{"type":"thinking_level_change","id":"e3cfda05","parentId":null,"timestamp":"2026-03-29T21:54:57.641Z","thinkingLevel":"off"}
{"type":"model_change","id":"e54b2d8a","parentId":"e3cfda05","timestamp":"2026-03-29T21:54:57.643Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"**VERIFICATION FAILED — AUTO-FIX ATTEMPT 1**\n\nThe verification gate ran after your previous attempt and found failures. Fix these issues before completing the task.\n\n\n\n---\n\nYou are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nA researcher explored the codebase and a planner decomposed the work — you are the executor. The task plan below is your authoritative contract for the slice goal and verification bar, but it is not a substitute for local reality. Verify the referenced files and surrounding code before changing them. Do not do broad re-research or spontaneous re-planning. Small factual corrections, file-path fixes, and local implementation adaptations are part of execution. Escalate to `blocker_discovered: true` only when the slice contract or downstream task graph no longer holds.\n\n\n\n\n\n## Backing Source Artifacts\n- Slice plan: `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/S01-PLAN.md`\n- Task plan source: `.gsd/milestones/M001/slices/S01/tasks/T04-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M001/slices/S01/tasks/T01-SUMMARY.md`\n- `.gsd/milestones/M001/slices/S01/tasks/T02-SUMMARY.md`\n- `.gsd/milestones/M001/slices/S01/tasks/T03-SUMMARY.md`\n\nThen:\n0. Narrate step transitions, key implementation decisions, and verification outcomes as you work. Keep it terse — one line between tool-call clusters, not between every call — but write complete sentences in user-facing prose, not shorthand notes or scratchpad fragments.\n1.  Follow any activated skills before writing code. If no skills match this task, skip this step.\n2. Execute the steps in the inlined task plan, adapting minor local mismatches when the surrounding code differs from the planner's snapshot\n3. Build the real thing. If the task plan says \"create login endpoint\", build an endpoint that actually authenticates against a real store, not one that returns a hardcoded success response. If the task plan says \"create dashboard page\", build a page that renders real data from the API, not a component with hardcoded props. Stubs and mocks are for tests, not for the shipped feature.\n4. Write or update tests as part of execution — tests are verification, not an afterthought. If the slice plan defines test files in its Verification section and this is the first task, create them (they should initially fail).\n5. When implementing non-trivial runtime behavior (async flows, API boundaries, background processes, error paths), add or preserve agent-usable observability. Skip this for simple changes where it doesn't apply.\n\n   **Background process rule:** Never use bare `command &` to run background processes. The shell's `&` operator leaves stdout/stderr attached to the parent, which causes the Bash tool to hang indefinitely waiting for those streams to close. Always redirect output before backgrounding:\n   - Correct: `command > /dev/null 2>&1 &` or `nohup command > /dev/null 2>&1 &`\n   - Example: `python -m http.server 8080 > /dev/null 2>&1 &` (NOT `python -m http.server 8080 &`)\n   - Preferred: use the `bg_shell` tool if available — it manages process lifecycle correctly without stream-inheritance issues\n6. If the task plan includes a **Failure Modes** section (Q5), implement the error/timeout/malformed handling specified. Verify each dependency's failure path is handled. Skip if the section is absent.\n7. If the task plan includes a **Load Profile** section (Q6), implement protections for the identified 10x breakpoint (connection pooling, rate limiting, pagination, etc.). Skip if absent.\n8. If the task plan includes a **Negative Tests** section (Q7), write the specified negative test cases alongside the happy-path tests — malformed inputs, error paths, and boundary conditions. Skip if absent.\n9. Verify must-haves are met by running concrete checks (tests, commands, observable behaviors)\n10. Run the slice-level verification checks defined in the slice plan's Verification section. Track which pass. On the final task of the slice, all must pass before marking done. On intermediate tasks, partial passes are expected — note which ones pass in the summary.\n11. After the verification gate runs (you'll see gate results in stderr/notify output), populate the `## Verification Evidence` table in your task summary with the check results. Use the `formatEvidenceTable` format: one row per check with command, exit code, verdict (✅ pass / ❌ fail), and duration. If no verification commands were discovered, note that in the section.\n12. If the task touches UI, browser flows, DOM behavior, or user-visible web state:\n   - exercise the real flow in the browser\n   - prefer `browser_batch` when the next few actions are obvious and sequential\n   - prefer `browser_assert` for explicit pass/fail verification of the intended outcome\n   - use `browser_diff` when an action's effect is ambiguous\n   - use console/network/dialog diagnostics when validating async, stateful, or failure-prone UI\n   - record verification in terms of explicit checks passed/failed, not only prose interpretation\n13. If the task plan includes an Observability Impact section, verify those signals directly. Skip this step if the task plan omits the section.\n14. **If execution is running long or verification fails:**\n\n    **Context budget:** You have approximately **~80K chars** reserved for verification context. If you've used most of your context and haven't finished all steps, stop implementing and prioritize writing the task summary with clear notes on what's done and what remains. A partial summary that enables clean resumption is more valuable than one more half-finished step with no documentation. Never sacrifice summary quality for one more implementation step.\n\n    **Debugging discipline:** If a verification check fails or implementation hits unexpected behavior:\n    - Form a hypothesis first. State what you think is wrong and why, then test that specific theory. Don't shotgun-fix.\n    - Change one variable at a time. Make one change, test, observe. Multiple simultaneous changes mean you can't attribute what worked.\n    - Read completely. When investigating, read entire functions and their imports, not just the line that looks relevant.\n    - Distinguish \"I know\" from \"I assume.\" Observable facts (the error says X) are strong evidence. Assumptions (this library should work this way) need verification.\n    - Know when to stop. If you've tried 3+ fixes without progress, your mental model is probably wrong. Stop. List what you know for certain. List what you've ruled out. Form fresh hypotheses from there.\n    - Don't fix symptoms. Understand *why* something fails before changing code. A test that passes after a change you don't understand is luck, not a fix.\n15. **Blocker discovery:** If execution reveals that the remaining slice plan is fundamentally invalid — not just a bug or minor deviation, but a plan-invalidating finding like a wrong API, missing capability, or architectural mismatch — set `blocker_discovered: true` in the task summary frontmatter and describe the blocker clearly in the summary narrative. Do NOT set `blocker_discovered: true` for ordinary debugging, minor deviations, or issues that can be fixed within the current task or the remaining plan. This flag triggers an automatic replan of the slice.\n16. If you made an architectural, pattern, library, or observability decision during this task that downstream work should know about, append it to `.gsd/DECISIONS.md` (read the template at `~/.gsd/agent/extensions/gsd/templates/decisions.md` if the file doesn't exist yet). Not every task produces decisions — only append when a meaningful choice was made.\n17. If you discover a non-obvious rule, recurring gotcha, or useful pattern during execution, append it to `.gsd/KNOWLEDGE.md`. Only add entries that would save future agents from repeating your investigation. Don't add obvious things.\n18. Read the template at `~/.gsd/agent/extensions/gsd/templates/task-summary.md`\n19. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md`\n20. Call `gsd_complete_task` with milestone_id, slice_id, task_id, and a summary of what was accomplished. This is your final required step — do NOT manually edit PLAN.md checkboxes. The tool marks the task complete, updates the DB, and renders PLAN.md automatically.