{"type":"thinking_level_change","id":"28bec48b","parentId":null,"timestamp":"2026-03-30T18:15:29.732Z","thinkingLevel":"medium"}
{"type":"model_change","id":"bd58c27c","parentId":"28bec48b","timestamp":"2026-03-30T18:15:29.733Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You 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\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-PLAN.md` and `.gsd/milestones/M007/slices/S01/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S01 planned.\"\n## UNIT: Plan Slice S01 (\"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\") — Milestone M007\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M007/M007-ROADMAP.md`\n\n# M007: \n\n## Vision\nMake the pipeline fully transparent — every LLM call's full prompt and response viewable, copyable, exportable, with per-stage token accounting. Automate transcript ingestion via folder monitoring instead of manual POST. Tighten the admin UX: prune dead weight, improve workflow clarity. Fix frontend overflow and mobile responsiveness bugs. Set the foundation for iterating on content quality through better visibility into what the LLM is actually doing.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting | medium | — | ⬜ | Toggle debug mode on for a video, trigger pipeline, see full prompt + response stored for every LLM call with per-stage token breakdown |\n| S02 | Debug Payload Viewer — Inline View, Copy, and Export in Admin UI | low | S01 | ⬜ | Admin clicks an LLM call event, sees full prompt and response in a readable viewer, copies to clipboard, or downloads as JSON |\n| S03 | Transcript Folder Watcher — Auto-Ingest Service | medium | — | ⬜ | Drop a transcript JSON into the watch folder on ub01 → file is detected, validated, POSTed to /ingest, pipeline triggers automatically |\n| S04 | Admin UX Audit — Prune, Streamline, and Polish | low | S02 | ⬜ | Admin pipeline page is cleaner and more efficient for daily content management. Dead UI elements removed. Workflow improvements visible. |\n| S05 | Key Moment Card Text Overflow Fix | low | — | ⬜ | Key moment cards with long filenames and timestamp ranges display cleanly — text truncated with ellipsis or wrapped within card bounds, no horizontal bleed |\n| S06 | Mobile Viewport Overflow Fix — Technique Pages and Global Content | low | S05 | ⬜ | Technique page on Galaxy S25 Ultra renders cleanly — body text wraps, tags don't overflow, metadata stays within viewport, no horizontal scroll |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M007/slices/S01/S01-RESEARCH.md`\n\n# S01 Research — Pipeline Debug Mode: Full LLM I/O Capture and Token Accounting\n\n## Summary\n\nThe existing `PipelineEvent` model and `_make_llm_callback` infrastructure captures token counts and a truncated 2000-char `content_preview` for each LLM call. **No prompt text is stored.** Full response text is discarded. This slice needs to:\n\n1. Add a debug mode toggle (global, Redis-backed like `review_mode`)\n2. When debug is on, capture full system prompt, user prompt, and full response text for every LLM call\n3. Expose per-stage token aggregation via API\n\nThe work is straightforward — extending an established event-capture pattern with two new columns, a toggle, and an aggregation query.\n\n## Recommendation\n\n**Targeted approach — extend existing infrastructure, don't rebuild it.**\n\n- Add `system_prompt_text` and `user_prompt_text` TEXT columns to `pipeline_events` (alongside existing `payload` JSONB which keeps the response content). Alternatively, store full response in a dedicated `response_text` TEXT column for symmetry and queryability, keeping `payload` for metadata only.\n- Debug mode: global Redis key `chrysopedia:debug_mode` (follows `review_mode` pattern from D007). Config default `debug_mode: bool = False`. When debug is **off**, behavior is unchanged (truncated preview only). When **on**, full prompts + full response stored.\n- Thread prompt text into the callback by expanding `_make_llm_callback` to accept `system_prompt` and `user_prompt` parameters. The callback closure already captures `video_id` and `stage`.\n- Token aggregation: add `GET /admin/pipeline/token-summary/{video_id}` that groups `pipeline_events` by `stage` and sums token columns.\n\n## Implementation Landscape\n\n### Existing Code (what to modify)\n\n| File | Role | What changes |\n|------|------|-------------|\n| `backend/models.py` (line ~386) | `PipelineEvent` model | Add `system_prompt_text`, `user_prompt_text`, `response_text` — all `Text, nullable=True` |\n| `backend/pipeline/stages.py` (line ~52) | `_emit_event()` | Accept new `system_prompt_text`, `user_prompt_text`, `response_text` kwargs, pass to model |\n| `backend/pipeline/stages.py` (line ~87) | `_make_llm_callback()` | Accept `system_prompt`, `user_prompt` params; conditionally capture full text when debug mode is on |\n| `backend/pipeline/stages.py` (lines 275-280, 365-372, 478-487, 720-726) | Stage `llm.complete()` call sites | Pass `system_prompt` and `user_prompt` to `_make_llm_callback()` |\n| `backend/config.py` | `Settings` | Add `debug_mode: bool = False` |\n| `backend/routers/pipeline.py` | Pipeline admin endpoints | Add debug mode GET/PUT, token summary endpoint, include new fields in event response |\n| `backend/schemas.py` | Pydantic schemas | Add `DebugModeResponse`, `DebugModeUpdate`, `TokenSummary` schemas |\n| `alembic/versions/006_debug_columns.py` | New migration | Add 3 TEXT columns to `pipeline_events` |\n\n### Key Design Details\n\n**Storage sizing:** System prompts are 4-12KB. User prompts include transcript text — can be 50-200KB for long videos. Full LLM responses are similar magnitude to user prompts. At ~4 LLM calls per video (stages 2-5, though stage 3 and 5 may loop), worst case is ~1-2MB per video pipeline run. PostgreSQL TEXT columns handle this fine. No need for filesystem or blob storage at this scale.\n\n**Stage 3 and 5 looping:** Stage 3 calls LLM once per topic group; stage 5 calls once per (creator, category) group. Each call already produces a separate `PipelineEvent` with `event_type=\"llm_call\"`. The existing infrastructure handles multi-call stages correctly — no special grouping needed.\n\n**Debug mode check location:** The `_make_llm_callback` factory runs in the Celery worker (sync context). It needs to read debug mode. Two options:\n- Read from config `settings.debug_mode` (simpler, requires worker restart to change)\n- Read from Redis via sync `redis.Redis` (matches `review_mode` runtime toggle pattern, no restart needed)\n\n**Recommendation:** Use sync Redis read in the callback. The worker already imports `config.get_settings()` which has `redis_url`. Create a thin sync Redis helper (or just `redis.from_url()` inline) — the async `redis_client.py` helper is for FastAPI only.\n\n**`_safe_parse_llm_response` retry calls:** When the first LLM parse fails, `_safe_parse_llm_response` makes a retry call with a nudge prompt. This retry call does NOT have an `on_complete` callback — it won't emit a debug event. This is acceptable for now (retry is a diagnostic edge case), but note it for S02 if full call tracing is needed.\n\n**Per-stage token aggregation query:**\n```sql\nSELECT stage,\n       COUNT(*) AS call_count,\n       SUM(prompt_tokens) AS total_prompt_tokens,\n       SUM(completion_tokens) AS total_completion_tokens,\n       SUM(total_tokens) AS total_tokens\nFROM pipeline_events\nWHERE video_id = :vid AND event_type = 'llm_call'\nGROUP BY stage\nORDER BY MIN(created_at)\n```\n\n### Natural Task Seams\n\n1. **DB migration + model** — Add 3 TEXT columns to `pipeline_events`, update `PipelineEvent` model. Pure schema work, verifiable with `alembic upgrade head`.\n2. **Debug mode toggle** — Config setting + Redis key + GET/PUT endpoints + schemas. Follows `review_mode` pattern exactly. Verifiable via API calls.\n3. **Capture instrumentation** — Modify `_make_llm_callback` to accept and conditionally store prompts/response. Modify all 4 stage call sites to pass prompt text. This is the core work. Verifiable by triggering pipeline with debug on and querying events.\n4. **Token summary endpoint** — New `GET /admin/pipeline/token-summary/{video_id}` with aggregation query. Verifiable via API call after pipeline run.\n\nTasks 1-2 are independent. Task 3 depends on 1 (columns must exist). Task 4 depends on nothing new (uses existing columns).