{"type":"thinking_level_change","id":"4df865ef","parentId":null,"timestamp":"2026-04-03T00:46:43.495Z","thinkingLevel":"medium"}
{"type":"model_change","id":"1f34dc6c","parentId":"4df865ef","timestamp":"2026-04-03T00:46:43.497Z","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/M014/slices/S01/S01-PLAN.md` and `.gsd/milestones/M014/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 (\"Synthesis Prompt v5 — Nested Sections + Citations\") — Milestone M014\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M014/M014-ROADMAP.md`\n\n# M014: \n\n## Vision\nRestructure technique pages to be broader (per-creator+category across videos), deeper (H2/H3 nesting with TOC), multi-source (compose new video content into existing pages), with inline citations linking prose claims to source moments, admin multi-source management, and section-level search with deep linking.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Synthesis Prompt v5 — Nested Sections + Citations | high | — | ⬜ | Run test harness with new prompt → output has list-of-objects body_sections with H2/H3 nesting, citation markers on key claims, broader page scope. |\n| S02 | Composition Prompt + Test Harness Compose Mode | high | S01 | ⬜ | Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations. |\n| S03 | Data Model + Migration | low | — | ⬜ | Alembic migration runs clean. API response includes body_sections_format and source_videos fields. |\n| S04 | Pipeline Compose-or-Create Logic | high | S01, S02, S03 | ⬜ | Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs. |\n| S05 | Frontend — Nested Rendering, TOC, Citations | medium | S03 | ⬜ | Format-2 page renders with TOC, nested sections, clickable citations. Format-1 pages unchanged. |\n| S06 | Admin UI — Multi-Source Pipeline Management | medium | S03, S04 | ⬜ | Admin view for multi-source page shows source dropdown, composition history, per-video chunking inspection. |\n| S07 | Search — Per-Section Embeddings + Deep Linking | medium | S04, S05 | ⬜ | Search 'LFO grain position' → section-level result → click → navigates to page#section and scrolls. |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M014/slices/S01/S01-RESEARCH.md`\n\n# S01 Research: Synthesis Prompt v5 — Nested Sections + Citations\n\n## Summary\n\nS01 rewrites the stage 5 synthesis prompt to produce a new output format: `body_sections` changes from a flat `dict[str, str]` to a list of section objects with H2/H3 nesting and inline citation markers. The scope is prompt + schema + test harness validation only — no DB migration, no pipeline wiring, no frontend changes.\n\nThe current prompt (`prompts/stage5_synthesis.txt`, ~400 lines) is mature and battle-tested. The v5 rewrite preserves its voice/tone/synthesis philosophy but restructures the output format section and adds citation instructions. The Pydantic schema (`SynthesizedPage`) needs a type change from `dict` to `list` plus a new nested model. The test harness needs updated word-count logic.\n\n**Primary risk:** LLM compliance with the new nested JSON structure. Flat dict output is simpler for LLMs. Nested objects with citation markers add complexity that may cause parse failures or inconsistent structure.\n\n## Recommendation\n\n**Approach:** Rewrite the prompt's output format section + schema + harness in three tasks. Use a structured list-of-objects format with explicit `level` fields rather than deep nesting.\n\n**Format design:**\n```json\n{\n  \"body_sections\": [\n    {\n      \"heading\": \"Why your bass disappears in mono\",\n      \"level\": 2,\n      \"content\": \"Mid-side processing separates a stereo signal [0]...\",\n      \"subsections\": [\n        {\n          \"heading\": \"The mid channel check\",\n          \"level\": 3,\n          \"content\": \"Solo the mid channel and listen for [2]...\"\n        }\n      ]\n    }\n  ]\n}\n```\n\n**Citation format:** `[N]` where N is the zero-indexed moment from the input. Multiple citations: `[0,2]`. Only on specific factual claims (settings, values, techniques), not on general observations. This maps directly to the `[0]`, `[1]` indices already present in the input prompt format.\n\n**Why list-of-objects, not nested dict:** A list enforces ordering (important for workflow-ordered sections). Objects with explicit `heading`/`level`/`content` fields are easier for LLMs to produce consistently than deeply nested structures. The `subsections` array is optional — sections without sub-topics just omit it.\n\n## Implementation Landscape\n\n### Files to Change\n\n| File | What Changes | Notes |\n|------|-------------|-------|\n| `prompts/stage5_synthesis.txt` | Rewrite output format section. Add citation rules. Update JSON example. Keep all voice/tone/synthesis guidance intact. | ~400 lines, majority stays. Output format section + example are ~80 lines that get rewritten. |\n| `backend/pipeline/schemas.py` | New `BodySection` model. Change `SynthesizedPage.body_sections` from `dict` to `list[BodySection]`. | Add `body_sections_format: str = \"v2\"` field to mark pages using new format. |\n| `backend/pipeline/test_harness.py` | Update word-count logic (lines 283, 457) and per-page summary logging. | Currently iterates `.values()` on a dict — needs to walk list-of-objects instead. |\n\n### Files That Read body_sections (NOT changed in S01 — awareness only)\n\n| File | How it reads body_sections | Impact |\n|------|---------------------------|--------|\n| `backend/pipeline/stages.py:1324,1357,1372` | Stores raw JSONB to DB column | **No change needed** — JSONB accepts any structure. |\n| `backend/pipeline/quality/scorer.py:552-554` | `.items()` iteration for scoring | **Breaks** — needs update in S03/S04 timeframe. |\n| `backend/pipeline/quality/__main__.py:337` | `.items()` iteration | **Breaks** — same. |\n| `backend/pipeline/quality/variant_generator.py:84` | Format marker check | Marker string \"body_sections\" still present — OK. |\n| `frontend/src/pages/TechniquePage.tsx:405+` | `Object.entries(displaySections)` | **Breaks** — fixed in S05. |\n| `frontend/src/api/public-client.ts:66` | Type: `Record<string, unknown>` | Needs type update in S05. |\n| Stage 6 embedding (`stages.py:1507-1510`) | Only reads `summary`, `title`, `topic_tags` | **Not affected** — never touches body_sections. |\n\n### New Pydantic Schema Design\n\n```python\nclass BodySubSection(BaseModel):\n    \"\"\"An H3 subsection within a body section.\"\"\"\n    heading: str = Field(description=\"H3 subsection heading\")\n    content: str = Field(description=\"Prose content with inline [N] citation markers\")\n\nclass BodySection(BaseModel):\n    \"\"\"A top-level H2 body section with optional H3 subsections.\"\"\"\n    heading: str = Field(description=\"H2 section heading\")\n    content: str = Field(description=\"Prose content with inline [N] citation markers\")\n    subsections: list[BodySubSection] = Field(\n        default_factory=list,\n        description=\"Optional H3 subsections for deeper topics\"\n    )\n\nclass SynthesizedPage(BaseModel):\n    # ... existing fields ...