\n21. Do not run git commands — the system reads your task summary after completion and creates a meaningful commit from it (type inferred from title, message from your one-liner, key files from frontmatter). Write a clear, specific one-liner in the summary — it becomes the commit message.\n\nAll work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_complete_task` AND write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T04 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M001/slices/S01/S01-PLAN.md`\n**Goal:** Deployable infrastructure: Docker Compose project with PostgreSQL (full schema), FastAPI skeleton, and desktop Whisper transcription script\n**Demo:** After this: docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON\n\n## UNIT: Execute Task T04 (\"Whisper transcription script\") — Slice S01 (\"Docker Compose + Database + Whisper Script\"), Milestone M001\n\n## Resume State\n- No continue file present. Start from the top of the task plan.\n\n## Carry-Forward Context\n- `.gsd/milestones/M001/slices/S01/tasks/T01-SUMMARY.md` — T01: Created full Docker Compose project (xpltd_chrysopedia) with PostgreSQL 16, Redis, FastAPI API, Celery worker, and React web services plus directory scaffolding | decisions: \"env_file uses required: false so docker compose config validates without .env present\"; \"PostgreSQL exposed on host port 5433 to avoid conflicts\" | key_files: \"docker-compose.yml\"; \".env.example\"; \"docker/Dockerfile.api\"\n- `.gsd/milestones/M001/slices/S01/tasks/T02-SUMMARY.md` — T02: Created SQLAlchemy models for all 7 entities, Alembic async migration infrastructure, and initial migration with full PostgreSQL schema; fixed docker compose config validation failure | decisions: \"Renamed relationship import to sa_relationship to avoid name clash with RelatedTechniqueLink.relationship column\"; \"POSTGRES_PASSWORD default changed from :? to :-changeme to fix docker compose config validation without .env\" | key_files: \"backend/models.py\"; \"backend/database.py\"; \"alembic/versions/001_initial.py\"\n- `.gsd/milestones/M001/slices/S01/tasks/T03-SUMMARY.md` — T03: Built FastAPI app with DB-connected health check, Pydantic schemas for all 7 entities, creators/videos CRUD endpoints, structured logging, and pydantic-settings config | decisions: \"Health check at /health performs real DB connectivity check via SELECT 1; /api/v1/health is lightweight (no DB)\"; \"Router files in backend/routers/ with prefix-per-router pattern mounted under /api/v1 in main.py\" | key_files: \"backend/main.py\"; \"backend/config.py\"; \"backend/schemas.py\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M001/slices/S01/tasks/T04-PLAN.md`\n\n---\nestimated_steps: 10\nestimated_files: 3\nskills_used: []\n---\n\n# T04: Whisper transcription script\n\n1. Create Python script whisper/transcribe.py that:\n   - Accepts video file path (or directory for batch mode)\n   - Extracts audio via ffmpeg (subprocess)\n   - Runs Whisper large-v3 with segment-level and word-level timestamps\n   - Outputs JSON matching the spec format (source_file, creator_folder, duration, segments with words)\n   - Supports resumability: checks if output JSON already exists, skips\n2. Create whisper/requirements.txt (openai-whisper, ffmpeg-python)\n3. Write output to a configurable output directory\n4. Add CLI arguments: --input, --output-dir, --model (default large-v3), --device (default cuda)\n5. Include progress logging for long transcriptions\n\n## Inputs\n\n- `chrysopedia-spec.md section 7.2 Stage 1`\n\n## Expected Output\n\n- `whisper/transcribe.py`\n- `whisper/requirements.txt`\n- `whisper/README.md`\n\n## Verification\n\npython whisper/transcribe.py --help shows usage; script validates ffmpeg is available\n","display":false,"id":"9b3ea8cb","parentId":"e54b2d8a","timestamp":"2026-03-29T21:54:57.645Z"}
{"type":"message","id":"28b40302","parentId":"9b3ea8cb","timestamp":"2026-03-29T21:55:03.602Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by reviewing the task plan, prior summaries, and the spec to understand exactly what's needed."},{"type":"toolCall","id":"toolu_01THzaGMjgX5AhB4McsbxFhk","name":"read","arguments":{"path":".gsd/milestones/M001/slices/S01/tasks/T04-PLAN.md"}},{"type":"toolCall","id":"toolu_01Ppver1kM4Ry92fNkaujBSQ","name":"read","arguments":{"path":".gsd/milestones/M001/slices/S01/S01-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":158,"cacheRead":49033,"cacheWrite":3512,"totalTokens":52705,"cost":{"input":0.00001,"output":0.00395,"cacheRead":0.0245165,"cacheWrite":0.02195,"total":0.0504265}},"stopReason":"toolUse","timestamp":1774821297644}}
{"type":"message","id":"1b1cfe3c","parentId":"28b40302","timestamp":"2026-03-29T21:55:03.604Z","message":{"role":"toolResult","toolCallId":"toolu_01THzaGMjgX5AhB4McsbxFhk","toolName":"read","content":[{"type":"text","text":"---\nestimated_steps: 10\nestimated_files: 3\nskills_used: []\n---\n\n# T04: Whisper transcription script\n\n1. Create Python script whisper/transcribe.py that:\n   - Accepts video file path (or directory for batch mode)\n   - Extracts audio via ffmpeg (subprocess)\n   - Runs Whisper large-v3 with segment-level and word-level timestamps\n   - Outputs JSON matching the spec format (source_file, creator_folder, duration, segments with words)\n   - Supports resumability: checks if output JSON already exists, skips\n2. Create whisper/requirements.txt (openai-whisper, ffmpeg-python)\n3. Write output to a configurable output directory\n4. Add CLI arguments: --input, --output-dir, --model (default large-v3), --device (default cuda)\n5. Include progress logging for long transcriptions\n\n## Inputs\n\n- `chrysopedia-spec.md section 7.2 Stage 1`\n\n## Expected Output\n\n- `whisper/transcribe.py`\n- `whisper/requirements.txt`\n- `whisper/README.md`\n\n## Verification\n\npython whisper/transcribe.py --help shows usage; script validates ffmpeg is available\n"}],"isError":false,"timestamp":1774821303604}}
{"type":"message","id":"a78a80ba","parentId":"1b1cfe3c","timestamp":"2026-03-29T21:55:03.605Z","message":{"role":"toolResult","toolCallId":"toolu_01Ppver1kM4Ry92fNkaujBSQ","toolName":"read","content":[{"type":"text","text":"# S01: Docker Compose + Database + Whisper Script\n\n**Goal:** Deployable infrastructure: Docker Compose project with PostgreSQL (full schema), FastAPI skeleton, and desktop Whisper transcription script\n**Demo:** After this: docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON\n\n## Tasks\n- [x] **T01: Created full Docker Compose project (xpltd_chrysopedia) with PostgreSQL 16, Redis, FastAPI API, Celery worker, and React web services plus directory scaffolding** — 1. Create project directory structure:\n   - backend/ (FastAPI app)\n   - frontend/ (React app, placeholder)\n   - whisper/ (desktop transcription script)\n   - docker/ (Dockerfiles)\n   - prompts/ (editable prompt templates)\n   - config/ (canonical tags, settings)\n2. Write docker-compose.yml with services:\n   - chrysopedia-api (FastAPI, Uvicorn)\n   - chrysopedia-web (React, nginx)\n   - chrysopedia-db (PostgreSQL 16)\n   - chrysopedia-worker (Celery)\n   - chrysopedia-redis (Redis for Celery broker)\n3. Follow XPLTD conventions: bind mounts, project naming xpltd_chrysopedia, dedicated bridge network\n4. Create .env.example with all required env vars\n5. Write Dockerfiles for API and web services\n  - Estimate: 2-3 hours\n  - Files: docker-compose.yml, .env.example, docker/Dockerfile.api, docker/Dockerfile.web, backend/main.py, backend/requirements.txt\n  - Verify: docker compose config validates without errors\n- [x] **T02: Created SQLAlchemy models for all 7 entities, Alembic async migration infrastructure, and initial migration with full PostgreSQL schema; fixed docker compose config validation failure** — 1. Create SQLAlchemy models for all 7 entities:\n   - Creator (id, name, slug, genres, folder_name, view_count, timestamps)\n   - SourceVideo (id, creator_id FK, filename, file_path, duration, content_type enum, transcript_path, processing_status enum, timestamps)\n   - TranscriptSegment (id, source_video_id FK, start_time, end_time, text, segment_index, topic_label)\n   - KeyMoment (id, source_video_id FK, technique_page_id FK nullable, title, summary, start/end time, content_type enum, plugins, review_status enum, raw_transcript, timestamps)\n   - TechniquePage (id, creator_id FK, title, slug, topic_category, topic_tags, summary, body_sections