\n\n### Constraints & Risks\n\n- **Celery worker sync context:** The debug mode Redis read must use sync `redis.Redis`, not the async client from `redis_client.py`. This is the same constraint that led to sync SQLAlchemy in the worker (D004).\n- **Large TEXT columns:** No index needed on prompt/response text. These are write-once, read-rarely columns for debugging. The existing `ix_pipeline_events_video_created` index handles all query patterns.\n- **Migration on production data:** The `pipeline_events` table has existing rows. Adding nullable TEXT columns is a non-blocking ALTER TABLE in PostgreSQL — no data rewrite needed.\n\n### Verification Strategy\n\n1. Migration: `docker exec chrysopedia-api alembic upgrade head` succeeds, columns visible in `\\d pipeline_events`\n2. Debug toggle: `curl` GET/PUT on debug mode endpoint returns expected values\n3. Full capture: Enable debug mode, trigger pipeline for a video, query events — `system_prompt_text`, `user_prompt_text`, `response_text` populated for `llm_call` events\n4. Token summary: `GET /admin/pipeline/token-summary/{video_id}` returns per-stage breakdown\n5. Debug off: Disable debug mode, trigger pipeline — new events have NULL prompt/response text columns (existing truncated `content_preview` in payload still works)\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n[...truncated 3 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Project**: `.gsd/PROJECT.md`\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M007/M007-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M007/slices/S01/S01-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"b126e648","parentId":"bd58c27c","timestamp":"2026-03-30T18:15:29.735Z"}
{"type":"message","id":"8ad93ae2","parentId":"b126e648","timestamp":"2026-03-30T18:15:39.800Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the codebase to ground the plan in reality before decomposing tasks.","thinkingSignature":"Eo4CClkIDBgCKkATtBW16aSum2KnSy7uWOzlYS/4UxsX5xXTs4ytHgFcM4s0wQdjtuw5Gkhholzm5+fwWb0MTHILFKYwQzGr38c6Mg9jbGF1ZGUtb3B1cy00LTY4ABIMH043GqV1xm+1xrwUGgwQEDAkHnSl2kObw2QiMArqSy+1nUoiw8Psn+dlCZiKTklMqHefVzHoCJ0VpVKNto2F46syojPm+sUxH0Gamipjaynlb3V8STYASwH96d8q9oGjK3OZm2bJH3UTlGIuWUiy9iNbqwaTI+GdR/Rc/fIPgzVjTex7tpO4/L77+K4yALjtmESiBpkW/G86eHpt885TSRkQI0wVQSpB2slV9LgNbCwBGAE="},{"type":"text","text":"Let me explore the relevant code to ground the plan."},{"type":"toolCall","id":"toolu_01Wv8ftC5CReqdbFNgjrEpxe","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class PipelineEvent\" backend/models.py"}},{"type":"toolCall","id":"toolu_01RELh6U6mDfuP2DLo8owAJG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"_make_llm_callback\\|_emit_event\\|def _safe_parse\" backend/pipeline/stages.py | head -30"}},{"type":"toolCall","id":"toolu_01Y5474y52GQjgUUJvYdxjWX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"review_mode\\|debug_mode\" backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":288,"cacheRead":58112,"cacheWrite":9313,"totalTokens":67715,"cost":{"input":0.00001,"output":0.007200000000000001,"cacheRead":0.029056,"cacheWrite":0.05820625,"total":0.09447225000000001}},"stopReason":"toolUse","timestamp":1774894529734}}
{"type":"message","id":"c4137738","parentId":"8ad93ae2","timestamp":"2026-03-30T18:15:39.814Z","message":{"role":"toolResult","toolCallId":"toolu_01Wv8ftC5CReqdbFNgjrEpxe","toolName":"bash","content":[{"type":"text","text":"rg: backend/models.py: No such file or directory (os error 2)\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1774894539814}}
{"type":"message","id":"c4afa910","parentId":"c4137738","timestamp":"2026-03-30T18:15:39.814Z","message":{"role":"toolResult","toolCallId":"toolu_01RELh6U6mDfuP2DLo8owAJG","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774894539814}}
{"type":"message","id":"e7c01eec","parentId":"c4afa910","timestamp":"2026-03-30T18:15:39.817Z","message":{"role":"toolResult","toolCallId":"toolu_01Y5474y52GQjgUUJvYdxjWX","toolName":"bash","content":[{"type":"text","text":"rg: backend/config.py: No such file or directory (os error 2)\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1774894539816}}
{"type":"message","id":"b847907f","parentId":"e7c01eec","timestamp":"2026-03-30T18:15:45.710Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The files aren't at the expected paths. Let me find the actual project structure.","thinkingSignature":"EowCClkIDBgCKkAG042be2ZgdPRpmoN+9/Zmvv3eoOhHASF+V0IaQG+a6dPnv1Gdr8lF8RN5gSbYFMm1j7OqsKjTPFukOLUT0Rl0Mg9jbGF1ZGUtb3B1cy00LTY4ABIM7icffMLWOn9BX0XKGgxe8DNOiiIsZxKyC0UiMIC6O9PK88Dbmtjg2K5Z6/I4OqLefuKOz6H6ubCPuhrNe2HdKFnL4uQF5wgkquiJJiph1KBATG6xT597ixUTI7PW05j0YSmoC0uX8eGdu5gNYPSonqqmd2PFX9hNkh65v6Pm+3kXAdbpF7iAgNEgGDJIhgslpwV0KoNYVsI/HaITOhQGGGZXxSJnYKrdkxd310b4SBgB"},{"type":"text","text":"The CLAUDE.md says the canonical dev directory is on ub01. Let me find where the code actually lives."},{"type":"toolCall","id":"toolu_01VJJDaJHt73XXPpLWoPfhyR","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"models.py\" -not -path \"./.gsd/*\" -not -path \"*/node_modules/*\" 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01VNnRc8cRFjB3xdUAdZ2ced","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":220,"cacheRead":67425,"cacheWrite":397,"totalTokens":68043,"cost":{"input":0.000005,"output":0.0055000000000000005,"cacheRead":0.0337125,"cacheWrite":0.00248125,"total":0.04169875}},"stopReason":"toolUse","timestamp":1774894539816}}
{"type":"message","id":"3c786778","parentId":"b847907f","timestamp":"2026-03-30T18:15:45.745Z","message":{"role":"toolResult","toolCallId":"toolu_01VJJDaJHt73XXPpLWoPfhyR","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774894545745}}
{"type":"message","id":"cfde7f3a","parentId":"3c786778","timestamp":"2026-03-30T18:15:45.747Z","message":{"role":"toolResult","toolCallId":"toolu_01VNnRc8cRFjB3xdUAdZ2ced","toolName":"bash","content":[{"type":"text","text":"total 48\ndrwxrwxr-x 10 aux aux 4096 Mar 30 18:11 .\ndrwxrwxr-x  4 aux aux 4096 Mar 29 17:47 ..\ndrwxrwxr-x  3 aux aux 4096 Mar 30 08:33 .artifacts\ndrwxrwxr-x  3 aux aux 4096 Mar 30 11:57 backend\ndrwxrwxr-x  2 aux aux 4096 Mar 30 07:38 .bg-shell\ndrwxrwxr-x  2 aux aux 4096 Mar 30 12:44 .claude\n-rw-rw-r--  1 aux aux 2119 Mar 30 12:37 CLAUDE.md\ndrwxrwxr-x  5 aux aux 4096 Mar 30 11:57 frontend\ndrwxrwxr-x  8 aux aux 4096 Mar 30 18:11 .git\n-rw-rw-r--  1 aux aux  353 Mar 29 21:39 .gitignore\ndrwxrwxr-x  6 aux aux 4096 Mar 30 18:15 .gsd\ndrwxrwxr-x  2 aux aux 4096 Mar 30 18:15 .gsd.lock\n"}],"isError":false,"timestamp":1774894545745}}
{"type":"message","id":"a2baaff9","parentId":"cfde7f3a","timestamp":"2026-03-30T18:15:49.524Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WgnsDXxc5F6y2625SnGgDX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -type f -name \"*.py\" | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":67822,"cacheWrite":608,"totalTokens":68512,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.033911,"cacheWrite":0.0038,"total":0.03974099999999999}},"stopReason":"toolUse","timestamp":1774894545745}}
{"type":"message","id":"d72bdc88","parentId":"a2baaff9","timestamp":"2026-03-30T18:15:49.539Z","message":{"role":"toolResult","toolCallId":"toolu_01WgnsDXxc5F6y2625SnGgDX","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/stages.py\n"}],"isError":false,"timestamp":1774894549537}}
{"type":"message","id":"9ee6858d","parentId":"d72bdc88","timestamp":"2026-03-30T18:15:57.283Z","message":{"role":"assistant","content":[{"type":"text","text":"Only one file locally. The real codebase is on ub01. Let me check what's accessible via SSH and look at the actual source files there."},{"type":"toolCall","id":"toolu_01NU3hoS5QsazBeAJgUqQnra","name":"bash","arguments":{"command":"ssh ub01 \"find /vmPool/r/repos/xpltdco/chrysopedia/backend -type f -name '*.py' | sort\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":129,"cacheRead":68430,"cacheWrite":101,"totalTokens":68661,"cost":{"input":0.000005,"output":0.003225,"cacheRead":0.034214999999999995,"cacheWrite":0.0006312500000000001,"total":0.03807624999999999}},"stopReason":"toolUse","timestamp":1774894549537}}
