{"type":"thinking_level_change","id":"2238641a","parentId":null,"timestamp":"2026-04-03T00:43:35.722Z","thinkingLevel":"medium"}
{"type":"model_change","id":"5f93b5a7","parentId":"2238641a","timestamp":"2026-04-03T00:43:35.725Z","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## UNIT: Research 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### 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### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (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## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n## R016 — Card Hover Animations & Staggered Entrance\n**Status:** validated\n**Description:** All technique/content cards animate on hover (scale + shadow transition). Card grids use staggered fade-in-up entrance animations on page load.\n**Validation:** Cards visually respond to hover with smooth 200ms transition. Grid cards appear sequentially on page load.\n**Primary Owner:** M011/S01\n\n## R017 — Featured Technique Visual Redesign\n**Status:** validated\n**Description:** Featured Technique section on homepage has a visually distinct treatment (gradient border, glow, or full-bleed) that differentiates it from ordinary cards.\n**Validation:** Featured technique is visually prominent and clearly distinct from regular technique cards.\n**Primary Owner:** M011/S01\n\n## R018 — Random Technique Discovery\n**Status:** validated\n**Description:** A \"Random Technique\" button exists that navigates to a randomly selected technique page.\n**Validation:** Clicking the button loads a different technique each time.\n**Primary Owner:** M011/S01\n\n## R019 — Topics Page Default-Collapsed\n**Status:** validated\n**Description:** Topics page loads with all categories collapsed, showing only category cards. Clicking a category expands its sub-topics with a smooth slide animation.\n**Validation:** Page loads collapsed. Click expands with animation. Click again collapses.\n**Primary Owner:** M011/S02\n\n## R020 — Global Search in Navigation\n**Status:** validated\n**Description:** A compact search input in the navigation bar is available on all pages except the homepage. Cmd+K (or /) keyboard shortcut focuses it from any page.\n**Validation:** Search bar visible in nav on Topics, Creators, Technique, About pages. Cmd+K focuses it.\n**Primary Owner:** M011/S03\n\n## R021 — Mobile Hamburger Menu\n**Status:** validated\n**Description:** Navigation uses a hamburger menu on viewports below 768px with generous touch targets (44×44px minimum).\n**Validation:** Mobile viewport shows hamburger icon. Tapping reveals nav items. Touch targets meet minimum size.\n**Primary Owner:** M011/S03\n\n## R022 — Heading Hierarchy Fix\n**Status:** validated\n**Description:** Every page has exactly one H1 element. Heading levels are sequential (no skips from H1 to H3).\n**Validation:** DOM inspection confirms single H1 per page and sequential heading levels.\n**Primary Owner:** M011/S04\n\n## R023 — Skip-to-Content Link\n**Status:** validated\n**Description:** A visually hidden skip link is the first focusable element on every page, visible on keyboard focus.\n**Validation:** Tab from page load shows \"Skip to content\" link. Clicking it jumps to main content area.\n**Primary Owner:** M011/S04\n\n## R024 — Text Contrast AA Compliance\n**Status:** validated\n**Description:** All text meets WCAG AA contrast ratios (4.5:1 for normal text, 3:1 for large text) against the dark background.\n**Validation:** Secondary text color passes 4.5:1 contrast check against page background.\n**Primary Owner:** M011/S04\n\n## R025 — Page-Specific Document Titles\n**Status:** validated\n**Description:** Each route sets a descriptive document title (e.g., \"Bass — Sound Design — Chrysopedia\", \"COPYCATT — Chrysopedia\").\n**Validation:** Browser tab shows distinct title per page. Navigating between pages updates the title.\n**Primary Owner:** M011/S04\n\n## R026 — Creator Stats Topic-Colored Pills\n**Status:** validated\n**Description:** Creator detail page stats line rendered as topic-colored pill badges instead of run-on text.\n**Validation:** Stats show as visually distinct colored pills grouped by topic category.\n**Primary Owner:** M011/S02\n\n## R027 — Tag Overflow Limit on Cards\n**Status:** validated\n**Description:** Technique cards show a maximum of 4 tags, with a \"+N more\" indicator for additional tags.\n**Validation:** Cards with >4 tags show exactly 4 plus a \"+N more\" badge.\n**Primary Owner:** M011/S02\n\n## R028 — Empty Subtopic Handling\n**Status:** validated\n**Description:** Sub-topics with 0 techniques either hidden by default or display a \"Coming soon\" badge.\n**Validation:** Empty subtopics are visually distinguished or hidden. No dead-end clicks.\n**Primary Owner:** M011/S02\n\n## Denied / Out of Scope\n\n## R029 — Beginner Learning Paths\n**Status:** out-of-scope\n**Description:** Curated beginner learning paths with skill-level tags and \"New to Production?\" section.\n**Validation:** n/a\n**Notes:** Denied — audience knows what they're looking for. Assessment F01.\n\n## R030 — YouTube Deep Links on Key Moments\n**Status:** out-of-scope\n**Description:** Key moment timestamps link to source YouTube videos at the correct time.\n**Validation:** n/a\n**Notes:** Denied — no video URLs in data model / don't want to link out. Assessment F02.\n\n## R031 — Hide Admin Dropdown\n**Status:** out-of-scope\n**Description:** Admin dropdown hidden from public visitors behind auth or flag.\n**Validation:** n/a\n**Notes:** Denied — fine as-is. Assessment F03.\n\n## R032 — CTA Label Changes\n**Status:** out-of-scope\n**Description:** Change \"Start Exploring\" to more descriptive CTA text.\n**Validation:** n/a\n**Notes:** Denied — skipped. Assessment F15.\n\n---\n\n[...truncated 6 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 to watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M014`, `slice_id: S01`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M014/slices/S01/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S01 researched.\"","display":false,"id":"becbad4c","parentId":"5f93b5a7","timestamp":"2026-04-03T00:43:35.729Z"}
{"type":"message","id":"879571cf","parentId":"becbad4c","timestamp":"2026-04-03T00:43:44.634Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research Slice S01: \"Synthesis Prompt v5 — Nested Sections + Citations\" for Milestone M014. This is about restructuring the technique page synthesis prompt to produce nested H2/H3 sections with citations.\n\nLet me start by understanding the current state of the synthesis prompt and pipeline.","thinkingSignature":"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"},{"type":"text","text":"Starting research on S01 — the synthesis prompt restructure. Need to understand the current prompt template (stage 5), the output format it produces, any test harness, and what \"v5 with nested sections + citations\" means concretely."},{"type":"toolCall","id":"toolu_01AoANpwWuKTjKCUokMHiSUH","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.md\" -path \"*/M014/*\" | head -20"}},{"type":"toolCall","id":"toolu_01TN1hZTzkKikeZV2K9e2ouE","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend/prompts -type f | sort"}},{"type":"toolCall","id":"toolu_012z5Ehd4bWMQLNNE1mdtdqY","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.py\" -path \"*/pipeline/*\" | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":355,"cacheRead":59865,"cacheWrite":8194,"totalTokens":68417,"cost":{"input":0.000015000000000000002,"output":0.008875000000000001,"cacheRead":0.029932499999999997,"cacheWrite":0.0512125,"total":0.090035}},"stopReason":"toolUse","timestamp":1775177015726}}
{"type":"message","id":"9f118d2d","parentId":"879571cf","timestamp":"2026-04-03T00:43:44.680Z","message":{"role":"toolResult","toolCallId":"toolu_01AoANpwWuKTjKCUokMHiSUH","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S05/S05-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/S01-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S03/S03-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S06/S06-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/S02-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S07/S07-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/S04-PLAN.md\n/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/M014-ROADMAP.md\n"}],"isError":false,"timestamp":1775177024680}}
{"type":"message","id":"244dc5a0","parentId":"9f118d2d","timestamp":"2026-04-03T00:43:44.681Z","message":{"role":"toolResult","toolCallId":"toolu_01TN1hZTzkKikeZV2K9e2ouE","toolName":"bash","content":[{"type":"text","text":"find: ‘/home/aux/projects/content-to-kb-automator/backend/prompts’: No such file or directory\n"}],"isError":false,"timestamp":1775177024680}}