JSONB, signal_chains JSONB, plugins, source_quality enum, view_count, review_status enum, timestamps)\n   - RelatedTechniqueLink (id, source_page_id FK, target_page_id FK, relationship enum)\n   - Tag (id, name, category, aliases)\n2. Set up Alembic for migrations\n3. Create initial migration\n4. Add seed data for canonical tags (6 top-level categories)\n  - Estimate: 2-3 hours\n  - Files: backend/models.py, backend/database.py, alembic.ini, alembic/versions/*.py, config/canonical_tags.yaml\n  - Verify: alembic upgrade head succeeds; all 7 tables exist with correct columns and constraints\n- [x] **T03: Built FastAPI app with DB-connected health check, Pydantic schemas for all 7 entities, creators/videos CRUD endpoints, structured logging, and pydantic-settings config** — 1. Set up FastAPI app with:\n   - CORS middleware\n   - Database session dependency\n   - Health check endpoint (/health)\n   - API versioning prefix (/api/v1)\n2. Create Pydantic schemas for all entities\n3. Implement basic CRUD endpoints:\n   - GET /api/v1/creators\n   - GET /api/v1/creators/{slug}\n   - GET /api/v1/videos\n   - GET /api/v1/health\n4. Add structured logging\n5. Configure environment variable loading from .env\n  - Estimate: 1-2 hours\n  - Files: backend/main.py, backend/schemas.py, backend/routers/__init__.py, backend/routers/health.py, backend/routers/creators.py, backend/config.py\n  - Verify: curl http://localhost:8000/health returns 200; curl http://localhost:8000/api/v1/creators returns empty list\n- [ ] **T04: Whisper transcription script** — 1. Create Python script whisper/transcribe.py that:\n   - Accepts video file path (or directory for batch mode)\n   - Extracts audio via ffmpeg (subprocess)\n   - Runs Whisper large-v3 with segment-level and word-level timestamps\n   - Outputs JSON matching the spec format (source_file, creator_folder, duration, segments with words)\n   - Supports resumability: checks if output JSON already exists, skips\n2. Create whisper/requirements.txt (openai-whisper, ffmpeg-python)\n3. Write output to a configurable output directory\n4. Add CLI arguments: --input, --output-dir, --model (default large-v3), --device (default cuda)\n5. Include progress logging for long transcriptions\n  - Estimate: 1-2 hours\n  - Files: whisper/transcribe.py, whisper/requirements.txt, whisper/README.md\n  - Verify: python whisper/transcribe.py --help shows usage; script validates ffmpeg is available\n- [ ] **T05: Integration verification and documentation** — 1. Write README.md with:\n   - Project overview\n   - Architecture diagram (text)\n   - Setup instructions (Docker Compose + desktop Whisper)\n   - Environment variable documentation\n   - Development workflow\n2. Verify Docker Compose stack starts with: docker compose up -d\n3. Verify PostgreSQL schema with: alembic upgrade head\n4. Verify API health check responds\n5. Create sample transcript JSON for testing subsequent slices\n  - Estimate: 1 hour\n  - Files: README.md, tests/fixtures/sample_transcript.json\n  - Verify: docker compose config validates; README covers all setup steps; sample transcript JSON is valid\n"}],"isError":false,"timestamp":1774821303604}}
{"type":"message","id":"a223ebc7","parentId":"a78a80ba","timestamp":"2026-03-29T21:55:08.879Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the spec section 7.2 for the transcript JSON format, and the current state of the whisper directory."},{"type":"toolCall","id":"toolu_01SZC6DFa627sUgXdQhnwcjt","name":"bash","arguments":{"command":"grep -n \"Stage 1\\|7\\.2\\|transcript\\|Whisper\\|whisper\\|segments\\|source_file\\|creator_folder\" chrysopedia-spec.md | head -60"}},{"type":"toolCall","id":"toolu_01PAxuAvyrZT4XhuawbbxYr2","name":"bash","arguments":{"command":"ls -la whisper/ 2>/dev/null || echo \"whisper/ directory doesn't exist yet\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":187,"cacheRead":52545,"cacheWrite":2057,"totalTokens":54790,"cost":{"input":0.000005,"output":0.004675,"cacheRead":0.026272499999999997,"cacheWrite":0.012856250000000001,"total":0.04380875}},"stopReason":"toolUse","timestamp":1774821303605}}
{"type":"message","id":"9727e17d","parentId":"a223ebc7","timestamp":"2026-03-29T21:55:08.891Z","message":{"role":"toolResult","toolCallId":"toolu_01SZC6DFa627sUgXdQhnwcjt","toolName":"bash","content":[{"type":"text","text":"17:1. **Transcribes** video content using local Whisper inference\n58:The audio track carries the vast majority of the value. Visual demonstrations (screen recordings of DAW work) are useful context but are not the primary extraction target. The transcript is the primary ore.\n76:| **Transcript** | The timestamped text output of Whisper processing a source video's audio. |\n263:transcript_path string (path to transcript JSON)\n292:raw_transcript  text (the original transcript text for this segment)\n337:| Vector DB | Semantic search embeddings for transcripts, key moments, and technique page content | Qdrant (already running on hypervisor) |\n338:| File store | Raw transcript JSON files, source video reference metadata | Local filesystem on hypervisor, organized by creator slug |\n346:- Transcript segments (for future RAG/chat retrieval)\n360:│ Whisper (local GPU)  │──2.5GbE──────▶│ ├─ API / pipeline service       │\n361:│ Output: transcript   │  (text only)  │ ├─ Web UI                       │\n374:**Bandwidth analysis:** Transcript JSON files are 200–500KB each. At 50Mbit upload, the entire library's transcripts could transfer in under a minute. The bandwidth constraint is irrelevant for this workload. The only large files (videos) stay on the desktop.\n376:**Future centralization:** The Docker Compose project should be structured so that when all hardware is co-located, the only change is config (moving Whisper into the compose stack and pointing file paths to local storage). No architectural rewrite.\n378:### 7.2 Processing stages\n380:#### Stage 1: Audio extraction and transcription (Desktop)\n382:**Tool:** Whisper large-v3 running locally on RTX 4090\n386:2. Run Whisper with word-level or segment-level timestamps\n387:3. Output: JSON file with timestamped transcript\n392:  \"source_file\": \"Skope — Sound Design Masterclass pt2.mp4\",\n393:  \"creator_folder\": \"Skope\",\n395:  \"segments\": [\n409:**Performance estimate:** Whisper large-v3 on a 4090 processes audio at roughly 10-20x real-time. A 2-hour video takes ~6-12 minutes to transcribe. For 300 videos averaging 1.5 hours each, the initial transcription pass is roughly 15-40 hours of GPU time.\n414:**Input:** Full timestamped transcript JSON\n415:**Process:** The LLM analyzes the transcript to identify topic boundaries — points where the creator shifts from one subject to another. Output is a segmented transcript with topic labels per segment.\n422:**Input:** Individual transcript segments from Stage 2\n484:| Transcription (Whisper, 4090) | 6–12 min | 30–60 hours |\n516:- Raw transcript segment displayed alongside for comparison\n543:2. **Trigger transcription:** Run the Whisper transcription stage on the new file(s). This could be a manual CLI command, a watched-folder daemon, or an n8n workflow trigger.\n544:3. **Ship transcript:** Transfer the transcript JSON to the hypervisor (automated via the pipeline)\n545:4. **Process:** Stages 2-5 run automatically on the new transcript\n561:For maximum automation, a filesystem watcher on the desktop could detect new video files and automatically trigger the transcription pipeline. This is a nice-to-have for v2, not a v1 requirement. In v1, transcription is triggered manually.\n569:The entire Chrysopedia stack (excluding Whisper, which runs on the desktop GPU) is packaged as a single `docker-compose.yml`:\n599:### 10.3 Whisper on desktop\n601:Whisper runs separately on the desktop with the RTX 4090. It is NOT part of the Docker Compose stack (for now). It should be packaged as a simple Python script or lightweight container that:\n605:3. Runs Whisper large-v3\n606:4. Outputs transcript JSON\n609:**Future centralization:** When all hardware is co-located, Whisper can be added to the Docker Compose stack with GPU passthrough, and the video files can be mounted directly. The pipeline should be designed so this migration is a config change, not a rewrite.\n613:- Desktop ↔ Hypervisor: 2.5GbE (ample for transcript JSON transfer)\n625:| Transcription | Whisper large-v3 (local, 4090) | Best accuracy, local processing keeps media files on-network |\n659:The extraction pipeline is fundamentally transcript-based. It could be extended to process podcast episodes, audio-only recordings, or even written tutorials/blog posts with minimal architectural changes.\n683:- Implement Whisper transcription script for desktop\n684:- Build transcript ingestion endpoint on the API\n"}],"isError":false,"timestamp":1774821308891}}
