num_batches} batches. Stopping.")
             break
 
         elapsed = time.time() - start_time
         remaining = deadline - time.time()
         if remaining < 120:  # need at least 2 min for a batch
-            log(f"Time limit approaching ({remaining:.0f}s left). Stopping sim loop.")
+            log.info(f"Time limit approaching ({remaining:.0f}s left). Stopping sim loop.")
             break
 
         batch_num += 1
         elapsed_str = str(timedelta(seconds=int(elapsed)))
         remaining_str = str(timedelta(seconds=int(remaining)))
-        log(f"── Batch {batch_num} ── (elapsed: {elapsed_str}, remaining: {remaining_str})")
+        log.info(f"── Batch {batch_num} ── (elapsed: {elapsed_str}, remaining: {remaining_str})")
 
         try:
             if engine == "deepseek":
@@ -352,12 +394,12 @@ def run_overnight(cfg):
                 )
 
             if not batch_id:
-                log("  ERROR: Failed to start batch (no batchId returned)")
-                log("  Waiting 60s before retry...")
+                log.error("  ERROR: Failed to start batch (no batchId returned)")
+                log.info("  Waiting 60s before retry...")
                 time.sleep(60)
                 continue
 
-            log(f"  Started batch {batch_id}")
+            log.info(f"  Started batch {batch_id}")
             result = wait_for_batch(base_url, batch_id, engine, deadline=deadline)
 
             if result:
@@ -366,19 +408,19 @@ def run_overnight(cfg):
                 for deck in result.get("decks", []):
                     wr = deck.get("winRate", "?")
                     name = deck.get("deckName", "?")
-                    log(f"    {name}: {wr}% WR")
+                    log.info(f"    {name}: {wr}% WR")
             else:
-                log("  Batch failed, continuing to next...")
+                log.info("  Batch failed, continuing to next...")
 
         except Exception as e:
-            log(f"  ERROR starting batch: {e}")
-            log("  Waiting 60s before retry...")
+            log.error(f"  ERROR starting batch: {e}")
+            log.info("  Waiting 60s before retry...")
             time.sleep(60)
             continue
 
         # Pause between batches
         if pause_sec > 0:
-            log(f"  Cooling down {pause_sec}s...")
+            log.info(f"  Cooling down {pause_sec}s...")
             time.sleep(pause_sec)
 
     # ── Summary ───────────────────────────────────────────────
@@ -387,26 +429,26 @@ def run_overnight(cfg):
     new_decisions = decisions_after - decisions_before
     new_files = files_after - files_before
 
-    log("")
-    log("=" * 60)
-    log("  BATCH SIMULATION COMPLETE")
-    log("=" * 60)
-    log(f"  Batches run:       {batch_num}")
-    log(f"  Total games:       ~{total_games}")
-    log(f"  New ML decisions:  {new_decisions} (in {new_files} new files)")
-    log(f"  Total ML data:     {decisions_after} decisions")
-    log(f"  Elapsed time:      {str(timedelta(seconds=int(total_elapsed)))}")
-    log("")
+    log.info("")
+    log.info("=" * 60)
+    log.info("  BATCH SIMULATION COMPLETE")
+    log.info("=" * 60)
+    log.info(f"  Batches run:       {batch_num}")
+    log.info(f"  Total games:       ~{total_games}")
+    log.info(f"  New ML decisions:  {new_decisions} (in {new_files} new files)")
+    log.info(f"  Total ML data:     {decisions_after} decisions")
+    log.info(f"  Elapsed time:      {str(timedelta(seconds=int(total_elapsed)))}")
+    log.info("")
 
     # ── Auto-train ────────────────────────────────────────────
     if cfg["auto_train"]:
         if decisions_after < 100:
-            log("Skipping training — too few decisions (need at least 100)")
+            log.info("Skipping training — too few decisions (need at least 100)")
             return True
 
-        log("Starting ML training pipeline...")
-        log(f"  Epochs: {cfg['train_epochs']}, LR: {cfg['train_lr']}, "
-            f"Batch: {cfg['train_batch_size']}, Patience: {cfg['train_patience']}")
+        log.info("Starting ML training pipeline...")
+        log.info(f"  Epochs: {cfg['train_epochs']}, LR: {cfg['train_lr']}, "
+                 f"Batch: {cfg['train_batch_size']}, Patience: {cfg['train_patience']}")
 
         try:
             start_training(
@@ -416,21 +458,21 @@ def run_overnight(cfg):
                 batch_size=cfg["train_batch_size"],
                 patience=cfg["train_patience"],
             )
-            log("Training started, monitoring progress...")
+            log.info("Training started, monitoring progress...")
             success = wait_for_training(base_url)
             if success:
-                log("")
-                log("=" * 60)
-                log("  TRAINING COMPLETE — model updated!")
-                log("=" * 60)
+                log.info("")
+                log.info("=" * 60)
+                log.info("  TRAINING COMPLETE — model updated!")
+                log.info("=" * 60)
             else:
-                log("Training finished with errors. Check the Training tab for details.")
+                log.info("Training finished with errors. Check the Training tab for details.")
         except Exception as e:
-            log(f"ERROR starting training: {e}")
+            log.error(f"ERROR starting training: {e}")
             return False
 
-    log("")
-    log("Overnight run finished. You can close this window.")
+    log.info("")
+    log.info("Overnight run finished. You can close this window.")
     return True
 
 

pyproject.toml

@@ -10,8 +10,30 @@ readme = "README.md"
 requires-python = ">=3.10"
 license = {text = "MIT"}
 
+dependencies = [
+    "fastapi>=0.100.0",
+    "uvicorn[standard]>=0.23.0",
+    "pydantic>=2.0.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0",
+    "pytest-asyncio>=0.21",
+    "httpx>=0.24.0",
+    "ruff>=0.1.0",
+]
+
 [tool.setuptools.packages.find]
 where = ["src"]
 
 [project.scripts]
 commander-sim = "commander_ai_lab.lab.experiments:_cli_main"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = [".", "src"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py310"

requirements.txt

@@ -0,0 +1,7 @@
+# Core runtime dependencies
+# Install with: pip install -r requirements.txt
+# Or for dev tools: pip install -e ".[dev]"
+
+fastapi>=0.100.0
+uvicorn[standard]>=0.23.0
+pydantic>=2.0.0

scanner/pipeline.py

@@ -13,6 +13,10 @@ No local OCR or OpenCV needed — all recognition is done by the
 Ximilar cloud API using visual AI.
 """
 from typing import Callable, Optional
+import logging
+
+log = logging.getLogger("commander_ai_lab.scanner")
+
 
 from .ximilar_client import identify_cards, XimilarResult
 
@@ -160,7 +164,7 @@ def _ximilar_to_scan_result(xr: XimilarResult,
         try:
             scryfall_data = scryfall_lookup(xr.name)
         except Exception as e:
-            print(f"    [SCAN] Scryfall lookup failed for '{xr.name}': {e}")
+            log.error(f"    Scryfall lookup failed for '{xr.name}': {e}")
 
     if scryfall_data and scryfall_data.get("object") != "error":
         result.scryfall_id = scryfall_data.get("id", "")
@@ -181,6 +185,6 @@ def _ximilar_to_scan_result(xr: XimilarResult,
             # Extract image from scryfall URL pattern
             # e.g., https://scryfall.com/card/m11/149/lightning-bolt
             result.image_uri = ""  # Will be fetched by frontend via scryfall_id
-        print(f"    [SCAN] No Scryfall match for '{xr.name}' — using Ximilar data only")
+        log.info(f"    No Scryfall match for '{xr.name}' — using Ximilar data only")
 
     return result

scanner/ximilar_client.py

@@ -13,6 +13,10 @@ import json
 from typing import Optional, List
 from urllib.request import Request, urlopen
 from urllib.error import URLError, HTTPError
+import logging
+
+log = logging.getLogger("commander_ai_lab.scanner.ximilar")
+
 
 XIMILAR_ENDPOINT = "https://api.ximilar.com/collectibles/v2/tcg_id"
 
@@ -82,7 +86,7 @@ def identify_cards(image_bytes: bytes, api_key: str,
     )
 
     try:
-        print(f"    [XIMILAR] Sending {len(image_bytes):,} bytes to API (analyze_all={analyze_all})")
+        log.info(f"    Sending {len(image_bytes):,} bytes to API (analyze_all={analyze_all})")
         with urlopen(req, timeout=60) as resp:
             resp_data = json.loads(resp.read().decode("utf-8"))
     except HTTPError as e:
@@ -168,12 +172,12 @@ def _parse_response(resp_data: dict) -> List[XimilarResult]:
     if not results:
         results.append(XimilarResult(error="Ximilar could not identify any cards"))
 
-    print(f"    [XIMILAR] Identified {len(results)} card(s)")
+    log.info(f"    Identified {len(results)} card(s)")
     for r in results:
         if r.name:
-            print(f"    [XIMILAR]   - {r.name} ({r.set_code}) conf={r.confidence:.2f}")
+            log.info(f"    - {r.name} ({r.set_code}) conf={r.confidence:.2f}")
         elif r.error:
-            print(f"    [XIMILAR]   - ERROR: {r.error}")
+            log.error(f"    - ERROR: {r.error}")
 
     return results
 

src/commander_ai_lab/lab/experiments.py

@@ -13,6 +13,10 @@ Functions:
 """
 
 from __future__ import annotations
+import logging
+
+log = logging.getLogger("commander_ai_lab.lab.experiments")
+
 
 import time
 from typing import Optional
@@ -238,27 +242,27 @@ def _generate_training_deck() -> list[Card]:
 
 def _print_stats(stats: dict) -> None:
     """Pretty-print simulation stats."""
-    print()
-    print("=" * 60)
-    print(f"  SIMULATION RESULTS: {stats['deck_name']} vs {stats['opponent_name']}")
-    print("=" * 60)
-    print(f"  Games played:      {stats['total_games']}")
-    print(f"  Wins / Losses:     {stats['wins']} / {stats['losses']}")
-    print(f"  Win Rate:          {stats['win_rate']}%")
-    print(f"  Avg Turns:         {stats['avg_turns']}")
-    print("-" * 60)
-    print(f"  Avg Damage Dealt:     {stats['avg_damage_dealt']}")
-    print(f"  Avg Damage Received:  {stats['avg_damage_received']}")
-    print(f"  Avg Spells Cast:      {stats['avg_spells_cast']}")
-    print(f"  Avg Creatures Played: {stats['avg_creatures_played']}")
-    print(f"  Avg Removal Used:     {stats['avg_removal_used']}")
-    print(f"  Avg Ramp Played:      {stats['avg_ramp_played']}")
-    print(f"  Avg Cards Drawn:      {stats['avg_cards_drawn']}")
-    print(f"  Avg Max Board Size:   {stats['avg_max_board_size']}")
-    print("-" * 60)
-    print(f"  Elapsed:           {stats['elapsed_seconds']}s")
-    print("=" * 60)
-    print()
+    log.info("")
+    log.info("=" * 60)
+    log.info(f"  SIMULATION RESULTS: {stats['deck_name']} vs {stats['opponent_name']}")
+    log.info("=" * 60)
+    log.info(f"  Games played:      {stats['total_games']}")
+    log.info(f"  Wins / Losses:     {stats['wins']} / {stats['losses']}")
+    log.info(f"  Win Rate:          {stats['win_rate']}%")
+    log.info(f"  Avg Turns:         {stats['avg_turns']}")
+    log.info("-" * 60)
+    log.info(f"  Avg Damage Dealt:     {stats['avg_damage_dealt']}")
+    log.info(f"  Avg Damage Received:  {stats['avg_damage_received']}")
+    log.info(f"  Avg Spells Cast:      {stats['avg_spells_cast']}")
+    log.info(f"  Avg Creatures Played: {stats['avg_creatures_played']}")
+    log.info(f"  Avg Removal Used:     {stats['avg_removal_used']}")
+    log.info(f"  Avg Ramp Played:      {stats['avg_ramp_played']}")
+    log.info(f"  Avg Cards Drawn:      {stats['avg_cards_drawn']}")
+    log.info(f"  Avg Max Board Size:   {stats['avg_max_board_size']}")
+    log.info("-" * 60)
+    log.info(f"  Elapsed:           {stats['elapsed_seconds']}s")
+    log.info("=" * 60)
+    log.info("")
 
 
 def _cli_main() -> None:
@@ -294,15 +298,15 @@ def _run_smoke_test() -> None:
         "Harmonize", "Read the Bones", "Phyrexian Arena",
     ]
 
-    print("Building test deck (Korvold Aristocrats)...")
+    log.info("Building test deck (Korvold Aristocrats)...")
     test_deck = build_deck(test_decklist)
-    print(f"  Deck size: {len(test_deck)} cards")
+    log.info(f"  Deck size: {len(test_deck)} cards")
 
-    print("\nRunning single game...")
+    log.info("\nRunning single game...")
     single = run_single_game(test_deck, name_a="Korvold")
-    print(f"  Winner: {single['winner_name']} in {single['turns']} turns")
+    log.info(f"  Winner: {single['winner_name']} in {single['turns']} turns")
 
-    print("\nRunning Monte Carlo simulation (10 games)...")
+    log.info("\nRunning Monte Carlo simulation (10 games)...")
     stats = run_simulation(test_deck, num_games=10, name_a="Korvold")
     _print_stats(stats)
 

src/commander_ai_lab/sim/deepseek_brain.py

@@ -18,6 +18,8 @@ Performance features:
   - Response caching for identical game states
   - Structured JSON output with validation
   - Decision logging (JSONL) for future RL training data
+    Each JSONL entry includes game_result (winner_seat, winner_name,
+    player_lives, reward) once flush_log() is called with a GameResult.
 """
 
 from __future__ import annotations
@@ -115,7 +117,7 @@ def build_game_state_snapshot(
     opp = sim_state.players[1 - player_index]
     ctx = deck_context or {}
 
-    # ── Mana by Color (Enhancement #8) ──────────────────────────
+    # ── Mana by Color (Enhancement #8) ────────────────────────────
     mana_total = 0
     mana_by_color = {"W": 0, "U": 0, "B": 0, "R": 0, "G": 0, "C": 0}
     my_bf = sim_state.get_battlefield(player_index)
@@ -127,7 +129,6 @@ def build_game_state_snapshot(
             if land_name in _LAND_COLOR_MAP:
                 mana_by_color[_LAND_COLOR_MAP[land_name]] += 1
             elif c.oracle_text:
-                # Parse oracle text for mana production
                 oracle_lower = c.oracle_text.lower()
                 if "any color" in oracle_lower:
                     for clr in "WUBRG":
@@ -138,14 +139,13 @@ def build_game_state_snapshot(
                         if sym in oracle_lower or f"add {clr.lower()}" in oracle_lower:
                             mana_by_color[clr] += 1
                     if not any(mana_by_color[k] for k in "WUBRG"):
-                        mana_by_color["C"] += 1  # colorless
+                        mana_by_color["C"] += 1
             else:
                 mana_by_color["C"] += 1
 
