Per-Talk Subagent Instructions — Detail

The Step 3 procedure each parallel subagent runs. The orchestrator passes the talk's DB entry plus the current rhetoric-style-summary.md; the subagent returns the JSON shape in schemas-db.md.

A. Acquire Transcript and Slides

Transcript download

YouTube talks — yt-dlp with auto-subtitles across likely languages:

yt-dlp --write-auto-sub --sub-lang "en,ru,he,fr,de,es,ja" --skip-download \
  --sub-format vtt \
  -o "{vault_root}/transcripts/{youtube_id}" \
  "https://www.youtube.com/watch?v={youtube_id}"

The VTT filename includes the language code (e.g., {youtube_id}.ru.vtt). Record the detected language as delivery_language. After download, clean the VTT:

python3 skills/vault-ingress/scripts/vtt-cleanup.py \
  "{vault_root}/transcripts/{youtube_id}.{lang}.vtt"

This strips timestamps, cue markers, and deduplicates lines. Output: transcripts/{youtube_id}.txt.

Fallback 1 — youtube-transcript-api (when yt-dlp has no auto-captions):

"{python_path}" -c "
from youtube_transcript_api import YouTubeTranscriptApi
transcript = YouTubeTranscriptApi.get_transcript('{youtube_id}', languages=['en','ru','he','fr','de'])
for entry in transcript:
    print(entry['text'])
" > "{vault_root}/transcripts/{youtube_id}.txt"

Fallback 2 — Whisper (no captions at all; requires downloaded video/audio):

ffmpeg -i "{vault_root}/slides-rebuild/{youtube_id}/{youtube_id}.mp4" \
  -vn -acodec libmp3lame \
  "{vault_root}/slides-rebuild/{youtube_id}/{youtube_id}.mp3"

Then transcribe with MLX Whisper (mlx_whisper.transcribe()) or OpenAI Whisper. Set transcript_source: "whisper".

Non-YouTube talks (InfoQ, Vimeo, conference platforms): attempt audio download via yt-dlp, then transcribe locally with Whisper. Falls back to processed_partial if audio fails.

Slide acquisition (per slide_source)

• pptx / both — run python3 skills/vault-ingress/scripts/pptx-extraction.py <path.pptx>.
• pdf — download via gdown:

  "{python_path}" -m gdown \
    "https://drive.google.com/uc?id={google_drive_id}" \
    -O "{vault_root}/slides/{google_drive_id}.pdf"

• video_extracted — download video at 720p, then extract slides:

  yt-dlp -f "bestvideo[height<=720][ext=mp4]+bestaudio[ext=m4a]/best[height<=720][ext=mp4]/best[height<=720]" \
    --merge-output-format mp4 \
    -o "{vault_root}/slides-rebuild/{youtube_id}/{youtube_id}.mp4" \
    "https://www.youtube.com/watch?v={youtube_id}"
  python3 skills/vault-ingress/scripts/video-slide-extraction.py \
    "{vault_root}/slides-rebuild/{youtube_id}/{youtube_id}.mp4" \
    "{vault_root}/slides-rebuild/{youtube_id}" "{youtube_id}"

  Copy the resulting PDF to slides/{youtube_id}.pdf. Delete the video after extraction. For batch downloads, use skills/vault-ingress/scripts/batch-download-videos.sh <vault_root> ID1 ID2 ....
• none — transcript-only, status processed_partial.
• Fallback — if primary slides fail but video_url exists, fall back to video extraction. A talk can still reach processed status this way.

transcript_source is required

Set transcript_source on the talk entry: youtube_auto (yt-dlp captions), whisper (local transcription), or manual. Downstream tools use it to gauge transcript reliability.

B. Analyze for Rhetoric & Style (NOT content)

Apply all 14 dimensions from rhetoric-dimensions.md, including dimension 14 (Areas for Improvement). Follow language policy and verbatim-quote rules in processing-rules.md.

Quote rule: verbatim quotes must be English-first — "English translation" (original text). Never quote non-English text without an English translation preceding it.

B2. Tag Presentation Patterns

Scan observations against the pattern taxonomy at skills/presentation-creator/references/patterns/_index.md. Skip patterns marked observable: false. Record confidence (strong/moderate/weak) and evidence per pattern. Compute per-talk score: count(patterns) − count(antipatterns). Store in pattern_observations. See processing-rules.md for full tagging rules.

C. Return JSON

Per the subagent return schema in schemas-db.md. Minimal structure:

{
  "talk_id": "...",
  "status": "processed",
  "transcript_source": "youtube",
  "slide_source": "pdf",
  "pattern_observations": [
    {"pattern_id": "...", "confidence": "strong", "evidence": "..."}
  ],
  "new_patterns": ["..."],
  "summary_updates": [{"section": 1, "content": "..."}],
  "structured_data": {"delivery_language": "en", "co_presenter": false}
}