{"type":"message","id":"4ff755b1","parentId":"9727e17d","timestamp":"2026-03-29T21:55:08.894Z","message":{"role":"toolResult","toolCallId":"toolu_01PAxuAvyrZT4XhuawbbxYr2","toolName":"bash","content":[{"type":"text","text":"total 12\ndrwxrwxr-x  2 aux aux 4096 Mar 29 21:41 .\ndrwxrwxr-x 15 aux aux 4096 Mar 29 21:45 ..\n-rw-rw-r--  1 aux aux  102 Mar 29 21:41 README.md\n"}],"isError":false,"timestamp":1774821308893}}
{"type":"message","id":"87c95a38","parentId":"4ff755b1","timestamp":"2026-03-29T21:55:13.678Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me read the full spec excerpt for the JSON format and check what's already in the whisper directory."},{"type":"toolCall","id":"toolu_01PndeaxqtTUzRert1dwPqjt","name":"read","arguments":{"limit":40,"offset":380,"path":"chrysopedia-spec.md"}},{"type":"toolCall","id":"toolu_012UpU8eX9k6jNTZThRTBqui","name":"read","arguments":{"path":"whisper/README.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":54602,"cacheWrite":1491,"totalTokens":56247,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.027301,"cacheWrite":0.00931875,"total":0.04044975}},"stopReason":"toolUse","timestamp":1774821308893}}
{"type":"message","id":"ad12a048","parentId":"87c95a38","timestamp":"2026-03-29T21:55:13.680Z","message":{"role":"toolResult","toolCallId":"toolu_01PndeaxqtTUzRert1dwPqjt","toolName":"read","content":[{"type":"text","text":"#### Stage 1: Audio extraction and transcription (Desktop)\n\n**Tool:** Whisper large-v3 running locally on RTX 4090\n**Input:** Video file (MP4/MKV)\n**Process:**\n1. Extract audio track from video (ffmpeg → WAV or direct pipe)\n2. Run Whisper with word-level or segment-level timestamps\n3. Output: JSON file with timestamped transcript\n\n**Output format:**\n```json\n{\n  \"source_file\": \"Skope — Sound Design Masterclass pt2.mp4\",\n  \"creator_folder\": \"Skope\",\n  \"duration_seconds\": 7243,\n  \"segments\": [\n    {\n      \"start\": 0.0,\n      \"end\": 4.52,\n      \"text\": \"Hey everyone welcome back to part two...\",\n      \"words\": [\n        {\"word\": \"Hey\", \"start\": 0.0, \"end\": 0.28},\n        {\"word\": \"everyone\", \"start\": 0.32, \"end\": 0.74}\n      ]\n    }\n  ]\n}\n```\n\n**Performance estimate:** Whisper large-v3 on a 4090 processes audio at roughly 10-20x real-time. A 2-hour video takes ~6-12 minutes to transcribe. For 300 videos averaging 1.5 hours each, the initial transcription pass is roughly 15-40 hours of GPU time.\n\n#### Stage 2: Transcript segmentation (Hypervisor → LLM)\n\n**Tool:** LLM (Qwen on DGX Sparks, or local Ollama as fallback)\n**Input:** Full timestamped transcript JSON\n**Process:** The LLM analyzes the transcript to identify topic boundaries — points where the creator shifts from one subject to another. Output is a segmented transcript with topic labels per segment.\n\n**This stage can use a lighter model** if needed (segmentation is more mechanical than extraction). However, for simplicity in v1, use the same model endpoint as stages 3-5.\n\n#### Stage 3: Key moment extraction (Hypervisor → LLM)\n\n[295 more lines in file. Use offset=420 to continue.]"}],"isError":false,"timestamp":1774821313680}}
{"type":"message","id":"6e3bf286","parentId":"ad12a048","timestamp":"2026-03-29T21:55:13.682Z","message":{"role":"toolResult","toolCallId":"toolu_012UpU8eX9k6jNTZThRTBqui","toolName":"read","content":[{"type":"text","text":"# Chrysopedia — Whisper Transcription\n\nDesktop transcription script. See `transcribe.py` for usage.\n"}],"isError":false,"timestamp":1774821313680}}
{"type":"message","id":"d97b8532","parentId":"6e3bf286","timestamp":"2026-03-29T21:55:17.973Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check if there's a previous failed attempt I need to fix."},{"type":"toolCall","id":"toolu_01WkVA4bEprxvXdKTLrCHF7Z","name":"bash","arguments":{"command":"ls -la whisper/transcribe.py 2>/dev/null && echo \"EXISTS\" || echo \"NOT FOUND\""}},{"type":"toolCall","id":"toolu_01Qe1uZG31oxzkogyYDnCXr1","name":"bash","arguments":{"command":"ls -la whisper/requirements.txt 2>/dev/null && echo \"EXISTS\" || echo \"NOT FOUND\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":157,"cacheRead":56093,"cacheWrite":760,"totalTokens":57011,"cost":{"input":0.000005,"output":0.0039250000000000005,"cacheRead":0.0280465,"cacheWrite":0.00475,"total":0.036726499999999995}},"stopReason":"toolUse","timestamp":1774821313681}}
{"type":"message","id":"e6f3f7bb","parentId":"d97b8532","timestamp":"2026-03-29T21:55:17.985Z","message":{"role":"toolResult","toolCallId":"toolu_01WkVA4bEprxvXdKTLrCHF7Z","toolName":"bash","content":[{"type":"text","text":"NOT FOUND\n"}],"isError":false,"timestamp":1774821317985}}
{"type":"message","id":"d372c3c0","parentId":"e6f3f7bb","timestamp":"2026-03-29T21:55:17.988Z","message":{"role":"toolResult","toolCallId":"toolu_01Qe1uZG31oxzkogyYDnCXr1","toolName":"bash","content":[{"type":"text","text":"NOT FOUND\n"}],"isError":false,"timestamp":1774821317987}}
{"type":"message","id":"dd0b098d","parentId":"d372c3c0","timestamp":"2026-03-29T21:55:24.840Z","message":{"role":"assistant","content":[{"type":"text","text":"The previous attempt didn't leave any files. The verification header says this is auto-fix attempt 1, so the prior attempt likely failed at the verification gate. Let me check if there's any existing T04 summary from a prior run."},{"type":"toolCall","id":"toolu_014pMhvbL5p9vCLMrHUr6edo","name":"bash","arguments":{"command":"ls -la .gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md 2>/dev/null || echo \"No prior summary\""}},{"type":"toolCall","id":"toolu_015t3hLr7Q5NMkvoJYWYH7gD","name":"bash","arguments":{"command":"python3 --version 2>&1; which ffmpeg 2>&1 || echo \"ffmpeg not found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":204,"cacheRead":56853,"cacheWrite":226,"totalTokens":57284,"cost":{"input":0.000005,"output":0.0051,"cacheRead":0.0284265,"cacheWrite":0.0014125000000000001,"total":0.034943999999999996}},"stopReason":"toolUse","timestamp":1774821317988}}
{"type":"message","id":"83187bdd","parentId":"dd0b098d","timestamp":"2026-03-29T21:55:24.852Z","message":{"role":"toolResult","toolCallId":"toolu_014pMhvbL5p9vCLMrHUr6edo","toolName":"bash","content":[{"type":"text","text":"No prior summary\n"}],"isError":false,"timestamp":1774821324852}}
{"type":"message","id":"c2470910","parentId":"83187bdd","timestamp":"2026-03-29T21:55:24.855Z","message":{"role":"toolResult","toolCallId":"toolu_015t3hLr7Q5NMkvoJYWYH7gD","toolName":"bash","content":[{"type":"text","text":"Python 3.12.3\nffmpeg not found\n"}],"isError":false,"timestamp":1774821324854}}
{"type":"message","id":"e0b3d86f","parentId":"c2470910","timestamp":"2026-03-29T21:55:29.633Z","message":{"role":"assistant","content":[{"type":"text","text":"ffmpeg is not installed on this system. The script needs to validate that ffmpeg is available but still work for `--help`. Let me check what's available and then build everything."},{"type":"toolCall","id":"toolu_01WNmBTPkJqFjGxHTkni5aGk","name":"bash","arguments":{"command":"apt list --installed 2>/dev/null | grep ffmpeg; which ffprobe 2>/dev/null || echo \"ffprobe not found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":124,"cacheRead":57079,"cacheWrite":282,"totalTokens":57486,"cost":{"input":0.000005,"output":0.0031000000000000003,"cacheRead":0.0285395,"cacheWrite":0.0017625000000000002,"total":0.033407}},"stopReason":"toolUse","timestamp":1774821324854}}
{"type":"message","id":"50b7639c","parentId":"e0b3d86f","timestamp":"2026-03-29T21:55:29.975Z","message":{"role":"toolResult","toolCallId":"toolu_01WNmBTPkJqFjGxHTkni5aGk","toolName":"bash","content":[{"type":"text","text":"ffprobe not found\n"}],"isError":false,"timestamp":1774821329974}}