\n    body_sections: list[BodySection] = Field(...)\n    body_sections_format: str = Field(default=\"v2\", description=\"Format version for body_sections\")\n```\n\n### Citation Rules for the Prompt\n\n- `[N]` after a specific claim (setting, value, routing decision) where N is the zero-indexed moment from the input\n- `[N,M]` when a claim draws from multiple moments\n- Citations only on factual/technical claims, not on general observations or orienting sentences\n- Every section should have at least one citation\n- A moment can be cited multiple times across sections\n- Citation is placed at the end of the sentence containing the claim, before the period: \"Set the LFO to 3/16th timing [2].\"\n\n### Test Harness Adaptations\n\nWord count: Walk `section.content` + `sum(sub.content for sub in section.subsections)` instead of `.values()`.\n\nSection count: `len(body_sections)` gives H2 count. Can also report subsection count.\n\nNew validation checks the harness should report:\n- Citation coverage: what % of moments are cited at least once\n- Citation format: all `[N]` markers reference valid moment indices\n- Nesting depth: H3 subsections are present (not all flat)\n- Format version: output includes `body_sections_format: \"v2\"`\n\n### Existing Test Fixtures\n\nThe test harness uses `export_fixture.py` to create fixture JSONs from the DB. There are small sample fixtures in `backend/pipeline/quality/fixtures/` but no full-sized synthesis fixtures checked in. The harness CLI (`python -m pipeline.test_harness run --fixture <path>`) is the primary verification tool.\n\n**To verify S01:** Run the test harness with an existing fixture (or create one from the live DB on ub01) against the new prompt and confirm:\n1. Output parses as the new `SynthesisResult` schema\n2. body_sections is a list of objects with heading/content/subsections\n3. Citation markers `[N]` appear in prose\n4. All citation indices are valid (within moment range)\n5. Word count and section count meet minimums\n\n### Prompt Backup\n\nA backup mechanism exists: `prompts/stage5_synthesis.20260401_101518.bak`. The promote subcommand also archives to `prompts/archive/`. Before overwriting the production prompt, the current version should be backed up.\n\n## Natural Task Seams\n\n1. **T01: Schema + citation validation utilities** — New `BodySection`/`BodySubSection` models in schemas.py. A small utility function to validate citation indices against moment count (reusable by harness and pipeline). ~30 min.\n\n2. **T02: Prompt v5 rewrite** — Rewrite the output format section of stage5_synthesis.txt. Add citation rules. Update the JSON example. Preserve all voice/tone/ordering guidance. Back up the current prompt first. ~45 min.\n\n3. **T03: Test harness update + verification run** — Update word-count/section-count logic in test_harness.py for new structure. Add citation coverage reporting. Run the harness against a fixture with the new prompt and capture output. ~30 min.\n\nT01 and T02 are independent. T03 depends on both.\n\n## Risks and Mitigations\n\n| Risk | Likelihood | Mitigation |\n|------|-----------|------------|\n| LLM doesn't reliably produce nested subsections | Medium | Make subsections optional. Prompt with explicit \"at least 2 sections must have subsections\" rule. |\n| LLM omits or misformats citation markers | Medium | Explicit citation examples in prompt. Harness reports citation coverage as a warning, not a hard fail. |\n| Schema change breaks quality scorer mid-milestone | Low | Scorer is only used during prompt optimization runs, not in the production pipeline. Can be updated separately. |\n| Very large prompts push token count over limit | Low | The v5 prompt will be similar length to v4 — format section grows but doesn't double. |\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| D021 | M011 | scope | Which UI/UX assessment findings to implement in M011 | 12 of 16 findings approved; F01 (beginner paths), F02 (YouTube links), F03 (hide admin), F15 (CTA label) denied | User triaged each finding individually. Denied F01 because audience knows what they want. Denied F02 because no video URLs / don't want to link out. Denied F03 because admin dropdown is fine as-is. Denied F15 as low-value. | Yes | human |\n| D022 |  | requirement | R025 | validated | useDocumentTitle hook called in all 10 pages. Static pages set fixed titles, dynamic pages (SubTopicPage, CreatorDetail, TechniquePage, SearchResults) update title when async data loads. | Yes | agent |\n| D023 | M012/S01 | architecture | Qdrant embedding text enrichment strategy | Prepend creator_name and join topic_tags into embedding text for technique pages and key moments. Batch-resolve creator names at stage 6 start. | Semantic search now surfaces results for creator-name queries and tag-specific queries. Batch resolution avoids N+1 lookups during embedding. Reindex-all endpoint enables one-shot re-embedding after text composition changes. | Yes | agent |\n\n---\n\n[...truncated 5 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/M014/M014-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M014/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":"ec1f3205","parentId":"1f34dc6c","timestamp":"2026-04-03T00:46:43.499Z"}
{"type":"message","id":"4fe2d7ea","parentId":"ec1f3205","timestamp":"2026-04-03T00:46:49.607Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the relevant code files to ground the plan in reality before decomposing.","thinkingSignature":"EpMCClkIDBgCKkAeowBEweESd0GHZY7O3Lwv583DooaHO95ExbNAAeXs2tM/rWbd9+l+X00R9TvvWukcWfXnxWX3EZ7ufDgnASq7Mg9jbGF1ZGUtb3B1cy00LTY4ABIMkwyywrmRfbjxZoNfGgy5h5i797HdWP3bvHEiMMCQ+m7QXA/u61EZqkQpDxFwbL6soJdn2tyCvqZ4/uAo/OkulEvApHEc7an1YvUK7CpotR/pABRov+TNoWlF4tYcE3N6SF5hpbNyLxK+7PqTextpKFb0zntihJ+RRBDiYn1ztM7q35iWypa5yvG7kzZrIpZGRg+vdhnmKF3lUCcMkue7aOF5Vt2GCdJ0jkJTU2B8GTBbKXx/t7UYAQ=="},{"type":"text","text":"Let me explore the current state of the relevant files before planning."},{"type":"toolCall","id":"toolu_01BuaGnGf6bkwV6zLpZMjQSL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat prompts/stage5_synthesis.txt | head -20 && echo \"---\" && wc -l prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01A1MKMANDVJjiehW13xdNfU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"body_sections\" backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01EB5q9oZuwVL5q9est2A1sS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"body_sections\" backend/pipeline/test_harness.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":287,"cacheRead":59865,"cacheWrite":8433,"totalTokens":68587,"cost":{"input":0.00001,"output":0.007175,"cacheRead":0.029932499999999997,"cacheWrite":0.05270625,"total":0.08982375000000001}},"stopReason":"toolUse","timestamp":1775177203498}}