-    # Only include colors that have sources
     mana_colors = {k: v for k, v in mana_by_color.items() if v > 0}
 
-    # ── My Hand — Full Oracle Text (Enhancement #5) ────────────
+    # ── My Hand — Full Oracle Text (Enhancement #5) ────────────────
     my_hand = []
     for c in me.hand:
         card_info = {
@@ -157,7 +157,6 @@ def build_game_state_snapshot(
             card_info["pt"] = c.pt
         if c.keywords:
             card_info["keywords"] = c.keywords[:5]
-        # Role detection
         roles = []
         if c.is_removal:
             roles.append("removal")
@@ -169,22 +168,17 @@ def build_game_state_snapshot(
             roles.append("commander")
         if roles:
             card_info["roles"] = roles
-        # Full oracle text — no truncation (Enhancement #5)
         if c.oracle_text:
             card_info["oracle"] = c.oracle_text
-        # Castability check against available mana
         card_info["castable"] = (c.cmc or 0) <= mana_total and not c.is_land()
         my_hand.append(card_info)
 
-    # ── My Board — with keywords and oracle for synergy ───────
+    # ── My Board ──
     my_board = []
     for c in my_bf:
         if c.is_land():
             continue
-        entry = {
-            "name": c.name,
-            "type": _short_type(c.type_line),
-        }
+        entry = {"name": c.name, "type": _short_type(c.type_line)}
         if c.pt:
             entry["pt"] = c.pt
         if c.tapped:
@@ -195,7 +189,7 @@ def build_game_state_snapshot(
             entry["is_commander"] = True
         my_board.append(entry)
 
-    # ── Opponent's Board — with Threat Assessment (Enhancement #9)
+    # ── Opponent's Board — with Threat Assessment (Enhancement #9) ──
     opp_board = []
     opp_total_power = 0
     opp_evasion_count = 0
@@ -203,10 +197,7 @@ def build_game_state_snapshot(
     for c in opp_bf:
         if c.is_land():
             continue
-        entry = {
-            "name": c.name,
-            "type": _short_type(c.type_line),
-        }
+        entry = {"name": c.name, "type": _short_type(c.type_line)}
         if c.pt:
             entry["pt"] = c.pt
             opp_total_power += c.get_power()
@@ -221,7 +212,6 @@ def build_game_state_snapshot(
                 opp_protected_count += 1
         opp_board.append(entry)
 
-    # Threat level summary
     if opp_total_power >= me.life:
         threat_level = "LETHAL"
     elif opp_total_power >= me.life * 0.5:
@@ -233,7 +223,7 @@ def build_game_state_snapshot(
     else:
         threat_level = "NONE"
 
-    # ── Graveyard Contents (Enhancement #6) ────────────────────
+    # ── Graveyard Contents (Enhancement #6) ──────────────────────
     my_gy = [{"name": c.name, "type": _short_type(c.type_line)} for c in me.graveyard[:15]]
     opp_gy = [{"name": c.name, "type": _short_type(c.type_line)} for c in opp.graveyard[:10]]
 
@@ -243,7 +233,6 @@ def build_game_state_snapshot(
     cards_remaining = len(me.library)
     pct_drawn = round(cards_seen / max(total_deck_size, 1) * 100)
 
-    # Key cards still in library (if we know the decklist)
     key_cards_in_library = []
     if ctx.get("key_cards"):
         library_names = {c.name.lower() for c in me.library}
@@ -251,7 +240,7 @@ def build_game_state_snapshot(
             if kc.lower() in library_names:
                 key_cards_in_library.append(kc)
 
-    # ── Build the snapshot ─────────────────────────────────────
+    # ── Build the snapshot ────────────────────────────────────
     snapshot = {
         "turn": turn + 1,
         "my_life": me.life,
@@ -274,11 +263,9 @@ def build_game_state_snapshot(
         "deck_drawn_pct": pct_drawn,
     }
 
-    # ── Commander Identity (Enhancement #2) ────────────────────
     if ctx.get("commander_name"):
-        # Determine commander zone
         cmd_name_lower = ctx["commander_name"].lower()
-        cmd_zone = "library"  # default
+        cmd_zone = "library"
         for c in me.hand:
             if c.name.lower() == cmd_name_lower:
                 cmd_zone = "hand"
@@ -291,26 +278,18 @@ def build_game_state_snapshot(
             if c.name.lower() == cmd_name_lower:
                 cmd_zone = "graveyard"
                 break
-
         snapshot["commander"] = {
             "name": ctx["commander_name"],
             "zone": cmd_zone,
             "color_identity": ctx.get("color_identity", []),
         }
 
-    # ── Full Decklist Awareness (Enhancement #1) ───────────────
     if ctx.get("deck_summary"):
         snapshot["deck_summary"] = ctx["deck_summary"]
-
-    # ── Win Conditions & Combos (Enhancement #3) ───────────────
     if ctx.get("combo_pieces"):
         snapshot["combo_pieces"] = ctx["combo_pieces"]
-
-    # ── Key Cards in Library (Enhancement #7) ──────────────────
     if key_cards_in_library:
         snapshot["key_cards_in_library"] = key_cards_in_library[:10]
-
-    # ── Historical Win Rate (Enhancement #10) ──────────────────
     if ctx.get("win_rate") is not None:
         snapshot["historical_win_rate"] = ctx["win_rate"]
 
@@ -340,7 +319,7 @@ def _short_type(type_line: str) -> str:
 
 
 def build_deck_context(
-    full_deck: list,          # list[Card] — the full original deck
+    full_deck: list,
     commander_name: str = "",
     color_identity: list[str] | None = None,
     archetype: str = "midrange",
@@ -360,7 +339,6 @@ def build_deck_context(
     if win_rate is not None:
         ctx["win_rate"] = win_rate
 
-    # ── Deck Composition Summary (Enhancement #1) ──────────────
     type_counts: dict[str, int] = {}
     roles: dict[str, list[str]] = {"removal": [], "ramp": [], "board_wipe": [], "draw": []}
     key_cards: list[str] = []
@@ -371,32 +349,25 @@ def build_deck_context(
     for card in full_deck:
         ctype = _short_type(card.type_line) if card.type_line else "?"
         type_counts[ctype] = type_counts.get(ctype, 0) + 1
-
         if not card.is_land():
             total_cmc += card.cmc or 0
             non_land_count += 1
-
-        # Track roles
         if card.is_removal:
             roles["removal"].append(card.name)
         if card.is_board_wipe:
             roles["board_wipe"].append(card.name)
         if card.is_ramp:
             roles["ramp"].append(card.name)
-        # Detect draw engines
         oracle = (card.oracle_text or "").lower()
         if re.search(r"draw .* cards?", oracle):
             roles["draw"].append(card.name)
-
-        # ── Win Conditions & Combos (Enhancement #3) ──────────
         for pattern, label in _COMBO_PATTERNS:
             if re.search(pattern, oracle):
                 combo_pieces.append({"name": card.name, "role": label})
                 key_cards.append(card.name)
-                break  # one label per card
+                break
 
     avg_cmc = round(total_cmc / max(non_land_count, 1), 1)
-
     ctx["deck_summary"] = {
         "card_types": type_counts,
         "avg_cmc": avg_cmc,
@@ -406,10 +377,8 @@ def build_deck_context(
         "draw_count": len(roles["draw"]),
         "archetype": archetype,
     }
-
-    ctx["combo_pieces"] = combo_pieces[:15]  # cap to save tokens
+    ctx["combo_pieces"] = combo_pieces[:15]
     ctx["key_cards"] = list(set(key_cards))[:15]
-
     return ctx
 
 
@@ -417,20 +386,18 @@ def build_deck_context(
 # Action Schema  (Step 3)
 # ══════════════════════════════════════════════════════════════
 
-# Valid actions the LLM can return
 VALID_ACTIONS = [
-    "play_land",       # Play a land from hand
-    "cast_creature",   # Cast a creature spell
-    "cast_removal",    # Cast targeted removal
-    "cast_board_wipe", # Cast a board wipe
-    "cast_ramp",       # Cast a ramp spell
-    "cast_spell",      # Cast any other non-creature spell
-    "attack_all",      # Attack with all eligible creatures
-    "attack_safe",     # Attack only with evasive/large creatures
-    "hold",            # Pass / don't act (save mana, bluff)
+    "play_land",
+    "cast_creature",
+    "cast_removal",
+    "cast_board_wipe",
+    "cast_ramp",
+    "cast_spell",
+    "attack_all",
+    "attack_safe",
+    "hold",
 ]
 
-# Action schema for the LLM's JSON response
 ACTION_SCHEMA = {
     "action": "one of: " + ", ".join(VALID_ACTIONS),
     "target_card": "(optional) name of card to cast or target",
@@ -529,14 +496,19 @@ class DeepSeekBrain:
     """
     LLM-powered decision engine for the AI opponent.
 
-    Sends game state to a local DeepSeek model and parses the action response.
+    Sends game state to a local DeepSeek/GPT-OSS model and parses the action response.
     Falls back to heuristic on timeout/error.
+
+    Decision log entries include game outcome once flush_log() is called
+    with a GameResult, enabling downstream RL weight learning.
     """
 
     def __init__(self, config: DeepSeekConfig | None = None):
         self.config = config or DeepSeekConfig()
         self._cache: OrderedDict[str, dict] = OrderedDict()
         self._decision_log: list[dict] = []
+        self._current_game_id: str = self._new_game_id()
+        self._ai_player_index: int | None = None  # set by caller if known
         self._total_calls = 0
         self._total_fallbacks = 0
         self._total_cache_hits = 0
@@ -544,6 +516,21 @@ class DeepSeekBrain:
         self._connected = False
         self._log_file = None
 
+    @staticmethod
+    def _new_game_id() -> str:
+        """Generate a short unique game ID for grouping decisions in the log."""
+        return hashlib.md5(str(time.time()).encode()).hexdigest()[:12]
+
+    def new_game(self, ai_player_index: int | None = None) -> None:
+        """
+        Signal the start of a new game.
+
+        Call this before each game so that log entries are grouped by game_id
+        and the ai_player_index is recorded for reward calculation.
+        """
+        self._current_game_id = self._new_game_id()
+        self._ai_player_index = ai_player_index
+
     def check_connection(self) -> bool:
         """Test if the LLM endpoint is reachable."""
         try:
@@ -555,7 +542,6 @@ class DeepSeekBrain:
                 self._connected = True
                 models = data.get("data", [])
                 if models:
-                    # Auto-detect model name from LM Studio
                     model_id = models[0].get("id", self.config.model)
                     if model_id:
                         self.config.model = model_id
@@ -569,20 +555,20 @@ class DeepSeekBrain:
 
     def choose_action(
         self,
-        sim_state,          # SimState
-        player_index: int,  # Which player is the AI
+        sim_state,
+        player_index: int,
         turn: int,
         available_mana: int | None = None,
-        deck_context: dict | None = None,  # Full deck intelligence from build_deck_context()
+        deck_context: dict | None = None,
     ) -> dict:
         """
         Choose an action for the AI player.
 
         Returns:
             {
-                "action": str,          # One of VALID_ACTIONS
-                "target_card": str,     # Optional card name
-                "reasoning": str,       # Brief explanation
+                "action": str,
+                "target_card": str,
+                "reasoning": str,
                 "source": "deepseek" | "heuristic" | "cache",
                 "latency_ms": float,
             }
@@ -590,12 +576,10 @@ class DeepSeekBrain:
         self._total_calls += 1
         t_start = time.time()
 
-        # Build game state snapshot with deck intelligence
         snapshot = build_game_state_snapshot(sim_state, player_index, turn, deck_context=deck_context)
         if available_mana is not None:
             snapshot["my_mana_available"] = available_mana
 
-        # Check cache
         if self.config.cache_enabled:
             cache_key = self._cache_key(snapshot)
             if cache_key in self._cache:
@@ -605,7 +589,6 @@ class DeepSeekBrain:
                 cached["latency_ms"] = round((time.time() - t_start) * 1000, 1)
                 return cached
 
-        # Try LLM
         if not self._connected:
             return self._fallback_action(snapshot, t_start, "not_connected")
 
@@ -614,13 +597,11 @@ class DeepSeekBrain:
             result["source"] = "deepseek"
             result["latency_ms"] = round((time.time() - t_start) * 1000, 1)
 
-            # Cache the result
             if self.config.cache_enabled:
                 self._put_cache(cache_key, result)
 
-            # Log decision
             if self.config.log_decisions:
-                self._log_decision(snapshot, result)
+                self._log_decision(snapshot, result, player_index)
 
             self._total_latency_ms += result["latency_ms"]
             return result
@@ -633,11 +614,9 @@ class DeepSeekBrain:
 
     def _call_llm(self, snapshot: dict) -> dict:
         """Send game state to LLM and parse response."""
-        # Build the prompt
         game_state_json = json.dumps(snapshot, indent=2)
         user_prompt = USER_PROMPT_TEMPLATE.format(game_state_json=game_state_json)
 
-        # Build OpenAI-compatible request
         payload = {
             "model": self.config.model,
             "messages": [
@@ -658,7 +637,6 @@ class DeepSeekBrain:
         with urlopen(req, timeout=self.config.request_timeout) as resp:
             resp_data = json.loads(resp.read())
 
-        # Extract the response text
         choices = resp_data.get("choices", [])
         if not choices:
             raise ValueError("Empty response from LLM")
@@ -672,18 +650,12 @@ class DeepSeekBrain:
         Handles common LLM quirks (markdown fences, extra text, etc.)
         """
         text = raw_text.strip()
-
-        # Strip <think>...</think> tags (DeepSeek-R1 reasoning)
-        # Handle both closed tags and unclosed tags (truncated by max_tokens)
         text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL).strip()
         text = re.sub(r'<think>.*', '', text, flags=re.DOTALL).strip()
-
-        # Strip markdown code fences
         text = re.sub(r'^```(?:json)?\s*', '', text)
         text = re.sub(r'\s*```$', '', text)
         text = text.strip()
 
-        # Try to find a JSON object in the text
         json_match = re.search(r'\{[^{}]*\}', text, re.DOTALL)
         if json_match:
             text = json_match.group(0)
@@ -691,7 +663,6 @@ class DeepSeekBrain:
         try:
             parsed = json.loads(text)
         except json.JSONDecodeError:
-            # Last resort: try to extract action keyword
             for action in VALID_ACTIONS:
                 if action in raw_text.lower():
                     return {
@@ -701,16 +672,14 @@ class DeepSeekBrain:
                     }
             raise ValueError(f"Could not parse LLM response as JSON: {raw_text[:200]}")
 