{"type":"message","id":"a4a841b3","parentId":"50b7639c","timestamp":"2026-03-29T21:56:20.316Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — ffmpeg isn't installed on this build machine, which is expected since this script runs on the desktop with the RTX 4090. The verification requires: (1) `--help` shows usage, and (2) the script validates ffmpeg is available. Let me now create all three files.\n\n**Creating `whisper/transcribe.py` — the core transcription script.**"},{"type":"toolCall","id":"toolu_01MrKvvVxUnXU66ba1M7Ge3y","name":"write","arguments":{"content":"#!/usr/bin/env python3\n\"\"\"\nChrysopedia — Whisper Transcription Script\n\nDesktop transcription tool for extracting timestamped text from video files\nusing OpenAI's Whisper model (large-v3). Designed to run on a machine with\nan NVIDIA GPU (e.g., RTX 4090).\n\nOutputs JSON matching the Chrysopedia spec format:\n{\n  \"source_file\": \"filename.mp4\",\n  \"creator_folder\": \"CreatorName\",\n  \"duration_seconds\": 7243,\n  \"segments\": [\n    {\n      \"start\": 0.0,\n      \"end\": 4.52,\n      \"text\": \"...\",\n      \"words\": [{\"word\": \"Hey\", \"start\": 0.0, \"end\": 0.28}, ...]\n    }\n  ]\n}\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport logging\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\nimport time\nfrom pathlib import Path\n\n# ---------------------------------------------------------------------------\n# Logging\n# ---------------------------------------------------------------------------\n\nLOG_FORMAT = \"%(asctime)s [%(levelname)s] %(message)s\"\nlogging.basicConfig(format=LOG_FORMAT, level=logging.INFO)\nlogger = logging.getLogger(\"chrysopedia.transcribe\")\n\n# ---------------------------------------------------------------------------\n# Constants\n# ---------------------------------------------------------------------------\n\nSUPPORTED_EXTENSIONS = {\".mp4\", \".mkv\", \".avi\", \".mov\", \".webm\", \".flv\", \".wmv\"}\nDEFAULT_MODEL = \"large-v3\"\nDEFAULT_DEVICE = \"cuda\"\n\n# ---------------------------------------------------------------------------\n# Helpers\n# ---------------------------------------------------------------------------\n\n\ndef check_ffmpeg() -> bool:\n    \"\"\"Return True if ffmpeg is available on PATH.\"\"\"\n    return shutil.which(\"ffmpeg\") is not None\n\n\ndef get_audio_duration(video_path: Path) -> float | None:\n    \"\"\"Use ffprobe to get duration in seconds. Returns None on failure.\"\"\"\n    ffprobe = shutil.which(\"ffprobe\")\n    if ffprobe is None:\n        return None\n    try:\n        result = subprocess.run(\n            [\n                ffprobe,\n                \"-v\", \"error\",\n                \"-show_entries\", \"format=duration\",\n                \"-of\", \"default=noprint_wrappers=1:nokey=1\",\n                str(video_path),\n            ],\n            capture_output=True,\n            text=True,\n            timeout=30,\n        )\n        return float(result.stdout.strip())\n    except (subprocess.TimeoutExpired, ValueError, OSError) as exc:\n        logger.warning(\"Could not determine duration for %s: %s\", video_path.name, exc)\n        return None\n\n\ndef extract_audio(video_path: Path, audio_path: Path) -> None:\n    \"\"\"Extract audio from video to 16kHz mono WAV using ffmpeg.\"\"\"\n    logger.info(\"Extracting audio: %s -> %s\", video_path.name, audio_path.name)\n    cmd = [\n        \"ffmpeg\",\n        \"-i\", str(video_path),\n        \"-vn\",                    # no video\n        \"-acodec\", \"pcm_s16le\",   # 16-bit PCM\n        \"-ar\", \"16000\",           # 16kHz (Whisper expects this)\n        \"-ac\", \"1\",               # mono\n        \"-y\",                     # overwrite\n        str(audio_path),\n    ]\n    result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)\n    if result.returncode != 0:\n        raise RuntimeError(\n            f\"ffmpeg audio extraction failed (exit {result.returncode}): {result.stderr[:500]}\"\n        )\n\n\ndef transcribe_audio(\n    audio_path: Path,\n    model_name: str = DEFAULT_MODEL,\n    device: str = DEFAULT_DEVICE,\n) -> dict:\n    \"\"\"Run Whisper on the audio file and return the raw result dict.\"\"\"\n    # Import whisper here so --help works without the dependency installed\n    try:\n        import whisper  # type: ignore[import-untyped]\n    except ImportError:\n        logger.error(\n            \"openai-whisper is not installed. \"\n            \"Install it with: pip install openai-whisper\"\n        )\n        sys.exit(1)\n\n    logger.info(\"Loading Whisper model '%s' on device '%s'...\", model_name, device)\n    t0 = time.time()\n    model = whisper.load_model(model_name, device=device)\n    logger.info(\"Model loaded in %.1f s\", time.time() - t0)\n\n    logger.info(\"Transcribing %s ...\", audio_path.name)\n    t0 = time.time()\n    result = model.transcribe(\n        str(audio_path),\n        word_timestamps=True,\n        verbose=False,\n    )\n    elapsed = time.time() - t0\n    logger.info(\n        \"Transcription complete in %.1f s (%.1fx real-time)\",\n        elapsed,\n        (result.get(\"duration\", elapsed) / elapsed) if elapsed > 0 else 0,\n    )\n    return result\n\n\ndef format_output(\n    whisper_result: dict,\n    source_file: str,\n    creator_folder: str,\n    duration_seconds: float | None,\n) -> dict:\n    \"\"\"Convert Whisper result to the Chrysopedia spec JSON format.\"\"\"\n    segments = []\n    for seg in whisper_result.get(\"segments\", []):\n        words = []\n        for w in seg.get(\"words\", []):\n            words.append(\n                {\n                    \"word\": w.get(\"word\", \"\").strip(),\n                    \"start\": round(w.get(\"start\", 0.0), 2),\n                    \"end\": round(w.get(\"end\", 0.0), 2),\n                }\n            )\n        segments.append(\n            {\n                \"start\": round(seg.get(\"start\", 0.0), 2),\n                \"end\": round(seg.get(\"end\", 0.0), 2),\n                \"text\": seg.get(\"text\", \"\").strip(),\n                \"words\": words,\n            }\n        )\n\n    # Use duration from ffprobe if available, otherwise from whisper\n    if duration_seconds is None:\n        duration_seconds = whisper_result.get(\"duration\", 0.0)\n\n    return {\n        \"source_file\": source_file,\n        \"creator_folder\": creator_folder,\n        \"duration_seconds\": round(duration_seconds),\n        \"segments\": segments,\n    }\n\n\ndef infer_creator_folder(video_path: Path) -> str:\n    \"\"\"\n    Infer creator folder name from directory structure.\n\n    Expected layout: /path/to/<CreatorName>/video.mp4\n    Falls back to parent directory name.\n    \"\"\"\n    return video_path.parent.name\n\n\ndef output_path_for(video_path: Path, output_dir: Path) -> Path:\n    \"\"\"Compute the output JSON path for a given video file.\"\"\"\n    return output_dir / f\"{video_path.stem}.json\"\n\n\ndef process_single(\n    video_path: Path,\n    output_dir: Path,\n    model_name: str,\n    device: str,\n    creator_folder: str | None = None,\n) -> Path | None:\n    \"\"\"\n    Process a single video file. Returns the output path on success, None if skipped.\n    \"\"\"\n    out_path = output_path_for(video_path, output_dir)\n\n    # Resumability: skip if output already exists\n    if out_path.exists():\n        logger.info(\"SKIP (output exists): %s\", out_path)\n        return None\n\n    logger.info(\"Processing: %s\", video_path)\n\n    # Determine creator folder\n    folder = creator_folder or infer_creator_folder(video_path)\n\n    # Get duration via ffprobe\n    duration = get_audio_duration(video_path)\n    if duration is not None:\n        logger.info(\"Video duration: %.0f s (%.1f min)\", duration, duration / 60)\n\n    # Extract audio to temp file\n    with tempfile.TemporaryDirectory(prefix=\"chrysopedia_\") as tmpdir:\n        audio_path = Path(tmpdir) / \"audio.wav\"\n        extract_audio(video_path, audio_path)\n\n        # Transcribe\n        whisper_result = transcribe_audio(audio_path, model_name, device)\n\n    # Format and write output\n    output = format_output(whisper_result, video_path.name, folder, duration)\n\n    output_dir.mkdir(parents=True, exist_ok=True)\n    with open(out_path, \"w\", encoding=\"utf-8\") as f:\n        json.dump(output, f, indent=2, ensure_ascii=False)\n\n    segment_count = len(output[\"segments\"])\n    logger.info(\"Wrote %s (%d segments)\", out_path, segment_count)\n    return out_path\n\n\ndef find_videos(input_path: Path) -> list[Path]:\n    \"\"\"Find all supported video files in a directory (non-recursive).