{"type":"message","id":"7a4dad6a","parentId":"4fe2d7ea","timestamp":"2026-04-03T00:46:49.621Z","message":{"role":"toolResult","toolCallId":"toolu_01BuaGnGf6bkwV6zLpZMjQSL","toolName":"bash","content":[{"type":"text","text":"You are writing a technique reference page for music producers. Your job is to teach. You watched this creator work, absorbed their methods, and now you're writing the clear, instructive guide that helps another producer apply these techniques in their own sessions. Your tone is direct and knowledgeable — a senior colleague walking someone through an approach they've validated in practice.\n\n## What you are creating\n\nA Chrysopedia technique page teaches a specific production method. Not a video summary. Not a blog post. A clear, instructive reference that a producer can follow in their DAW.\n\nA producer should be able to read this page and apply the technique. If they can't, the page failed.\n\nThe page contains:\n1. **Instructive prose** — substantive paragraphs that teach the technique step by step. Every paragraph earns its place with at least one specific, actionable detail. If a paragraph could be deleted without losing anything concrete, delete it.\n2. **Key moments index** — reference list of the source moments for readers who want to trace information back to the original content.\n\n## Voice and tone\n\nYour tone is instructive and confident. You're teaching a technique, not narrating someone's session. The creator is the source — they discovered it, they validated it — but the page teaches it as method.\n\n- **Instructive, not narrative** — write \"Route the effect at 100% wet on a parallel lane, then control level via gain\" rather than \"He routes the effect at 100% wet.\" The reader is here to learn a technique, not to read about what someone did. Use imperative/instructive phrasing (\"set\", \"route\", \"use\", \"check\") for methods. The page reads like a lesson, not a biography.\n- **No hesitation** — state techniques as established method. \"Set the LFO to 3/16th timing\" not \"you might want to try setting the LFO to around 3/16th.\"\n- **Direct quotes for personality** — pull 2-3 direct quotes per page where the creator's phrasing is vivid or memorable. These give the page life. Introduce them with a brief attribution (\"as he puts it,\" or \"in his words,\") then let the quote do the work. Outside of quotes, the prose teaches — no attribution needed.\n- **Specific and grounded** — every technical claim needs concrete backing: plugin names, settings, frequencies, ratios, time values, routing decisions. \"Use some compression\" is worthless. \"OTT at 30% depth on the bus\" is useful.\n---\n206 prompts/stage5_synthesis.txt\n"}],"isError":false,"timestamp":1775177209621}}
{"type":"message","id":"c609c349","parentId":"7a4dad6a","timestamp":"2026-04-03T00:46:49.623Z","message":{"role":"toolResult","toolCallId":"toolu_01A1MKMANDVJjiehW13xdNfU","toolName":"bash","content":[{"type":"text","text":"    body_sections: dict = Field(\n"}],"isError":false,"timestamp":1775177209623}}
{"type":"message","id":"cfa39539","parentId":"c609c349","timestamp":"2026-04-03T00:46:49.627Z","message":{"role":"toolResult","toolCallId":"toolu_01EB5q9oZuwVL5q9est2A1sS","toolName":"bash","content":[{"type":"text","text":"            sections = page.body_sections or {}\n                sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n"}],"isError":false,"timestamp":1775177209626}}
{"type":"message","id":"4e89c73f","parentId":"cfa39539","timestamp":"2026-04-03T00:46:54.207Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SzDss8ZPXcjj6SctE2uwZL","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_012ifgUPsMXuCH3UM4XR5sTP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"body_sections|word.?count|section.*count\" backend/pipeline/test_harness.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":140,"cacheRead":68298,"cacheWrite":962,"totalTokens":69401,"cost":{"input":0.000005,"output":0.0035,"cacheRead":0.034149,"cacheWrite":0.0060125000000000005,"total":0.0436665}},"stopReason":"toolUse","timestamp":1775177209626}}
{"type":"message","id":"b2106ef8","parentId":"4e89c73f","timestamp":"2026-04-03T00:46:54.216Z","message":{"role":"toolResult","toolCallId":"toolu_01SzDss8ZPXcjj6SctE2uwZL","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775177214216}}
{"type":"message","id":"fbc5f0a0","parentId":"b2106ef8","timestamp":"2026-04-03T00:46:54.227Z","message":{"role":"toolResult","toolCallId":"toolu_012ifgUPsMXuCH3UM4XR5sTP","toolName":"bash","content":[{"type":"text","text":"283:            sections = page.body_sections or {}\n284:            word_count = sum(len(str(v).split()) for v in sections.values())\n285:            total_words += word_count\n289:                f\"{len(sections)} sections, {word_count} words, \"\n457:                sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n"}],"isError":false,"timestamp":1775177214225}}
{"type":"message","id":"1f1bd5f7","parentId":"fbc5f0a0","timestamp":"2026-04-03T00:46:58.307Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TE2gQPosh6hGsXL6f3kXK1","name":"read","arguments":{"offset":150,"path":"prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01Kiq6jtwEGwgqWArsCk13HG","name":"read","arguments":{"limit":40,"offset":270,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":69260,"cacheWrite":1319,"totalTokens":70733,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.03463,"cacheWrite":0.008243750000000001,"total":0.04670375}},"stopReason":"toolUse","timestamp":1775177214225}}
{"type":"message","id":"ac82487b","parentId":"1f1bd5f7","timestamp":"2026-04-03T00:46:58.311Z","message":{"role":"toolResult","toolCallId":"toolu_01TE2gQPosh6hGsXL6f3kXK1","toolName":"read","content":[{"type":"text","text":"- Bad: [\"Vital\", \"Kilohearts\", \"EQ\", \"Trash 2\"]\n\n## Source quality assessment\n\nAssess source_quality based on the nature of the input moments:\n- **structured**: Moments come from a planned tutorial with clear instructional flow. Most details are explicitly taught.\n- **mixed**: Some moments are well-structured, others are scattered or conversational. Common for track breakdowns.\n- **unstructured**: Moments are extracted from livestreams, Q&A sessions, or very informal content. Insights were scattered across a long session.\n\n## Input format\n\nThe creator name is provided in a <creator> tag. Key moments are provided inside <moments> tags as a JSON array, enriched with classification metadata (topic_category, topic_tags). All moments are from the same creator and related topic area. Use the creator name from the <creator> tag in the title, slug, and summary. Do NOT use the creator name repeatedly in body section prose — see the name usage rules above. Never invent or guess a creator name from transcript content.