{"type":"message","id":"3aefc74b","parentId":"244dc5a0","timestamp":"2026-04-03T00:43:44.690Z","message":{"role":"toolResult","toolCallId":"toolu_012z5Ehd4bWMQLNNE1mdtdqY","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/pipeline/embedding_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/export_fixture.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fitness.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/__main__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/optimizer.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/variant_generator.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/voice_dial.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/test_harness.py\n"}],"isError":false,"timestamp":1775177024689}}
{"type":"message","id":"b67a4938","parentId":"3aefc74b","timestamp":"2026-04-03T00:43:51.049Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — there's already a `test_harness.py`. Let me read the key files: the stage 5 synthesis prompt, the stages.py (stage 5 section), the test harness, and the existing schemas."},{"type":"toolCall","id":"toolu_013LB3MNeLeoA5eSqi5Fc7BB","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.txt\" -o -name \"*.prompt\" -o -name \"*.j2\" -o -name \"*.jinja\" | head -20; echo \"---\"; find /home/aux/projects/content-to-kb-automator -path \"*/prompts*\" -type f 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_01EqFAi5Nhx3DZB1N47tdEvR","name":"bash","arguments":{"command":"rg -l \"stage_5\\|stage5\\|synthesis\\|synthesize\\|technique_page\" /home/aux/projects/content-to-kb-automator/backend/pipeline/ --type py | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":283,"cacheRead":68059,"cacheWrite":1119,"totalTokens":69462,"cost":{"input":0.000005,"output":0.0070750000000000006,"cacheRead":0.0340295,"cacheWrite":0.00699375,"total":0.04810325}},"stopReason":"toolUse","timestamp":1775177024689}}
{"type":"message","id":"3592a1d4","parentId":"b67a4938","timestamp":"2026-04-03T00:43:51.131Z","message":{"role":"toolResult","toolCallId":"toolu_013LB3MNeLeoA5eSqi5Fc7BB","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/whisper/requirements.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage3_extraction.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_synthesis.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage2_segmentation.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v038.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v027.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v025.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v048.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v010.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v096.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v041.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v039.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v059.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v053.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v022.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v095.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v073.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v019.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v082.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v089.txt\n---\n/home/aux/projects/content-to-kb-automator/prompts/stage3_extraction.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_synthesis.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage2_segmentation.txt\n/home/aux/projects/content-to-kb-automator/prompts/README.md\n/home/aux/projects/content-to-kb-automator/prompts/stage5_synthesis.20260401_101518.bak\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v038.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v027.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v025.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v048.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v010.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v096.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v041.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v039.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v059.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v053.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/manifest.json\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v022.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v095.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v073.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_variants/v019.txt\n"}],"isError":false,"timestamp":1775177031130}}
{"type":"message","id":"d7dba02f","parentId":"3592a1d4","timestamp":"2026-04-03T00:43:51.133Z","message":{"role":"toolResult","toolCallId":"toolu_01EqFAi5Nhx3DZB1N47tdEvR","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177031130}}
{"type":"message","id":"fb6a5862","parentId":"d7dba02f","timestamp":"2026-04-03T00:43:55.491Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CMjLQFLvmiKtPLwoL7wEZm","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01RdLsgZhffDbB96JngUhucQ","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_013D3jiQKmNbXwLJ9B7zCJZh","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":190,"cacheRead":69178,"cacheWrite":1483,"totalTokens":70852,"cost":{"input":0.000005,"output":0.00475,"cacheRead":0.034589,"cacheWrite":0.009268750000000001,"total":0.04861275}},"stopReason":"toolUse","timestamp":1775177031131}}
{"type":"message","id":"75ce9796","parentId":"fb6a5862","timestamp":"2026-04-03T00:43:55.501Z","message":{"role":"toolResult","toolCallId":"toolu_01CMjLQFLvmiKtPLwoL7wEZm","toolName":"read","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- **Match the energy of the source** — if the creator is hyped about a technique, let that land. If they're cautionary, make it feel urgent.\n- **Efficiency** — say it once, say it well. If a sentence doesn't teach something specific or make the next specific thing clearer, cut it.\n\n### Creator name usage — hard rule\n\nThe body sections are written as instructional content, not a biography. The creator's name appears in the **title** and **summary** for attribution — that's where the reader learns whose technique this is. The body sections then TEACH the technique directly, like a lesson book or a well-written blog post.\n\nThe creator's name should appear **at most once** in the entire body_sections content — and only when introducing a direct quote for the first time. Otherwise, the prose is instructive: \"do this\", \"set this to X\", \"route the signal through Y.\" Not \"he does this\", \"she sets this to X.\"\n\nBAD — third-person narration in every section:\n> **Section 1:** \"Keota solves this by creating dedicated analog modules...\"\n> **Section 2:** \"Keota uses Ableton's Remap plugin with a quantized staircase...\"\n> **Section 3:** \"Keota separates the modulation effect from the carrier...\"\n> **Section 4:** \"Keota compares options systematically...\"\n\nEach section re-introduces the creator as if the reader forgot whose page they're on. This reads like a Wikipedia article about a person, not a technique reference.\n\nGOOD — instructive voice, teaches the reader directly:\n> **Section 1:** \"The approach builds each harmonic as a separate parallel module with independent gain. Start with the 3rd harmonic, then optionally add the 5th, 6th, or 7th...\"\n> **Section 2:** \"Use Ableton's Remap with a quantized staircase curve to force any wavetable into discrete harmonic steps. Set up a harmonic control knob, map it to a quantized 8-step curve...\"\n> **Section 3:** \"To separate the modulation effect from the carrier signal, turn off the oscillator output and route through a mix module instead...\"\n> **Section 4:** \"When choosing waveforms for FM-style patches, compare the harmonic series wavetable against sync smooth sine...\"\n\nThe second version teaches a technique. The first version describes a person. Write the second version. Direct quotes naturally attribute themselves: \"as he puts it, '...'\" — that's the only place a pronoun reference to the creator is needed.\n\n## 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## Synthesis approach\n\nEach section follows a consistent rhythm that orients the reader before teaching them:\n\n**Orientation → Situation → Method → Implementation → Why it works**\n\n- **Orientation** (1-2 sentences): Briefly define or describe the concept being discussed. What IS this thing? This gives the reader a foothold before diving in. Example: \"Mid-side processing separates a stereo signal into its center (mid) and edges (side) components, letting you EQ or compress each independently.\"\n- **Situation** (1 sentence): When would a producer need this? Frame it as something they've experienced. Example: \"This becomes essential when a mix sounds wide in headphones but collapses to mush on a mono club system.\"\n- **Method** (the bulk): What to do and exactly how. Specific settings, routing, plugin choices. Instructive phrasing — \"Set the filter to 133Hz\" not \"he sets the filter to 133Hz.\"\n- **Why it works** (1-2 sentences): What problem this solves or what breaks if you skip it.\n\nThis structure prevents the prose from feeling like a giant run-on stream of techniques. The orientation sentence gives the reader context. The situation tells them why to care. Then the method teaches them what to do.