A descriptive ingestion layer feeding the downstream reasoning model: monitors YouTube
playlists, transcribes new uploads, and extracts structured, verifiable CLAIM records
(with provenance + a mandatory verbatim quote). It records WHAT was claimed; it never
judges correctness or tradeability -- all judgment stays downstream, in one place.
Built per the operator's spec. Two clocks coordinated through Postgres (§2): a 30-min
detection poll + a paced concurrency-1 transcription/extraction worker. Reuses the app's
infra: shared Postgres (isolated `transcripts` schema), the app's Ollama for extraction
(free, on-LAN, private -- transcripts never leave the network), the config.py host resolver.
youtube_claims/schema.sql playlists / videos / claims (§7-8); claims.verbatim_quote NOT NULL
youtube_claims/db.py psycopg2; claim-by-atomic-status-flip under FOR UPDATE SKIP LOCKED
youtube_claims/detection.py playlistItems.list over urllib; dedupe by video_id (§5)
youtube_claims/extraction.py local Ollama -> descriptive claims; drops any claim lacking an
exact verbatim quote present in the source window (anti-hallucination)
youtube_claims/transcription.py yt-dlp -> faster-whisper (medium/cpu/int8, option A;
initial_prompt=watchlist) with youtube-transcript-api captions fallback
youtube_claims/worker.py paced drain; per-video state machine + retries/backoff
youtube_claims/run.py APScheduler poll + worker loop in one process; CLI for setup
VERIFIED LIVE: schema applied; playlist "Anatomy of a Trade" (Ross Cameron) added; poll
ingested all 12 videos (1 API unit); Ollama extraction pulled clean descriptive claims
(TC/DXST/CF) with exact verbatim quotes from a real transcript. Whisper transcription pending
ffmpeg (`apt install ffmpeg`, needs sudo) + the yt-dlp/faster-whisper deps (installing via uv).
Secrets (YOUTUBE_API_KEY) live in gitignored .env only. Whisper is option A (CPU); env-flippable
to option B (LAN GPU large-v3) later.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