\n\n## Output format\n\nReturn a JSON object with a single key \"pages\" containing a list of synthesized pages. Most inputs should produce a **single page** — only split into multiple pages if the moments cover two genuinely unrelated techniques that a producer would never look up together (e.g., moments about both \"kick design\" and \"reverb automation\" that happen to share a category). When in doubt, keep it as one page. Every page must meet the minimum thresholds: at least 3 sections, at least 400 words, at least 3 source moments. If a split would create a page below these thresholds, don't split — merge into one richer page instead. When splitting, assign each moment to exactly one page via moment_indices — every input moment index must appear in exactly one page's array.\n\n```json\n{\n  \"pages\": [\n    {\n      \"title\": \"Snare Design by ExampleCreator\",\n      \"slug\": \"snare-design-examplecreator\",\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"drums\", \"snare\", \"layering\", \"saturation\", \"transient shaping\"],\n      \"summary\": \"ExampleCreator builds snares as three independent layers — transient click, tonal body, and noise tail — each shaped by a transient shaper before any bus processing. The signature crunch comes from parallel soft-clip saturation with a pre-delay that preserves the clean transient.\",\n      \"body_sections\": {\n        \"Three-layer snare construction\": \"Layered snare design splits the sound into three independent components, each serving a distinct role: the transient click (a short noise burst, 2-5ms decay), the tonal body (a pitched sine or triangle wave around 180-220Hz, tuned to the track's key), and the noise tail (filtered white noise with a fast exponential decay). Vital's noise oscillator works well for the click layer, sometimes with a bandpass around 2-4kHz to control the character.\\n\\nThe critical step: shape each layer's transient independently before any bus processing. Use a transient shaper (Kilohearts, attack +4 to +6dB, sustain -6 to -8dB) rather than compression for this — 'compression adds sustain as a side effect while a transient shaper gives you direct independent control of both.'\",\n        \"Parallel saturation for crunch\": \"The crunch character comes from parallel saturation, not inline processing. Route the summed snare to a send with Trash 2 using the tape algorithm at 30-40% wet. The key detail: add a pre-delay of approximately 5ms on the saturation send. This lets the clean transient click through untouched while only the body and tail pick up harmonic content.\\n\\nAvoid saturating the transient directly — it 'smears the snap into mush' and you lose the precision that makes the snare cut through a dense mix.\",\n        \"Bus processing in dense arrangements\": \"When the snare needs to punch through a busy mix, prioritize transient clarity over sustain. On the snare bus compressor, set a high-pass sidechain filter around 200-300Hz so low-end energy from the body layer doesn't trigger gain reduction. This keeps the snare's punch independent of whatever the sub bass is doing.\\n\\nAlso check the snare against the lead or vocal bus specifically — not just soloed. The 2-4kHz presence range is where both elements compete. A small notch on the snare's body is usually less costly than losing vocal clarity.\"\n      },\n      \"signal_chains\": [\n        {\n          \"name\": \"Snare layer processing\",\n          \"steps\": [\n            \"Noise osc (Vital) → Transient Shaper (Kilohearts, attack +6dB, sustain -8dB) → EQ (Pro-Q 3, shelf -3dB @ 12kHz)\",\n            \"Dry path → snare bus\",\n            \"Send → Pre-delay (5ms) → Trash 2 (tape algorithm, 35% wet) → snare bus\"\n          ]\n        }\n      ],\n      \"plugins\": [\"Vital\", \"Kilohearts Transient Shaper\", \"FabFilter Pro-Q 3\", \"iZotope Trash 2\"],\n      \"source_quality\": \"structured\",\n      \"moment_indices\": [0, 1, 2, 3, 4]\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **title**: Short, practical technique name followed by \"by {name from <creator> tag}\" — maximum 8 words before \"by\". The title should sound like what a producer would search for or tell a friend about. Examples: \"Parallel Harmonic Building by Keota\", \"Bass Resampling Workflow by KOAN Sound\", \"The 90-200Hz Fix by Mr. Bill\". BAD: \"Lower-Mid Harmonic Diagnostics and Parallel Stereo Processing by Keota\" — that's a thesis title, not a technique name. Use title case.\n- **slug**: URL-safe, lowercase, hyphenated version of the title including creator name. Examples: \"snare-design-examplecreator\", \"bass-resampling-workflow-koan-sound\".\n- **topic_tags**: Merge and deduplicate from input moment tags. Add any clearly relevant tags the moments missed. Keep tags specific — \"sidechain compression\" not \"audio processing\".\n- **summary**: 2-4 sentences. The most important insight first, then the method, then the distinguishing detail. A reader should get the core idea from the summary alone.\n- **body_sections**: Dict of section_name → prose content. Section names derived from content (never generic). Each section 2-5 substantive paragraphs.\n- **plugins**: List of string plugin names. Plain strings only — never objects. Include only plugins the creator mentioned or demonstrated. Use standard/common plugin names.\n- **moment_indices**: Zero-indexed list referencing which input moments this page covers. Every input moment must appear in exactly one page's moment_indices."}],"isError":false,"timestamp":1775177218310}}
{"type":"message","id":"cc75ed03","parentId":"ac82487b","timestamp":"2026-04-03T00:46:58.314Z","message":{"role":"toolResult","toolCallId":"toolu_01Kiq6jtwEGwgqWArsCk13HG","toolName":"read","content":[{"type":"text","text":"        # Parse response\n        try:\n            result = SynthesisResult.model_validate_json(str(raw))\n        except (ValidationError, json.JSONDecodeError) as exc:\n            _log(\"ERROR\", f\"  Parse failed: {exc}\", level=\"ERROR\")\n            _log(\"ERROR\", f\"  Raw response (first 2000 chars): {str(raw)[:2000]}\", level=\"ERROR\")\n            exit_code = 2\n            continue\n\n        # Log per-page summary\n        _log(\"SYNTH\", f\"  Parsed: {len(result.pages)} pages synthesized\")\n        total_words = 0\n        for page in result.pages:\n            sections = page.body_sections or {}\n            word_count = sum(len(str(v).split()) for v in sections.values())\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{len(sections)} sections, {word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            all_pages.append(page.model_dump())\n\n    # Summary\n    _log(\"SUMMARY\", f\"Total: {len(all_pages)} pages across {len(categories)} categories\")\n    _log(\"SUMMARY\", f\"Tokens: {total_prompt_tokens} prompt + {total_completion_tokens} completion = {total_prompt_tokens + total_completion_tokens} total\")\n    _log(\"SUMMARY\", f\"Duration: {total_duration_ms}ms ({total_duration_ms / 1000:.1f}s)\")\n\n    return all_pages, exit_code\n\n\n# ── Promote: deploy a prompt to production ─────────────────────────────────\n\n_STAGE_PROMPT_MAP = {\n    2: \"stage2_segmentation.txt\",\n    3: \"stage3_extraction.txt\",\n    4: \"stage4_classification.txt\",\n\n[173 more lines in file. Use offset=310 to continue.]"}],"isError":false,"timestamp":1775177218312}}