\n\nExample of the full rhythm:\n> \"Parallel routing sends the dry signal and processed signal through separate paths before summing them at the output. This matters when you need to process the wet signal independently — adding EQ, compression, or distortion to just the reverb or chorus without touching the dry sound. Set up the effect at 100% wet on its own lane, then control the blend via channel gain rather than the plugin's internal mix knob. This gives you full processing access to the wet signal that an inline dry/wet knob can never provide.\"\n\nMerge moments that address the same problem. When the creator contradicts themselves across moments, explain the context for each approach — don't hide the contradiction, it's usually the most interesting part.\n\n## Section ordering — follow the workflow\n\n**This is the single most important structural rule.** The order of body_sections in your output MUST mirror the actual production workflow. This is non-negotiable.\n\nBefore you write, mentally assign each section a workflow stage number:\n- Stage 1: Conceptual framework / what the building blocks are\n- Stage 2: Building or shaping individual elements (the core construction)\n- Stage 3: Combining, processing, or refining the result (glue, bus processing, distortion)\n- Stage 4: Quality checks, validation, visual tests, mix-context adjustments\n\nThen output body_sections in that order. **A section about validating or checking the result can NEVER come before the sections about building the thing being checked.** A \"visual waveform validation\" section belongs at the END, not the beginning — the reader hasn't built anything to validate yet. A \"distortion as binding agent\" section comes AFTER the layers it's binding are explained.\n\nThe test is simple: read your section headings top to bottom and ask \"could a producer follow these steps in this exact sequence in their DAW?\" If the answer is no — if any section references, depends on, or validates something from a later section — your ordering is wrong. Fix it before outputting.\n\nWrong ordering (quality check before construction):\n1. \"Visual waveform validation\" ← checking a result that hasn't been built yet\n2. \"The three-layer framework\"\n3. \"Shaping the body tone\"\n4. \"Distortion as binding agent\"\n\nCorrect ordering (build → shape → combine → validate):\n1. \"The three-layer framework\"\n2. \"Shaping the body tone\"\n3. \"Distortion as binding agent\"\n4. \"Visual waveform validation\" ← now the reader knows what they're checking\n\n## Reader context\n\nYour reader could be a beginner or an expert. Write so both get value: use production terminology naturally (don't over-explain fundamentals), but when the creator explains a non-obvious concept in an illuminating way, include that explanation — it helps beginners and often contains nuance that experts appreciate too.\n\nThe page should be immediately scannable (clear section names, specific details prominent) for the expert who knows what they're looking for, and readable end-to-end for the learner who wants the full picture.\n\n## Summary requirements\n\nThe summary (2-3 sentences) is the hook. It opens with the most surprising or valuable thing — the one detail that makes a producer stop scrolling.\n\nBad summary: \"This page covers Keota's approach to harmonic synthesis using parallel modules, quantized remapping, and LFO modulation in Phase Plant.\"\nGood summary: \"Instead of relying on a single oscillator's harmonic series, he builds each harmonic as its own parallel module with independent gain — meaning you can automate the 3rd harmonic disappearing while the 5th swells in. The trick that makes it work: a quantized Remap curve that forces any wavetable into discrete harmonic steps.\"\n\nThe bad version describes what the page is about. The good version teaches you something in two sentences. Always write the good version. Use the creator's name once in the summary for attribution, then pronouns only.\n\n## Writing engagement\n\nThe page must be something a producer actually wants to read, not something they have to read:\n\n- **Open every section with a brief orientation, then get concrete** — start with 1-2 sentences that define or describe the concept so the reader has a foothold. Then immediately get specific. NEVER open with vague importance statements like \"A critical prerequisite...\" or \"Before building anything, you need to understand...\" — instead orient concisely then teach: \"Mid-side processing separates a stereo signal into center and edge components for independent processing. This matters when a mix sounds wide in headphones but collapses in mono — boost the side channel to widen, but only if actual stereo content exists there.\"\n- **Vary sentence rhythm** — a long technical sentence explaining a routing chain, then a short punchy one: \"That's the whole trick.\" Monotone paragraph structure kills engagement.\n- **Let the creator's personality drive the energy** — if they discovered something by accident, let that surprise come through. If they're warning you about something that burned them, make it feel urgent.\n- **End sections with something that sticks** — a direct quote, a warning, a key insight. Not \"This approach provides flexibility for future workflow adjustments.\" That's a nothing sentence. Instead: \"The modulation routing stays intact no matter what you add to the chain — that's the point.\"\n\n## Signal chains\n\nWhen the source moments describe a signal routing chain (oscillator → effects → processing → bus), represent it as a structured signal chain object. Signal chains are only included when the creator explicitly walks through routing — do not infer chains from casual plugin mentions.\n\nFormat signal chain steps to include the role of each stage, not just the plugin name:\n- Good: [\"Noise osc (Vital)\", \"Transient Shaper (Kilohearts, attack +6dB)\", \"EQ (Pro-Q 3, shelf -3dB @ 12kHz)\", \"Send → Trash 2 (tape algo, 35% wet)\"]\n- 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":1775177035498}}
{"type":"message","id":"e086e95a","parentId":"75ce9796","timestamp":"2026-04-03T00:43:55.506Z","message":{"role":"toolResult","toolCallId":"toolu_01RdLsgZhffDbB96JngUhucQ","toolName":"read","content":[{"type":"text","text":"\"\"\"Offline prompt test harness for Chrysopedia synthesis.\n\nLoads a fixture JSON (exported by export_fixture.py) and a prompt file,\ncalls the LLM, and outputs the synthesized result. No Docker, no database,\nno Redis, no Celery — just prompt + fixture + LLM endpoint.\n\nUsage:\n    python -m pipeline.test_harness \\\\\n      --fixture fixtures/real_video_xyz.json \\\\\n      --prompt prompts/stage5_synthesis.txt \\\\\n      --output /tmp/result.json\n\n    # Run all categories in a fixture:\n    python -m pipeline.test_harness --fixture fixtures/video.json\n\n    # Run a specific category only:\n    python -m pipeline.test_harness --fixture fixtures/video.json --category \"Sound Design\"\n\nExit codes: 0=success, 1=LLM error, 2=parse error, 3=fixture error\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter, defaultdict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import NamedTuple\n\nfrom pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesisResult\n\n\n# ── Lightweight stand-in for KeyMoment ORM model ───────────────────────────\n\nclass _MockContentType:\n    \"\"\"Mimics KeyMomentContentType enum with a .value property.\"\"\"\n    def __init__(self, value: str) -> None:\n        self.value = value\n\n\nclass MockKeyMoment(NamedTuple):\n    \"\"\"Lightweight stand-in for the ORM KeyMoment.\n\n    Has the same attributes that _build_moments_text() accesses:\n    title, summary, content_type, start_time, end_time, plugins, raw_transcript.\n    \"\"\"\n    title: str\n    summary: str\n    content_type: object  # _MockContentType\n    start_time: float\n    end_time: float\n    plugins: list[str]\n    raw_transcript: str\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[HARNESS] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\n# ── Moment text builder (mirrors stages.py _build_moments_text) ────────────\n\ndef build_moments_text(\n    moment_group: list[tuple[MockKeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text — matches _build_moments_text in stages.py.\"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\n# ── Fixture loading ────────────────────────────────────────────────────────\n\n@dataclass\nclass FixtureData:\n    \"\"\"Parsed fixture with moments grouped by category.\"\"\"\n    creator_name: str\n    video_id: str\n    content_type: str\n    filename: str\n    # Groups: category -> list of (MockKeyMoment, cls_info_dict)\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]]\n    total_moments: int\n\n\ndef load_fixture(path: str) -> FixtureData:\n    \"\"\"Load and parse a fixture JSON file into grouped moments.