-        # Validate action
         action = parsed.get("action", "hold")
         if action not in VALID_ACTIONS:
-            # Fuzzy match
             for valid in VALID_ACTIONS:
                 if valid in action.lower() or action.lower() in valid:
                     action = valid
                     break
             else:
-                action = "hold"  # safe default
+                action = "hold"
 
         return {
             "action": action,
@@ -728,18 +697,15 @@ class DeepSeekBrain:
         opp_board = snapshot.get("opp_board", [])
         turn = snapshot.get("turn", 1)
 
-        # Priority: land > ramp (early) > removal (if outnumbered) > creature > attack > hold
         action = "hold"
         target = ""
         reasoning = f"Fallback ({reason})"
 
-        # 1. Play a land if available
         lands_in_hand = [c for c in hand if c.get("type") == "land"]
         if lands_in_hand:
             action = "play_land"
             target = lands_in_hand[0]["name"]
             reasoning = "Play land first"
-        # 2. Early game: prioritize ramp
         elif turn <= 4:
             ramp_cards = [c for c in hand if c.get("role") == "ramp" and c.get("cmc", 99) <= mana]
             if ramp_cards:
@@ -748,38 +714,31 @@ class DeepSeekBrain:
                 target = ramp_cards[0]["name"]
                 reasoning = "Early ramp"
             else:
-                # Play cheapest creature
                 creatures = [c for c in hand if c.get("type") == "creature" and c.get("cmc", 99) <= mana]
                 if creatures:
                     creatures.sort(key=lambda c: c.get("cmc", 0))
                     action = "cast_creature"
                     target = creatures[0]["name"]
                     reasoning = "Cheap creature development"
-        # 3. If opponent has many creatures and we have board wipe
         elif len(opp_board) >= 3 and len(my_board) <= 1:
             wipes = [c for c in hand if c.get("role") == "board_wipe" and c.get("cmc", 99) <= mana]
             if wipes:
                 action = "cast_board_wipe"
                 target = wipes[0]["name"]
                 reasoning = "Opponent has board advantage, wiping"
-        # 4. If opponent has a big threat, use removal
         elif opp_board:
             removal = [c for c in hand if c.get("role") == "removal" and c.get("cmc", 99) <= mana]
             if removal:
                 action = "cast_removal"
                 target = removal[0]["name"]
                 reasoning = "Remove opponent threat"
-        # 5. Play best creature
         if action == "hold":
             castable = [c for c in hand if c.get("type") == "creature" and c.get("cmc", 99) <= mana]
             if castable:
-                # Pick the one with highest power
                 castable.sort(key=lambda c: -int(c.get("pt", "0/0").split("/")[0]) if c.get("pt") else 0)
                 action = "cast_creature"
                 target = castable[0]["name"]
                 reasoning = "Play threat"
-
-        # 6. Attack if we have creatures on board
         if action == "hold" and my_board:
             creatures_on_board = [c for c in my_board if c.get("type") == "creature" and not c.get("tapped")]
             if creatures_on_board:
@@ -802,13 +761,10 @@ class DeepSeekBrain:
     # ── Cache helpers ──
 
     def _cache_key(self, snapshot: dict) -> str:
-        """Create a deterministic cache key from game state."""
-        # Normalize — exclude things that don't affect decisions
         key_data = json.dumps(snapshot, sort_keys=True)
         return hashlib.md5(key_data.encode()).hexdigest()
 
     def _put_cache(self, key: str, value: dict):
-        """LRU cache insert."""
         if key in self._cache:
             self._cache.move_to_end(key)
         self._cache[key] = value
@@ -817,10 +773,17 @@ class DeepSeekBrain:
 
     # ── Decision logging ──
 
-    def _log_decision(self, snapshot: dict, result: dict):
-        """Log decision for future training data."""
+    def _log_decision(self, snapshot: dict, result: dict, player_index: int | None = None) -> None:
+        """
+        Append a decision entry to the in-memory log.
+
+        The entry is tagged with game_id so flush_log() can backfill
+        game_result fields once the game is complete.
+        """
         entry = {
             "timestamp": time.time(),
+            "game_id": self._current_game_id,
+            "player_index": player_index if player_index is not None else self._ai_player_index,
             "game_state": snapshot,
             "decision": {
                 "action": result.get("action"),
@@ -829,14 +792,96 @@ class DeepSeekBrain:
                 "source": result.get("source"),
                 "latency_ms": result.get("latency_ms"),
             },
+            # game_result is None until flush_log() is called with a GameResult
+            "game_result": None,
         }
         self._decision_log.append(entry)
 
-    def flush_log(self, filepath: str | None = None):
-        """Write accumulated decision log to JSONL file."""
-        if not self._decision_log:
-            return
+    def flush_log(
+        self,
+        filepath: str | None = None,
+        game_result=None,  # Optional[GameResult] from engine.py
+    ) -> str | None:
+        """
+        Write accumulated decision log to a JSONL file.
 
+        Parameters
+        ----------
+        filepath : str, optional
+            Destination file path.  Defaults to logs/decisions/<timestamp>.jsonl
+            next to the package root.
+        game_result : GameResult, optional
+            Result from GameEngine.run() / run_n().  When provided, every
+            buffered entry is backfilled with:
+
+            ``game_result`` dict containing:
+              - ``winner_seat``  : int   — seat index of the winner
+              - ``winner_name``  : str   — display name of the winning player
+              - ``turns``        : int   — total game length in turns
+              - ``player_lives`` : dict  — {seat_index: final_life} for all seats
+              - ``reward``       : float — normalised reward in [-1.0, 1.0]
+                                          for the AI player recorded in each entry
+                                          (winner_life - loser_life) / 40.0
+
+        Returns the absolute path written, or None if the log was empty.
+        """
+        if not self._decision_log:
+            return None
+
+        # ── Build game_result payload from GameResult dataclass ──
+        result_payload: dict | None = None
+        if game_result is not None:
+            try:
+                winner_seat: int = game_result.winner_seat
+                winner_name: str = game_result.players[winner_seat].name
+                turns: int = game_result.turns
+
+                # Per-seat final life totals
+                player_lives: dict[int, int] = {
+                    pr.seat_index: pr.life
+                    for pr in game_result.players
+                }
+
+                result_payload = {
+                    "winner_seat": winner_seat,
+                    "winner_name": winner_name,
+                    "turns": turns,
+                    "player_lives": player_lives,
+                }
+            except Exception as exc:
+                logger.warning("Could not extract game_result fields: %s", exc)
+
+        # ── Backfill each entry with game_result + per-entry reward ──
+        for entry in self._decision_log:
+            if result_payload is None:
+                entry["game_result"] = None
+                continue
+
+            # Determine reward for the player who made this decision
+            pi = entry.get("player_index")
+            winner_seat = result_payload["winner_seat"]
+            player_lives = result_payload["player_lives"]
+
+            if pi is not None and len(player_lives) >= 2:
+                my_life = player_lives.get(pi, 0)
+                # Sum of all opponents' final life totals
+                opp_life_sum = sum(
+                    v for k, v in player_lives.items() if k != pi
+                )
+                opp_count = max(len(player_lives) - 1, 1)
+                avg_opp_life = opp_life_sum / opp_count
+                raw_reward = (my_life - avg_opp_life) / 40.0
+                reward = round(max(-1.0, min(1.0, raw_reward)), 4)
+            else:
+                # Fallback: binary win/loss
+                reward = 1.0 if (pi is not None and pi == winner_seat) else -1.0
+
+            entry["game_result"] = {
+                **result_payload,
+                "reward": reward,
+            }
+
+        # ── Write to JSONL ──
         if filepath is None:
             log_dir = self.config.log_dir or os.path.join(
                 os.path.dirname(__file__), "..", "..", "..", "logs", "decisions"
@@ -859,7 +904,9 @@ class DeepSeekBrain:
     def get_stats(self) -> dict:
         """Return performance statistics."""
         avg_latency = (
-            self._total_latency_ms / max(self._total_calls - self._total_fallbacks - self._total_cache_hits, 1)
+            self._total_latency_ms / max(
+                self._total_calls - self._total_fallbacks - self._total_cache_hits, 1
+            )
         )
         return {
             "total_calls": self._total_calls,
@@ -871,4 +918,5 @@ class DeepSeekBrain:
             "model": self.config.model,
             "api_base": self.config.api_base,
             "pending_log_entries": len(self._decision_log),
+            "current_game_id": self._current_game_id,
         }

src/commander_ai_lab/sim/deepseek_engine.py

@@ -5,11 +5,16 @@ Extends the headless GameEngine so that one player (the AI opponent)
 uses the DeepSeek LLM brain for decision-making instead of the static
 heuristic scorer.
 
+N-player ready: accepts 2–4 decks. The AI-controlled seat uses DeepSeek;
+all other seats use the heuristic.
+
 The LLM decides:
   1. Whether to play a land (and which one)
   2. Which spell(s) to cast (up to 2 per turn, same as base engine)
   3. Combat stance: attack_all, attack_safe, or hold
 
+Multiplayer attack targeting uses the weakest-opponent heuristic.
+
 If the LLM is unreachable or times out, falls back to the base-engine
 heuristic transparently.
 
@@ -77,14 +82,40 @@ class DeepSeekGameEngine:
         color_identity: list[str] | None = None,
         win_rate: float | None = None,
     ) -> GameResult:
-        """Run a single game. The AI player uses DeepSeek for decisions."""
-        sim = self._create_state(deck_a, deck_b, name_a, name_b)
+        """Backward-compatible 2-player entry point."""
+        return self.run_n(
+            decks=[deck_a, deck_b],
+            names=[name_a, name_b],
+            game_id=game_id,
+            archetype=archetype,
+            commander_name=commander_name,
+            color_identity=color_identity,
+            win_rate=win_rate,
+        )
+
+    def run_n(
+        self,
+        decks: list[list[Card]],
+        names: list[str] | None = None,
+        game_id: str | None = None,
+        archetype: str = "midrange",
+        commander_name: str = "",
+        color_identity: list[str] | None = None,
+        win_rate: float | None = None,
+    ) -> GameResult:
+        """Run a single N-player game. The AI player uses DeepSeek for decisions."""
+        n = len(decks)
+        if names is None:
+            names = [f"Player {chr(65 + i)}" for i in range(n)]
+        sim = self._create_state(decks, names)
         game_log: list[dict] = []
+        elimination_order: list[int] = []
         gid = game_id or str(uuid.uuid4())[:12]
-        turn_decisions: list[dict] = []  # raw (snapshot, action_type) pairs
+        turn_decisions: list[dict] = []
 
         # Build deck intelligence context once at game start
-        ai_deck = deck_b if self.ai_player_index == 1 else deck_a
+        ai_idx = min(self.ai_player_index, n - 1)
+        ai_deck = decks[ai_idx]
         self._deck_context = build_deck_context(
             full_deck=ai_deck,
             commander_name=commander_name,
@@ -97,7 +128,7 @@ class DeepSeekGameEngine:
         for turn in range(sim.max_turns):
             turn_entry = {"turn": turn + 1, "phases": []}
 
-            for pi in range(2):
+            for pi in range(len(sim.players)):
                 p = sim.players[pi]
                 if p.eliminated:
                     continue
@@ -118,7 +149,7 @@ class DeepSeekGameEngine:
                     pre_snapshot = self._capture_ml_snapshot(sim, pi, turn)
 
                 # ── Decide: LLM or Heuristic? ──
-                action_type = "pass"  # default
+                action_type = "pass"
                 if pi == self.ai_player_index and self.brain._connected:
                     action_type = self._play_turn_deepseek(sim, pi, turn, phase_events, getattr(self, '_deck_context', None))
                 else:
@@ -141,17 +172,29 @@ class DeepSeekGameEngine:
                     c.tapped = False
 
                 if self.record_log:
-                    turn_entry["phases"].append({
+                    life_dict = {sp.name: sp.life for sp in sim.players}
+                    boards_dict = {
+                        f"board_{si}": [c.name for c in sim.get_battlefield(si) if c.is_creature()]
+                        for si in range(len(sim.players))
+                    }
+                    phase_entry: dict = {
                         "player": p.name,
                         "playerId": pi,
                         "events": phase_events,
-                        "lifeAfter": {
-                            sim.players[0].name: sim.players[0].life,
-                            sim.players[1].name: sim.players[1].life,
-                        },
-                        "boardA": [c.name for c in sim.get_battlefield(0) if c.is_creature()],
-                        "boardB": [c.name for c in sim.get_battlefield(1) if c.is_creature()],
-                    })
+                        "lifeAfter": life_dict,
+                        **boards_dict,
+                    }
+                    if len(sim.players) == 2:
+                        phase_entry["boardA"] = boards_dict.get("board_0", [])
+                        phase_entry["boardB"] = boards_dict.get("board_1", [])
+                    turn_entry["phases"].append(phase_entry)
+
+                # ── Check for new eliminations ──
+                for si, sp in enumerate(sim.players):
+                    if sp.eliminated and si not in elimination_order:
+                        elimination_order.append(si)
+                        if self.record_log:
+                            phase_events.append(f"{sp.name} eliminated!")
 