{"type":"message","id":"5ea13c7e","parentId":"cc75ed03","timestamp":"2026-04-03T00:47:03.452Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RCxA78nDmxpNnCbEmCM5bW","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"body_sections\" backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01PVPjdkMrnXkzB8Z4PchjLM","name":"read","arguments":{"limit":30,"offset":445,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":164,"cacheRead":70579,"cacheWrite":2371,"totalTokens":73115,"cost":{"input":0.000005,"output":0.0041,"cacheRead":0.0352895,"cacheWrite":0.01481875,"total":0.05421325}},"stopReason":"toolUse","timestamp":1775177218313}}
{"type":"message","id":"bc673489","parentId":"5ea13c7e","timestamp":"2026-04-03T00:47:03.463Z","message":{"role":"toolResult","toolCallId":"toolu_01RCxA78nDmxpNnCbEmCM5bW","toolName":"bash","content":[{"type":"text","text":"283:            sections = page.body_sections or {}\n457:                sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n"}],"isError":false,"timestamp":1775177223463}}
{"type":"message","id":"59420b18","parentId":"bc673489","timestamp":"2026-04-03T00:47:03.466Z","message":{"role":"toolResult","toolCallId":"toolu_01PVPjdkMrnXkzB8Z4PchjLM","toolName":"read","content":[{"type":"text","text":"        return exit_code\n\n    output = {\n        \"fixture_source\": args.fixture,\n        \"prompt_source\": prompt_path,\n        \"creator_name\": fixture.creator_name,\n        \"video_id\": fixture.video_id,\n        \"category_filter\": args.category,\n        \"pages\": pages,\n        \"metadata\": {\n            \"page_count\": len(pages),\n            \"total_words\": sum(\n                sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n                for p in pages\n            ),\n            \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n        },\n    }\n\n    output_json = json.dumps(output, indent=2, ensure_ascii=False)\n\n    if args.output:\n        Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n        Path(args.output).write_text(output_json, encoding=\"utf-8\")\n        _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n    else:\n        print(output_json)\n        _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n\n    total_elapsed = time.monotonic() - overall_start\n\n[8 more lines in file. Use offset=475 to continue.]"}],"isError":false,"timestamp":1775177223463}}
{"type":"message","id":"723a1c49","parentId":"59420b18","timestamp":"2026-04-03T00:47:07.983Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the templates."},{"type":"toolCall","id":"toolu_01RVZs49EC1BSSvEPiXnWrdj","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01RAnDLTgxAXm9HUHDHqu92R","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":133,"cacheRead":72950,"cacheWrite":613,"totalTokens":73697,"cost":{"input":0.000005,"output":0.0033250000000000003,"cacheRead":0.036475,"cacheWrite":0.00383125,"total":0.04363625}},"stopReason":"toolUse","timestamp":1775177223464}}
{"type":"message","id":"693a18dd","parentId":"723a1c49","timestamp":"2026-04-03T00:47:07.986Z","message":{"role":"toolResult","toolCallId":"toolu_01RVZs49EC1BSSvEPiXnWrdj","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":1775177227985}}
{"type":"message","id":"8740f90d","parentId":"693a18dd","timestamp":"2026-04-03T00:47:07.990Z","message":{"role":"toolResult","toolCallId":"toolu_01RAnDLTgxAXm9HUHDHqu92R","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":1775177227988}}
{"type":"message","id":"8b4fca02","parentId":"8740f90d","timestamp":"2026-04-03T00:47:15.327Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016wF5utUJpnzf4WzMNtJ7GY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/REQUIREMENTS.md | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":73563,"cacheWrite":2741,"totalTokens":76386,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.0367815,"cacheWrite":0.01713125,"total":0.05594275}},"stopReason":"toolUse","timestamp":1775177227989}}
{"type":"message","id":"005679e6","parentId":"8b4fca02","timestamp":"2026-04-03T00:47:15.344Z","message":{"role":"toolResult","toolCallId":"toolu_016wF5utUJpnzf4WzMNtJ7GY","toolName":"bash","content":[{"type":"text","text":"# 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 (7 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic loads a dedicated page with creator-grouped techniques and breadcrumbs.\n**Validation:** Hierarchy renders, filtering works, sub-topics have dedicated pages at /topics/:category/:subtopic with backend filtering and creator grouping. Verified: M010/S01.\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"}],"isError":false,"timestamp":1775177235341}}
{"type":"message","id":"06ae9739","parentId":"005679e6","timestamp":"2026-04-03T00:47:19.168Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01MHVcAkysQHbjFz496AUCg1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"^## R0\" .gsd/REQUIREMENTS.md | tail -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":76304,"cacheWrite":1471,"totalTokens":77865,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.038152,"cacheWrite":0.00919375,"total":0.04957575}},"stopReason":"toolUse","timestamp":1775177235342}}
{"type":"message","id":"c70034ab","parentId":"06ae9739","timestamp":"2026-04-03T00:47:19.183Z","message":{"role":"toolResult","toolCallId":"toolu_01MHVcAkysQHbjFz496AUCg1","toolName":"bash","content":[{"type":"text","text":"15:## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n21:## R004 — Review Queue UI\n27:## R005 — Search-First Web UI\n33:## R006 — Technique Page Display\n39:## R007 — Creators Browse Page\n45:## R008 — Topics Browse Page\n51:## R009 — Qdrant Vector Search Integration\n57:## R010 — Docker Compose Deployment\n63:## R011 — Canonical Tag System\n69:## R012 — Incremental Content Addition\n75:## R013 — Prompt Template System\n81:## R014 — Creator Equity\n87:## R015 — 30-Second Retrieval Target\n93:## R016 — Card Hover Animations & Staggered Entrance\n99:## R017 — Featured Technique Visual Redesign\n105:## R018 — Random Technique Discovery\n111:## R019 — Topics Page Default-Collapsed\n117:## R020 — Global Search in Navigation\n123:## R021 — Mobile Hamburger Menu\n129:## R022 — Heading Hierarchy Fix\n135:## R023 — Skip-to-Content Link\n141:## R024 — Text Contrast AA Compliance\n147:## R025 — Page-Specific Document Titles\n153:## R026 — Creator Stats Topic-Colored Pills\n159:## R027 — Tag Overflow Limit on Cards\n165:## R028 — Empty Subtopic Handling\n173:## R029 — Beginner Learning Paths\n179:## R030 — YouTube Deep Links on Key Moments\n185:## R031 — Hide Admin Dropdown\n191:## R032 — CTA Label Changes\n"}],"isError":false,"timestamp":1775177239181}}