\"\"\"\n    fixture_path = Path(path)\n    if not fixture_path.exists():\n        raise FileNotFoundError(f\"Fixture not found: {path}\")\n\n    raw = fixture_path.read_text(encoding=\"utf-8\")\n    size_kb = len(raw.encode(\"utf-8\")) / 1024\n    data = json.loads(raw)\n\n    moments_raw = data.get(\"moments\", [])\n    if not moments_raw:\n        raise ValueError(f\"Fixture has no moments: {path}\")\n\n    _log(\"FIXTURE\", f\"Loading: {path} ({size_kb:.1f} KB, {len(moments_raw)} moments)\")\n\n    # Build MockKeyMoment objects and group by category\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]] = defaultdict(list)\n\n    for m in moments_raw:\n        cls = m.get(\"classification\", {})\n        category = cls.get(\"topic_category\", m.get(\"topic_category\", \"Uncategorized\"))\n        tags = cls.get(\"topic_tags\", m.get(\"topic_tags\", []))\n\n        mock = MockKeyMoment(\n            title=m.get(\"title\", m.get(\"summary\", \"\")[:80]),\n            summary=m.get(\"summary\", \"\"),\n            content_type=_MockContentType(m.get(\"content_type\", \"technique\")),\n            start_time=m.get(\"start_time\", 0.0),\n            end_time=m.get(\"end_time\", 0.0),\n            plugins=m.get(\"plugins\", []),\n            raw_transcript=m.get(\"raw_transcript\", m.get(\"transcript_excerpt\", \"\")),\n        )\n        cls_info = {\"topic_category\": category, \"topic_tags\": tags}\n        groups[category].append((mock, cls_info))\n\n    # Log breakdown\n    cat_counts = {cat: len(moms) for cat, moms in groups.items()}\n    counts = list(cat_counts.values())\n    _log(\n        \"FIXTURE\",\n        f\"Breakdown: {len(groups)} categories, \"\n        f\"moments per category: min={min(counts)}, max={max(counts)}, \"\n        f\"avg={sum(counts)/len(counts):.1f}\",\n    )\n    for cat, count in sorted(cat_counts.items(), key=lambda x: -x[1]):\n        _log(\"FIXTURE\", f\"  {cat}: {count} moments\")\n\n    return FixtureData(\n        creator_name=data.get(\"creator_name\", \"Unknown\"),\n        video_id=data.get(\"video_id\", \"unknown\"),\n        content_type=data.get(\"content_type\", \"tutorial\"),\n        filename=data.get(\"filename\", \"unknown\"),\n        groups=dict(groups),\n        total_moments=len(moments_raw),\n    )\n\n\n# ── Synthesis runner ───────────────────────────────────────────────────────\n\ndef run_synthesis(\n    fixture: FixtureData,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run synthesis on fixture data, returns (pages, exit_code).\n\n    Returns all synthesized pages as dicts plus an exit code.\n    \"\"\"\n    # Load prompt\n    prompt_file = Path(prompt_path)\n    if not prompt_file.exists():\n        _log(\"ERROR\", f\"Prompt file not found: {prompt_path}\", level=\"ERROR\")\n        return [], 3\n\n    system_prompt = prompt_file.read_text(encoding=\"utf-8\")\n    _log(\"PROMPT\", f\"Loading: {prompt_path} ({len(system_prompt)} chars)\")\n\n    # Setup LLM\n    settings = get_settings()\n    llm = LLMClient(settings)\n\n    stage_model = model_override or settings.llm_stage5_model or settings.llm_model\n    stage_modality = modality or settings.llm_stage5_modality or \"thinking\"\n    hard_limit = settings.llm_max_tokens_hard_limit\n\n    _log(\"LLM\", f\"Model: {stage_model}, modality: {stage_modality}, hard_limit: {hard_limit}\")\n\n    # Filter categories if requested\n    categories = fixture.groups\n    if category_filter:\n        if category_filter not in categories:\n            _log(\"ERROR\", f\"Category '{category_filter}' not found. Available: {list(categories.keys())}\", level=\"ERROR\")\n            return [], 3\n        categories = {category_filter: categories[category_filter]}\n\n    all_pages: list[dict] = []\n    total_prompt_tokens = 0\n    total_completion_tokens = 0\n    total_duration_ms = 0\n    exit_code = 0\n\n    for cat_idx, (category, moment_group) in enumerate(categories.items(), 1):\n        _log(\"SYNTH\", f\"Category {cat_idx}/{len(categories)}: '{category}' ({len(moment_group)} moments)\")\n\n        # Build user prompt (same format as stages.py _synthesize_chunk)\n        moments_text, all_tags = build_moments_text(moment_group, category)\n        user_prompt = f\"<creator>{fixture.creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n        estimated_tokens = estimate_max_tokens(\n            system_prompt, user_prompt,\n            stage=\"stage5_synthesis\",\n            hard_limit=hard_limit,\n        )\n        _log(\n            \"SYNTH\",\n            f\"  Building prompt: {len(moment_group)} moments, \"\n            f\"max_tokens={estimated_tokens}, tags={sorted(all_tags)[:5]}{'...' if len(all_tags) > 5 else ''}\",\n        )\n\n        # Call LLM\n        call_start = time.monotonic()\n        _log(\"LLM\", f\"  Calling: model={stage_model}, max_tokens={estimated_tokens}, modality={stage_modality}\")\n\n        try:\n            raw = llm.complete(\n                system_prompt,\n                user_prompt,\n                response_model=SynthesisResult,\n                modality=stage_modality,\n                model_override=stage_model,\n                max_tokens=estimated_tokens,\n            )\n        except Exception as exc:\n            _log(\"ERROR\", f\"  LLM call failed: {exc}\", level=\"ERROR\")\n            exit_code = 1\n            continue\n\n        call_duration_ms = int((time.monotonic() - call_start) * 1000)\n        prompt_tokens = getattr(raw, \"prompt_tokens\", None) or 0\n        completion_tokens = getattr(raw, \"completion_tokens\", None) or 0\n        finish_reason = getattr(raw, \"finish_reason\", \"unknown\")\n\n        total_prompt_tokens += prompt_tokens\n        total_completion_tokens += completion_tokens\n        total_duration_ms += call_duration_ms\n\n        _log(\n            \"LLM\",\n            f\"  Response: {prompt_tokens} prompt + {completion_tokens} completion tokens, \"\n            f\"{call_duration_ms}ms, finish_reason={finish_reason}\",\n        )\n\n        if finish_reason == \"length\":\n            _log(\n                \"WARN\",\n                \"  finish_reason=length — output likely truncated! \"\n                \"Consider reducing fixture size or increasing max_tokens.\",\n                level=\"WARN\",\n            )\n\n        # 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    5: \"stage5_synthesis.txt\",\n}\n\n\ndef promote_prompt(prompt_path: str, stage: int, reason: str, commit: bool = False) -> int:\n    \"\"\"Copy a winning prompt to the canonical path and create a backup.\n\n    The worker reads prompts from disk at runtime — no restart needed.\n    \"\"\"\n    import hashlib\n    import shutil\n\n    if stage not in _STAGE_PROMPT_MAP:\n        _log(\"ERROR\", f\"Invalid stage {stage}. Valid: {sorted(_STAGE_PROMPT_MAP)}\", level=\"ERROR\")\n        return 1\n\n    settings = get_settings()\n    template_name = _STAGE_PROMPT_MAP[stage]\n    canonical = Path(settings.prompts_path) / template_name\n    source = Path(prompt_path)\n\n    if not source.exists():\n        _log(\"ERROR\", f\"Source prompt not found: {prompt_path}\", level=\"ERROR\")\n        return 1\n\n    new_prompt = source.read_text(encoding=\"utf-8\")\n    new_hash = hashlib.sha256(new_prompt.encode()).hexdigest()[:12]\n\n    # Backup current prompt\n    old_prompt = \"\"\n    old_hash = \"none\"\n    if canonical.exists():\n        old_prompt = canonical.read_text(encoding=\"utf-8\")\n        old_hash = hashlib.sha256(old_prompt.encode()).hexdigest()[:12]\n\n        if old_prompt.strip() == new_prompt.strip():\n            _log(\"PROMOTE\", \"No change — new prompt is identical to current prompt\")\n            return 0\n\n        archive_dir = Path(settings.prompts_path) / \"archive\"\n        archive_dir.mkdir(parents=True, exist_ok=True)\n        ts = time.strftime(\"%Y%m%d_%H%M%S\", time.gmtime())\n        backup = archive_dir / f\"{template_name.replace('.txt', '')}_{ts}.txt\"\n        shutil.copy2(canonical, backup)\n        _log(\"PROMOTE\", f\"Backed up current prompt: {old_hash} -> {backup}\")\n\n    # Write new prompt\n    canonical.write_text(new_prompt, encoding=\"utf-8\")\n\n    old_lines = old_prompt.strip().splitlines()\n    new_lines = new_prompt.strip().splitlines()\n    _log(\"PROMOTE\", f\"Installed new prompt: {new_hash} ({len(new_prompt)} chars, {len(new_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Previous: {old_hash} ({len(old_prompt)} chars, {len(old_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Reason: {reason}\")\n    _log(\"PROMOTE\", \"Worker reads prompts from disk at runtime — no restart needed\")\n\n    if commit:\n        import subprocess\n        try:\n            subprocess.run(\n                [\"git\", \"add\", str(canonical)],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            msg = f\"prompt: promote stage{stage} — {reason}\"\n            subprocess.run(\n                [\"git\", \"commit\", \"-m\", msg],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            _log(\"PROMOTE\", f\"Git commit created: {msg}\")\n        except subprocess.CalledProcessError as exc:\n            _log(\"PROMOTE\", f\"Git commit failed: {exc}\", level=\"WARN\")\n\n    return 0\n\n\n# ── CLI ────────────────────────────────────────────────────────────────────\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.test_harness\",\n        description=\"Offline prompt test harness for Chrysopedia synthesis\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- run subcommand (default behavior) --\n    run_parser = sub.add_parser(\"run\", help=\"Run synthesis against a fixture\")\n    run_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"Fixture JSON file\")\n    run_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Prompt file (default: stage5_synthesis.txt)\")\n    run_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n    run_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n    run_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n    run_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n\n    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n\n    args = parser.parse_args()\n\n    # If no subcommand, check for --fixture for backward compat\n    if args.command is None:\n        # Support running without subcommand for backward compat\n        parser.print_help()\n        return 1\n\n    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n\n    # -- run command --\n    prompt_path = args.prompt\n    if prompt_path is None:\n        settings = get_settings()\n        prompt_path = str(Path(settings.prompts_path) / \"stage5_synthesis.txt\")\n\n    overall_start = time.monotonic()\n    try:\n        fixture = load_fixture(args.fixture)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"Fixture error: {exc}\", level=\"ERROR\")\n        return 3\n\n    pages, exit_code = run_synthesis(\n        fixture=fixture,\n        prompt_path=prompt_path,\n        category_filter=args.category,\n        model_override=args.model,\n        modality=args.modality,\n    )\n\n    if not pages and exit_code != 0:\n        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    _log(\"DONE\", f\"Completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n\n    return exit_code\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775177035504}}
{"type":"message","id":"30b8bb3d","parentId":"e086e95a","timestamp":"2026-04-03T00:43:55.512Z","message":{"role":"toolResult","toolCallId":"toolu_013D3jiQKmNbXwLJ9B7zCJZh","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":1775177035509}}
{"type":"message","id":"5f3fa3c8","parentId":"30b8bb3d","timestamp":"2026-04-03T00:44:04.106Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have the current state. Key observations:\n1. `body_sections` is currently `dict` (flat `section_name → content string`)\n2. No citations in the current format\n3. The prompt (v4 effectively) produces flat sections, no nesting\n\nLet me check the M014 context/research docs and the S01 plan for specifics on what v5 needs."},{"type":"toolCall","id":"toolu_0168toRpp4uYGt9FXZyV8MGn","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/S01-PLAN.md"}},{"type":"toolCall","id":"toolu_01FLEQWnBxAn3TELSAFZvrMj","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/M014-*.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":244,"cacheRead":70661,"cacheWrite":11735,"totalTokens":82641,"cost":{"input":0.000005,"output":0.0061,"cacheRead":0.0353305,"cacheWrite":0.07334375,"total":0.11477925}},"stopReason":"toolUse","timestamp":1775177035510}}
{"type":"message","id":"88a40826","parentId":"5f3fa3c8","timestamp":"2026-04-03T00:44:04.116Z","message":{"role":"toolResult","toolCallId":"toolu_0168toRpp4uYGt9FXZyV8MGn","toolName":"read","content":[{"type":"text","text":"# S01: Synthesis Prompt v5 — Nested Sections + Citations\n\n**Goal:** Rewrite stage5_synthesis.txt to produce nested output format with inline citations. Validate via test harness.\n**Demo:** After this: 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\n## Tasks\n"}],"isError":false,"timestamp":1775177044116}}
{"type":"message","id":"159f6c82","parentId":"88a40826","timestamp":"2026-04-03T00:44:04.122Z","message":{"role":"toolResult","toolCallId":"toolu_01FLEQWnBxAn3TELSAFZvrMj","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/M014-ROADMAP.md\n"}],"isError":false,"timestamp":1775177044120}}
{"type":"message","id":"3a95954d","parentId":"159f6c82","timestamp":"2026-04-03T00:44:10.007Z","message":{"role":"assistant","content":[{"type":"text","text":"The plan is bare — just the goal. Let me look at the milestone roadmap more carefully for context, and also understand how stage 5 is called in `stages.py` to know the exact integration points."},{"type":"toolCall","id":"toolu_01ULiowYqEJhb9bcinGFHxys","name":"bash","arguments":{"command":"rg -n \"stage_5\\|_synthesize\\|body_sections\\|SynthesisResult\\|SynthesizedPage\" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py | head -40"}},{"type":"toolCall","id":"toolu_015tjqvCYxNogKLRvPo1ufB9","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/ -r --type py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":227,"cacheRead":82396,"cacheWrite":433,"totalTokens":83057,"cost":{"input":0.000005,"output":0.005675,"cacheRead":0.041198,"cacheWrite":0.0027062500000000003,"total":0.049584249999999996}},"stopReason":"toolUse","timestamp":1775177044121}}
{"type":"message","id":"ad7165e6","parentId":"3a95954d","timestamp":"2026-04-03T00:44:10.023Z","message":{"role":"toolResult","toolCallId":"toolu_01ULiowYqEJhb9bcinGFHxys","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177050023}}
{"type":"message","id":"8aa8f79f","parentId":"ad7165e6","timestamp":"2026-04-03T00:44:10.037Z","message":{"role":"toolResult","toolCallId":"toolu_015tjqvCYxNogKLRvPo1ufB9","toolName":"bash","content":[{"type":"text","text":"rg: py: No such file or directory (os error 2)\n/home/aux/projects/content-to-kb-automator/backend/tests/test_pipeline.py:436:        assert page.--type is not None\n/home/aux/projects/content-to-kb-automator/backend/tests/test_pipeline.py:437:        assert \"Overview\" in page.--type\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/mock_llm_responses.py:84:            \"--type\": {\n/home/aux/projects/content-to-kb-automator/backend/tests/test_public_api.py:89:            --type={\"intro\": \"Getting started with reese bass\"},\n/home/aux/projects/content-to-kb-automator/backend/tests/test_public_api.py:545:    snapshot = {\"title\": \"Old Title\", \"summary\": \"Old summary\", \"--type\": {\"intro\": \"Old intro\"}}\n/home/aux/projects/content-to-kb-automator/backend/pipeline/test_harness.py:283:            sections = page.--type or {}\n/home/aux/projects/content-to-kb-automator/backend/pipeline/test_harness.py:457:                sum(len(str(v).split()) for v in p.get(\"--type\", {}).values())\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py:81:    --type: dict = Field(\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py:1324:                            \"--type\": existing.--type,\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py:1357:                    existing.--type = page_data.--type\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py:1372:                        --type=page_data.--type,\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/__main__.py:337:        \"--type\": [\n/home/aux/projects/content-to-kb-automator/backend/models.py:218:    --type: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/variant_generator.py:84:_FORMAT_MARKERS = [\"SynthesisResult\", '\"pages\"', \"--type\", \"title\", \"summary\"]\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:200:1. A synthesized technique page (JSON with title, summary, --type)\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:283:        format_markers=[\"SynthesisResult\", '\"pages\"', \"--type\", \"title\", \"summary\"],\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:552:            \"--type\": [\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:554:                for heading, content in page.--type.items()\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/results/optimize_stage5_20260401_100005.json:8:  \"best_prompt\": \"You are an expert technical writer specializing in music production education. Your task is to synthesize a set of related key moments from the same creator into a single, high-quality technique page that serves as a definitive reference on the topic.\\n\\n## What you are creating\\n\\nA Chrysopedia technique page is NOT a generic article or wiki entry. It is a focused reference document that a music producer will consult mid-session when they need to understand and apply a specific technique. The reader is Alt+Tabbing from their DAW, looking for actionable knowledge, and wants to absorb the key insight and get back to work in under 2 minutes.\\n\\nThe page has two complementary sections:\\n\\n1. **Study guide prose** \\u2014 rich, detailed paragraphs organized by sub-aspect of the technique. This is for learning and deep understanding. It reads like notes from an expert mentor, not a textbook.