\"\"\"\n    videos = sorted(\n        p for p in input_path.iterdir()\n        if p.is_file() and p.suffix.lower() in SUPPORTED_EXTENSIONS\n    )\n    return videos\n\n\n# ---------------------------------------------------------------------------\n# CLI\n# ---------------------------------------------------------------------------\n\n\ndef build_parser() -> argparse.ArgumentParser:\n    parser = argparse.ArgumentParser(\n        prog=\"transcribe\",\n        description=(\n            \"Chrysopedia Whisper Transcription — extract timestamped transcripts \"\n            \"from video files using OpenAI's Whisper model.\"\n        ),\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        epilog=(\n            \"Examples:\\n\"\n            \"  # Single file\\n\"\n            \"  python transcribe.py --input video.mp4 --output-dir ./transcripts\\n\"\n            \"\\n\"\n            \"  # Batch mode (all videos in directory)\\n\"\n            \"  python transcribe.py --input ./videos/ --output-dir ./transcripts\\n\"\n            \"\\n\"\n            \"  # Use a smaller model on CPU\\n\"\n            \"  python transcribe.py --input video.mp4 --model base --device cpu\\n\"\n        ),\n    )\n    parser.add_argument(\n        \"--input\",\n        required=True,\n        type=str,\n        help=\"Path to a video file or directory of video files\",\n    )\n    parser.add_argument(\n        \"--output-dir\",\n        required=True,\n        type=str,\n        help=\"Directory to write transcript JSON files\",\n    )\n    parser.add_argument(\n        \"--model\",\n        default=DEFAULT_MODEL,\n        type=str,\n        help=f\"Whisper model name (default: {DEFAULT_MODEL})\",\n    )\n    parser.add_argument(\n        \"--device\",\n        default=DEFAULT_DEVICE,\n        type=str,\n        help=f\"Compute device: cuda, cpu (default: {DEFAULT_DEVICE})\",\n    )\n    parser.add_argument(\n        \"--creator\",\n        default=None,\n        type=str,\n        help=\"Override creator folder name (default: inferred from parent directory)\",\n    )\n    parser.add_argument(\n        \"-v\", \"--verbose\",\n        action=\"store_true\",\n        help=\"Enable debug logging\",\n    )\n    return parser\n\n\ndef main(argv: list[str] | None = None) -> int:\n    parser = build_parser()\n    args = parser.parse_args(argv)\n\n    if args.verbose:\n        logging.getLogger().setLevel(logging.DEBUG)\n\n    # Validate ffmpeg availability\n    if not check_ffmpeg():\n        logger.error(\n            \"ffmpeg is not installed or not on PATH. \"\n            \"Install it with: sudo apt install ffmpeg  (or equivalent)\"\n        )\n        return 1\n\n    input_path = Path(args.input).resolve()\n    output_dir = Path(args.output_dir).resolve()\n\n    if not input_path.exists():\n        logger.error(\"Input path does not exist: %s\", input_path)\n        return 1\n\n    # Single file mode\n    if input_path.is_file():\n        if input_path.suffix.lower() not in SUPPORTED_EXTENSIONS:\n            logger.error(\n                \"Unsupported file type '%s'. Supported: %s\",\n                input_path.suffix,\n                \", \".join(sorted(SUPPORTED_EXTENSIONS)),\n            )\n            return 1\n        result = process_single(\n            input_path, output_dir, args.model, args.device, args.creator\n        )\n        if result is None:\n            logger.info(\"Nothing to do (output already exists).\")\n        return 0\n\n    # Batch mode (directory)\n    if input_path.is_dir():\n        videos = find_videos(input_path)\n        if not videos:\n            logger.warning(\"No supported video files found in %s\", input_path)\n            return 0\n\n        logger.info(\"Found %d video(s) in %s\", len(videos), input_path)\n        processed = 0\n        skipped = 0\n        failed = 0\n\n        for i, video in enumerate(videos, 1):\n            logger.info(\"--- [%d/%d] %s ---\", i, len(videos), video.name)\n            try:\n                result = process_single(\n                    video, output_dir, args.model, args.device, args.creator\n                )\n                if result is not None:\n                    processed += 1\n                else:\n                    skipped += 1\n            except Exception:\n                logger.exception(\"FAILED: %s\", video.name)\n                failed += 1\n\n        logger.info(\n            \"Batch complete: %d processed, %d skipped, %d failed\",\n            processed, skipped, failed,\n        )\n        return 1 if failed > 0 else 0\n\n    logger.error(\"Input is neither a file nor a directory: %s\", input_path)\n    return 1\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","path":"whisper/transcribe.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3631,"cacheRead":57361,"cacheWrite":142,"totalTokens":61135,"cost":{"input":0.000005,"output":0.09077500000000001,"cacheRead":0.028680499999999998,"cacheWrite":0.0008875,"total":0.12034800000000001}},"stopReason":"toolUse","timestamp":1774821329975}}
{"type":"message","id":"11298d66","parentId":"a4a841b3","timestamp":"2026-03-29T21:56:20.320Z","message":{"role":"toolResult","toolCallId":"toolu_01MrKvvVxUnXU66ba1M7Ge3y","toolName":"write","content":[{"type":"text","text":"Successfully wrote 12201 bytes to whisper/transcribe.py"}],"isError":false,"timestamp":1774821380319}}
{"type":"message","id":"d0616b83","parentId":"11298d66","timestamp":"2026-03-29T21:56:25.498Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014Q5Dcxyxx3a6TcTfp41rh5","name":"write","arguments":{"content":"# Chrysopedia — Whisper transcription dependencies\n# Install: pip install -r requirements.txt\n#\n# Note: openai-whisper requires ffmpeg to be installed on the system.\n#   sudo apt install ffmpeg   (Debian/Ubuntu)\n#   brew install ffmpeg        (macOS)\n\nopenai-whisper>=20231117\nffmpeg-python>=0.2.0\n","path":"whisper/requirements.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":165,"cacheRead":57503,"cacheWrite":3658,"totalTokens":61327,"cost":{"input":0.000005,"output":0.004125,"cacheRead":0.0287515,"cacheWrite":0.0228625,"total":0.055744}},"stopReason":"toolUse","timestamp":1774821380319}}
{"type":"message","id":"c8829f8d","parentId":"d0616b83","timestamp":"2026-03-29T21:56:25.501Z","message":{"role":"toolResult","toolCallId":"toolu_014Q5Dcxyxx3a6TcTfp41rh5","toolName":"write","content":[{"type":"text","text":"Successfully wrote 298 bytes to whisper/requirements.txt"}],"isError":false,"timestamp":1774821385500}}
{"type":"message","id":"254113d4","parentId":"c8829f8d","timestamp":"2026-03-29T21:56:41.825Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RtEbhSd7SFiVYBRqChDMu6","name":"write","arguments":{"content":"# Chrysopedia — Whisper Transcription\n\nDesktop transcription tool for extracting timestamped text from video files\nusing OpenAI's Whisper model (large-v3). Designed to run on a machine with\nan NVIDIA GPU (e.g., RTX 4090).\n\n## Prerequisites\n\n- **Python 3.10+**\n- **ffmpeg** installed and on PATH\n- **NVIDIA GPU** with CUDA support (recommended; CPU fallback available)\n\n### Install ffmpeg\n\n```bash\n# Debian/Ubuntu\nsudo apt install ffmpeg\n\n# macOS\nbrew install ffmpeg\n```\n\n### Install Python dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n## Usage\n\n### Single file\n\n```bash\npython transcribe.py --input \"path/to/video.mp4\" --output-dir ./transcripts\n```\n\n### Batch mode (all videos in a directory)\n\n```bash\npython transcribe.py --input ./videos/ --output-dir ./transcripts\n```\n\n### Options\n\n| Flag            | Default     | Description                                     |\n| --------------- | ----------- | ----------------------------------------------- |\n| `--input`       | (required)  | Path to a video file or directory of videos      |\n| `--output-dir`  | (required)  | Directory to write transcript JSON files         |\n| `--model`       | `large-v3`  | Whisper model name (`tiny`, `base`, `small`, `medium`, `large-v3`) |\n| `--device`      | `cuda`      | Compute device (`cuda` or `cpu`)                 |\n| `--creator`     | (inferred)  | Override creator folder name in output JSON      |\n| `-v, --verbose` | off         | Enable debug logging                             |\n\n## Output Format\n\nEach video produces a JSON file matching the Chrysopedia spec:\n\n```json\n{\n  \"source_file\": \"Skope — Sound Design Masterclass pt2.mp4\",\n  \"creator_folder\": \"Skope\",\n  \"duration_seconds\": 7243,\n  \"segments\": [\n    {\n      \"start\": 0.0,\n      \"end\": 4.52,\n      \"text\": \"Hey everyone welcome back to part two...\",\n      \"words\": [\n        { \"word\": \"Hey\", \"start\": 0.0, \"end\": 0.28 },\n        { \"word\": \"everyone\", \"start\": 0.32, \"end\": 0.74 }\n      ]\n    }\n  ]\n}\n```\n\n## Resumability\n\nThe script automatically skips videos whose output JSON already exists. To\nre-transcribe a file, delete its output JSON first.\n\n## Performance\n\nWhisper large-v3 on an RTX 4090 processes audio at roughly 10–20× real-time.\nA 2-hour video takes ~6–12 minutes. For 300 videos averaging 1.5 hours each,\nthe initial transcription pass takes roughly 15–40 hours of GPU time.\n\n## Directory Convention\n\nThe script infers the `creator_folder` field from the parent directory of each\nvideo file. Organize videos like:\n\n```\nvideos/\n├── Skope/\n│   ├── Sound Design Masterclass pt1.mp4\n│   └── Sound Design Masterclass pt2.mp4\n├── Mr Bill/\n│   └── Glitch Techniques.mp4\n```\n\nOverride with `--creator` when processing files outside this structure.\n","path":"whisper/README.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":892,"cacheRead":61161,"cacheWrite":190,"totalTokens":62244,"cost":{"input":0.000005,"output":0.0223,"cacheRead":0.0305805,"cacheWrite":0.0011875,"total":0.054073}},"stopReason":"toolUse","timestamp":1774821385500}}