{"type":"message","id":"2a5f22b0","parentId":"9ee6858d","timestamp":"2026-03-30T18:15:58.060Z","message":{"role":"toolResult","toolCallId":"toolu_01NU3hoS5QsazBeAJgUqQnra","toolName":"bash","content":[{"type":"text","text":"/vmPool/r/repos/xpltdco/chrysopedia/backend/config.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/database.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/main.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/embedding_client.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/__init__.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/llm_client.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/qdrant_client.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/schemas.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/redis_client.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/creators.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/health.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/ingest.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/__init__.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/reports.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/search.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/techniques.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/topics.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/videos.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/search_service.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/conftest.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/fixtures/mock_llm_responses.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/__init__.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_ingest.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_pipeline.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_public_api.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_search.py\n/vmPool/r/repos/xpltdco/chrysopedia/backend/worker.py\n"}],"isError":false,"timestamp":1774894558059}}
{"type":"message","id":"6da35229","parentId":"2a5f22b0","timestamp":"2026-03-30T18:16:04.877Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Let me pull the key files I need to ground the plan."},{"type":"toolCall","id":"toolu_01WmBaCS2ZcE3g3oahpnt7uJ","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'class PipelineEvent\\|system_prompt\\|user_prompt\\|response_text\\|content_preview\\|payload\\|prompt_tokens\\|completion_tokens\\|total_tokens' /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_0141u7jcyzQiTojrLnnz2PaX","name":"bash","arguments":{"command":"ssh ub01 \"grep -n '_make_llm_callback\\|_emit_event\\|def _safe_parse\\|llm\\.complete\\|on_complete' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01VbiVR6R8omovL5vxX8uB1B","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'review_mode\\|debug_mode\\|redis_url' /vmPool/r/repos/xpltdco/chrysopedia/backend/config.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":348,"cacheRead":68531,"cacheWrite":1028,"totalTokens":69908,"cost":{"input":0.000005,"output":0.008700000000000001,"cacheRead":0.0342655,"cacheWrite":0.006425,"total":0.049395499999999995}},"stopReason":"toolUse","timestamp":1774894558059}}
{"type":"message","id":"d7d8e1dd","parentId":"6da35229","timestamp":"2026-03-30T18:16:05.247Z","message":{"role":"toolResult","toolCallId":"toolu_01WmBaCS2ZcE3g3oahpnt7uJ","toolName":"bash","content":[{"type":"text","text":"386:class PipelineEvent(Base):\n390:    token usage and optional response payloads for debugging.\n404:    prompt_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n405:    completion_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n406:    total_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n409:    payload: Mapped[dict | None] = mapped_column(\n"}],"isError":false,"timestamp":1774894565246}}
{"type":"message","id":"04243acd","parentId":"d7d8e1dd","timestamp":"2026-03-30T18:16:05.248Z","message":{"role":"toolResult","toolCallId":"toolu_0141u7jcyzQiTojrLnnz2PaX","toolName":"bash","content":[{"type":"text","text":"53:def _emit_event(\n88:def _make_llm_callback(video_id: str, stage: str):\n89:    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\"\"\"\n95:        _emit_event(\n196:def _safe_parse_llm_response(\n222:        retry_raw = llm.complete(\n242:    _emit_event(video_id, \"stage2_segmentation\", \"start\")\n278:        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n292:        _emit_event(video_id, \"stage2_segmentation\", \"complete\")\n303:        _emit_event(video_id, \"stage2_segmentation\", \"error\", payload={\"error\": str(exc)})\n323:    _emit_event(video_id, \"stage3_extraction\", \"start\")\n370:            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n404:        _emit_event(video_id, \"stage3_extraction\", \"complete\")\n415:        _emit_event(video_id, \"stage3_extraction\", \"error\", payload={\"error\": str(exc)})\n438:    _emit_event(video_id, \"stage4_classification\", \"start\")\n485:        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\"),\n517:        _emit_event(video_id, \"stage4_classification\", \"complete\")\n528:        _emit_event(video_id, \"stage4_classification\", \"error\", payload={\"error\": str(exc)})\n660:    _emit_event(video_id, \"stage5_synthesis\", \"start\")\n724:            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult, on_complete=_make_llm_callback(video_id, \"stage5_synthesis\"),\n835:        _emit_event(video_id, \"stage5_synthesis\", \"complete\")\n846:        _emit_event(video_id, \"stage5_synthesis\", \"error\", payload={\"error\": str(exc)})\n"}],"isError":false,"timestamp":1774894565248}}
{"type":"message","id":"566c4f1c","parentId":"04243acd","timestamp":"2026-03-30T18:16:05.251Z","message":{"role":"toolResult","toolCallId":"toolu_01VbiVR6R8omovL5vxX8uB1B","toolName":"bash","content":[{"type":"text","text":"19:    redis_url: str = \"redis://localhost:6379/0\"\n63:    review_mode: bool = True\n"}],"isError":false,"timestamp":1774894565249}}
{"type":"message","id":"1fc0c1ae","parentId":"566c4f1c","timestamp":"2026-03-30T18:16:11.566Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CiGRpSCRsNLs4tj6HTZXvD","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '50,120p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_013cCG8YPWKX6xsB5a73VxwA","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '386,430p' /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":177,"cacheRead":69559,"cacheWrite":1187,"totalTokens":70924,"cost":{"input":0.000005,"output":0.004425,"cacheRead":0.0347795,"cacheWrite":0.00741875,"total":0.046628249999999996}},"stopReason":"toolUse","timestamp":1774894565250}}
{"type":"message","id":"eaf63f56","parentId":"1fc0c1ae","timestamp":"2026-03-30T18:16:11.915Z","message":{"role":"toolResult","toolCallId":"toolu_01CiGRpSCRsNLs4tj6HTZXvD","toolName":"bash","content":[{"type":"text","text":"\n# ── Pipeline event persistence ───────────────────────────────────────────────\n\ndef _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)\n\n\ndef _make_llm_callback(video_id: str, stage: str):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\"\"\"\n    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n                 total_tokens=None, content=None, finish_reason=None,\n                 is_fallback=False, **_kwargs):\n        # Truncate content for storage — keep first 2000 chars for debugging\n        truncated = content[:2000] if content and len(content) > 2000 else content\n        _emit_event(\n            video_id=video_id,\n            stage=stage,\n            event_type=\"llm_call\",\n            model=model,\n            prompt_tokens=prompt_tokens,\n            completion_tokens=completion_tokens,\n            total_tokens=total_tokens,\n            payload={\n                \"content_preview\": truncated,\n                \"content_length\": len(content) if content else 0,\n                \"finish_reason\": finish_reason,\n                \"is_fallback\": is_fallback,\n            },\n        )\n    return callback\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n_engine = None\n_SessionLocal = None\n\n\ndef _get_sync_engine():\n    \"\"\"Create a sync SQLAlchemy engine, converting the async URL if needed.\"\"\"\n    global _engine\n"}],"isError":false,"timestamp":1774894571914}}