\\n2. **Key moments index** \\u2014 a compact list of the individual source moments that contributed to this page, each with a descriptive title that enables quick scanning.\\n\\nBoth sections are essential. The prose synthesizes and explains; the moment index lets readers quickly locate the specific insight they need.\\n\\n## Voice and tone\\n\\nWrite as if you are a knowledgeable colleague explaining what you learned from watching this creator's content. The tone should be:\\n\\n- **Direct and confident** \\u2014 state what the creator does, not \\\"the creator appears to\\\" or \\\"it seems like they\\\"\\n- **Technical but accessible** \\u2014 use production terminology naturally, but explain non-obvious concepts when the creator's explanation adds value\\n- **Preserving the creator's voice** \\u2014 This is a critical priority. You must aggressively capture the creator's unique personality and phrasing. Do not just summarize their points; extract their exact words for any memorable metaphors, strong opinions, or colorful descriptions. Quote them directly with quotation marks. These direct quotes are often the most valuable parts of the page. Examples: 'He warns against using OTT on snares \\u2014 says it \\\"smears the snap into mush.\\\"' or 'Her reasoning: \\\"every bus you add is another place you'll be tempted to put a compressor that doesn't need to be there.\\\"' If the creator uses slang or specific adjectives (e.g., \\\"muddy,\\\" \\\"punchy,\\\" \\\"surgical\\\"), retain those exact words in your description rather than substituting synonyms.\\n- **Specific over general** \\u2014 always prefer concrete details (frequencies, ratios, ms values, plugin names, specific settings) over vague descriptions. \\\"Uses compression\\\" is never acceptable if the source moments contain specifics.\\n\\n## Body sections structure\\n\\nDo NOT use generic section names like \\\"Overview,\\\" \\\"Step-by-Step Process,\\\" \\\"Key Settings,\\\" or \\\"Tips and Variations.\\\" These produce lifeless, formulaic output.\\n\\nInstead, derive section names from the actual content. Each section should cover one distinct sub-aspect of the technique. Use descriptive names that tell the reader exactly what they'll learn:\\n\\nGood section names (examples):\\n- \\\"Layer construction\\\" / \\\"Saturation and the crunch character\\\" / \\\"Mix context and bus processing\\\"\\n- \\\"Resampling loop\\\" / \\\"Preserving transient information\\\" / \\\"Wavetable import settings\\\"\\n- \\\"Overall philosophy\\\" / \\\"Bus structure\\\" / \\\"Gain staging mindset\\\"\\n- \\\"Oscillator setup and FM routing\\\" / \\\"Effects chain per-layer\\\" / \\\"Automating movement\\\"\\n\\nBad section names (never use these):\\n- \\\"Overview\\\" / \\\"Introduction\\\" / \\\"Step-by-Step Process\\\" / \\\"Key Settings\\\" / \\\"Tips and Variations\\\" / \\\"Conclusion\\\" / \\\"Summary\\\"\\n\\nEach section must be 2-5 paragraphs of substantive prose. A section with only 1-2 sentences is too thin \\u2014 either merge it with another section or expand it with the detail available in the source moments. Ensure each paragraph flows logically into the next, building a complete picture of that specific sub-aspect.\\n\\n## Signal chains\\n\\nWhen the source moments describe a signal routing chain (oscillator \\u2192 effects \\u2192 processing \\u2192 bus), represent it as a structured signal chain object. Signal chains are only included when the creator explicitly walks through routing \\u2014 do not infer chains from casual plugin mentions.\\n\\nFormat signal chain steps to include the role of each stage, not just the plugin name:\\n- Good: [\\\"Noise osc (Vital)\\\", \\\"Transient Shaper (Kilohearts, attack +6dB)\\\", \\\"EQ (Pro-Q 3, shelf -3dB @ 12kHz)\\\", \\\"Send \\u2192 Trash 2 (tape algo, 35% wet)\\\"]\\n- Bad: [\\\"Vital\\\", \\\"Kilohearts\\\", \\\"EQ\\\", \\\"Trash 2\\\"]\\n\\n## Plugin detail rule\\n\\nInclude specific plugin names, settings, and parameters ONLY when the creator was teaching that setting \\u2014 spending time explaining why they chose it, what it does, or how to configure it. If a plugin is merely visible or briefly mentioned without explanation, include it in the plugins list but do not feature it in the body prose.\\n\\nThis distinction is critical for page quality. A page that lists every plugin the creator happened to have open reads like a gear list. A page that explains the plugins the creator intentionally demonstrated reads like education.\\n\\n## Synthesis, not concatenation\\n\\nYou are synthesizing knowledge, not summarizing a video. This means:\\n\\n- **Merge related information**: If the creator discusses snare transient shaping at timestamp 1:42:00 and then returns to refine the point at 2:15:00, these should be woven into one coherent section, not presented as two separate observations.\\n- **Build a logical flow**: Organize sections in the order a producer would naturally encounter these decisions (e.g., sound source \\u2192 processing \\u2192 mixing context), even if the creator covered them in a different order.\\n- **Resolve redundancy**: If two moments say essentially the same thing, combine them into one clear statement. Don't repeat yourself.\\n- **Note contradictions**: If the creator says contradictory things in different moments (e.g., recommends different settings for the same parameter), note both and provide the context for each (\\\"In dense arrangements, he pulls the sustain back further; for sparse sections, he leaves more room for the tail\\\").\\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. ALWAYS use the creator name from the <creator> tag in titles, slugs, and prose \\u2014 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 produce a single page, but if the moments clearly cover two distinctly separate techniques (e.g., moments about both \\\"kick design\\\" and \\\"hi-hat design\\\" that happen to share a topic_category), split them into separate pages. When splitting, you MUST assign each moment to exactly one page via the moment_indices field \\u2014 every input moment index must appear in exactly one page's moment_indices 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 \\u2014 transient click, tonal body, and noise tail \\u2014 with 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. In dense mixes, he uses HP sidechaining on the snare bus to maintain punch without competing with sub content.\\\",\\n      \\\"--type\\\": {\\n        \\\"Layer construction\\\": \\\"ExampleCreator builds snares as three independent layers, each shaped before they are summed. The transient click is a short noise burst (2-5ms decay) \\u2014 he uses Vital's noise oscillator for this, sometimes with a bandpass around 2-4kHz to control the character. The tonal body is a pitched sine or triangle wave around 180-220Hz, tuned to complement the key of the track. The tail is filtered white noise with a fast exponential decay.\\\\n\\\\nThe critical insight: he shapes each layer's transient independently before any bus processing. He uses Kilohearts Transient Shaper (attack +4 to +6dB, sustain -6 to -8dB) rather than compression for this, because \\\\\\\"compression adds sustain as a side effect while a transient shaper gives you direct independent control of both.\\\\\\\"\\\",\\n        \\\"Saturation and the crunch character\\\": \\\"The signature ExampleCreator snare crunch comes from parallel saturation \\u2014 not inline. He routes the summed snare to a send with Trash 2 using the tape algorithm at 30-40% wet. The key detail: he puts a pre-delay of approximately 5ms on the saturation send, which lets the clean transient click through untouched while only the body and tail pick up harmonic content.\\\\n\\\\nHe explicitly warns against saturating the transient directly \\u2014 says it \\\\\\\"smears the snap into mush\\\\\\\" and you lose the precision that makes the snare cut through.\\\",\\n        \\\"Mix context and bus processing\\\": \\\"In dense arrangements, ExampleCreator prioritizes punch over sustain. On the snare bus compressor, he uses a high-pass sidechain filter (around 200-300Hz) so low-end energy from the body layer does not trigger gain reduction. This keeps the snare's ability to cut through the mix independent of whatever the sub bass is doing.\\\\n\\\\nHe also checks the snare against the lead or vocal bus specifically, not just soloed \\u2014 because the 2-4kHz presence range is where both elements compete, and he would rather notch the snare's body slightly than lose vocal clarity.\\\"\\n      },\\n      \\\"signal_chains\\\": [\\n        {\\n          \\\"name\\\": \\\"Snare layer processing\\\",\\n          \\\"steps\\\": [\\n            \\\"Noise osc (Vital) \\u2192 Transient Shaper (Kilohearts, attack +6dB, sustain -8dB) \\u2192 EQ (Pro-Q 3, shelf -3dB @ 12kHz)\\\",\\n            \\\"Dry path \\u2192 snare bus\\\",\\n            \\\"Send \\u2192 Pre-delay (5ms) \\u2192 Trash 2 (tape algorithm, 35% wet) \\u2192 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**: The technique or concept name followed by \\\"by {name from <creator> tag}\\\" \\u2014 concise and search-friendly. Examples: \\\"Snare Design by Break\\\", \\\"Bass Resampling Workflow by KOAN Sound\\\", \\\"Mid-Side EQ for Width by Mr. Bill\\\". 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/home/aux/projects/content-to-kb-automator/backend/schemas.py:125:    --type: dict | None = None\n"}],"isError":false,"timestamp":1775177050034}}