{"type":"message","id":"d7c0e131","parentId":"254113d4","timestamp":"2026-03-29T21:56:41.828Z","message":{"role":"toolResult","toolCallId":"toolu_01RtEbhSd7SFiVYBRqChDMu6","toolName":"write","content":[{"type":"text","text":"Successfully wrote 2732 bytes to whisper/README.md"}],"isError":false,"timestamp":1774821401827}}
{"type":"message","id":"10b0e18f","parentId":"d7c0e131","timestamp":"2026-03-29T21:56:46.784Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me run the two verification checks: `--help` shows usage, and the script validates ffmpeg availability."},{"type":"toolCall","id":"toolu_01RBhEuzuwLecoXdsNdHVQyb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python3 whisper/transcribe.py --help 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":61351,"cacheWrite":918,"totalTokens":62379,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.030675499999999998,"cacheWrite":0.0057375,"total":0.039143}},"stopReason":"toolUse","timestamp":1774821401828}}
{"type":"message","id":"6bbd2581","parentId":"10b0e18f","timestamp":"2026-03-29T21:56:46.834Z","message":{"role":"toolResult","toolCallId":"toolu_01RBhEuzuwLecoXdsNdHVQyb","toolName":"bash","content":[{"type":"text","text":"usage: transcribe [-h] --input INPUT --output-dir OUTPUT_DIR [--model MODEL]\n                  [--device DEVICE] [--creator CREATOR] [-v]\n\nChrysopedia Whisper Transcription — extract timestamped transcripts from video files using OpenAI's Whisper model.\n\noptions:\n  -h, --help            show this help message and exit\n  --input INPUT         Path to a video file or directory of video files\n  --output-dir OUTPUT_DIR\n                        Directory to write transcript JSON files\n  --model MODEL         Whisper model name (default: large-v3)\n  --device DEVICE       Compute device: cuda, cpu (default: cuda)\n  --creator CREATOR     Override creator folder name (default: inferred from\n                        parent directory)\n  -v, --verbose         Enable debug logging\n\nExamples:\n  # Single file\n  python transcribe.py --input video.mp4 --output-dir ./transcripts\n\n  # Batch mode (all videos in directory)\n  python transcribe.py --input ./videos/ --output-dir ./transcripts\n\n  # Use a smaller model on CPU\n  python transcribe.py --input video.mp4 --model base --device cpu\n"}],"isError":false,"timestamp":1774821406832}}
{"type":"message","id":"6fe508e5","parentId":"6bbd2581","timestamp":"2026-03-29T21:56:51.889Z","message":{"role":"assistant","content":[{"type":"text","text":"`--help` works. Now verify that the script detects missing ffmpeg and exits with error code 1:"},{"type":"toolCall","id":"toolu_01B58hRZKpozR98GT8xVv7rE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python3 whisper/transcribe.py --input /tmp/fake.mp4 --output-dir /tmp/out 2>&1; echo \"EXIT_CODE=$?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":133,"cacheRead":62269,"cacheWrite":405,"totalTokens":62808,"cost":{"input":0.000005,"output":0.0033250000000000003,"cacheRead":0.0311345,"cacheWrite":0.00253125,"total":0.03699575}},"stopReason":"toolUse","timestamp":1774821406833}}
{"type":"message","id":"10effd0b","parentId":"6fe508e5","timestamp":"2026-03-29T21:56:51.940Z","message":{"role":"toolResult","toolCallId":"toolu_01B58hRZKpozR98GT8xVv7rE","toolName":"bash","content":[{"type":"text","text":"2026-03-29 21:56:51,932 [ERROR] ffmpeg is not installed or not on PATH. Install it with: sudo apt install ffmpeg  (or equivalent)\nEXIT_CODE=1\n"}],"isError":false,"timestamp":1774821411939}}
{"type":"message","id":"cdfe92e0","parentId":"10effd0b","timestamp":"2026-03-29T21:56:56.312Z","message":{"role":"assistant","content":[{"type":"text","text":"Both verification checks pass. Let me also verify the script is syntactically valid and importable:"},{"type":"toolCall","id":"toolu_017E1WKhYcXpmCJBPHKBfN4i","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python3 -c \"import ast; ast.parse(open('whisper/transcribe.py').read()); print('Syntax OK')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":122,"cacheRead":62674,"cacheWrite":194,"totalTokens":62991,"cost":{"input":0.000005,"output":0.00305,"cacheRead":0.031337,"cacheWrite":0.0012125,"total":0.0356045}},"stopReason":"toolUse","timestamp":1774821411939}}
{"type":"message","id":"7a549a03","parentId":"cdfe92e0","timestamp":"2026-03-29T21:56:56.352Z","message":{"role":"toolResult","toolCallId":"toolu_017E1WKhYcXpmCJBPHKBfN4i","toolName":"bash","content":[{"type":"text","text":"Syntax OK\n"}],"isError":false,"timestamp":1774821416351}}
{"type":"message","id":"83202922","parentId":"7a549a03","timestamp":"2026-03-29T21:57:00.350Z","message":{"role":"assistant","content":[{"type":"text","text":"All checks pass. Now let me read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_013NWPKi58hZr6HMKUKGmZjZ","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":87,"cacheRead":62868,"cacheWrite":138,"totalTokens":63094,"cost":{"input":0.000005,"output":0.0021750000000000003,"cacheRead":0.031434,"cacheWrite":0.0008625000000000001,"total":0.0344765}},"stopReason":"toolUse","timestamp":1774821416352}}
{"type":"message","id":"175d4fbf","parentId":"83202922","timestamp":"2026-03-29T21:57:00.355Z","message":{"role":"toolResult","toolCallId":"toolu_013NWPKi58hZr6HMKUKGmZjZ","toolName":"read","content":[{"type":"text","text":"---\nid: {{taskId}}\nparent: {{sliceId}}\nmilestone: {{milestoneId}}\nprovides:\n  - {{whatThisTaskProvides}}\nkey_files:\n  - {{filePath}}\nkey_decisions:\n  - {{decision}}\npatterns_established:\n  - {{pattern}}\nobservability_surfaces:\n  - {{status endpoint, structured log, persisted failure state, diagnostic command, or none}}\nduration: {{duration}}\nverification_result: passed\ncompleted_at: {{date}}\n# Set blocker_discovered: true only if execution revealed the remaining slice plan\n# is fundamentally invalid (wrong API, missing capability, architectural mismatch).\n# Do NOT set true for ordinary bugs, minor deviations, or fixable issues.\nblocker_discovered: false\n---\n\n# {{taskId}}: {{taskTitle}}\n\n<!-- One-liner must say what actually shipped, not just that work completed.\n     Good: \"Added retry-aware worker status logging\"\n     Bad: \"Implemented logging improvements\" -->\n\n**{{oneLiner}}**\n\n## What Happened\n\n{{narrative}}\n\n## Verification\n\n{{whatWasVerifiedAndHow — commands run, tests passed, behavior confirmed}}\n\n## Verification Evidence\n\n<!-- Populated from verification gate output. If the gate ran, fill in the table below.\n     If no gate ran (e.g., no verification commands discovered), note that. -->\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| {{row}} | {{command}} | {{exitCode}} | {{verdict}} | {{duration}} |\n\n## Diagnostics\n\n{{howToInspectWhatThisTaskBuiltLater — status surfaces, logs, error shapes, failure artifacts, or none}}\n\n## Deviations\n\n<!-- Deviations are unplanned changes to the written task plan, not ordinary debugging during implementation. -->\n\n{{deviationsFromPlan_OR_none}}\n\n## Known Issues\n\n{{issuesDiscoveredButNotFixed_OR_none}}\n\n## Files Created/Modified\n\n- `{{filePath}}` — {{description}}\n- `{{filePath}}` — {{description}}\n"}],"isError":false,"timestamp":1774821420352}}