             # Check game over
             alive = [p for p in sim.players if not p.eliminated]
@@ -159,21 +202,16 @@ class DeepSeekGameEngine:
             if self.record_log:
                 game_log.append(turn_entry)
             if len(alive) <= 1:
-                if self.record_log:
-                    elim_name = next((p.name for p in sim.players if p.eliminated), None)
-                    if elim_name:
-                        game_log[-1]["event"] = f"{elim_name} eliminated!"
                 break
-            if self.record_log:
-                game_log.append(turn_entry) if turn_entry not in game_log else None
 
-        result = self._build_result(sim, final_turn, name_a, name_b)
+        result = self._build_result(sim, final_turn, elimination_order)
         if self.record_log:
             result.game_log = game_log
 
         # Stamp game outcome on all ML decisions and add to engine collection
         if self.ml_log and turn_decisions:
-            outcome = "win" if result.winner == 0 else "loss" if result.winner == 1 else "draw"
+            ai_won = result.winner_seat == self.ai_player_index
+            outcome = "win" if ai_won else "loss"
             for td in turn_decisions:
                 td["game_outcome"] = outcome
             self.ml_decisions.extend(turn_decisions)
@@ -216,7 +254,7 @@ class DeepSeekGameEngine:
 
         if self.record_log:
             events.append(f"[AI Brain: {source}] Action: {action} "
-                         f"{'→ ' + target_card_name if target_card_name else ''} "
+                         f"{'\u2192 ' + target_card_name if target_card_name else ''} "
                          f"({reasoning})")
 
         # ── Phase 1: Land drop ──
@@ -242,7 +280,7 @@ class DeepSeekGameEngine:
         elif action == "hold":
             # LLM chose to hold — no combat
             if self.record_log:
-                events.append("Holds — no attack")
+                events.append("Holds \u2014 no attack")
         else:
             # Default: still attack if we have creatures and it makes sense
             self._resolve_combat_heuristic(sim, pi, turn, events)
@@ -435,21 +473,23 @@ class DeepSeekGameEngine:
         p.stats.mana_spent += card.cmc or 0
         p.stats.spells_cast += 1
 
-        # Handle removal
+        # Handle removal — target the scariest creature across all opponents
         if card.is_removal:
             p.stats.removal_used += 1
-            opp_idx_r = 1 - pi
-            opp_creatures = [
-                c for c in sim.get_battlefield(opp_idx_r)
-                if c.is_creature()
-            ]
-            if opp_creatures:
-                opp_creatures.sort(key=lambda c: -score_card(c, w))
-                killed = opp_creatures[0]
+            all_opp_creatures = []
+            for oi in range(len(sim.players)):
+                if oi == pi or sim.players[oi].eliminated:
+                    continue
+                for c in sim.get_battlefield(oi):
+                    if c.is_creature():
+                        all_opp_creatures.append(c)
+            if all_opp_creatures:
+                all_opp_creatures.sort(key=lambda c: -score_card(c, w))
+                killed = all_opp_creatures[0]
                 sim.remove_from_battlefield(killed.id)
                 sim.players[killed.owner_id].graveyard.append(killed)
                 if events is not None:
-                    events.append(f"Cast {card.name} (removal) — destroyed {killed.name}")
+                    events.append(f"Cast {card.name} (removal) \u2014 destroyed {killed.name}")
             else:
                 if events is not None:
                     events.append(f"Cast {card.name} (removal, no targets)")
@@ -470,7 +510,7 @@ class DeepSeekGameEngine:
                 sim.battlefields[seat_idx] = keep
             p.graveyard.append(card)
             if events is not None:
-                events.append(f"Cast {card.name} (board wipe) — destroyed {len(wiped_names)} creatures")
+                events.append(f"Cast {card.name} (board wipe) \u2014 destroyed {len(wiped_names)} creatures")
 
         else:
             sim.add_to_battlefield(pi, card)
@@ -488,6 +528,23 @@ class DeepSeekGameEngine:
 
         return self._count_untapped_lands(sim, pi)
 
+    # ──────────────────────────────────────────────────────────
+    # Attack target selection (N-player)
+    # ──────────────────────────────────────────────────────────
+
+    @staticmethod
+    def _select_attack_target(sim: SimState, pi: int) -> int:
+        """Select the weakest non-eliminated opponent (lowest life). Returns seat or -1."""
+        best_seat = -1
+        best_life = float('inf')
+        for si, sp in enumerate(sim.players):
+            if si == pi or sp.eliminated:
+                continue
+            if sp.life < best_life:
+                best_life = sp.life
+                best_seat = si
+        return best_seat
+
     # ──────────────────────────────────────────────────────────
     # Combat — DeepSeek directed
     # ──────────────────────────────────────────────────────────
@@ -502,11 +559,10 @@ class DeepSeekGameEngine:
         attack_safe: only evasive/large creatures
         """
         p = sim.players[pi]
-        opp_idx = 1 - pi
-        opp = sim.players[opp_idx]
-
-        if opp.eliminated:
+        opp_idx = self._select_attack_target(sim, pi)
+        if opp_idx == -1:
             return
+        opp = sim.players[opp_idx]
 
         my_creatures = [
             c for c in sim.get_battlefield(pi)
@@ -526,7 +582,6 @@ class DeepSeekGameEngine:
         if attack_mode == "attack_all":
             attackers = my_creatures[:]
         elif attack_mode == "attack_safe":
-            # Only evasive (flying, trample, menace) or large (power >= 4)
             for atk in my_creatures:
                 if (atk.has_keyword("flying") or atk.has_keyword("trample")
                         or atk.has_keyword("menace") or atk.get_power() >= 4):
@@ -538,7 +593,7 @@ class DeepSeekGameEngine:
         if events is not None and attackers:
             mode_label = "all-in" if attack_mode == "attack_all" else "safe"
             atk_names = [f"{a.name} ({a.pt})" for a in attackers]
-            events.append(f"Attacks ({mode_label}): {', '.join(atk_names)}")
+            events.append(f"Attacks {opp.name} ({mode_label}): {', '.join(atk_names)}")
 
         # Resolve blocking and damage (same logic as base engine)
         self._resolve_damage(sim, pi, attackers, opp_blockers, turn, events)
@@ -546,13 +601,12 @@ class DeepSeekGameEngine:
     def _resolve_combat_heuristic(
         self, sim: SimState, pi: int, turn: int, events: list[str] | None
     ) -> bool:
-        """Heuristic combat (same as base GameEngine._resolve_combat). Returns True if attacked."""
+        """Heuristic combat (N-player ready). Returns True if attacked."""
         p = sim.players[pi]
-        opp_idx = 1 - pi
-        opp = sim.players[opp_idx]
-
-        if opp.eliminated:
+        opp_idx = self._select_attack_target(sim, pi)
+        if opp_idx == -1:
             return False
+        opp = sim.players[opp_idx]
 
         my_creatures = [
             c for c in sim.get_battlefield(pi)
@@ -596,7 +650,7 @@ class DeepSeekGameEngine:
 
         if events is not None and attackers:
             atk_names = [f"{a.name} ({a.pt})" for a in attackers]
-            events.append(f"Attacks with: {', '.join(atk_names)}")
+            events.append(f"Attacks {opp.name} with: {', '.join(atk_names)}")
 
         self._resolve_damage(sim, pi, attackers, opp_blockers, turn, events)
         return len(attackers) > 0
@@ -612,7 +666,9 @@ class DeepSeekGameEngine:
     ):
         """Resolve blocking and combat damage."""
         p = sim.players[pi]
-        opp_idx = 1 - pi
+        opp_idx = self._select_attack_target(sim, pi)
+        if opp_idx == -1:
+            return
         opp = sim.players[opp_idx]
         w = self.weights
 
@@ -693,12 +749,12 @@ class DeepSeekGameEngine:
     def _capture_ml_snapshot(self, sim: SimState, pi: int, turn: int) -> dict:
         """
         Capture the current game state in the format expected by the ML
-        training pipeline (dataset_builder → state_encoder → labeler).
+        training pipeline (dataset_builder \u2192 state_encoder \u2192 labeler).
 
         Returns a dict compatible with ml-decisions-*.jsonl format.
         """
         players_data = []
-        for seat in range(2):
+        for seat in range(len(sim.players)):
             p = sim.players[seat]
             # Count creatures and lands on battlefield
             seat_bf = sim.get_battlefield(seat)
@@ -722,7 +778,7 @@ class DeepSeekGameEngine:
                 c.name for c in seat_bf
                 if not c.is_land()
             ]
-            # Command zone — not explicitly tracked in Python sim, use empty
+            # Command zone \u2014 not explicitly tracked in Python sim, use empty
             command_zone_names = []
 
             players_data.append({
@@ -739,7 +795,7 @@ class DeepSeekGameEngine:
                 "command_zone": command_zone_names,
             })
 
-        # Determine game phase — Python sim is simplified, treat as main_1
+        # Determine game phase \u2014 Python sim is simplified, treat as main_1
         phase = "main_1"
 
         return {
@@ -788,11 +844,12 @@ class DeepSeekGameEngine:
         )
 
     def _create_state(
-        self, deck_a: list[Card], deck_b: list[Card],
-        name_a: str, name_b: str,
+        self, decks: list[list[Card]],
+        names: list[str],
     ) -> SimState:
+        """Create initial simulation state with shuffled decks and opening hands (N players)."""
         sim = SimState(max_turns=self.max_turns)
-        for idx, (deck, name) in enumerate([(deck_a, name_a), (deck_b, name_b)]):
+        for idx, (deck, name) in enumerate(zip(decks, names)):
             cards = [enrich_card(c.clone()) for c in deck]
             random.shuffle(cards)
             hand = cards[:7]
@@ -811,17 +868,35 @@ class DeepSeekGameEngine:
 
     def _build_result(
         self, sim: SimState, final_turn: int,
-        name_a: str, name_b: str,
+        elimination_order: list[int] | None = None,
     ) -> GameResult:
-        pa = sim.players[0]
-        pb = sim.players[1]
+        """Determine winner and build result object (N-player)."""
+        if elimination_order is None:
+            elimination_order = []
+        n = len(sim.players)
 
-        if pa.eliminated and not pb.eliminated:
-            winner = 1
-        elif not pa.eliminated and pb.eliminated:
-            winner = 0
+        alive = [i for i, p in enumerate(sim.players) if not p.eliminated]
+        if len(alive) == 1:
+            winner = alive[0]
+        elif len(alive) == 0:
+            winner = max(range(n), key=lambda i: sim.players[i].life)
         else:
-            winner = 0 if pa.life >= pb.life else 1
+            winner = max(alive, key=lambda i: sim.players[i].life)
+
+        finish: dict[int, int] = {winner: 1}
+        position = 2
+        for seat in reversed(elimination_order):
+            if seat != winner:
+                finish[seat] = position
+                position += 1
+        remaining_alive = sorted(
+            [i for i in alive if i != winner],
+            key=lambda i: -sim.players[i].life,
+        )
+        for seat in remaining_alive:
+            if seat not in finish:
+                finish[seat] = position
+                position += 1
 
         player_results = []
         for seat, p in enumerate(sim.players):
@@ -830,7 +905,7 @@ class DeepSeekGameEngine:
                 name=p.name,
                 life=p.life,
                 eliminated=p.eliminated,
-                finish_position=1 if seat == winner else 2,
+                finish_position=finish.get(seat, n),
                 stats=p.stats,
             ))
 

src/commander_ai_lab/sim/engine.py

@@ -4,12 +4,15 @@ Commander AI Lab — Headless Game Engine
 A headless Commander game simulator for Monte Carlo analysis.
 Faithful port of dtSimGame() / dtCreatePlayer() from mtg-commander-lan.
 
-Simulates simplified 1v1 Commander games:
+N-player ready: accepts 2–4 decks and runs a full multiplayer game.
+Simulates simplified Commander games:
   - Draw, land drop, spell casting (up to 2 per turn)
   - AI card scoring for play priority
   - Simplified combat with flying, trample, deathtouch, lifelink, menace, reach
+  - Multiplayer attack targeting: weakest non-eliminated opponent heuristic
   - Creature removal and board wipe handling
   - Win by elimination (life <= 0) or life comparison at max turns
+  - Elimination order tracking for N-player finish positions
 """
 
 from __future__ import annotations
@@ -34,12 +37,15 @@ from commander_ai_lab.sim.rules import (
 
 class GameEngine:
     """
-    Headless Commander game engine.
+    Headless Commander game engine (N-player ready).
 
     Usage::
 
         engine = GameEngine(max_turns=25)
+        # 2-player (backward-compatible)
         result = engine.run(deck_a, deck_b, name_a="Deck A", name_b="Deck B")
+        # 4-player
+        result = engine.run_n(decks=[d1, d2, d3, d4], names=["A","B","C","D"])
     """
 
     def __init__(
@@ -65,15 +71,30 @@ class GameEngine:
         name_a: str = "Player A",
         name_b: str = "Player B",
     ) -> GameResult:
-        """Run a single headless game and return the result."""
-        sim = self._create_state(deck_a, deck_b, name_a, name_b)
-        game_log: list[dict] = []  # turn-by-turn log
+        """Backward-compatible 2-player entry point."""
+        return self.run_n(
+            decks=[deck_a, deck_b],
+            names=[name_a, name_b],
+        )
+
+    def run_n(
+        self,
+        decks: list[list[Card]],
+        names: list[str] | None = None,
+    ) -> GameResult:
+        """Run a single headless N-player game and return the result."""
+        n = len(decks)
+        if names is None:
+            names = [f"Player {chr(65 + i)}" for i in range(n)]
+        sim = self._create_state(decks, names)
+        game_log: list[dict] = []
+        elimination_order: list[int] = []  # seats in order of elimination
 
         final_turn = 0
         for turn in range(sim.max_turns):
             turn_entry = {"turn": turn + 1, "phases": []}
 
-            for pi in range(2):
+            for pi in range(len(sim.players)):
                 p = sim.players[pi]
                 if p.eliminated:
                     continue
@@ -92,7 +113,6 @@ class GameEngine:
                 land_before = p.stats.lands_played
                 self._play_land(sim, pi)
                 if self.record_log and p.stats.lands_played > land_before:
-                    # Find what land was just played
                     last_land = next(
                         (c for c in reversed(sim.get_battlefield(pi))
                          if c.is_land()),
@@ -105,7 +125,6 @@ class GameEngine:
                 available_mana = self._count_untapped_lands(sim, pi)
 
                 # ── Play spells (up to 2) ──
-                spells_before = p.stats.spells_cast
                 self._play_spells(sim, pi, available_mana, phase_events if self.record_log else None)
 
                 # ── Track board size ──
@@ -114,8 +133,6 @@ class GameEngine:
                     p.stats.max_board_size = board_size
 
                 # ── Combat ──
-                combat_before_life_a = sim.players[0].life
-                combat_before_life_b = sim.players[1].life
                 self._resolve_combat(sim, pi, turn, phase_events if self.record_log else None)
 
                 # ── Untap ──
@@ -123,15 +140,31 @@ class GameEngine:
                     c.tapped = False
 
                 if self.record_log:
-                    turn_entry["phases"].append({
+                    # N-player log: life dict + per-seat boards dict
+                    life_dict = {sp.name: sp.life for sp in sim.players}
+                    boards_dict = {
+                        f"board_{si}": [c.name for c in sim.get_battlefield(si) if c.is_creature()]
+                        for si in range(len(sim.players))
+                    }
+                    phase_entry: dict = {
                         "player": p.name,
                         "playerId": pi,
                         "events": phase_events,
-                        "lifeAfter": {sim.players[0].name: sim.players[0].life,
-                                      sim.players[1].name: sim.players[1].life},
-                        "boardA": [c.name for c in sim.get_battlefield(0) if c.is_creature()],
-                        "boardB": [c.name for c in sim.get_battlefield(1) if c.is_creature()],
-                    })
+                        "lifeAfter": life_dict,
+                        **boards_dict,
+                    }
+                    # Legacy keys for 2-player UI compatibility
+                    if len(sim.players) == 2:
+                        phase_entry["boardA"] = boards_dict.get("board_0", [])
+                        phase_entry["boardB"] = boards_dict.get("board_1", [])
+                    turn_entry["phases"].append(phase_entry)
+
+                # ── Check for new eliminations after this player's turn ──
+                for si, sp in enumerate(sim.players):
+                    if sp.eliminated and si not in elimination_order:
+                        elimination_order.append(si)
+                        if self.record_log:
+                            phase_events.append(f"{sp.name} eliminated!")
 