{"type":"message","id":"2e47678f","parentId":"eaf63f56","timestamp":"2026-03-30T18:16:11.917Z","message":{"role":"toolResult","toolCallId":"toolu_013cCG8YPWKX6xsB5a73VxwA","toolName":"bash","content":[{"type":"text","text":"class PipelineEvent(Base):\n    \"\"\"Structured log entry for pipeline execution.\n\n    Captures per-stage start/complete/error/llm_call events with\n    token usage and optional response payloads for debugging.\n    \"\"\"\n    __tablename__ = \"pipeline_events\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        UUID(as_uuid=True), nullable=False, index=True,\n    )\n    stage: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"stage2_segmentation, stage3_extraction, etc.\"\n    )\n    event_type: Mapped[str] = mapped_column(\n        String(30), nullable=False, doc=\"start, complete, error, llm_call\"\n    )\n    prompt_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    completion_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    total_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    model: Mapped[str | None] = mapped_column(String(100), nullable=True)\n    duration_ms: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    payload: Mapped[dict | None] = mapped_column(\n        JSONB, nullable=True, doc=\"LLM response content, error details, stage metadata\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n"}],"isError":false,"timestamp":1774894571914}}
{"type":"message","id":"4fa9145a","parentId":"2e47678f","timestamp":"2026-03-30T18:16:16.742Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01MZeagFc6agK3xkgLCBjmVQ","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'review_mode\\|chrysopedia:review' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01Pgemv8kPuQrJuYL2spA3Fk","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'review_mode\\|chrysopedia:review' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01EJijRZZzuTp6NxTquokjte","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/repos/xpltdco/chrysopedia/alembic/versions/\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":266,"cacheRead":70746,"cacheWrite":1292,"totalTokens":72305,"cost":{"input":0.000005,"output":0.0066500000000000005,"cacheRead":0.035373,"cacheWrite":0.008075,"total":0.050103}},"stopReason":"toolUse","timestamp":1774894571915}}
{"type":"message","id":"786b3c2a","parentId":"4fa9145a","timestamp":"2026-03-30T18:16:17.053Z","message":{"role":"toolResult","toolCallId":"toolu_01MZeagFc6agK3xkgLCBjmVQ","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774894577053}}
{"type":"message","id":"d324b7d7","parentId":"786b3c2a","timestamp":"2026-03-30T18:16:17.059Z","message":{"role":"toolResult","toolCallId":"toolu_01Pgemv8kPuQrJuYL2spA3Fk","toolName":"bash","content":[{"type":"text","text":"35:REDIS_MODE_KEY = \"chrysopedia:review_mode\"\n346:                return ReviewModeResponse(review_mode=value.lower() == \"true\")\n353:    return ReviewModeResponse(review_mode=settings.review_mode)\n364:            await redis.set(REDIS_MODE_KEY, str(body.review_mode))\n374:    logger.info(\"Review mode set to %s\", body.review_mode)\n375:    return ReviewModeResponse(review_mode=body.review_mode)\n"}],"isError":false,"timestamp":1774894577059}}
{"type":"message","id":"97713514","parentId":"d324b7d7","timestamp":"2026-03-30T18:16:17.065Z","message":{"role":"toolResult","toolCallId":"toolu_01EJijRZZzuTp6NxTquokjte","toolName":"bash","content":[{"type":"text","text":"001_initial.py\n002_technique_page_versions.py\n003_content_reports.py\n004_pipeline_events.py\n005_content_hash.py\n"}],"isError":false,"timestamp":1774894577064}}
{"type":"message","id":"25509382","parentId":"97713514","timestamp":"2026-03-30T18:16:21.189Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GsGZqnM9wUobpQHqfb1yCY","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '330,380p' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01MeL75dpitFE82Az9X3BbBq","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'def complete\\|system_prompt\\|user_prompt\\|on_complete' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/llm_client.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":197,"cacheRead":72038,"cacheWrite":528,"totalTokens":72764,"cost":{"input":0.000005,"output":0.0049250000000000006,"cacheRead":0.036018999999999995,"cacheWrite":0.0033,"total":0.044249}},"stopReason":"toolUse","timestamp":1774894577064}}
{"type":"message","id":"1daefbfb","parentId":"25509382","timestamp":"2026-03-30T18:16:21.502Z","message":{"role":"toolResult","toolCallId":"toolu_01GsGZqnM9wUobpQHqfb1yCY","toolName":"bash","content":[{"type":"text","text":"    result = await db.execute(stmt)\n    row = result.one_or_none()\n    if row is None:\n        raise HTTPException(status_code=404, detail=f\"Moment {moment_id} not found\")\n    moment, file_path, creator_name = row\n    return _moment_to_queue_item(moment, file_path or \"\", creator_name)\n\n@router.get(\"/mode\", response_model=ReviewModeResponse)\nasync def get_mode() -> ReviewModeResponse:\n    \"\"\"Get the current review mode (review vs auto).\"\"\"\n    settings = get_settings()\n    try:\n        redis = await get_redis()\n        try:\n            value = await redis.get(REDIS_MODE_KEY)\n            if value is not None:\n                return ReviewModeResponse(review_mode=value.lower() == \"true\")\n        finally:\n            await redis.aclose()\n    except Exception as exc:\n        # Redis unavailable — fall back to config default\n        logger.warning(\"Redis unavailable for mode read, using config default: %s\", exc)\n\n    return ReviewModeResponse(review_mode=settings.review_mode)\n\n\n@router.put(\"/mode\", response_model=ReviewModeResponse)\nasync def set_mode(\n    body: ReviewModeUpdate,\n) -> ReviewModeResponse:\n    \"\"\"Set the review mode (review vs auto).\"\"\"\n    try:\n        redis = await get_redis()\n        try:\n            await redis.set(REDIS_MODE_KEY, str(body.review_mode))\n        finally:\n            await redis.aclose()\n    except Exception as exc:\n        logger.error(\"Failed to set review mode in Redis: %s\", exc)\n        raise HTTPException(\n            status_code=503,\n            detail=f\"Redis unavailable: {exc}\",\n        )\n\n    logger.info(\"Review mode set to %s\", body.review_mode)\n    return ReviewModeResponse(review_mode=body.review_mode)\n"}],"isError":false,"timestamp":1774894581501}}
{"type":"message","id":"b48e7c3f","parentId":"1daefbfb","timestamp":"2026-03-30T18:16:21.534Z","message":{"role":"toolResult","toolCallId":"toolu_01MeL75dpitFE82Az9X3BbBq","toolName":"bash","content":[{"type":"text","text":"83:    system_prompt: str,\n84:    user_prompt: str,\n95:    system_prompt:\n97:    user_prompt:\n110:    input_tokens = estimate_tokens(system_prompt) + estimate_tokens(user_prompt)\n143:    def complete(\n145:        system_prompt: str,\n146:        user_prompt: str,\n150:        on_complete: \"Callable | None\" = None,\n157:        system_prompt:\n159:        user_prompt:\n181:        effective_system = system_prompt\n192:                effective_system = system_prompt + json_schema_hint\n200:            {\"role\": \"user\", \"content\": user_prompt},\n233:            if on_complete is not None:\n235:                    on_complete(\n244:                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n273:            if on_complete is not None:\n275:                    on_complete(\n285:                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n"}],"isError":false,"timestamp":1774894581532}}
{"type":"message","id":"9bc3751f","parentId":"b48e7c3f","timestamp":"2026-03-30T18:16:27.355Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how `llm.complete` is called at the stage sites to understand what prompt text is available."},{"type":"toolCall","id":"toolu_01Sfsqc3rUcpXHPZSxLmTbLK","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '265,285p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01NcGzbL68zpZyYNGgRrBjqZ","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'class ReviewModeResponse\\|class ReviewModeUpdate\\|class TokenSummary\\|class DebugMode\\|class PipelineEventResponse' /vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":234,"cacheRead":72566,"cacheWrite":985,"totalTokens":73786,"cost":{"input":0.000005,"output":0.00585,"cacheRead":0.036282999999999996,"cacheWrite":0.00615625,"total":0.04829425}},"stopReason":"toolUse","timestamp":1774894581533}}