{"type":"message","id":"d749bfd2","parentId":"8aa8f79f","timestamp":"2026-04-03T00:44:14.573Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at how stage 5 is wired in stages.py to understand the integration boundary."},{"type":"toolCall","id":"toolu_01LBMdxx3ZCPxfYSRMueP3bh","name":"bash","arguments":{"command":"rg -n \"def.*synth\\|stage.5\\|_synthesize\\|_build_moments\" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":124,"cacheRead":82829,"cacheWrite":4206,"totalTokens":87160,"cost":{"input":0.000005,"output":0.0031000000000000003,"cacheRead":0.0414145,"cacheWrite":0.026287500000000002,"total":0.07080700000000001}},"stopReason":"toolUse","timestamp":1775177050035}}
{"type":"message","id":"faf5eee3","parentId":"d749bfd2","timestamp":"2026-04-03T00:44:14.592Z","message":{"role":"toolResult","toolCallId":"toolu_01LBMdxx3ZCPxfYSRMueP3bh","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775177054589}}
{"type":"message","id":"7688891a","parentId":"faf5eee3","timestamp":"2026-04-03T00:44:18.007Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013ZnCEU1o28u8h4o6RqEPXV","name":"bash","arguments":{"command":"rg -n \"def \" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":85,"cacheRead":87035,"cacheWrite":155,"totalTokens":87276,"cost":{"input":0.000005,"output":0.002125,"cacheRead":0.0435175,"cacheWrite":0.00096875,"total":0.04661625}},"stopReason":"toolUse","timestamp":1775177054590}}
{"type":"message","id":"a8513ed7","parentId":"7688891a","timestamp":"2026-04-03T00:44:18.020Z","message":{"role":"toolResult","toolCallId":"toolu_013ZnCEU1o28u8h4o6RqEPXV","toolName":"bash","content":[{"type":"text","text":"59:def _set_error_status(video_id: str, stage_name: str, error: Exception) -> None:\n79:def _emit_event(\n122:def _is_debug_mode() -> bool:\n137:def _make_llm_callback(\n160:    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n189:def _build_request_params(\n224:def _get_sync_engine():\n236:def _get_sync_session() -> Session:\n244:def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n280:def _get_llm_client() -> LLMClient:\n285:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n298:def _load_canonical_tags() -> dict:\n314:def _format_taxonomy_for_prompt(tags_data: dict) -> str:\n325:def _safe_parse_llm_response(\n386:def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\n468:def stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\n579:def _classify_moment_batch(\n636:def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n741:def _store_classification_data(video_id: str, data: list[dict]) -> None:\n791:def _load_classification_data(video_id: str) -> list[dict]:\n847:def _get_git_commit_sha() -> str:\n884:def _capture_pipeline_metadata() -> dict:\n935:def _compute_page_tags(\n957:def _build_moments_text(\n983:def _synthesize_chunk(\n1025:def _slug_base(slug: str) -> str:\n1034:def _merge_pages_by_slug(\n1127:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n1427:def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n1602:def _snapshot_prior_pages(video_id: str) -> None:\n"}],"isError":false,"timestamp":1775177058019}}
{"type":"message","id":"99898cdd","parentId":"a8513ed7","timestamp":"2026-04-03T00:44:21.789Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EJ7tm67SiNm5mf8kfeZYrv","name":"read","arguments":{"limit":120,"offset":957,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":107,"cacheRead":87190,"cacheWrite":674,"totalTokens":87972,"cost":{"input":0.000005,"output":0.0026750000000000003,"cacheRead":0.043594999999999995,"cacheWrite":0.0042125,"total":0.0504875}},"stopReason":"toolUse","timestamp":1775177058019}}
{"type":"message","id":"7ec096cc","parentId":"99898cdd","timestamp":"2026-04-03T00:44:21.795Z","message":{"role":"toolResult","toolCallId":"toolu_01EJ7tm67SiNm5mf8kfeZYrv","toolName":"read","content":[{"type":"text","text":"def _build_moments_text(\n    moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text and collect all tags for a group of moments.\n\n    Returns (moments_text, all_tags).\n    \"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n    E.g. 'wavetable-sound-design-copycatt' → 'wavetable-sound-design'\n    Also normalizes casing.\n    \"\"\"\n    return slug.lower().strip()\n\n\ndef _merge_pages_by_slug(\n    all_pages: list,\n    creator_name: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n) -> list:\n    \"\"\"Detect pages with the same slug across chunks and merge them via LLM.\n\n    Pages with unique slugs pass through unchanged. Pages sharing a slug\n    get sent to a merge prompt that combines them into one cohesive page.\n\n    Returns the final list of SynthesizedPage objects.\n    \"\"\"\n    from pipeline.schemas import SynthesizedPage\n\n    # Group pages by slug\n    by_slug: dict[str, list] = defaultdict(list)\n    for page in all_pages:\n        by_slug[_slug_base(page.slug)].append(page)\n\n    final_pages = []\n    for slug, pages_group in by_slug.items():\n        if len(pages_group) == 1:\n            # Unique slug — no merge needed\n            final_pages.append(pages_group[0])\n            continue\n\n        # Multiple pages share this slug — merge via LLM\n        logger.info(\n            \"Stage 5: Merging %d partial pages with slug '%s' for video_id=%s\",\n            len(pages_group), slug, video_id,\n        )\n\n        # Serialize partial pages to JSON for the merge prompt\n        pages_json = json.dumps(\n            [p.model_dump() for p in pages_group],\n            indent=2, ensure_ascii=False,\n        )\n\n\n[1027 more lines in file. Use offset=1077 to continue.]"}],"isError":false,"timestamp":1775177061792}}
{"type":"message","id":"17ba6eea","parentId":"7ec096cc","timestamp":"2026-04-03T00:44:27.493Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how `body_sections` is stored in the DB model and consumed by the frontend/API."},{"type":"toolCall","id":"toolu_01GpXYmGiKFDTXgFDVxVamuF","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/models.py"}},{"type":"toolCall","id":"toolu_01SKsiqN72QJGjfQTLCYzn3H","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/schemas.py"}},{"type":"toolCall","id":"toolu_01QZjuAKDrAdmN4c9t7Hj5aP","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/frontend/src/ -r --include=\"*.tsx\" --include=\"*.ts\" | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":251,"cacheRead":87864,"cacheWrite":1456,"totalTokens":89572,"cost":{"input":0.000005,"output":0.006275,"cacheRead":0.043932,"cacheWrite":0.0091,"total":0.059312000000000004}},"stopReason":"toolUse","timestamp":1775177061793}}
{"type":"message","id":"2fb76601","parentId":"17ba6eea","timestamp":"2026-04-03T00:44:27.513Z","message":{"role":"toolResult","toolCallId":"toolu_01GpXYmGiKFDTXgFDVxVamuF","toolName":"bash","content":[{"type":"text","text":"218:    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n"}],"isError":false,"timestamp":1775177067513}}
{"type":"message","id":"425a8ece","parentId":"2fb76601","timestamp":"2026-04-03T00:44:27.518Z","message":{"role":"toolResult","toolCallId":"toolu_01SKsiqN72QJGjfQTLCYzn3H","toolName":"bash","content":[{"type":"text","text":"125:    body_sections: dict | None = None\n"}],"isError":false,"timestamp":1775177067518}}
{"type":"message","id":"9eea247e","parentId":"425a8ece","timestamp":"2026-04-03T00:44:27.524Z","message":{"role":"toolResult","toolCallId":"toolu_01QZjuAKDrAdmN4c9t7Hj5aP","toolName":"bash","content":[{"type":"text","text":"rg: unrecognized flag --include\n\nsimilar flags that are available: --include-zero\n"}],"isError":false,"timestamp":1775177067521}}
{"type":"message","id":"432e4242","parentId":"9eea247e","timestamp":"2026-04-03T00:44:31.393Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01UGK1uByPwhJac7Rj1ybAD7","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/frontend/src/ -t ts -t tsx 2>/dev/null || rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/frontend/src/ --glob \"*.tsx\" --glob \"*.ts\" | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":138,"cacheRead":89320,"cacheWrite":399,"totalTokens":89858,"cost":{"input":0.000005,"output":0.0034500000000000004,"cacheRead":0.04466,"cacheWrite":0.0024937500000000003,"total":0.05060875}},"stopReason":"toolUse","timestamp":1775177067522}}