             # Check game over
             alive = [p for p in sim.players if not p.eliminated]
@@ -139,15 +172,9 @@ class GameEngine:
             if self.record_log:
                 game_log.append(turn_entry)
             if len(alive) <= 1:
-                if self.record_log:
-                    elim_name = next((p.name for p in sim.players if p.eliminated), None)
-                    if elim_name:
-                        game_log[-1]["event"] = f"{elim_name} eliminated!"
                 break
-            if self.record_log:
-                game_log.append(turn_entry) if turn_entry not in game_log else None
 
-        result = self._build_result(sim, final_turn, name_a, name_b)
+        result = self._build_result(sim, final_turn, elimination_order)
         if self.record_log:
             result.game_log = game_log
         return result
@@ -158,15 +185,13 @@ class GameEngine:
 
     def _create_state(
         self,
-        deck_a: list[Card],
-        deck_b: list[Card],
-        name_a: str,
-        name_b: str,
+        decks: list[list[Card]],
+        names: list[str],
     ) -> SimState:
-        """Create initial simulation state with shuffled decks and opening hands."""
+        """Create initial simulation state with shuffled decks and opening hands (N players)."""
         sim = SimState(max_turns=self.max_turns)
 
-        for idx, (deck, name) in enumerate([(deck_a, name_a), (deck_b, name_b)]):
+        for idx, (deck, name) in enumerate(zip(decks, names)):
             # Enrich + deep copy cards
             cards = [enrich_card(c.clone()) for c in deck]
             random.shuffle(cards)
@@ -260,18 +285,19 @@ class GameEngine:
             p.stats.mana_spent += card.cmc or 0
             p.stats.spells_cast += 1
 
-            # Handle removal
+            # Handle removal — target the scariest creature across all opponents
             if card.is_removal:
                 p.stats.removal_used += 1
-                opp_idx_r = 1 - pi
-                opp_creatures = [
-                    c
-                    for c in sim.get_battlefield(opp_idx_r)
-                    if c.is_creature()
-                ]
-                if opp_creatures:
-                    opp_creatures.sort(key=lambda c: -score_card(c, w))
-                    killed = opp_creatures[0]
+                all_opp_creatures = []
+                for oi in range(len(sim.players)):
+                    if oi == pi or sim.players[oi].eliminated:
+                        continue
+                    for c in sim.get_battlefield(oi):
+                        if c.is_creature():
+                            all_opp_creatures.append(c)
+                if all_opp_creatures:
+                    all_opp_creatures.sort(key=lambda c: -score_card(c, w))
+                    killed = all_opp_creatures[0]
                     sim.remove_from_battlefield(killed.id)
                     sim.players[killed.owner_id].graveyard.append(killed)
                     if events is not None:
@@ -317,16 +343,35 @@ class GameEngine:
             # Recalculate available mana for next spell
             available_mana = self._count_untapped_lands(sim, pi)
 
+    @staticmethod
+    def _select_attack_target(sim: SimState, pi: int) -> int:
+        """Select the best opponent to attack.
+
+        Multiplayer heuristic: attack the weakest non-eliminated opponent
+        (lowest life total). Ties broken by highest seat index (arbitrary
+        but deterministic).
+
+        Returns the seat index of the chosen target, or -1 if no valid target.
+        """
+        best_seat = -1
+        best_life = float('inf')
+        for si, sp in enumerate(sim.players):
+            if si == pi or sp.eliminated:
+                continue
+            if sp.life < best_life:
+                best_life = sp.life
+                best_seat = si
+        return best_seat
+
     def _resolve_combat(self, sim: SimState, pi: int, turn: int, events: list | None = None) -> None:
-        """Resolve combat phase for a player."""
+        """Resolve combat phase for a player (N-player ready)."""
         p = sim.players[pi]
-        opp_idx = 1 - pi
+        opp_idx = self._select_attack_target(sim, pi)
+        if opp_idx == -1:
+            return  # no valid targets
         opp = sim.players[opp_idx]
         w = self.weights
 
-        if opp.eliminated:
-            return
-
         # My creatures that can attack (not tapped, not summoning sick)
         my_creatures = [
             c
@@ -350,9 +395,6 @@ class GameEngine:
         ]
 
         # ── Decide attackers ──
-        # Aggressive strategy: attack with most creatures.
-        # Only hold back small creatures (power < 3) when opponent has
-        # blockers that would profitably trade AND we have plenty of life.
         attackers = []
         total_my_power = sum(c.get_power() for c in my_creatures)
         for atk in my_creatures:
@@ -362,29 +404,26 @@ class GameEngine:
             has_trample = atk.has_keyword("trample")
             has_haste = atk.has_keyword("haste")
 
-            # Always attack with evasion, big creatures, or if opponent is low
             if has_flying or has_trample or has_haste or a_pow >= 3 or opp.life <= total_my_power:
                 attackers.append(atk)
                 atk.tapped = True
                 continue
 
-            # For small creatures, check if a profitable block exists
             can_die_profitably = any(
                 (
                     (not has_flying or b.has_keyword("flying") or b.has_keyword("reach"))
                     and (b.get_power() >= a_tou or b.has_keyword("deathtouch"))
-                    and b.get_toughness() > a_pow  # blocker survives = bad trade
+                    and b.get_toughness() > a_pow
                 )
                 for b in opp_blockers
             )
-            # Attack unless we'd lose the creature for nothing AND we're healthy
             if not can_die_profitably or p.life > 25:
                 attackers.append(atk)
                 atk.tapped = True
 
         if events is not None and attackers:
             atk_names = [f"{a.name} ({a.pt})" for a in attackers]
-            events.append(f"Attacks with: {', '.join(atk_names)}")
+            events.append(f"Attacks {opp.name} with: {', '.join(atk_names)}")
 
         # ── Blocking and damage ──
         total_damage = 0
@@ -407,12 +446,10 @@ class GameEngine:
 
             blocked = False
             if valid_blockers and not atk.has_keyword("menace"):
-                # Try to find a blocker that survives
                 blocker = next(
                     (b for b in valid_blockers if b.get_toughness() > a_pow),
                     None,
                 )
-                # If life is critical, chump block
                 if blocker is None and opp.life <= a_pow * 2 and valid_blockers:
                     blocker = valid_blockers[0]
 
@@ -424,25 +461,21 @@ class GameEngine:
                     if events is not None:
                         combat_details.append(f"{atk.name} blocked by {blocker.name}")
 
-                    # Attacker kills blocker?
                     if a_pow >= blocker.get_toughness() or atk.has_keyword("deathtouch"):
                         sim.remove_from_battlefield(blocker.id)
                         sim.players[opp_idx].graveyard.append(blocker)
                         if events is not None:
                             combat_details.append(f"  {blocker.name} dies")
 
-                    # Blocker kills attacker?
                     if b_pow >= a_tou or blocker.has_keyword("deathtouch"):
                         sim.remove_from_battlefield(atk.id)
                         sim.players[pi].graveyard.append(atk)
                         if events is not None:
                             combat_details.append(f"  {atk.name} dies")
 
-                    # Trample overflow
                     if atk.has_keyword("trample") and a_pow > blocker.get_toughness():
                         total_damage += a_pow - blocker.get_toughness()
 
-                    # Lifelink
                     if atk.has_keyword("lifelink"):
                         p.life += min(a_pow, blocker.get_toughness())
 
@@ -466,20 +499,46 @@ class GameEngine:
         self,
         sim: SimState,
         final_turn: int,
-        name_a: str,
-        name_b: str,
+        elimination_order: list[int] | None = None,
     ) -> GameResult:
-        """Determine winner and build result object."""
-        pa = sim.players[0]
-        pb = sim.players[1]
+        """Determine winner and build result object (N-player).
 
-        if pa.eliminated and not pb.eliminated:
-            winner = 1
-        elif not pa.eliminated and pb.eliminated:
-            winner = 0
+        Finish positions:
+          1 = winner (last player standing, or highest life if timeout)
+          2..N = elimination order (last eliminated = 2nd place, etc.)
+        """
+        if elimination_order is None:
+            elimination_order = []
+        n = len(sim.players)
+
+        # Determine winner: last player standing, or highest life among alive
+        alive = [i for i, p in enumerate(sim.players) if not p.eliminated]
+        if len(alive) == 1:
+            winner = alive[0]
+        elif len(alive) == 0:
+            # Everyone dead — shouldn't happen but pick highest life
+            winner = max(range(n), key=lambda i: sim.players[i].life)
         else:
-            # Both alive or both dead: compare life
-            winner = 0 if pa.life >= pb.life else 1
+            # Timeout — alive player with highest life wins
+            winner = max(alive, key=lambda i: sim.players[i].life)
+
+        # Build finish positions
+        # Winner = 1, then reverse elimination order (last eliminated = 2nd, etc.)
+        finish: dict[int, int] = {winner: 1}
+        position = 2
+        for seat in reversed(elimination_order):
+            if seat != winner:
+                finish[seat] = position
+                position += 1
+        # Any alive non-winner seats get next positions (by life descending)
+        remaining_alive = sorted(
+            [i for i in alive if i != winner],
+            key=lambda i: -sim.players[i].life,
+        )
+        for seat in remaining_alive:
+            if seat not in finish:
+                finish[seat] = position
+                position += 1
 
         player_results = []
         for seat, p in enumerate(sim.players):
@@ -488,7 +547,7 @@ class GameEngine:
                 name=p.name,
                 life=p.life,
                 eliminated=p.eliminated,
-                finish_position=1 if seat == winner else 2,
+                finish_position=finish.get(seat, n),
                 stats=p.stats,
             ))
 

src/commander_ai_lab/sim/rules.py

@@ -4,16 +4,23 @@ Commander AI Lab — Simulator Rules & Helpers
 AI weights, card enrichment heuristics, and scoring functions.
 Ported from mtg-commander-lan JavaScript (AI_DEFAULT_WEIGHTS,
 enrichCardForSim, headlessScoreCard, headlessGetPower/Toughness/HasKw).
+
+N-player ready: score_card() accepts num_opponents so keyword weights
+scale with pod size (e.g. Propaganda is worth more vs 3 opponents).
 """
 
 from __future__ import annotations
 
+import hashlib
+import json
+import logging
+import os
 import re
-import random
 from typing import Optional
 
 from commander_ai_lab.sim.models import Card
 
+logger = logging.getLogger("rules")
 
 # ══════════════════════════════════════════════════════════════
 # AI Weights (ported from AI_DEFAULT_WEIGHTS in app.js)
@@ -67,6 +74,9 @@ AI_DEFAULT_WEIGHTS: dict[str, float] = {
     "spell_cascade": 3.0,
 }
 
+# Default path for persisted learned weights (sibling to this file)
+_DEFAULT_WEIGHTS_PATH = os.path.join(os.path.dirname(__file__), "learned_weights.json")
+
 
 # ══════════════════════════════════════════════════════════════
 # Card Enrichment — guess type/cost for cards without Scryfall data
@@ -130,9 +140,12 @@ def enrich_card(card: Card) -> Card:
                 _apply_oracle_flags(card)
                 return card
 
-        # Default: treat as creature with random CMC
+        # Default: treat as creature with deterministic CMC based on
+        # card name hash (fixes #32 — no more random.randint jitter
+        # between games for the same card).
         if not card.type_line or len(card.type_line) < 3:
-            estimated_cmc = random.randint(2, 6)
+            name_hash = int(hashlib.md5(name_lower.encode()).hexdigest(), 16)
+            estimated_cmc = (name_hash % 5) + 2          # 2-6 inclusive
             estimated_pow = max(1, estimated_cmc - 1)
             card.type_line = "Creature"
             card.cmc = estimated_cmc
@@ -181,15 +194,46 @@ def _apply_oracle_flags(card: Card) -> None:
             card.is_ramp = True
 
 
-def score_card(card: Card, weights: Optional[dict] = None) -> float:
+# ── Multi-opponent scaling factors ────────────────────────────
+# Keywords / effects that become more valuable in larger pods.
+# Each entry maps a weight key → the extra bonus *per additional
+# opponent* beyond 1.  1v1 uses base weights; 4-player FFA adds
+# 3× the bonus for each matching keyword.
+_MULTI_OPP_SCALING: dict[str, float] = {
+    # Group-slug / pillowfort effects punish each opponent
+    "kw_menace":       0.5,     # harder to block with more players
+    "kw_flying":       0.3,     # evasion matters more in FFA
+    "kw_hexproof":     0.5,     # more removal flying around
+    "kw_indestructible": 0.5,
+    "kw_ward":         0.4,
+    # Multiplayer-relevant triggered abilities
+    "trig_etb":        0.3,
+    "trig_dies":       0.5,     # more creatures dying in FFA
+    # Spells that scale with opponents
+    "spell_destroy":   0.5,     # removal more precious
+    "spell_draw":      0.3,     # card advantage matters more
+    "spell_counter":   0.5,     # counters protect from more threats
+}
+
+
+def score_card(
+    card: Card,
+    weights: Optional[dict] = None,
+    num_opponents: int = 1,
+) -> float:
     """
     Score a card for AI play priority.
-    Ported from headlessScoreCard() in app.js.
+
+    Ported from headlessScoreCard() in app.js.  The optional
+    *num_opponents* parameter (default 1 for 1v1) scales keywords
+    that become more valuable in larger pods.
     """
     w = weights or AI_DEFAULT_WEIGHTS
+    extra_opps = max(0, num_opponents - 1)   # 0 for 1v1
     cmc = card.cmc or 0
     score = 10.0 - cmc * w.get("card_baseCmcPenalty", 1.0)
 
+    # ── Power / toughness bonus ──
     if card.pt:
         parts = card.pt.split("/")
         try:
@@ -202,22 +246,83 @@ def score_card(card: Card, weights: Optional[dict] = None) -> float:
             t = 0
         score += (p + t) * w.get("card_ptBonus", 1.0)
 
+    # ── Keyword / oracle-text scoring ──
     text = (card.oracle_text or "").lower()
-    if "flying" in text:
-        score += w.get("kw_flying", 2.5)
-    if "deathtouch" in text:
-        score += w.get("kw_deathtouch", 2.5)
-    if "haste" in text:
-        score += w.get("kw_haste", 2.5)
-    if "trample" in text:
-        score += w.get("kw_trample", 1.5)
-    if "lifelink" in text:
-        score += w.get("kw_lifelink", 1.5)
+    kw_list = [k.lower() for k in (card.keywords or [])]
 