{"type":"message","id":"f6d1d75c","parentId":"9bc3751f","timestamp":"2026-03-30T18:16:27.683Z","message":{"role":"toolResult","toolCallId":"toolu_01Sfsqc3rUcpXHPZSxLmTbLK","toolName":"bash","content":[{"type":"text","text":"                f\"[{seg.segment_index}] ({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n            )\n        transcript_text = \"\\n\".join(transcript_lines)\n\n        # Load prompt and call LLM\n        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(2)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage2_segmentation\", hard_limit=hard_limit)\n        logger.info(\"Stage 2 using model=%s, modality=%s, max_tokens=%d\", model_override or \"default\", modality, max_tokens)\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n                           modality=modality, model_override=model_override, max_tokens=max_tokens)\n        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override)\n\n        # Update topic_label on each segment row\n        seg_by_index = {s.segment_index: s for s in segments}\n        for topic_seg in result.segments:\n"}],"isError":false,"timestamp":1774894587683}}
{"type":"message","id":"93f8d6c9","parentId":"f6d1d75c","timestamp":"2026-03-30T18:16:27.686Z","message":{"role":"toolResult","toolCallId":"toolu_01NcGzbL68zpZyYNGgRrBjqZ","toolName":"bash","content":[{"type":"text","text":"245:class ReviewModeResponse(BaseModel):\n250:class ReviewModeUpdate(BaseModel):\n"}],"isError":false,"timestamp":1774894587683}}
{"type":"message","id":"365ceb7f","parentId":"93f8d6c9","timestamp":"2026-03-30T18:16:36.610Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I now have enough context. Let me also check what the existing pipeline router endpoints look like and the existing schemas for pipeline events."},{"type":"toolCall","id":"toolu_01BxKH3ayHLd9FKNih8wYXrn","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'def \\|@router' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_015URYKYwM7oUZc9oCr8Q5En","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'class Pipeline\\|class .*Event' /vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":214,"cacheRead":73551,"cacheWrite":725,"totalTokens":74491,"cost":{"input":0.000005,"output":0.005350000000000001,"cacheRead":0.036775499999999996,"cacheWrite":0.0045312500000000006,"total":0.046661749999999995}},"stopReason":"toolUse","timestamp":1774894587684}}
{"type":"message","id":"14364deb","parentId":"365ceb7f","timestamp":"2026-03-30T18:16:36.951Z","message":{"role":"toolResult","toolCallId":"toolu_01BxKH3ayHLd9FKNih8wYXrn","toolName":"bash","content":[{"type":"text","text":"32:@router.post(\"/pipeline/trigger/{video_id}\")\n33:async def trigger_pipeline(\n66:@router.get(\"/admin/pipeline/videos\")\n67:async def list_pipeline_videos(\n129:@router.post(\"/admin/pipeline/trigger/{video_id}\")\n130:async def admin_trigger_pipeline(\n140:@router.post(\"/admin/pipeline/revoke/{video_id}\")\n141:async def revoke_pipeline(video_id: str):\n178:@router.get(\"/admin/pipeline/events/{video_id}\")\n179:async def list_pipeline_events(\n236:@router.get(\"/admin/pipeline/worker-status\")\n237:async def worker_status():\n"}],"isError":false,"timestamp":1774894596951}}
{"type":"message","id":"5231e4c8","parentId":"14364deb","timestamp":"2026-03-30T18:16:36.954Z","message":{"role":"toolResult","toolCallId":"toolu_015URYKYwM7oUZc9oCr8Q5En","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774894596951}}
{"type":"message","id":"5b2b71fa","parentId":"5231e4c8","timestamp":"2026-03-30T18:16:43.898Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TPGFxgKTKh6N1kJabExotP","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '178,235p' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":74276,"cacheWrite":454,"totalTokens":74830,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.037138,"cacheWrite":0.0028375,"total":0.0424555}},"stopReason":"toolUse","timestamp":1774894596952}}
{"type":"message","id":"e9c5d118","parentId":"5b2b71fa","timestamp":"2026-03-30T18:16:44.217Z","message":{"role":"toolResult","toolCallId":"toolu_01TPGFxgKTKh6N1kJabExotP","toolName":"bash","content":[{"type":"text","text":"@router.get(\"/admin/pipeline/events/{video_id}\")\nasync def list_pipeline_events(\n    video_id: str,\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=200)] = 100,\n    stage: Annotated[str | None, Query(description=\"Filter by stage name\")] = None,\n    event_type: Annotated[str | None, Query(description=\"Filter by event type\")] = None,\n    order: Annotated[str, Query(description=\"Sort order: asc or desc\")] = \"desc\",\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Get pipeline events for a video. Default: newest first (desc).\"\"\"\n    stmt = select(PipelineEvent).where(PipelineEvent.video_id == video_id)\n    \n    if stage:\n        stmt = stmt.where(PipelineEvent.stage == stage)\n    if event_type:\n        stmt = stmt.where(PipelineEvent.event_type == event_type)\n    \n    # Validate order param\n    if order not in (\"asc\", \"desc\"):\n        raise HTTPException(status_code=400, detail=\"order must be 'asc' or 'desc'\")\n\n    # Count\n    count_stmt = select(func.count()).select_from(stmt.subquery())\n    total = (await db.execute(count_stmt)).scalar() or 0\n    \n    # Fetch\n    order_clause = PipelineEvent.created_at.asc() if order == \"asc\" else PipelineEvent.created_at.desc()\n    stmt = stmt.order_by(order_clause)\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    events = result.scalars().all()\n    \n    return {\n        \"items\": [\n            {\n                \"id\": str(e.id),\n                \"video_id\": str(e.video_id),\n                \"stage\": e.stage,\n                \"event_type\": e.event_type,\n                \"prompt_tokens\": e.prompt_tokens,\n                \"completion_tokens\": e.completion_tokens,\n                \"total_tokens\": e.total_tokens,\n                \"model\": e.model,\n                \"duration_ms\": e.duration_ms,\n                \"payload\": e.payload,\n                \"created_at\": e.created_at.isoformat() if e.created_at else None,\n            }\n            for e in events\n        ],\n        \"total\": total,\n        \"offset\": offset,\n        \"limit\": limit,\n    }\n\n\n# ── Admin: Worker status ─────────────────────────────────────────────────────\n\n"}],"isError":false,"timestamp":1774894604216}}
{"type":"message","id":"fa92bbe0","parentId":"e9c5d118","timestamp":"2026-03-30T18:16:50.540Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me read the templates and plan."},{"type":"toolCall","id":"toolu_01NN9xJB7kWkYRtjDKNeprfp","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01MPpnZSWiTHg4R3L3Sj2H7n","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":74730,"cacheWrite":785,"totalTokens":75651,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.037364999999999995,"cacheWrite":0.00490625,"total":0.04565125}},"stopReason":"toolUse","timestamp":1774894604216}}
{"type":"message","id":"2ca4de97","parentId":"fa92bbe0","timestamp":"2026-03-30T18:16:50.547Z","message":{"role":"toolResult","toolCallId":"toolu_01NN9xJB7kWkYRtjDKNeprfp","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1774894610546}}
{"type":"message","id":"f4a263f8","parentId":"2ca4de97","timestamp":"2026-03-30T18:16:50.550Z","message":{"role":"toolResult","toolCallId":"toolu_01MPpnZSWiTHg4R3L3Sj2H7n","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1774894610546}}