{"type":"message","id":"88a06dae","parentId":"c70034ab","timestamp":"2026-04-03T00:47:26.918Z","message":{"role":"assistant","content":[{"type":"text","text":"No new requirements are owned by M014/S01 — this is a prompt+schema+harness slice. The roadmap doesn't list specific requirement IDs for S01. It advances R003 (extraction pipeline) and R013 (prompt templates) indirectly.\n\nNow let me check what the prompt output format section looks like more precisely, and confirm the test harness fixture infrastructure:"},{"type":"toolCall","id":"toolu_01FuRd68VrMsP2ktZKZ7tSvR","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/quality/fixtures/ 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01SLm7YU4NDEYSdGmrYLaoqZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def run\\|def validate\\|def compare\\|class.*Fixture\\|class.*Harness\" backend/pipeline/test_harness.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":265,"cacheRead":76304,"cacheWrite":1987,"totalTokens":78557,"cost":{"input":0.000005,"output":0.006625000000000001,"cacheRead":0.038152,"cacheWrite":0.012418750000000001,"total":0.05720075}},"stopReason":"toolUse","timestamp":1775177239182}}
{"type":"message","id":"ea8477af","parentId":"88a06dae","timestamp":"2026-04-03T00:47:26.932Z","message":{"role":"toolResult","toolCallId":"toolu_01FuRd68VrMsP2ktZKZ7tSvR","toolName":"bash","content":[{"type":"text","text":"__init__.py\nsample_classifications.json\nsample_moments.json\nsample_segments.json\nsample_topic_group.json\n"}],"isError":false,"timestamp":1775177246931}}
{"type":"message","id":"db3f18b7","parentId":"ea8477af","timestamp":"2026-04-03T00:47:26.937Z","message":{"role":"toolResult","toolCallId":"toolu_01SLm7YU4NDEYSdGmrYLaoqZ","toolName":"bash","content":[{"type":"text","text":"96:class FixtureData:\n167:def run_synthesis(\n"}],"isError":false,"timestamp":1775177246935}}
{"type":"message","id":"b4eb36fe","parentId":"db3f18b7","timestamp":"2026-04-03T00:47:32.268Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I have a clear picture now. The research doc's three-task decomposition (T01: schema, T02: prompt, T03: harness+verification) is well-reasoned. T01 and T02 are independent, T03 depends on both. Let me verify there are no existing tests for schemas:"},{"type":"toolCall","id":"toolu_01QWFLwdEL4yafAiyTLQnxH6","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"test_schemas*\" -o -name \"*test*schema*\" 2>/dev/null | grep -v node_modules | grep -v .gsd"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":78291,"cacheWrite":375,"totalTokens":78847,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.0391455,"cacheWrite":0.00234375,"total":0.04599425}},"stopReason":"toolUse","timestamp":1775177246935}}
{"type":"message","id":"ded21f25","parentId":"b4eb36fe","timestamp":"2026-04-03T00:47:32.308Z","message":{"role":"toolResult","toolCallId":"toolu_01QWFLwdEL4yafAiyTLQnxH6","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775177252306}}
{"type":"message","id":"c723fcd3","parentId":"ded21f25","timestamp":"2026-04-03T00:47:38.009Z","message":{"role":"assistant","content":[{"type":"text","text":"No existing schema tests. The verification for this slice is running the test harness with the new prompt against a fixture — but that requires an LLM call, which we can't do in a CI-style verification. The schema validation and citation utilities can be unit tested though.\n\nLet me check the prompt's output format section boundaries more precisely:"},{"type":"toolCall","id":"toolu_011njx6mgDEBSKzHdGG2uoLe","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"^## \" prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":156,"cacheRead":78666,"cacheWrite":211,"totalTokens":79034,"cost":{"input":0.000005,"output":0.0039000000000000003,"cacheRead":0.039333,"cacheWrite":0.00131875,"total":0.04455675}},"stopReason":"toolUse","timestamp":1775177252307}}
{"type":"message","id":"922540b0","parentId":"c723fcd3","timestamp":"2026-04-03T00:47:38.024Z","message":{"role":"toolResult","toolCallId":"toolu_011njx6mgDEBSKzHdGG2uoLe","toolName":"bash","content":[{"type":"text","text":"3:## What you are creating\n13:## Voice and tone\n46:## Body sections structure\n66:## Plugin and detail rules\n76:## Synthesis approach\n94:## Section ordering — follow the workflow\n120:## Reader context\n126:## Summary requirements\n135:## Writing engagement\n144:## Signal chains\n152:## Source quality assessment\n159:## Input format\n163:## Output format\n199:## Field rules\n"}],"isError":false,"timestamp":1775177258021}}
{"type":"message","id":"f6b79dca","parentId":"922540b0","timestamp":"2026-04-03T00:47:41.138Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FCywF4SFyDYiu8WDDrtVjq","name":"read","arguments":{"limit":30,"offset":46,"path":"prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":78877,"cacheWrite":264,"totalTokens":79235,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.0394385,"cacheWrite":0.00165,"total":0.0434185}},"stopReason":"toolUse","timestamp":1775177258022}}
{"type":"message","id":"4fa89fab","parentId":"f6b79dca","timestamp":"2026-04-03T00:47:41.143Z","message":{"role":"toolResult","toolCallId":"toolu_01FCywF4SFyDYiu8WDDrtVjq","toolName":"read","content":[{"type":"text","text":"## Body sections structure\n\nFrame sections around the problems the creator is solving or the situations a producer would recognize, not the tools being used.\n\nGood section names sound like something you'd say to a friend in the studio:\n- \"Why your bass disappears in mono\" / \"Getting the snare to cut without killing the vocal\" / \"The 90-200Hz gap that makes everything feel disconnected\" / \"Adding movement without losing the pocket\"\n\nBad section names sound like a textbook table of contents:\n- \"Overview\" / \"EQ Settings\" / \"Understanding Phase Plant signal flow\" / \"Compression Techniques\" / \"Final Thoughts\"\n\nThe test: would a producer ever search for this phrase when they're stuck? \"Why your bass disappears in mono\" — yes. \"Understanding Phase Plant signal flow\" — no, that's a manual heading, not a studio problem.\n\nEach section: 2-4 paragraphs of substantive prose, minimum 150 words per section. Flow logically within each section. **Merge thin topics aggressively** — if a section would only have 1 paragraph, fold it into the most related section. Never generate a section with fewer than 2 paragraphs.\n\n**Page minimum thresholds — hard rules:**\n- A page MUST have at least 3 sections and at least 400 words total. If the content can't fill this, it is too thin to be a standalone page.\n- If a category has fewer than 3 moments, DO NOT generate a standalone page for it. Instead, fold those moments into the most related page from another category.\n- NEVER generate a page with only 1 section. That's a paragraph, not a technique page.\n- Prefer fewer, richer pages over many thin ones. 3 substantial pages is always better than 6 stubs.\n\n## Plugin and detail rules\n\nEvery specific value needs its context — why this number, what problem it solves, when you'd change it.\n\nDon't just list \"attack +6dB\" — explain that the transient shaper is set to +6dB attack because the goal is to punch the initial click through without relying on compression (which adds sustain as a side effect). The value and the reasoning form a unit.\n\nInclude plugin names and settings only when the creator was teaching that setting — spending time on why they chose it. A plugin merely visible in their session belongs in the plugins list, not the prose.\n\nNever use vague fill: \"experiment with settings,\" \"adjust to taste,\" \"use your ears.\" If the creator gave a specific value, use it. If they gave a range, state the range.\n\n\n[132 more lines in file. Use offset=76 to continue.]"}],"isError":false,"timestamp":1775177261140}}