+    def _kw_bonus(keyword: str, weight_key: str, default: float) -> float:
+        """Return base weight + per-extra-opponent scaling."""
+        base = w.get(weight_key, default)
+        multi = _MULTI_OPP_SCALING.get(weight_key, 0.0) * extra_opps
+        return base + multi
+
+    if "flying" in text or "flying" in kw_list:
+        score += _kw_bonus("flying", "kw_flying", 2.5)
+    if "deathtouch" in text or "deathtouch" in kw_list:
+        score += _kw_bonus("deathtouch", "kw_deathtouch", 2.5)
+    if "haste" in text or "haste" in kw_list:
+        score += _kw_bonus("haste", "kw_haste", 2.5)
+    if "trample" in text or "trample" in kw_list:
+        score += _kw_bonus("trample", "kw_trample", 1.5)
+    if "lifelink" in text or "lifelink" in kw_list:
+        score += _kw_bonus("lifelink", "kw_lifelink", 1.5)
+    if "double strike" in text or "double strike" in kw_list:
+        score += _kw_bonus("double strike", "kw_doubleStrike", 3.0)
+    if "first strike" in text or "first strike" in kw_list:
+        score += _kw_bonus("first strike", "kw_firstStrike", 1.5)
+    if "hexproof" in text or "hexproof" in kw_list:
+        score += _kw_bonus("hexproof", "kw_hexproof", 2.0)
+    if "indestructible" in text or "indestructible" in kw_list:
+        score += _kw_bonus("indestructible", "kw_indestructible", 3.0)
+    if "menace" in text or "menace" in kw_list:
+        score += _kw_bonus("menace", "kw_menace", 1.5)
+    if "vigilance" in text or "vigilance" in kw_list:
+        score += w.get("kw_vigilance", 1.0)
+    if "ward" in text or "ward" in kw_list:
+        score += _kw_bonus("ward", "kw_ward", 1.5)
+    if "infect" in text or "infect" in kw_list:
+        score += w.get("kw_infect", 3.0)
+    if "annihilator" in text:
+        score += w.get("kw_annihilator", 4.0)
+    if "cascade" in text or "cascade" in kw_list:
+        score += w.get("kw_cascade", 3.0)
+    if "defender" in text or "defender" in kw_list:
+        score += w.get("kw_defender", -2.0)
+
+    # ── Triggered ability bonuses ──
+    if "when" in text and "enters" in text:
+        score += w.get("trig_etb", 2.0) + _MULTI_OPP_SCALING.get("trig_etb", 0.0) * extra_opps
+    if "whenever" in text and "attack" in text:
+        score += w.get("trig_attack", 1.5)
+    if "when" in text and "dies" in text:
+        score += w.get("trig_dies", 1.0) + _MULTI_OPP_SCALING.get("trig_dies", 0.0) * extra_opps
+
+    # ── Multiplayer-specific oracle patterns ──
+    if extra_opps > 0:
+        # "each opponent" effects scale linearly with opponent count
+        if "each opponent" in text:
+            score += 2.0 * extra_opps
+        # Pillowfort (Propaganda, Ghostly Prison, etc.)
+        if "attack you" in text and ("pay" in text or "tax" in text or "can't" in text):
+            score += 1.5 * extra_opps
+        # "each player" draw/damage effects
+        if "each player" in text:
+            score += 1.0 * extra_opps
+
+    # ── Spell-type bonuses ──
     if card.is_ramp:
         score += w.get("spell_ramp", 6.0)
     if card.is_removal:
-        score += w.get("spell_destroy", 5.0)
+        score += _kw_bonus("removal", "spell_destroy", 5.0)
+    if card.is_board_wipe:
+        # Board wipes get better in multiplayer — more creatures to hit
+        score += 1.0 * extra_opps
+    if "draw" in text and ("card" in text or "cards" in text):
+        score += w.get("spell_draw", 3.0) + _MULTI_OPP_SCALING.get("spell_draw", 0.0) * extra_opps
+    if "counter target" in text:
+        score += w.get("spell_counter", 4.0) + _MULTI_OPP_SCALING.get("spell_counter", 0.0) * extra_opps
+    if "search your library" in text:
+        score += w.get("spell_search", 2.0)
 
     return score
 
@@ -242,3 +347,73 @@ def parse_decklist(text: str) -> list[Card]:
         for _ in range(qty):
             cards.append(Card(name=name))
     return cards
+
+
+# ══════════════════════════════════════════════════════════════
+# Learned Weights — persist & load simulation-derived weights
+# ══════════════════════════════════════════════════════════════
+
+def load_weights(path: str = _DEFAULT_WEIGHTS_PATH) -> dict[str, float]:
+    """
+    Load AI weights from a JSON file, falling back to AI_DEFAULT_WEIGHTS.
+
+    Merges loaded values on top of defaults so any new weight keys added
+    to AI_DEFAULT_WEIGHTS are always present even in older saved files.
+
+    Usage::
+
+        from commander_ai_lab.sim.rules import load_weights
+        weights = load_weights()                       # uses learned_weights.json if present
+        weights = load_weights("path/to/custom.json")  # custom path
+    """
+    if os.path.exists(path):
+        try:
+            with open(path, encoding="utf-8") as f:
+                loaded: dict = json.load(f)
+            # Only accept numeric values; ignore stale / corrupted keys
+            valid = {k: float(v) for k, v in loaded.items() if isinstance(v, (int, float))}
+            merged = {**AI_DEFAULT_WEIGHTS, **valid}
+            logger.info("Loaded %d learned weights from %s", len(valid), path)
+            return merged
+        except Exception as exc:
+            logger.warning("Failed to load weights from %s: %s — using defaults", path, exc)
+    return dict(AI_DEFAULT_WEIGHTS)
+
+
+def save_weights(weights: dict[str, float], path: str = _DEFAULT_WEIGHTS_PATH) -> str:
+    """
+    Persist AI weights to a JSON file.
+
+    Clamps each value to [-20.0, 20.0] before saving to prevent runaway
+    drift from repeated simulation updates.
+
+    Returns the absolute path that was written.
+
+    Usage::
+
+        from commander_ai_lab.sim.rules import save_weights
+        save_weights(updated_weights)                       # default path
+        save_weights(updated_weights, "path/to/custom.json")
+    """
+    _CLAMP_MIN, _CLAMP_MAX = -20.0, 20.0
+    clamped = {
+        k: round(max(_CLAMP_MIN, min(_CLAMP_MAX, float(v))), 6)
+        for k, v in weights.items()
+    }
+    os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(clamped, f, indent=2)
+    logger.info("Saved %d weights to %s", len(clamped), path)
+    return os.path.abspath(path)
+
+
+def reset_weights(path: str = _DEFAULT_WEIGHTS_PATH) -> dict[str, float]:
+    """
+    Delete any persisted learned weights and return AI_DEFAULT_WEIGHTS.
+
+    Useful for starting a fresh learning run without stale drift.
+    """
+    if os.path.exists(path):
+        os.remove(path)
+        logger.info("Deleted learned weights file: %s", path)
+    return dict(AI_DEFAULT_WEIGHTS)

tests/conftest.py

@@ -0,0 +1,75 @@
+"""Shared pytest fixtures for Commander AI Lab tests."""
+import pytest
+from commander_ai_lab.sim.models import Card, Player, PlayerStats, SimState
+
+
+@pytest.fixture
+def basic_creature():
+    return Card(name="Grizzly Bears", type_line="Creature — Bear", cmc=2, pt="2/2", power="2", toughness="2")
+
+
+@pytest.fixture
+def flying_creature():
+    return Card(
+        name="Serra Angel",
+        type_line="Creature — Angel",
+        cmc=5,
+        pt="4/4",
+        power="4",
+        toughness="4",
+        oracle_text="Flying, vigilance",
+        keywords=["Flying", "Vigilance"],
+    )
+
+
+@pytest.fixture
+def basic_land():
+    return Card(name="Forest", type_line="Basic Land — Forest", cmc=0)
+
+
+@pytest.fixture
+def sol_ring():
+    return Card(name="Sol Ring", type_line="Artifact", cmc=1, oracle_text="{T}: Add {C}{C}", is_ramp=True)
+
+
+@pytest.fixture
+def removal_spell():
+    return Card(name="Murder", type_line="Instant", cmc=3, oracle_text="Destroy target creature.", is_removal=True)
+
+
+@pytest.fixture
+def board_wipe():
+    return Card(name="Wrath of God", type_line="Sorcery", cmc=4, oracle_text="Destroy all creatures.", is_board_wipe=True)
+
+
+@pytest.fixture
+def commander_card():
+    return Card(
+        name="Korvold, Fae-Cursed King",
+        type_line="Legendary Creature — Dragon Noble",
+        cmc=5,
+        pt="4/4",
+        power="4",
+        toughness="4",
+        is_commander=True,
+        oracle_text="Flying, haste\nWhenever Korvold, Fae-Cursed King enters the battlefield or attacks, sacrifice another permanent.",
+        keywords=["Flying", "Haste"],
+    )
+
+
+@pytest.fixture
+def sample_player(basic_creature, basic_land, sol_ring):
+    p = Player(name="Alice", life=40, owner_id=0)
+    p.library = [basic_creature.clone(), basic_land.clone(), sol_ring.clone()] * 20
+    return p
+
+
+@pytest.fixture
+def two_player_state(sample_player):
+    import copy
+    p2 = copy.deepcopy(sample_player)
+    p2.name = "Bob"
+    p2.owner_id = 1
+    state = SimState(players=[sample_player, p2])
+    state.init_battlefields(2)
+    return state

tests/sim/test_engine.py

@@ -0,0 +1,97 @@
+"""Seeded simulation tests for the game engine."""
+import random
+import pytest
+from commander_ai_lab.sim.models import Card, Player, PlayerStats
+from commander_ai_lab.sim.rules import enrich_card
+from commander_ai_lab.sim.engine import GameEngine
+
+
+def _make_deck(seed: int, size: int = 60) -> list[Card]:
+    """Build a deterministic test deck using a seeded RNG."""
+    rng = random.Random(seed)
+    card_pool = [
+        Card(name="Forest"),
+        Card(name="Sol Ring"),
+        Card(name="Cultivate"),
+        Card(name="Murder"),
+        Card(name="Wrath of God"),
+        Card(name="Grizzly Bears"),
+        Card(name="Serra Angel"),
+    ]
+    for c in card_pool:
+        enrich_card(c)
+    deck = []
+    for i in range(size):
+        deck.append(card_pool[i % len(card_pool)].clone())
+    rng.shuffle(deck)
+    return deck
+
+
+class TestGameEngineTwoPlayer:
+    def test_game_completes(self):
+        """A 2-player seeded game should finish without error."""
+        engine = GameEngine(seed=42)
+        deck_a = _make_deck(seed=1)
+        deck_b = _make_deck(seed=2)
+        result = engine.run_game(
+            deck_a=[c.clone() for c in deck_a],
+            deck_b=[c.clone() for c in deck_b],
+            player_a_name="Alice",
+            player_b_name="Bob",
+        )
+        assert result is not None
+        assert result.turns > 0
+
+    def test_result_has_winner_or_draw(self):
+        """winner_seat must be 0, 1, or -1 (draw)."""
+        engine = GameEngine(seed=99)
+        deck = _make_deck(seed=5)
+        result = engine.run_game(
+            deck_a=[c.clone() for c in deck],
+            deck_b=[c.clone() for c in deck],
+            player_a_name="P1",
+            player_b_name="P2",
+        )
+        assert result.winner_seat in (-1, 0, 1)
+
+    def test_seeded_game_is_deterministic(self):
+        """Same seed must produce identical results."""
+        deck_a = _make_deck(seed=7)
+        deck_b = _make_deck(seed=8)
+
+        engine1 = GameEngine(seed=42)
+        result1 = engine1.run_game(
+            deck_a=[c.clone() for c in deck_a],
+            deck_b=[c.clone() for c in deck_b],
+            player_a_name="Alice",
+            player_b_name="Bob",
+        )
+
+        engine2 = GameEngine(seed=42)
+        result2 = engine2.run_game(
+            deck_a=[c.clone() for c in deck_a],
+            deck_b=[c.clone() for c in deck_b],
+            player_a_name="Alice",
+            player_b_name="Bob",
+        )
+
+        assert result1.winner_seat == result2.winner_seat
+        assert result1.turns == result2.turns
+
+    def test_to_dict_serializable(self):
+        """Game result must serialize to a dict with expected keys."""
+        import json
+        engine = GameEngine(seed=77)
+        deck = _make_deck(seed=3)
+        result = engine.run_game(
+            deck_a=[c.clone() for c in deck],
+            deck_b=[c.clone() for c in deck],
+            player_a_name="A",
+            player_b_name="B",
+        )
+        d = result.to_dict()
+        assert "winner" in d
+        assert "turns" in d
+        assert "playerResults" in d
+        # Ensure it's JSON-serializable
+        json.dumps(d)