{"type":"message","id":"69dfb42a","parentId":"f4a263f8","timestamp":"2026-03-30T18:18:29.206Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me plan this slice.\n\nThe research doc identified 4 natural task seams, but some can be combined. Let me think about the right decomposition:\n\n1. DB migration + model columns (schema work)\n2. Debug mode toggle (config + Redis + API endpoints + schemas)\n3. Capture instrumentation (modify _make_llm_callback and call sites)\n4. Token summary endpoint\n\nTasks 1 and 2 are independent. Task 3 depends on 1. Task 4 is independent.\n\nGiven the 200K context window and the scope, I think 3 tasks is right:\n\n\n- T01: Schema + migration + model + debug toggle endpoints (combines seams 1 and 2)\n- T02: LLM I/O capture instrumentation (seam 3 - the core work)\n- T03: Token summary endpoint + end-to-end verification (seam 4 + verification)\n\nWait, let me reconsider the constraints. The executor works on ub01 via SSH, so each task needs to be self-contained and independently deployable. Two tasks might be cleaner: T01 handles the database migration, model updates, and debug mode toggle infrastructure, while T02 bundles the LLM callback instrumentation with the token summary endpoint and end-to-end verification.\n\nActually, I need to account for the fact that the executor will need to edit files, run migrations, and rebuild containers after each change. Let me settle on two tasks: T01 covers all the surface area—schema migration, model updates, debug mode toggle, and the token summary endpoint—while T02 handles the core capture logic in the pipeline callback plus the rebuild and deployment. I'm settling on a two-task split instead: T01 handles the database schema, model definitions, debug toggle, and token summary endpoints with their Pydantic schemas, while T02 tackles the pipeline callback instrumentation, container rebuild, and end-to-end verification. This way the infrastructure work stays contained in T01, and the riskier pipeline modifications get their own focused task with proper testing.","thinkingSignature":"[encoded data, 4096 characters]"},{"type":"text","text":"I have enough context now. The research doc is solid and matches the codebase. The work breaks naturally into two tasks: infrastructure (schema + API endpoints) and pipeline instrumentation (the risky core work + verification).\n\nNo active requirements are owned by this slice — the roadmap doesn't list any R-numbers. All requirements are already validated. This is new functionality, not requirement-advancing work.\n\nLet me plan."},{"type":"toolCall","id":"toolu_01PJxJtWrB8RGHugiScYd2w1","name":"gsd_plan_slice","arguments":{"goal":"Debug mode toggle (Redis-backed) controls full LLM I/O capture in the pipeline. When on, every LLM call stores system prompt, user prompt, and full response text. Per-stage token summary available via API.","integrationClosure":"Pipeline worker stores data → API reads it back. S02 builds the viewer UI.","milestoneId":"M007","observabilityImpact":"Full LLM I/O capture is itself an observability feature. Adds debug mode toggle, token summary, and full prompt/response storage.","proofLevel":"integration","sliceId":"S01","successCriteria":"## Must-Haves\n\n- Three new TEXT columns on `pipeline_events`: `system_prompt_text`, `user_prompt_text`, `response_text`\n- Debug mode GET/PUT endpoints following the `review_mode` Redis pattern (D007)\n- When debug on: pipeline LLM calls store full prompt + response text in new columns\n- When debug off: new columns remain NULL, existing truncated `content_preview` in payload unchanged\n- `GET /admin/pipeline/token-summary/{video_id}` returns per-stage token breakdown\n- Event listing endpoint includes new fields when present\n\n## Threat Surface\n\n- **Abuse**: Debug mode stores full LLM prompts/responses — large TEXT columns. Toggle is admin-only (no auth layer exists, single-admin tool). No external attack surface.\n- **Data exposure**: Prompts contain transcript text (not PII). No secrets in prompt templates. Acceptable for admin-only tool.\n- **Input trust**: Debug toggle is a boolean. Token summary video_id is a UUID path param. No new untrusted input.\n\n## Requirement Impact\n\n- **Requirements touched**: None directly — all R001-R014 are validated. This adds new observability, doesn't modify existing behavior.\n- **Re-verify**: Pipeline must still work identically when debug is off (existing behavior preserved).\n- **Decisions revisited**: None. Extends D007 pattern (Redis toggle) to a new key.\n\n## Proof Level\n\n- This slice proves: integration (pipeline stores data → API reads it back)\n- Real runtime required: yes (must trigger actual pipeline on ub01 Docker stack)\n- Human/UAT required: no\n\n## Verification\n\n- `ssh ub01 \"docker exec chrysopedia-api alembic upgrade head\"` succeeds — migration applies cleanly\n- `curl -s http://ub01:8096/admin/pipeline/debug-mode | jq .debug_mode` returns boolean\n- `curl -s -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":true}' | jq .debug_mode` returns true\n- Trigger pipeline for a test video with debug on → query events → `system_prompt_text` and `user_prompt_text` and `response_text` are non-null for `llm_call` events\n- Trigger pipeline with debug off → new events have null prompt/response columns\n- `curl -s http://ub01:8096/admin/pipeline/token-summary/{video_id} | jq .stages` returns per-stage aggregation\n\n## Observability / Diagnostics\n\n- Runtime signals: `_emit_event` already logs warnings on failure; debug mode adds full prompt/response capture for LLM diagnostics\n- Inspection surfaces: `GET /admin/pipeline/events/{video_id}` (existing, extended), `GET /admin/pipeline/token-summary/{video_id}` (new), `GET /admin/pipeline/debug-mode` (new)\n- Failure visibility: Debug mode Redis read failure falls back to config default (matches review_mode pattern). Column storage failure is best-effort (existing `_emit_event` try/except).\n- Redaction constraints: None — prompts contain transcript text, not secrets\n\n## Integration Closure\n\n- Upstream surfaces consumed: `PipelineEvent` model, `_emit_event()`, `_make_llm_callback()`, `LLMClient.complete()`, Redis client pattern from review router\n- New wiring: debug mode Redis key read in sync Celery worker context (new sync Redis read, matches D004 constraint), 3 new API endpoints in pipeline router\n- What remains before milestone is truly usable end-to-end: S02 (viewer UI for the captured data)","tasks":[{"description":"Set up all infrastructure for debug mode: Alembic migration adding 3 TEXT columns to pipeline_events, PipelineEvent model update, debug mode GET/PUT endpoints (Redis-backed, following review_mode pattern from D007), token summary aggregation endpoint, and extended event listing response.\n\nAll work is on ub01 via SSH at /vmPool/r/repos/xpltdco/chrysopedia.\n\n## Steps\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` columns (all `Text, nullable=True`) to `PipelineEvent` model in `backend/models.py` after the existing `payload` column.\n\n2. Create Alembic migration `alembic/versions/006_debug_columns.py` that adds the 3 TEXT columns to `pipeline_events`. Use `op.add_column()` with `nullable=True` — no data rewrite needed.\n\n3. Add `debug_mode: bool = False` to `Settings` class in `backend/config.py`.\n\n4. Add Pydantic schemas in `backend/schemas.py`:\n   - `DebugModeResponse(BaseModel)` with `debug_mode: bool`\n   - `DebugModeUpdate(BaseModel)` with `debug_mode: bool`\n   - `TokenStageSummary(BaseModel)` with `stage: str`, `call_count: int`, `total_prompt_tokens: int`, `total_completion_tokens: int`, `total_tokens: int`\n   - `TokenSummaryResponse(BaseModel)` with `video_id: str`, `stages: list[TokenStageSummary]`, `grand_total_tokens: int`\n\n5. Add 3 new endpoints in `backend/routers/pipeline.py`:\n   - `GET /admin/pipeline/debug-mode` — read from Redis key `chrysopedia:debug_mode`, fall back to `settings.debug_mode`. Follow exact pattern from review router's `get_mode()`.\n   - `PUT /admin/pipeline/debug-mode` — write to Redis. Follow exact pattern from review router's `set_mode()`.\n   - `GET /admin/pipeline/token-summary/{video_id}` — query `pipeline_events` grouped by stage where `event_type='llm_call'`, sum token columns, return `TokenSummaryResponse`.\n\n6. Extend the existing `list_pipeline_events` response dict to include `system_prompt_text`, `user_prompt_text`, `response_text` fields (nullable).\n\n7. Run migration: `docker exec chrysopedia-api alembic upgrade head`. Rebuild API container if needed: `docker compose build chrysopedia-api && docker compose up -d chrysopedia-api`.\n\n8. Verify: curl debug-mode GET returns `{\"debug_mode\": false}`, PUT sets to true, GET reads back true. Curl token-summary for any video_id returns valid (possibly empty) response.