{"type":"message","id":"14c3e301","parentId":"4fa89fab","timestamp":"2026-04-03T00:48:46.255Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have a solid understanding of all three files that need changes, the exact structure of each, and the boundaries of the work. Let me plan the slice.\n\nThe research doc's three-task split maps cleanly to the work:\n- **T01**: Schema models + citation validation utility (independent)  \n- **T02**: Prompt v5 rewrite (independent, but I'll combine with T01 since schema+prompt are tightly coupled and small)\n- **T03**: Test harness update + verification\n\nActually — the research says T01 and T02 are independent, and T03 depends on both. But the prompt rewrite is the high-risk piece (LLM compliance). And schema changes are tiny (~30 lines). Let me keep the three-task structure since T03 needs to validate the combination, and the prompt rewrite is significant enough to warrant its own focused task.\n\nWait — re-reading the constraint: \"Each task gets ~320K chars of inline context.\" These tasks are all small. Two tasks would be more efficient: T01 (schema + prompt) and T02 (harness + verification). But the prompt file is 206 lines and needs careful surgical editing — keeping it separate helps the executor focus. Three tasks is right."},{"type":"toolCall","id":"toolu_011gp5coa9bwrEBPxqaV1rke","name":"gsd_plan_slice","arguments":{"goal":"Stage 5 synthesis prompt produces body_sections as a list of BodySection objects with H2/H3 nesting and inline [N] citation markers, validated by schema and test harness.","integrationClosure":"- Upstream: reads `prompts/stage5_synthesis.txt` (rewritten), `backend/pipeline/schemas.py` (modified)\n- New wiring: none — schema change is backward-compatible at DB level (JSONB accepts any structure), downstream consumers (quality scorer, frontend) break but are fixed in S03-S05\n- Remaining: S03/S04 must update quality scorer `.items()` calls, S05 must update frontend rendering","milestoneId":"M014","observabilityImpact":"None — this slice changes static artifacts (prompt text, Pydantic schema, harness logic). No runtime services modified.","proofLevel":"Contract — schema validates, prompt is syntactically correct, harness logic handles new structure. No LLM call required for proof (that's a manual verification step post-merge).","sliceId":"S01","successCriteria":"- `SynthesizedPage.body_sections` is `list[BodySection]` with heading/content/subsections fields\n- Prompt v5 includes citation rules and nested section JSON example\n- Test harness word-count and section-count logic works with new list-of-objects structure\n- Citation validation utility checks marker format and index bounds\n- `body_sections_format: \"v2\"` field distinguishes new-format pages","tasks":[{"description":"Add BodySubSection and BodySection Pydantic models to schemas.py. Change SynthesizedPage.body_sections from dict to list[BodySection]. Add body_sections_format field. Create a small citation validation utility (validate_citations) that checks [N] markers in section content against a moment count. This utility is reused by the test harness in T03.\n\n## Steps\n\n1. Read `backend/pipeline/schemas.py` fully.\n2. Add `BodySubSection` model with `heading: str` and `content: str` fields.\n3. Add `BodySection` model with `heading: str`, `content: str`, and `subsections: list[BodySubSection]` (default_factory=list).\n4. Change `SynthesizedPage.body_sections` type from `dict` to `list[BodySection]` with updated description.\n5. Add `body_sections_format: str = Field(default='v2')` to `SynthesizedPage`.\n6. Create `backend/pipeline/citation_utils.py` with:\n   - `extract_citations(text: str) -> list[int]` — regex extracts all `[N]` and `[N,M]` markers, returns sorted unique indices.\n   - `validate_citations(sections: list[BodySection], moment_count: int) -> dict` — returns `{valid: bool, total_citations: int, invalid_indices: list[int], uncited_moments: list[int], coverage_pct: float}`.\n7. Add unit tests in `backend/pipeline/test_citation_utils.py` covering: valid citations, out-of-range indices, multi-citation markers `[0,2]`, no citations, empty sections.\n\n## Must-Haves\n\n- [ ] BodySubSection and BodySection models exist with correct field types\n- [ ] SynthesizedPage.body_sections is list[BodySection]\n- [ ] body_sections_format field defaults to 'v2'\n- [ ] extract_citations parses [N] and [N,M] markers correctly\n- [ ] validate_citations returns coverage stats and flags invalid indices\n- [ ] Unit tests pass","estimate":"30m","expectedOutput":["`backend/pipeline/schemas.py` — updated with BodySection, BodySubSection models and modified SynthesizedPage","`backend/pipeline/citation_utils.py` — citation extraction and validation utilities","`backend/pipeline/test_citation_utils.py` — unit tests for citation utilities"],"files":["backend/pipeline/schemas.py","backend/pipeline/citation_utils.py","backend/pipeline/test_citation_utils.py"],"inputs":["`backend/pipeline/schemas.py` — existing schema file with SynthesizedPage model"],"taskId":"T01","title":"Add BodySection schema models and citation validation utility","verify":"cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_citation_utils.py -v"},{"description":"Rewrite the output format section of `prompts/stage5_synthesis.txt` to produce the new list-of-objects body_sections structure with inline [N] citation markers. Preserve all voice, tone, synthesis philosophy, and body section naming guidance. Back up the current prompt before editing.\n\n## Steps\n\n1. Back up: `cp prompts/stage5_synthesis.txt prompts/stage5_synthesis.$(date +%Y%m%d_%H%M%S).bak`\n2. Read the full prompt file.\n3. Update the `## Body sections structure` section (line ~46): add a paragraph about H3 subsections. Sections can contain 2-3 subsections when a topic has distinct sub-aspects. Subsections are optional — flat sections (no subsections) are fine when the topic doesn't warrant splitting.\n4. Add a new `## Citation rules` section after `## Body sections structure`:\n   - `[N]` after specific factual claims (settings, values, frequencies, routing decisions) where N is the zero-indexed moment from the input.