{"type":"message","id":"ff6cac58","parentId":"175d4fbf","timestamp":"2026-03-29T21:57:27.356Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DT5Y5Qb9Vp44ATH6xivigo","name":"write","arguments":{"content":"---\nid: T04\nparent: S01\nmilestone: M001\nprovides:\n  - whisper/transcribe.py — CLI transcription script for desktop GPU\n  - whisper/requirements.txt — Python dependency manifest\n  - whisper/README.md — Usage documentation\nkey_files:\n  - whisper/transcribe.py\n  - whisper/requirements.txt\n  - whisper/README.md\nkey_decisions:\n  - \"Whisper import is deferred (inside transcribe_audio) so --help and ffmpeg validation work without openai-whisper installed\"\n  - \"Audio extracted to 16kHz mono WAV via ffmpeg subprocess, matching Whisper's expected input format\"\n  - \"Creator folder inferred from parent directory name by default, overridable with --creator flag\"\npatterns_established:\n  - \"Resumability pattern: check if output JSON exists before processing; skip if present\"\nobservability_surfaces:\n  - \"Structured logging with timestamps, elapsed time, real-time ratio, batch progress counters\"\nduration: 15min\nverification_result: passed\ncompleted_at: 2026-03-29\nblocker_discovered: false\n---\n\n# T04: Whisper transcription script\n\n**Created desktop Whisper transcription script with single-file/batch modes, resumability, spec-compliant JSON output, and ffmpeg validation**\n\n## What Happened\n\nBuilt `whisper/transcribe.py` implementing all five requirements from the task plan:\n\n1. **CLI with argparse** — `--input` (file or directory), `--output-dir`, `--model` (default large-v3), `--device` (default cuda), `--creator` override, `-v` verbose flag.\n2. **Audio extraction** — ffmpeg subprocess extracts audio to 16kHz mono WAV in a temp directory, matching Whisper's expected input format.\n3. **Whisper transcription** — loads model on specified device, runs with `word_timestamps=True`, logs elapsed time and real-time processing ratio.\n4. **Spec-compliant JSON output** — matches the exact format from chrysopedia-spec.md §7.2: `source_file`, `creator_folder`, `duration_seconds`, and `segments` array with `start`, `end`, `text`, and `words` sub-array.\n5. **Resumability** — checks if output JSON already exists before processing; logs skip and moves on.\n6. **Batch mode** — directory input finds all supported video files (.mp4, .mkv, .avi, .mov, .webm, .flv, .wmv), processes sequentially with progress logging (`[N/M]`), and reports processed/skipped/failed counts.\n\nThe whisper import is deferred to inside `transcribe_audio()` so that `--help` and ffmpeg validation work on machines without openai-whisper installed (like this CI environment).\n\nAlso created `whisper/requirements.txt` (openai-whisper, ffmpeg-python) and `whisper/README.md` with prerequisites, usage examples, options table, output format documentation, and performance estimates.\n\n## Verification\n\n1. `python3 whisper/transcribe.py --help` — exits 0, displays full usage with all CLI arguments and examples.\n2. `python3 whisper/transcribe.py --input /tmp/fake.mp4 --output-dir /tmp/out` — exits 1 with clear error \"ffmpeg is not installed or not on PATH\", confirming ffmpeg validation works.\n3. `python3 -c \"import ast; ast.parse(open('whisper/transcribe.py').read())\"` — syntax validation passes.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python3 whisper/transcribe.py --help` | 0 | ✅ pass | <1s |\n| 2 | `python3 whisper/transcribe.py --input /tmp/fake.mp4 --output-dir /tmp/out` | 1 | ✅ pass (expected error for missing ffmpeg) | <1s |\n| 3 | `python3 -c \"import ast; ast.parse(open('whisper/transcribe.py').read())\"` | 0 | ✅ pass | <1s |\n\n## Diagnostics\n\n- Run `python3 whisper/transcribe.py --help` to see all options\n- Run with `-v` flag for debug-level logging during transcription\n- Batch mode logs `[N/M]` progress and final processed/skipped/failed counts\n- Resumability: delete output JSON to force re-transcription\n\n## Deviations\n\n- Added `--creator` CLI flag not in original plan — needed for files outside the expected `<CreatorName>/video.mp4` directory structure.\n- Added `ffmpeg-python` to requirements.txt as specified, though the script uses subprocess directly for ffmpeg calls (ffmpeg-python is available for future use if needed).\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `whisper/transcribe.py` — Main transcription script with single-file and batch modes\n- `whisper/requirements.txt` — Python dependencies (openai-whisper, ffmpeg-python)\n- `whisper/README.md` — Full usage documentation with examples, options, and output format\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1375,"cacheRead":63006,"cacheWrite":597,"totalTokens":64979,"cost":{"input":0.000005,"output":0.034375,"cacheRead":0.031502999999999996,"cacheWrite":0.00373125,"total":0.06961425}},"stopReason":"toolUse","timestamp":1774821420354}}
{"type":"message","id":"e84fcb91","parentId":"ff6cac58","timestamp":"2026-03-29T21:57:27.359Z","message":{"role":"toolResult","toolCallId":"toolu_01DT5Y5Qb9Vp44ATH6xivigo","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4429 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md"}],"isError":false,"timestamp":1774821447358}}
{"type":"message","id":"6735cacd","parentId":"e84fcb91","timestamp":"2026-03-29T21:57:39.523Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01K3aVSFMef2P44epGgceYu2","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"Added --creator CLI flag for overriding inferred creator folder name. ffmpeg-python included in requirements.txt per plan but script uses subprocess directly for reliability.","keyDecisions":["Whisper import deferred inside transcribe_audio() so --help and ffmpeg validation work without openai-whisper installed","Audio extracted to 16kHz mono WAV via ffmpeg subprocess matching Whisper expected format","Creator folder inferred from parent directory name by default, overridable with --creator"],"keyFiles":["whisper/transcribe.py","whisper/requirements.txt","whisper/README.md"],"knownIssues":"None.","milestoneId":"M001","narrative":"Built whisper/transcribe.py implementing all task plan requirements: argparse CLI with --input, --output-dir, --model (default large-v3), --device (default cuda); ffmpeg audio extraction to 16kHz mono WAV; Whisper transcription with word-level timestamps; JSON output matching the Chrysopedia spec format (source_file, creator_folder, duration_seconds, segments with words); resumability via output-exists check; batch mode with progress logging. Deferred whisper import so --help works without the dependency. Created requirements.txt and comprehensive README.md.","oneLiner":"Created desktop Whisper transcription script with single-file/batch modes, resumability, spec-compliant JSON output, and ffmpeg validation","sliceId":"S01","taskId":"T04","verification":"1. python3 whisper/transcribe.py --help — exits 0, shows full usage with all CLI args and examples. 2. python3 whisper/transcribe.py --input /tmp/fake.mp4 --output-dir /tmp/out — exits 1 with clear ffmpeg-not-found error, confirming validation works. 3. Python AST parse confirms syntax validity.","verificationEvidence":[{"command":"python3 whisper/transcribe.py --help","durationMs":200,"exitCode":0,"verdict":"✅ pass"},{"command":"python3 whisper/transcribe.py --input /tmp/fake.mp4 --output-dir /tmp/out","durationMs":200,"exitCode":1,"verdict":"✅ pass (expected ffmpeg error)"},{"command":"python3 -c \"import ast; ast.parse(open('whisper/transcribe.py').read())\"","durationMs":100,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":799,"cacheRead":63603,"cacheWrite":1436,"totalTokens":65839,"cost":{"input":0.000005,"output":0.019975,"cacheRead":0.031801499999999996,"cacheWrite":0.008975,"total":0.06075649999999999}},"stopReason":"toolUse","timestamp":1774821447358}}
{"type":"message","id":"509c78d1","parentId":"6735cacd","timestamp":"2026-03-29T21:57:39.536Z","message":{"role":"toolResult","toolCallId":"toolu_01K3aVSFMef2P44epGgceYu2","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T04 (S01/M001)"}],"details":{"operation":"complete_task","taskId":"T04","sliceId":"S01","milestoneId":"M001","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S01/tasks/T04-SUMMARY.md"},"isError":false,"timestamp":1774821459535}}
{"type":"message","id":"8f775858","parentId":"509c78d1","timestamp":"2026-03-29T21:57:42.539Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T04 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":65039,"cacheWrite":828,"totalTokens":65877,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.0325195,"cacheWrite":0.005175,"total":0.0379245}},"stopReason":"stop","timestamp":1774821459535}}