\n\n## Must-Haves\n\n- [ ] 3 TEXT columns added to pipeline_events table via migration\n- [ ] PipelineEvent model has matching mapped columns\n- [ ] Debug mode GET/PUT endpoints work with Redis (fall back to config)\n- [ ] Token summary endpoint returns per-stage aggregation\n- [ ] Event listing includes new fields\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Redis (debug mode read) | Fall back to config.debug_mode default | Same as error — use default | N/A (simple GET/SET of string) |\n| PostgreSQL (token summary) | Return 500 with error detail | Standard DB timeout handling | N/A (aggregation query) |","estimate":"1.5h","expectedOutput":["`alembic/versions/006_debug_columns.py` — migration adding 3 TEXT columns","`backend/models.py` — PipelineEvent with system_prompt_text, user_prompt_text, response_text columns","`backend/config.py` — Settings with debug_mode field","`backend/schemas.py` — DebugModeResponse, DebugModeUpdate, TokenStageSummary, TokenSummaryResponse","`backend/routers/pipeline.py` — 3 new endpoints + extended event listing"],"files":["backend/models.py","backend/config.py","backend/schemas.py","backend/routers/pipeline.py","alembic/versions/006_debug_columns.py"],"inputs":["`backend/models.py` — existing PipelineEvent model (line ~386)","`backend/config.py` — existing Settings class with review_mode pattern","`backend/schemas.py` — existing ReviewModeResponse/Update as template","`backend/routers/pipeline.py` — existing pipeline endpoints and list_pipeline_events","`backend/routers/review.py` — review_mode GET/PUT pattern to follow (lines 337-375)","`alembic/versions/` — existing migrations for revision chain"],"taskId":"T01","title":"Add debug columns, migration, debug toggle endpoints, and token summary API","verify":"ssh ub01 'docker exec chrysopedia-api alembic upgrade head' && curl -sf http://ub01:8096/admin/pipeline/debug-mode | grep debug_mode"},{"description":"Modify the pipeline's `_make_llm_callback` to conditionally capture full prompt and response text when debug mode is enabled. Thread system_prompt and user_prompt through to the callback at all 4 stage call sites. Rebuild worker container and verify end-to-end on ub01.\n\nAll work is on ub01 via SSH at /vmPool/r/repos/xpltdco/chrysopedia.\n\n## Steps\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` kwargs to `_emit_event()` in `backend/pipeline/stages.py`. Pass them through to the `PipelineEvent` constructor.\n\n2. Add a sync Redis helper at the top of `backend/pipeline/stages.py` to read debug mode:\n   ```python\n   def _is_debug_mode() -> bool:\n       \"\"\"Check if debug mode is enabled via Redis. Falls back to config.\"\"\"\n       try:\n           import redis\n           settings = get_settings()\n           r = redis.from_url(settings.redis_url)\n           val = r.get(\"chrysopedia:debug_mode\")\n           r.close()\n           if val is not None:\n               return val.decode().lower() == \"true\"\n       except Exception:\n           pass\n       return getattr(get_settings(), 'debug_mode', False)\n   ```\n   Note: uses sync `redis.from_url()` because Celery worker is sync (D004). The `redis` package is already installed (Celery broker dependency).\n\n3. Modify `_make_llm_callback(video_id, stage)` to accept `system_prompt: str | None = None` and `user_prompt: str | None = None` parameters. Inside the callback closure:\n   - Check `_is_debug_mode()`\n   - If debug on: pass `system_prompt_text=system_prompt`, `user_prompt_text=user_prompt`, `response_text=content` to `_emit_event()`\n   - If debug off: pass None for all three (existing behavior preserved)\n\n4. Update all 4 `llm.complete()` call sites to pass prompt text to the callback factory:\n   - Stage 2 (~line 278): `_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt)`\n   - Stage 3 (~line 370): same pattern\n   - Stage 4 (~line 485): same pattern  \n   - Stage 5 (~line 724): same pattern\n\n5. Rebuild and restart worker + API containers:\n   ```bash\n   cd /vmPool/r/repos/xpltdco/chrysopedia\n   docker compose build chrysopedia-api chrysopedia-worker\n   docker compose up -d\n   docker compose restart chrysopedia-web-8096  # nginx DNS cache\n   ```\n\n6. End-to-end verification:\n   a. Enable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":true}'`\n   b. Trigger pipeline for a video: `curl -X POST http://ub01:8096/admin/pipeline/trigger/{video_id}`\n   c. Wait for pipeline completion (check logs or events)\n   d. Query events: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — verify `system_prompt_text`, `user_prompt_text`, `response_text` are populated\n   e. Query token summary: `curl http://ub01:8096/admin/pipeline/token-summary/{video_id}` — verify per-stage breakdown\n   f. Disable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":false}'`\n   g. (Optional) Trigger another run — verify new events have null prompt/response columns\n\n## Must-Haves\n\n- [ ] `_emit_event` accepts and stores prompt/response text columns\n- [ ] `_is_debug_mode()` reads from sync Redis, falls back to config\n- [ ] `_make_llm_callback` conditionally captures full I/O when debug is on\n- [ ] All 4 stage call sites pass system_prompt and user_prompt to callback\n- [ ] Pipeline still works when debug is off (existing behavior preserved)\n- [ ] End-to-end: debug on → pipeline → events have full prompt/response text\n\n## Observability Impact\n\n- Signals added: Full LLM system prompt, user prompt, and response text stored per llm_call event when debug mode is on\n- How a future agent inspects this: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — check system_prompt_text/user_prompt_text/response_text fields\n- Failure state exposed: `_is_debug_mode()` failure is silent (falls back to off) — matches existing best-effort pattern in `_emit_event`","estimate":"1.5h","expectedOutput":["`backend/pipeline/stages.py` — _emit_event with prompt/response params, _is_debug_mode() helper, _make_llm_callback with conditional capture, all 4 call sites updated"],"files":["backend/pipeline/stages.py"],"inputs":["`backend/pipeline/stages.py` — existing _emit_event, _make_llm_callback, and 4 stage call sites","`backend/models.py` — PipelineEvent model with new columns (from T01)","`backend/routers/pipeline.py` — debug-mode and token-summary endpoints (from T01)","`backend/config.py` — Settings.debug_mode field (from T01)"],"taskId":"T02","title":"Wire debug-mode-aware LLM I/O capture into pipeline callback and verify end-to-end","verify":"ssh ub01 'curl -sf http://ub01:8096/admin/pipeline/debug-mode | python3 -c \"import sys,json; assert json.load(sys.stdin)[\\\"debug_mode\\\"] in [True,False]\"' && ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1 | head -5'"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":5107,"cacheRead":75515,"cacheWrite":2743,"totalTokens":83366,"cost":{"input":0.000005,"output":0.127675,"cacheRead":0.0377575,"cacheWrite":0.01714375,"total":0.18258125000000003}},"stopReason":"toolUse","timestamp":1774894610547}}
{"type":"message","id":"b7d56089","parentId":"69dfb42a","timestamp":"2026-03-30T18:18:29.242Z","message":{"role":"toolResult","toolCallId":"toolu_01PJxJtWrB8RGHugiScYd2w1","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S01 (M007)"}],"details":{"operation":"plan_slice","milestoneId":"M007","sliceId":"S01","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1774894709240}}
{"type":"message","id":"dad5a3ad","parentId":"b7d56089","timestamp":"2026-03-30T18:18:35.728Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. The plan persisted. Let me do a quick self-audit by reading back the generated plan."},{"type":"toolCall","id":"toolu_01LNSMzauHRsS5SobgDeqSxW","name":"read","arguments":{"path":".gsd/milestones/M007/slices/S01/S01-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":78258,"cacheWrite":4326,"totalTokens":82682,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.039129,"cacheWrite":0.027037500000000003,"total":0.0685965}},"stopReason":"toolUse","timestamp":1774894709241}}