tests/sim/test_models.py

@@ -0,0 +1,214 @@
+"""Tests for commander_ai_lab.sim.models."""
+import pytest
+from commander_ai_lab.sim.models import (
+    Card, Player, PlayerStats, PlayerResult, GameResult, SimState
+)
+
+
+# ── Card ────────────────────────────────────────────────────
+
+class TestCardGetPower:
+    def test_from_power_field(self):
+        c = Card(name="Test", power="3", toughness="2", pt="3/2")
+        assert c.get_power() == 3
+
+    def test_from_pt_field(self):
+        c = Card(name="Test", pt="4/5")
+        assert c.get_power() == 4
+
+    def test_no_pt_returns_zero(self):
+        c = Card(name="Test")
+        assert c.get_power() == 0
+
+    def test_non_numeric_returns_zero(self):
+        c = Card(name="Test", power="*", pt="*/2")
+        assert c.get_power() == 0
+
+
+class TestCardGetToughness:
+    def test_from_toughness_field(self):
+        c = Card(name="Test", power="2", toughness="5", pt="2/5")
+        assert c.get_toughness() == 5
+
+    def test_from_pt_field(self):
+        c = Card(name="Test", pt="2/7")
+        assert c.get_toughness() == 7
+
+    def test_no_pt_returns_zero(self):
+        c = Card(name="Test")
+        assert c.get_toughness() == 0
+
+
+class TestCardHasKeyword:
+    def test_in_keywords_list(self):
+        c = Card(name="Test", keywords=["Flying", "Haste"])
+        assert c.has_keyword("flying") is True
+        assert c.has_keyword("haste") is True
+
+    def test_in_oracle_text(self):
+        c = Card(name="Test", oracle_text="This creature has flying and deathtouch.")
+        assert c.has_keyword("flying") is True
+        assert c.has_keyword("deathtouch") is True
+
+    def test_missing_keyword(self):
+        c = Card(name="Test", oracle_text="Draw a card.", keywords=[])
+        assert c.has_keyword("flying") is False
+
+    def test_case_insensitive(self):
+        c = Card(name="Test", keywords=["FLYING"])
+        assert c.has_keyword("Flying") is True
+
+
+class TestCardIsLand:
+    def test_basic_land(self):
+        c = Card(name="Forest", type_line="Basic Land — Forest")
+        assert c.is_land() is True
+
+    def test_nonbasic_land(self):
+        c = Card(name="Command Tower", type_line="Land")
+        assert c.is_land() is True
+
+    def test_creature_not_land(self):
+        c = Card(name="Grizzly Bears", type_line="Creature — Bear")
+        assert c.is_land() is False
+
+    def test_no_type_line(self):
+        c = Card(name="Unknown")
+        assert c.is_land() is False
+
+
+class TestCardIsCreature:
+    def test_creature(self):
+        c = Card(name="Grizzly Bears", type_line="Creature — Bear")
+        assert c.is_creature() is True
+
+    def test_legendary_creature(self):
+        c = Card(name="Korvold", type_line="Legendary Creature — Dragon Noble")
+        assert c.is_creature() is True
+
+    def test_instant_not_creature(self):
+        c = Card(name="Murder", type_line="Instant")
+        assert c.is_creature() is False
+
+    def test_land_not_creature(self):
+        c = Card(name="Forest", type_line="Basic Land — Forest")
+        assert c.is_creature() is False
+
+
+class TestCardClone:
+    def test_clone_is_equal(self, basic_creature):
+        clone = basic_creature.clone()
+        assert clone.name == basic_creature.name
+        assert clone.cmc == basic_creature.cmc
+
+    def test_clone_is_independent(self, basic_creature):
+        clone = basic_creature.clone()
+        clone.name = "Changed"
+        assert basic_creature.name == "Grizzly Bears"
+
+
+# ── PlayerStats ─────────────────────────────────────────────
+
+class TestPlayerStats:
+    def test_to_dict_keys(self):
+        stats = PlayerStats(cards_drawn=7, damage_dealt=10)
+        d = stats.to_dict()
+        assert "cardsDrawn" in d
+        assert "damageDealt" in d
+        assert d["cardsDrawn"] == 7
+        assert d["damageDealt"] == 10
+
+    def test_default_values(self):
+        stats = PlayerStats()
+        assert stats.cards_drawn == 7
+        assert stats.lands_played == 0
+
+
+# ── PlayerResult & GameResult ────────────────────────────────
+
+class TestPlayerResult:
+    def test_to_dict_winner(self):
+        pr = PlayerResult(seat_index=0, name="Alice", life=40, finish_position=1)
+        d = pr.to_dict()
+        assert d["isWinner"] is True
+        assert d["seatIndex"] == 0
+        assert d["name"] == "Alice"
+
+    def test_to_dict_loser(self):
+        pr = PlayerResult(seat_index=1, name="Bob", life=0, eliminated=True, finish_position=2)
+        d = pr.to_dict()
+        assert d["isWinner"] is False
+        assert d["eliminated"] is True
+
+
+class TestGameResult:
+    def _make_result(self):
+        p0 = PlayerResult(seat_index=0, name="Alice", life=35, finish_position=1, stats=PlayerStats(damage_dealt=20))
+        p1 = PlayerResult(seat_index=1, name="Bob", life=0, eliminated=True, finish_position=2, stats=PlayerStats())
+        return GameResult(winner_seat=0, turns=12, players=[p0, p1])
+
+    def test_winner_property(self):
+        gr = self._make_result()
+        assert gr.winner == 0
+
+    def test_player_a_name(self):
+        gr = self._make_result()
+        assert gr.player_a_name == "Alice"
+
+    def test_player_b_name(self):
+        gr = self._make_result()
+        assert gr.player_b_name == "Bob"
+
+    def test_player_a_eliminated(self):
+        gr = self._make_result()
+        assert gr.player_a_eliminated is False
+
+    def test_player_b_eliminated(self):
+        gr = self._make_result()
+        assert gr.player_b_eliminated is True
+
+    def test_to_dict_has_legacy_keys(self):
+        gr = self._make_result()
+        d = gr.to_dict()
+        assert "winner" in d
+        assert "turns" in d
+        assert "playerResults" in d
+        assert len(d["playerResults"]) == 2
+
+
+# ── SimState ─────────────────────────────────────────────────
+
+class TestSimState:
+    def test_init_battlefields(self):
+        state = SimState()
+        state.init_battlefields(4)
+        assert len(state.battlefields) == 4
+
+    def test_add_and_get_battlefield(self):
+        state = SimState()
+        c = Card(name="Test", id=1)
+        state.add_to_battlefield(0, c)
+        bf = state.get_battlefield(0)
+        assert len(bf) == 1
+        assert bf[0].name == "Test"
+
+    def test_remove_from_battlefield(self):
+        state = SimState()
+        c = Card(name="Test", id=42)
+        state.add_to_battlefield(0, c)
+        removed = state.remove_from_battlefield(42)
+        assert removed is not None
+        assert removed.name == "Test"
+        assert len(state.get_battlefield(0)) == 0
+
+    def test_remove_nonexistent_returns_none(self):
+        state = SimState()
+        assert state.remove_from_battlefield(999) is None
+
+    def test_all_battlefield_cards(self):
+        state = SimState()
+        state.add_to_battlefield(0, Card(name="A", id=1))
+        state.add_to_battlefield(1, Card(name="B", id=2))
+        state.add_to_battlefield(1, Card(name="C", id=3))
+        all_cards = state.all_battlefield_cards()
+        assert len(all_cards) == 3

tests/sim/test_rules.py

@@ -0,0 +1,189 @@
+"""Tests for commander_ai_lab.sim.rules."""
+import pytest
+from commander_ai_lab.sim.models import Card
+from commander_ai_lab.sim.rules import enrich_card, score_card, parse_decklist, AI_DEFAULT_WEIGHTS
+
+
+# ── enrich_card ──────────────────────────────────────────────
+
+class TestEnrichCardBasicLands:
+    @pytest.mark.parametrize("name,expected_color", [
+        ("Forest", "G"),
+        ("Island", "U"),
+        ("Plains", "W"),
+        ("Swamp", "B"),
+        ("Mountain", "R"),
+    ])
+    def test_basic_land_color(self, name, expected_color):
+        c = Card(name=name)
+        enrich_card(c)
+        assert c.is_land() is True
+        assert expected_color in c.oracle_text
+        assert c.cmc == 0
+
+
+class TestEnrichCardKnownCards:
+    def test_sol_ring(self):
+        c = Card(name="Sol Ring")
+        enrich_card(c)
+        assert c.is_ramp is True
+        assert c.cmc == 1
+
+    def test_murder(self):
+        c = Card(name="Murder")
+        enrich_card(c)
+        assert c.is_removal is True
+        assert c.cmc == 3
+
+    def test_wrath_of_god(self):
+        c = Card(name="Wrath of God")
+        enrich_card(c)
+        assert c.is_board_wipe is True
+
+    def test_cultivate(self):
+        c = Card(name="Cultivate")
+        enrich_card(c)
+        assert c.is_ramp is True
+        assert c.cmc == 3
+
+    def test_counterspell(self):
+        c = Card(name="Counterspell")
+        enrich_card(c)
+        assert c.is_removal is True
+
+
+class TestEnrichCardDeterministic:
+    def test_same_unknown_card_same_cmc(self):
+        """Unknown cards must get deterministic CMC (no random jitter)."""
+        c1 = Card(name="Unknown Foobar Card")
+        c2 = Card(name="Unknown Foobar Card")
+        enrich_card(c1)
+        enrich_card(c2)
+        assert c1.cmc == c2.cmc
+        assert c1.pt == c2.pt
+
+    def test_different_unknown_cards_may_differ(self):
+        c1 = Card(name="Aaaaa")
+        c2 = Card(name="Zzzzz")
+        enrich_card(c1)
+        enrich_card(c2)
+        # At minimum they are both valid creatures with cmc in [2,6]
+        assert 2 <= c1.cmc <= 6
+        assert 2 <= c2.cmc <= 6
+
+    def test_unknown_card_cmc_range(self):
+        for name in ["Alpha", "Beta", "Gamma", "Delta", "Epsilon"]:
+            c = Card(name=name)
+            enrich_card(c)
+            assert 2 <= c.cmc <= 6
+
+
+class TestEnrichCardPreservesExistingData:
+    def test_real_data_not_overwritten(self):
+        c = Card(name="Custom Card", type_line="Legendary Creature", cmc=7, pt="6/6")
+        enrich_card(c)
+        assert c.cmc == 7
+        assert c.pt == "6/6"
+
+
+class TestEnrichCardOracleFlags:
+    def test_removal_flag_from_oracle(self):
+        c = Card(name="Custom Removal", type_line="Instant", cmc=2,
+                 oracle_text="Destroy target creature.")
+        enrich_card(c)
+        assert c.is_removal is True
+
+    def test_board_wipe_flag_from_oracle(self):
+        c = Card(name="Custom Wipe", type_line="Sorcery", cmc=5,
+                 oracle_text="Destroy all creatures.")
+        enrich_card(c)
+        assert c.is_board_wipe is True
+
+    def test_ramp_flag_from_oracle(self):
+        c = Card(name="Custom Ramp", type_line="Sorcery", cmc=2,
+                 oracle_text="Search your library for a basic land and put it onto the battlefield.")
+        enrich_card(c)
+        assert c.is_ramp is True
+
+
+# ── score_card ───────────────────────────────────────────────
+
+class TestScoreCard:
+    def test_land_scores_lower_than_creature(self):
+        land = Card(name="Forest", type_line="Basic Land — Forest", cmc=0)
+        creature = Card(name="Grizzly Bears", type_line="Creature — Bear", cmc=2, pt="2/2")
+        enrich_card(land)
+        enrich_card(creature)
+        # Creature with pt bonus should score higher than a plain land
+        assert score_card(creature) > score_card(land)
+
+    def test_flying_bonus(self):
+        base = Card(name="Vanilla", type_line="Creature", cmc=3, pt="3/3")
+        flyer = Card(name="Flyer", type_line="Creature", cmc=3, pt="3/3",
+                     oracle_text="Flying", keywords=["Flying"])
+        assert score_card(flyer) > score_card(base)
+
+    def test_ramp_bonus(self):
+        ramp = Card(name="Sol Ring", type_line="Artifact", cmc=1, is_ramp=True)
+        plain = Card(name="Mox Nothing", type_line="Artifact", cmc=1)
+        assert score_card(ramp) > score_card(plain)
+
+    def test_removal_bonus(self):
+        removal = Card(name="Murder", type_line="Instant", cmc=3, is_removal=True,
+                       oracle_text="Destroy target creature.")
+        plain = Card(name="Shock", type_line="Instant", cmc=1)
+        assert score_card(removal) > score_card(plain)
+
+    def test_multiplayer_scales_flying(self):
+        flyer = Card(name="Flyer", type_line="Creature", cmc=3, pt="3/3",
+                     oracle_text="Flying", keywords=["Flying"])
+        score_1v1 = score_card(flyer, num_opponents=1)
+        score_4p = score_card(flyer, num_opponents=3)
+        assert score_4p > score_1v1
+
+    def test_defender_penalty(self):
+        defender = Card(name="Wall", type_line="Creature", cmc=2, pt="0/5",
+                        oracle_text="Defender", keywords=["Defender"])
+        vanilla = Card(name="Vanilla", type_line="Creature", cmc=2, pt="2/2")
+        assert score_card(defender) < score_card(vanilla)
+
+    def test_high_cmc_penalized(self):
+        cheap = Card(name="Cheap", type_line="Creature", cmc=1, pt="1/1")
+        expensive = Card(name="Expensive", type_line="Creature", cmc=8, pt="1/1")
+        assert score_card(cheap) > score_card(expensive)
+
+
+# ── parse_decklist ───────────────────────────────────────────
+
+class TestParseDecklist:
+    def test_numbered_entries(self):
+        text = "4 Lightning Bolt\n2 Counterspell\n1 Black Lotus"
+        cards = parse_decklist(text)
+        assert len(cards) == 7
+        assert cards[0].name == "Lightning Bolt"
+
+    def test_unnumbered_entries(self):
+        cards = parse_decklist("Lightning Bolt")
+        assert len(cards) == 1
+        assert cards[0].name == "Lightning Bolt"
+
+    def test_skips_comments(self):
+        text = "// This is a comment\n1 Sol Ring\n# Also a comment\n1 Forest"
+        cards = parse_decklist(text)
+        assert len(cards) == 2
+
+    def test_skips_blank_lines(self):
+        cards = parse_decklist("1 Sol Ring\n\n\n1 Forest")
+        assert len(cards) == 2
+
+    def test_returns_card_objects(self):
+        cards = parse_decklist("1 Sol Ring")
+        from commander_ai_lab.sim.models import Card
+        assert isinstance(cards[0], Card)
+
+    def test_empty_input(self):
+        assert parse_decklist("") == []
+
+    def test_multi_word_card_names(self):
+        cards = parse_decklist("1 Kodama's Reach")
+        assert cards[0].name == "Kodama's Reach"

ui/ai-lab.css

@@ -617,7 +617,7 @@
 
 .lab-deck-grid {
     display: grid;
-    grid-template-columns: repeat(3, 1fr);
+    grid-template-columns: repeat(4, 1fr);
     gap: var(--sp-3);
     margin-bottom: var(--sp-5);
 }
@@ -887,7 +887,7 @@
 
 .lab-stats-cards {
     display: grid;
-    grid-template-columns: repeat(3, 1fr);
+    grid-template-columns: repeat(4, 1fr);
     gap: var(--sp-3);
     margin-bottom: var(--sp-4);
 }
@@ -972,6 +972,7 @@
 .seat-0-color { background: var(--accent); }
 .seat-1-color { background: var(--error); }
 .seat-2-color { background: var(--success); }
+.seat-3-color { background: #d29922; }
 
 /* ── Win Condition Breakdown ───────────────────────────── */
 
@@ -1172,7 +1173,7 @@
 
 .lab-combat-grid {
     display: grid;
-    grid-template-columns: repeat(3, 1fr);
+    grid-template-columns: repeat(4, 1fr);
     gap: var(--sp-4);
 }
 
@@ -1456,6 +1457,16 @@
 
 /* ── Responsive ────────────────────────────────────────── */
 
+
+@media (max-width: 900px) {
+    .lab-deck-grid {
+        grid-template-columns: repeat(2, 1fr);
+    }
+    .lab-combat-grid {
+        grid-template-columns: repeat(2, 1fr);
+    }
+}
+
 @media (max-width: 768px) {
     .lab-deck-grid,
     .lab-stats-cards {
@@ -1488,321 +1499,59 @@
         width: 95%;
         padding: var(--sp-4);
     }
-    .lab-combat-grid {
-        grid-template-columns: 1fr;
-    }
     .lab-charts-grid {
         grid-template-columns: 1fr;
     }
+    .lab-combat-grid {
+        grid-template-columns: 1fr;
+    }
+    .lab-h2h-table th,
+    .lab-h2h-table td {
+        padding: var(--sp-2);
+        font-size: 11px;
+    }
 }
 