\n   - `[N,M]` when a claim draws from multiple moments.\n   - Place citation at end of sentence before period: \"Set the LFO to 3/16th timing [2].\"\n   - Every section must have at least one citation.\n   - Citations only on technical/factual claims, not general observations.\n   - A moment can be cited multiple times.\n5. Rewrite `## Output format` section (line ~163): replace the JSON example with new structure using `body_sections` as a list of objects with heading/content/subsections. Include citation markers in the example prose. Add `body_sections_format: \"v2\"` field.\n6. Update `## Field rules` section (line ~199): change body_sections description from \"Dict of section_name → prose\" to \"List of section objects with heading, content, and optional subsections. Each section's content contains inline [N] citation markers.\"\n7. Verify: the prompt file is valid text, contains the new JSON example structure, has citation rules, and all other sections (voice, tone, plugin rules, etc.) are unchanged.\n\n## Must-Haves\n\n- [ ] Current prompt backed up before changes\n- [ ] body_sections in JSON example is a list of objects with heading/content/subsections\n- [ ] Citation rules section exists with [N] and [N,M] format\n- [ ] body_sections_format: \"v2\" in JSON example\n- [ ] All non-output-format sections preserved unchanged\n- [ ] Subsections shown as optional (empty array or omitted in some example sections)","estimate":"45m","expectedOutput":["`prompts/stage5_synthesis.txt` — rewritten with nested sections format, citation rules, and v2 JSON example"],"files":["prompts/stage5_synthesis.txt"],"inputs":["`prompts/stage5_synthesis.txt` — current v4 prompt (206 lines)"],"taskId":"T02","title":"Rewrite stage 5 prompt output format for nested sections and citations","verify":"cd /home/aux/projects/content-to-kb-automator && python -c \"import json; t=open('prompts/stage5_synthesis.txt').read(); assert 'body_sections_format' in t; assert 'Citation rules' in t or 'citation rules' in t.lower(); assert '\\\"subsections\\\"' in t; print('Prompt v5 structure OK')\""},{"description":"Update the test harness word-count and section-count logic to walk the new list-of-objects body_sections structure instead of dict.values(). Add citation coverage reporting using the citation_utils from T01. This closes the slice — the harness can parse and report on v2 synthesis output.\n\n## Steps\n\n1. Read `backend/pipeline/test_harness.py` fully.\n2. Read `backend/pipeline/schemas.py` (updated in T01) and `backend/pipeline/citation_utils.py` (created in T01).\n3. Update line ~283-289 (per-page summary in `run_synthesis`): replace `sections.values()` iteration with walking `BodySection` objects. Word count = sum of `section.content` words + sum of all `subsection.content` words. Section count = len(body_sections). Report subsection count too.\n4. Update line ~457 (metadata total_words): same dict→list-of-objects change for the output metadata calculation.\n5. Add citation reporting after per-page summary: import `validate_citations` from citation_utils, call it with `page.body_sections` and `len(page.moment_indices)`, log citation coverage % and any invalid indices.\n6. Create a small test script `backend/pipeline/test_harness_v2_format.py` that:\n   - Constructs a mock SynthesisResult with the new body_sections format (list of BodySection objects)\n   - Verifies word counting works correctly (known content → expected count)\n   - Verifies citation validation integrates correctly\n   - Does NOT require an LLM call\n\n## Must-Haves\n\n- [ ] Word count logic handles list[BodySection] including subsection content\n- [ ] Section count reports H2 count and subsection count\n- [ ] Citation coverage logged per-page\n- [ ] Metadata total_words uses new structure\n- [ ] Test script passes with mock v2 data","estimate":"30m","expectedOutput":["`backend/pipeline/test_harness.py` — updated word-count/section-count logic for list[BodySection]","`backend/pipeline/test_harness_v2_format.py` — test script validating harness handles v2 format"],"files":["backend/pipeline/test_harness.py","backend/pipeline/test_harness_v2_format.py"],"inputs":["`backend/pipeline/test_harness.py` — current harness with dict-based word counting","`backend/pipeline/schemas.py` — updated schema from T01","`backend/pipeline/citation_utils.py` — citation utilities from T01"],"taskId":"T03","title":"Update test harness for new body_sections structure and add citation reporting","verify":"cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_harness_v2_format.py -v && python -m pytest backend/pipeline/test_citation_utils.py -v"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3137,"cacheRead":79141,"cacheWrite":701,"totalTokens":82980,"cost":{"input":0.000005,"output":0.07842500000000001,"cacheRead":0.0395705,"cacheWrite":0.004381250000000001,"total":0.12238175000000001}},"stopReason":"toolUse","timestamp":1775177261141}}
{"type":"message","id":"7ea5aa6a","parentId":"14c3e301","timestamp":"2026-04-03T00:48:46.292Z","message":{"role":"toolResult","toolCallId":"toolu_011gp5coa9bwrEBPxqaV1rke","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S01 (M014)"}],"details":{"operation":"plan_slice","milestoneId":"M014","sliceId":"S01","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/S01-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T02-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T03-PLAN.md"]},"isError":false,"timestamp":1775177326291}}
{"type":"message","id":"f8dfc0de","parentId":"7ea5aa6a","timestamp":"2026-04-03T00:48:56.792Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n\n1. **Completion semantics**: T01 delivers the schema + citation utility, T02 delivers the prompt, T03 wires the harness to use both and validates. If all three pass, the slice goal (\"body_sections as list of objects, citation markers, validated by harness\") is met.\n\n2. **Requirement coverage**: No Active requirements are directly owned by S01. The slice advances R003/R013 indirectly. All must-haves map to tasks.\n\n3. **Task completeness**: Each task has steps, must-haves, verification commands, and backtick-wrapped input/output paths.\n\n4. **Dependency correctness**: T01 and T02 are independent. T03 depends on both (reads schemas.py from T01, prompt from T02). Order is correct.\n\n5. **Key links planned**: T03 explicitly imports citation_utils from T01. The prompt (T02) and schema (T01) must agree on format — both use the same structure from the research doc.\n\n6. **Scope sanity**: T01 has 7 steps/3 files, T02 has 7 steps/1 file, T03 has 6 steps/2 files. All within bounds.\n\nSlice S01 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":297,"cacheRead":79842,"cacheWrite":3164,"totalTokens":83304,"cost":{"input":0.000005,"output":0.007425,"cacheRead":0.039921,"cacheWrite":0.019775,"total":0.06712599999999999}},"stopReason":"stop","timestamp":1775177326291}}
