chore: track claude skills, tools, templates, reference code and research-wiki

- Add all claude skills (brainstorming, commit, debugging, TDD, etc.) - Add claude hooks (pre-commit-guard, post-edit-quality) - Add research templates (experiment plan, research brief, etc.) - Add claude tools (arxiv/semantic_scholar/openalex fetch, wiki, exa) - Add TRM4 reference implementation as algorithm fidelity baseline - Add research-wiki content (plans, index, graph, query_pack) - Update .gitignore to exclude .graphify_version runtime state
2026-07-06 20:59:03 -04:00
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+# graphify reference: transcribe video and audio
+
+Load this only when `detect` reported one or more `video` files. A corpus with no video never reads this.
+
+### Step 2.5 - Transcribe video / audio files (only if video files detected)
+
+Skip this step entirely if `detect` returned zero `video` files.
+
+Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.
+
+**Strategy:** Read the god nodes from `graphify-out/.graphify_detect.json` (or the analysis file if it exists from a previous run). You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.
+
+**However**, if the corpus has *only* video files and no other docs/code, use the generic fallback prompt: `"Use proper punctuation and paragraph breaks."`
+
+**Step 1 - Write the Whisper prompt yourself.**
+
+Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:
+
+- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
+- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
+
+Set it as `WHISPER_PROMPT` to use in the next command.
+
+**Step 2 - Transcribe:**
+
+```bash
+GRAPHIFY_WHISPER_MODEL=base  # or whatever --whisper-model the user passed
+$(cat graphify-out/.graphify_python) -c "
+import json, os
+from pathlib import Path
+from graphify.transcribe import transcribe_all
+
+detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+video_files = detect.get('files', {}).get('video', [])
+prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
+
+transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
+print(json.dumps(transcript_paths, ensure_ascii=False))
+" > graphify-out/.graphify_transcripts.json
+```
+
+After transcription:
+- Read the transcript paths from `graphify-out/.graphify_transcripts.json`
+- Add them to the docs list before dispatching semantic subagents in Step 3B
+- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
+- If transcription fails for a file, print a warning and continue with the rest
+
+**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.