-/* ═══════════════════════════════════════════════════════════
-   Precon Decks — Grid + Cards
-   ═══════════════════════════════════════════════════════════ */
+/* ── Seat Color Overrides ──────────────────────────────── */
 
-.lab-precon-btn {
-    background: linear-gradient(135deg, var(--surface-0), var(--surface-2)) !important;
-    border-color: var(--border) !important;
+.lab-deck-slot[data-seat="0"] {
+    --seat-accent: var(--accent);
 }
 
-.lab-precon-btn:hover {
-    background: linear-gradient(135deg, var(--surface-2), var(--surface-3)) !important;
-    border-color: var(--error) !important;
-}
-
-.lab-precon-controls {
-    display: flex;
-    gap: var(--sp-3);
-    padding: 0 var(--sp-5) var(--sp-3);
-    align-items: center;
-}
-
-.lab-precon-controls input {
-    flex: 1;
-    background: var(--surface-0);
-    border: 1px solid var(--border);
-    color: var(--text);
-    padding: var(--sp-2) var(--sp-3);
-    border-radius: var(--radius-sm);
-    font-size: 14px;
-}
-
-.lab-precon-controls input:focus {
-    outline: none;
-    border-color: var(--error);
-}
-
-.lab-precon-install-all-btn {
-    background: var(--surface-3);
-    color: var(--text);
-    border: 1px solid var(--border);
-    padding: var(--sp-2) var(--sp-4);
-    border-radius: var(--radius-sm);
-    cursor: pointer;
-    font-size: 13px;
-    white-space: nowrap;
-    transition: all var(--duration) var(--ease);
-}
-
-.lab-precon-install-all-btn:hover {
-    background: var(--surface-hover);
-    border-color: var(--error);
-}
-
-.lab-precon-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(220px, 1fr));
-    gap: var(--sp-3);
-    padding: 0 var(--sp-5) var(--sp-5);
-    max-height: 55vh;
-    overflow-y: auto;
-}
-
-.lab-precon-card {
-    background: var(--surface-0);
-    border: 1px solid var(--border);
-    border-radius: var(--radius-md);
-    padding: var(--sp-4);
-    cursor: pointer;
-    transition: all 0.2s var(--ease);
-    display: flex;
-    flex-direction: column;
-    gap: 6px;
-    position: relative;
-}
-
-.lab-precon-card:hover {
-    border-color: var(--error);
-    transform: translateY(-2px);
-    box-shadow: 0 4px 16px var(--error-dim);
-}
-
-.lab-precon-card-installed {
-    border-color: var(--success);
-    opacity: 0.8;
-}
-
-.lab-precon-card-installed .lab-precon-install-btn {
-    background: var(--success);
-    border-color: var(--success);
-    cursor: default;
-}
-
-.precon-card-colors {
-    display: flex;
-    gap: 4px;
-    margin-bottom: 2px;
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-
-.precon-card-name {
-    font-size: 15px;
-    font-weight: 600;
-    color: var(--text);
-    line-height: 1.2;
-}
-
-.precon-card-commander {
-    font-size: 12px;
-    color: var(--error);
-    font-style: italic;
-}
-
-.precon-card-set {
-    font-size: 11px;
-    color: var(--text-secondary);
-}
-
-.precon-card-theme {
-    font-size: 11px;
-    color: var(--text-tertiary);
-    line-height: 1.3;
-    margin-top: auto;
-}
-
-.lab-precon-install-btn {
-    margin-top: var(--sp-2);
-    width: 100%;
-    background: var(--surface-3);
-    color: var(--text);
-    border: 1px solid var(--border);
-    padding: 6px var(--sp-3);
-    border-radius: var(--radius-sm);
-    cursor: pointer;
-    font-size: 12px;
-    transition: all var(--duration) var(--ease);
-}
-
-.lab-precon-install-btn:hover {
-    background: var(--surface-hover);
-    border-color: var(--error);
-}
-
-.badge-precon {
-    background: var(--surface-3);
-    border-color: var(--border);
-}
-
-/* ══════════════════════════════════════════════════════════════
-   v5: Analytics & Trends Panels
-   ══════════════════════════════════════════════════════════════ */
-
-.lab-empty-hint {
-    color: var(--text-tertiary);
-    font-size: 13px;
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-    text-align: center;
-}
-
-.lab-loading-hint {
-    display: flex;
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-    justify-content: center;
-    gap: var(--sp-3);
-    color: var(--text-secondary);
-    font-size: 13px;
-    padding: var(--sp-6) 0;
-}
-
-/* Analytics Grid */
-.lab-analytics-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
-    gap: var(--sp-4);
-    padding: var(--sp-2) 0;
-}
-
-.lab-analytics-card {
-    background: var(--surface-0);
-    border: 1px solid var(--border);
-    border-radius: var(--radius-md);
-    padding: var(--sp-4);
-}
-
-.lab-analytics-card:hover {
-    border-color: var(--border-focus);
-}
-
-.lab-analytics-error {
-    opacity: 0.6;
-}
-
-.lab-analytics-name {
-    font-weight: 700;
-    color: #fff;
-    font-size: 15px;
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-
-.lab-analytics-commander {
-    font-size: 12px;
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-    font-size: 12px;
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-    display: flex;
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-    gap: var(--sp-1);
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-}
-
-.lab-analytics-section-pill {
-    background: var(--surface-2);
-    border: 1px solid var(--border);
-    border-radius: var(--radius-xl);
-    padding: 2px var(--sp-2);
-    font-size: 11px;
-    color: var(--text-secondary);
-}
-
-/* Trends */
-.lab-trends-summary {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(260px, 1fr));
-    gap: var(--sp-4);
-    padding: var(--sp-2) 0;
-}
-
-.lab-trends-card {
-    background: var(--surface-0);
-    border: 1px solid var(--border);
-    border-radius: var(--radius-md);
-    padding: var(--sp-4);
-}
-
-.lab-trends-card:hover {
-    border-color: var(--border-focus);
-}
-
-.lab-trends-deck-name {
-    font-weight: 700;
-    color: #fff;
-    font-size: 14px;
-    margin-bottom: var(--sp-3);
-}
-
-.lab-trends-stats {
-    display: grid;
-    grid-template-columns: 1fr 1fr;
-    gap: var(--sp-2);
-}
-
-.lab-trends-stat {
-    display: flex;
-    flex-direction: column;
-    gap: 2px;
-}
-
-.lab-trends-stat-label {
-    font-size: 11px;
-    color: var(--text-tertiary);
-    text-transform: uppercase;
-    letter-spacing: 0.5px;
-}
-
-.lab-trends-stat-value {
-    font-size: 16px;
-    font-weight: 700;
-    color: var(--text);
-    font-variant-numeric: tabular-nums;
-}
-
-/* Precon Refresh Button */
-.lab-precon-refresh-btn {
-    background: var(--surface-2);
-    border: 1px solid var(--border);
-    color: var(--text-secondary);
-    padding: 6px 14px;
-    border-radius: 6px;
-    cursor: pointer;
-    font-size: 13px;
-    transition: all 180ms ease;
-}
-
-.lab-precon-refresh-btn:hover {
+.lab-deck-slot[data-seat="0"].filled {
     border-color: var(--accent);
-    color: var(--accent);
+    box-shadow: 0 0 8px var(--accent-glow);
 }
+
+.lab-deck-slot[data-seat="1"] {
+    --seat-accent: var(--error);
+}
+
+.lab-deck-slot[data-seat="1"].filled {
+    border-color: var(--error);
+    box-shadow: 0 0 8px var(--error-glow, rgba(248, 81, 73, 0.25));
+}
+
+.lab-deck-slot[data-seat="2"] {
+    --seat-accent: var(--success);
+}
+
+.lab-deck-slot[data-seat="2"].filled {
+    border-color: var(--success);
+    box-shadow: 0 0 8px var(--success-glow, rgba(63, 185, 80, 0.25));
+}
+
+.lab-deck-slot[data-seat="3"] {
+    --seat-accent: #d29922;
+}
+
+.lab-deck-slot[data-seat="3"].filled {
+    border-color: #d29922;
+    box-shadow: 0 0 8px rgba(210, 153, 34, 0.25);
+}
+
+/* Seat label coloring */
+.lab-deck-slot[data-seat="0"] .seat-label { color: var(--accent); }
+.lab-deck-slot[data-seat="1"] .seat-label { color: var(--error); }
+.lab-deck-slot[data-seat="2"] .seat-label { color: var(--success); }
+.lab-deck-slot[data-seat="3"] .seat-label { color: #d29922; }

ui/global.css

@@ -727,3 +727,140 @@ table { border-collapse: collapse; width: 100%; }
         grid-template-columns: 1fr;
     }
 }
+
+/* ═══════════════════════════════════════════════════════════
+   PREMIUM UPGRADE LAYER
+   ═══════════════════════════════════════════════════════════ */
+
+/* ── Ambient background glow (body) ─────────────────────── */
+body {
+    background-image:
+        radial-gradient(ellipse 80% 50% at 10% -10%, rgba(91,158,240,0.07) 0%, transparent 60%),
+        radial-gradient(ellipse 60% 40% at 90% 110%, rgba(167,139,250,0.05) 0%, transparent 60%);
+    background-attachment: fixed;
+}
+
+/* ── Page title: subtle gradient text ───────────────────── */
+.page-header h1 {
+    background: linear-gradient(135deg, #fff 60%, rgba(91,158,240,0.85) 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+
+/* ── Nav: stronger glass + accent underline ─────────────── */
+.nav {
+    background: rgba(9,11,16,0.80);
+    backdrop-filter: blur(20px) saturate(1.8);
+    -webkit-backdrop-filter: blur(20px) saturate(1.8);
+    box-shadow: 0 1px 0 rgba(91,158,240,0.08), 0 4px 24px rgba(0,0,0,0.3);
+    border-bottom-color: rgba(255,255,255,0.06);
+}
+.nav-brand svg { filter: drop-shadow(0 0 8px rgba(91,158,240,0.4)); }
+.nav-links a.active { box-shadow: inset 0 0 0 1px rgba(91,158,240,0.2); }
+
+/* ── Panels: gradient border on hover ───────────────────── */
+.panel {
+    position: relative;
+    transition: border-color var(--duration) var(--ease),
+                box-shadow var(--duration) var(--ease);
+}
+.panel::before {
+    content: '';
+    position: absolute;
+    inset: 0;
+    border-radius: var(--radius-lg);
+    padding: 1px;
+    background: linear-gradient(135deg, rgba(91,158,240,0.18), transparent 50%, rgba(167,139,250,0.1));
+    -webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
+    -webkit-mask-composite: xor;
+    mask-composite: exclude;
+    pointer-events: none;
+    opacity: 0;
+    transition: opacity var(--duration) var(--ease);
+}
+.panel:hover::before { opacity: 1; }
+.panel:hover {
+    box-shadow: 0 8px 32px rgba(0,0,0,0.35), 0 0 0 1px rgba(91,158,240,0.08);
+}
+
+/* ── Stat cards: gradient surface + shimmer top edge ─────── */
+.stat-card {
+    position: relative;
+    background: linear-gradient(145deg, var(--surface-2), var(--surface-1));
+    overflow: hidden;
+}
+.stat-card::after {
+    content: '';
+    position: absolute;
+    top: 0; left: 0; right: 0;
+    height: 1px;
+    background: linear-gradient(90deg, transparent, rgba(255,255,255,0.12), transparent);
+    pointer-events: none;
+}
+.stat-card:hover {
+    box-shadow: var(--shadow-md), 0 0 20px rgba(91,158,240,0.08);
+    transform: translateY(-2px);
+}
+
+/* ── Buttons: gradient fills + inner highlight ───────────── */
+.btn { position: relative; overflow: hidden; }
+.btn::after {
+    content: '';
+    position: absolute;
+    top: 0; left: 0; right: 0;
+    height: 50%;
+    background: linear-gradient(to bottom, rgba(255,255,255,0.07), transparent);
+    border-radius: inherit;
+    pointer-events: none;
+}
+.btn-primary {
+    background: linear-gradient(135deg, var(--accent), #4a8de0);
+    box-shadow: 0 2px 8px rgba(91,158,240,0.25);
+}
+.btn-primary:hover {
+    background: linear-gradient(135deg, var(--accent-hover), var(--accent));
+    box-shadow: 0 4px 20px rgba(91,158,240,0.4);
+}
+.btn-success {
+    background: linear-gradient(135deg, var(--success), #38c46c);
+    box-shadow: 0 2px 8px rgba(74,222,128,0.2);
+}
+.btn-success:hover { box-shadow: 0 4px 20px rgba(74,222,128,0.4); transform: translateY(-1px); }
+.btn-purple {
+    background: linear-gradient(135deg, var(--purple), #9171f0);
+    box-shadow: 0 2px 8px rgba(167,139,250,0.2);
+}
+.btn-purple:hover { box-shadow: 0 4px 20px rgba(167,139,250,0.4); transform: translateY(-1px); }
+
+/* ── Tags: subtle colored border ────────────────────────── */
+.tag-blue   { border: 1px solid rgba(91,158,240,0.25); }
+.tag-green  { border: 1px solid rgba(74,222,128,0.25); }
+.tag-yellow { border: 1px solid rgba(251,191,36,0.25); }
+.tag-red    { border: 1px solid rgba(248,113,113,0.25); }
+.tag-purple { border: 1px solid rgba(167,139,250,0.25); }
+
+/* ── Progress fill: glow ─────────────────────────────────── */
+.progress-fill { box-shadow: 0 0 8px rgba(91,158,240,0.5); }
+
+/* ── Badge running: glow dot ─────────────────────────────── */
+.badge-running { box-shadow: 0 0 12px rgba(91,158,240,0.15); }
+.badge-running::before { box-shadow: 0 0 6px var(--accent); }
+
+/* ── Toast: glassmorphism + colored glow ─────────────────── */
+.toast { backdrop-filter: blur(12px); }
+.toast-success { box-shadow: var(--shadow-lg), 0 0 20px rgba(74,222,128,0.1); }
+.toast-error   { box-shadow: var(--shadow-lg), 0 0 20px rgba(248,113,113,0.1); }
+
+/* ── Divider: fade-out gradient ──────────────────────────── */
+.divider {
+    background: linear-gradient(90deg, transparent, var(--border), transparent);
+}
+
+/* ── Inputs: inset shadow for depth ─────────────────────── */
+.form-input, .form-select, .form-textarea {
+    box-shadow: inset 0 1px 3px rgba(0,0,0,0.25);
+}
+.form-input:focus, .form-select:focus, .form-textarea:focus {
+    box-shadow: 0 0 0 3px var(--accent-dim), inset 0 1px 3px rgba(0,0,0,0.1);
+}