{"type":"message","id":"43c4d8ec","parentId":"dad5a3ad","timestamp":"2026-03-30T18:18:35.734Z","message":{"role":"toolResult","toolCallId":"toolu_01LNSMzauHRsS5SobgDeqSxW","toolName":"read","content":[{"type":"text","text":"# S01: Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\n\n**Goal:** Debug mode toggle (Redis-backed) controls full LLM I/O capture in the pipeline. When on, every LLM call stores system prompt, user prompt, and full response text. Per-stage token summary available via API.\n**Demo:** After this: Toggle debug mode on for a video, trigger pipeline, see full prompt + response stored for every LLM call with per-stage token breakdown\n\n## Tasks\n- [ ] **T01: Add debug columns, migration, debug toggle endpoints, and token summary API** — Set up all infrastructure for debug mode: Alembic migration adding 3 TEXT columns to pipeline_events, PipelineEvent model update, debug mode GET/PUT endpoints (Redis-backed, following review_mode pattern from D007), token summary aggregation endpoint, and extended event listing response.\n\nAll work is on ub01 via SSH at /vmPool/r/repos/xpltdco/chrysopedia.\n\n## Steps\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` columns (all `Text, nullable=True`) to `PipelineEvent` model in `backend/models.py` after the existing `payload` column.\n\n2. Create Alembic migration `alembic/versions/006_debug_columns.py` that adds the 3 TEXT columns to `pipeline_events`. Use `op.add_column()` with `nullable=True` — no data rewrite needed.\n\n3. Add `debug_mode: bool = False` to `Settings` class in `backend/config.py`.\n\n4. Add Pydantic schemas in `backend/schemas.py`:\n   - `DebugModeResponse(BaseModel)` with `debug_mode: bool`\n   - `DebugModeUpdate(BaseModel)` with `debug_mode: bool`\n   - `TokenStageSummary(BaseModel)` with `stage: str`, `call_count: int`, `total_prompt_tokens: int`, `total_completion_tokens: int`, `total_tokens: int`\n   - `TokenSummaryResponse(BaseModel)` with `video_id: str`, `stages: list[TokenStageSummary]`, `grand_total_tokens: int`\n\n5. Add 3 new endpoints in `backend/routers/pipeline.py`:\n   - `GET /admin/pipeline/debug-mode` — read from Redis key `chrysopedia:debug_mode`, fall back to `settings.debug_mode`. Follow exact pattern from review router's `get_mode()`.\n   - `PUT /admin/pipeline/debug-mode` — write to Redis. Follow exact pattern from review router's `set_mode()`.\n   - `GET /admin/pipeline/token-summary/{video_id}` — query `pipeline_events` grouped by stage where `event_type='llm_call'`, sum token columns, return `TokenSummaryResponse`.\n\n6. Extend the existing `list_pipeline_events` response dict to include `system_prompt_text`, `user_prompt_text`, `response_text` fields (nullable).\n\n7. Run migration: `docker exec chrysopedia-api alembic upgrade head`. Rebuild API container if needed: `docker compose build chrysopedia-api && docker compose up -d chrysopedia-api`.\n\n8. Verify: curl debug-mode GET returns `{\"debug_mode\": false}`, PUT sets to true, GET reads back true. Curl token-summary for any video_id returns valid (possibly empty) response.\n\n## Must-Haves\n\n- [ ] 3 TEXT columns added to pipeline_events table via migration\n- [ ] PipelineEvent model has matching mapped columns\n- [ ] Debug mode GET/PUT endpoints work with Redis (fall back to config)\n- [ ] Token summary endpoint returns per-stage aggregation\n- [ ] Event listing includes new fields\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Redis (debug mode read) | Fall back to config.debug_mode default | Same as error — use default | N/A (simple GET/SET of string) |\n| PostgreSQL (token summary) | Return 500 with error detail | Standard DB timeout handling | N/A (aggregation query) |\n  - Estimate: 1.5h\n  - Files: backend/models.py, backend/config.py, backend/schemas.py, backend/routers/pipeline.py, alembic/versions/006_debug_columns.py\n  - Verify: ssh ub01 'docker exec chrysopedia-api alembic upgrade head' && curl -sf http://ub01:8096/admin/pipeline/debug-mode | grep debug_mode\n- [ ] **T02: Wire debug-mode-aware LLM I/O capture into pipeline callback and verify end-to-end** — Modify the pipeline's `_make_llm_callback` to conditionally capture full prompt and response text when debug mode is enabled. Thread system_prompt and user_prompt through to the callback at all 4 stage call sites. Rebuild worker container and verify end-to-end on ub01.\n\nAll work is on ub01 via SSH at /vmPool/r/repos/xpltdco/chrysopedia.\n\n## Steps\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` kwargs to `_emit_event()` in `backend/pipeline/stages.py`. Pass them through to the `PipelineEvent` constructor.\n\n2. Add a sync Redis helper at the top of `backend/pipeline/stages.py` to read debug mode:\n   ```python\n   def _is_debug_mode() -> bool:\n       \"\"\"Check if debug mode is enabled via Redis. Falls back to config.\"\"\"\n       try:\n           import redis\n           settings = get_settings()\n           r = redis.from_url(settings.redis_url)\n           val = r.get(\"chrysopedia:debug_mode\")\n           r.close()\n           if val is not None:\n               return val.decode().lower() == \"true\"\n       except Exception:\n           pass\n       return getattr(get_settings(), 'debug_mode', False)\n   ```\n   Note: uses sync `redis.from_url()` because Celery worker is sync (D004). The `redis` package is already installed (Celery broker dependency).\n\n3. Modify `_make_llm_callback(video_id, stage)` to accept `system_prompt: str | None = None` and `user_prompt: str | None = None` parameters. Inside the callback closure:\n   - Check `_is_debug_mode()`\n   - If debug on: pass `system_prompt_text=system_prompt`, `user_prompt_text=user_prompt`, `response_text=content` to `_emit_event()`\n   - If debug off: pass None for all three (existing behavior preserved)\n\n4. Update all 4 `llm.complete()` call sites to pass prompt text to the callback factory:\n   - Stage 2 (~line 278): `_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt)`\n   - Stage 3 (~line 370): same pattern\n   - Stage 4 (~line 485): same pattern  \n   - Stage 5 (~line 724): same pattern\n\n5. Rebuild and restart worker + API containers:\n   ```bash\n   cd /vmPool/r/repos/xpltdco/chrysopedia\n   docker compose build chrysopedia-api chrysopedia-worker\n   docker compose up -d\n   docker compose restart chrysopedia-web-8096  # nginx DNS cache\n   ```\n\n6. End-to-end verification:\n   a. Enable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":true}'`\n   b. Trigger pipeline for a video: `curl -X POST http://ub01:8096/admin/pipeline/trigger/{video_id}`\n   c. Wait for pipeline completion (check logs or events)\n   d. Query events: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — verify `system_prompt_text`, `user_prompt_text`, `response_text` are populated\n   e. Query token summary: `curl http://ub01:8096/admin/pipeline/token-summary/{video_id}` — verify per-stage breakdown\n   f. Disable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":false}'`\n   g. (Optional) Trigger another run — verify new events have null prompt/response columns\n\n## Must-Haves\n\n- [ ] `_emit_event` accepts and stores prompt/response text columns\n- [ ] `_is_debug_mode()` reads from sync Redis, falls back to config\n- [ ] `_make_llm_callback` conditionally captures full I/O when debug is on\n- [ ] All 4 stage call sites pass system_prompt and user_prompt to callback\n- [ ] Pipeline still works when debug is off (existing behavior preserved)\n- [ ] End-to-end: debug on → pipeline → events have full prompt/response text\n\n## Observability Impact\n\n- Signals added: Full LLM system prompt, user prompt, and response text stored per llm_call event when debug mode is on\n- How a future agent inspects this: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — check system_prompt_text/user_prompt_text/response_text fields\n- Failure state exposed: `_is_debug_mode()` failure is silent (falls back to off) — matches existing best-effort pattern in `_emit_event`\n  - Estimate: 1.5h\n  - Files: backend/pipeline/stages.py\n  - Verify: ssh ub01 'curl -sf http://ub01:8096/admin/pipeline/debug-mode | python3 -c \"import sys,json; assert json.load(sys.stdin)[\\\"debug_mode\\\"] in [True,False]\"' && ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1 | head -5'\n"}],"isError":false,"timestamp":1774894715732}}
{"type":"message","id":"2257542c","parentId":"43c4d8ec","timestamp":"2026-03-30T18:18:43.556Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n\n- **Completion semantics**: T01 builds all infrastructure (schema, migration, endpoints). T02 wires the capture into the pipeline and verifies end-to-end. Both together achieve the slice goal.\n- **Requirement coverage**: No active requirements owned — this is new functionality. All must-haves map to tasks.\n- **Task completeness**: Both tasks have steps, must-haves, verification, concrete file paths for inputs/outputs.\n- **Dependency correctness**: T02 depends on T01's model columns. Order is correct.\n- **Scope sanity**: T01 has 8 steps / 5 files, T02 has 6 steps / 1 file. Both within limits.\n- **Feature completeness**: Both produce real working functionality, not scaffolding.\n\nSlice S01 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":196,"cacheRead":82584,"cacheWrite":2606,"totalTokens":85387,"cost":{"input":0.000005,"output":0.0049,"cacheRead":0.041291999999999995,"cacheWrite":0.0162875,"total":0.0624845}},"stopReason":"stop","timestamp":1774894715733}}