{"type":"message","id":"8ee75b55","parentId":"432e4242","timestamp":"2026-04-03T00:44:31.412Z","message":{"role":"toolResult","toolCallId":"toolu_01UGK1uByPwhJac7Rj1ybAD7","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/frontend/src/api/public-client.ts:66:  body_sections: Record<string, unknown> | null;\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx:52:    body_sections:\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx:53:      typeof snapshot.body_sections === \"object\" && snapshot.body_sections !== null\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx:54:        ? (snapshot.body_sections as Record<string, unknown>)\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx:208:  const displaySections = overlay?.body_sections ?? technique.body_sections;\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx:405:      {/* Study guide prose — body_sections */}\n"}],"isError":false,"timestamp":1775177071410}}
{"type":"message","id":"db20c2b8","parentId":"8ee75b55","timestamp":"2026-04-03T00:44:35.114Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me see how the frontend renders `body_sections` to understand the current rendering code:"},{"type":"toolCall","id":"toolu_01GxaSyDW1aW7p6CFN69rRCg","name":"read","arguments":{"limit":80,"offset":395,"path":"/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":132,"cacheRead":89719,"cacheWrite":424,"totalTokens":90276,"cost":{"input":0.000005,"output":0.0033,"cacheRead":0.0448595,"cacheWrite":0.00265,"total":0.0508145}},"stopReason":"toolUse","timestamp":1775177071411}}
{"type":"message","id":"577e1a01","parentId":"db20c2b8","timestamp":"2026-04-03T00:44:35.117Z","message":{"role":"toolResult","toolCallId":"toolu_01GxaSyDW1aW7p6CFN69rRCg","toolName":"read","content":[{"type":"text","text":"            </div>\n          )}\n\n          {/* Summary */}\n      {displaySummary && (\n        <section className=\"technique-summary\">\n          <p>{displaySummary}</p>\n        </section>\n      )}\n\n      {/* Study guide prose — body_sections */}\n      {displaySections &&\n        Object.keys(displaySections).length > 0 && (\n          <section className=\"technique-prose\">\n            {Object.entries(displaySections).map(\n              ([sectionTitle, content]: [string, unknown]) => (\n                <div key={sectionTitle} className=\"technique-prose__section\">\n                  <h2>{sectionTitle}</h2>\n                  {typeof content === \"string\" ? (\n                    <p>{content as string}</p>\n                  ) : typeof content === \"object\" && content !== null ? (\n                    <pre className=\"technique-prose__json\">\n                      {JSON.stringify(content, null, 2)}\n                    </pre>\n                  ) : (\n                    <p>{String(content as string)}</p>\n                  )}\n                </div>\n              ),\n            )}\n          </section>\n        )}\n\n        </div>\n        <div className=\"technique-columns__sidebar\">\n          {/* Key moments (always from live data — not versioned) */}\n      {technique.key_moments.length > 0 && (\n        <section className=\"technique-moments\">\n          <h2>Key Moments</h2>\n          <ol className=\"technique-moments__list\">\n            {technique.key_moments.map((km) => (\n              <li key={km.id} id={`km-${km.id}`} className=\"technique-moment\">\n                <h3 className=\"technique-moment__title\">{km.title}</h3>\n                <div className=\"technique-moment__meta\">\n                  {km.video_filename && (\n                    <span className=\"technique-moment__source\">\n                      {km.video_filename}\n                    </span>\n                  )}\n                  <span className=\"technique-moment__time\">\n                    {formatTime(km.start_time)} – {formatTime(km.end_time)}\n                  </span>\n                  <span className=\"badge badge--content-type\">\n                    {km.content_type}\n                  </span>\n                </div>\n                <p className=\"technique-moment__summary\">{km.summary}</p>\n              </li>\n            ))}\n          </ol>\n        </section>\n      )}\n\n      {/* Signal chains */}\n      {displayChains &&\n        displayChains.length > 0 && (\n          <section className=\"technique-chains\">\n            <h2>Signal Chains</h2>\n            {displayChains.map((chain, i) => {\n              const chainObj = chain as Record<string, unknown>;\n              const chainName =\n                typeof chainObj[\"name\"] === \"string\"\n                  ? chainObj[\"name\"]\n                  : `Chain ${i + 1}`;\n              const steps = Array.isArray(chainObj[\"steps\"])\n                ? (chainObj[\"steps\"] as string[])\n                : [];\n              return (\n                <div key={i} className=\"technique-chain\">\n                  <h3>{chainName}</h3>\n\n[77 more lines in file. Use offset=475 to continue.]"}],"isError":false,"timestamp":1775177075116}}
{"type":"message","id":"d6115d91","parentId":"577e1a01","timestamp":"2026-04-03T00:44:40.501Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check if there's an existing fixture I can reference:"},{"type":"toolCall","id":"toolu_01ST5cWmKS743nA9QxyZXgUb","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name \"*.json\" -path \"*/fixtures/*\" -not -path \"*/node_modules/*\" | head -10"}},{"type":"toolCall","id":"toolu_01MQKk5KJhfLUgsY6vz7TLaA","name":"bash","arguments":{"command":"rg -n \"body_sections_format\\|format.*version\\|v2.*format\\|format_v\" /home/aux/projects/content-to-kb-automator/backend/ --glob \"*.py\" | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":199,"cacheRead":90143,"cacheWrite":949,"totalTokens":91292,"cost":{"input":0.000005,"output":0.004975,"cacheRead":0.0450715,"cacheWrite":0.00593125,"total":0.05598275}},"stopReason":"toolUse","timestamp":1775177075116}}
{"type":"message","id":"3741b1bf","parentId":"d6115d91","timestamp":"2026-04-03T00:44:40.557Z","message":{"role":"toolResult","toolCallId":"toolu_01ST5cWmKS743nA9QxyZXgUb","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_classifications.json\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_segments.json\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_moments.json\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/sample_transcript.json\n"}],"isError":false,"timestamp":1775177080556}}
{"type":"message","id":"07d84b78","parentId":"3741b1bf","timestamp":"2026-04-03T00:44:40.561Z","message":{"role":"toolResult","toolCallId":"toolu_01MQKk5KJhfLUgsY6vz7TLaA","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177080556}}
{"type":"message","id":"1f04c67e","parentId":"07d84b78","timestamp":"2026-04-03T00:44:43.822Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Qk8vYDHVXwRG7sK7nMNgFa","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name \"*.json\" -path \"*fixture*\" -size +10k | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":91092,"cacheWrite":437,"totalTokens":91618,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.045545999999999996,"cacheWrite":0.00273125,"total":0.05048224999999999}},"stopReason":"toolUse","timestamp":1775177080558}}
{"type":"message","id":"354e4a02","parentId":"1f04c67e","timestamp":"2026-04-03T00:44:43.854Z","message":{"role":"toolResult","toolCallId":"toolu_01Qk8vYDHVXwRG7sK7nMNgFa","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177083852}}
{"type":"message","id":"8705ef0d","parentId":"354e4a02","timestamp":"2026-04-03T00:44:47.811Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0159Knbfbt7yrXje5SdQ2Rdh","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json; wc -l /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":119,"cacheRead":91092,"cacheWrite":539,"totalTokens":91751,"cost":{"input":0.000005,"output":0.002975,"cacheRead":0.045545999999999996,"cacheWrite":0.00336875,"total":0.05189475}},"stopReason":"toolUse","timestamp":1775177083853}}
{"type":"message","id":"ed06c2c4","parentId":"8705ef0d","timestamp":"2026-04-03T00:44:47.827Z","message":{"role":"toolResult","toolCallId":"toolu_0159Knbfbt7yrXje5SdQ2Rdh","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json\n18 /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json\n"}],"isError":false,"timestamp":1775177087824}}
{"type":"message","id":"f794f22d","parentId":"ed06c2c4","timestamp":"2026-04-03T00:44:50.791Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Xwv7pGn3ALqvHbiGumsiBC","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/fixtures/sample_topic_group.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":91631,"cacheWrite":196,"totalTokens":91909,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.045815499999999995,"cacheWrite":0.001225,"total":0.04907049999999999}},"stopReason":"toolUse","timestamp":1775177087825}}
{"type":"message","id":"afb96789","parentId":"f794f22d","timestamp":"2026-04-03T00:44:50.800Z","message":{"role":"toolResult","toolCallId":"toolu_01Xwv7pGn3ALqvHbiGumsiBC","toolName":"read","content":[{"type":"text","text":"{\n  \"topic_segments\": [\n    {\n      \"start_index\": 0,\n      \"end_index\": 2,\n      \"topic_label\": \"Frequency-specific sidechain compression with Trackspacer\",\n      \"summary\": \"Using Trackspacer for frequency-band sidechain ducking instead of traditional volume compression\",\n      \"transcript_text\": \"Hey everyone, today we're going to talk about sidechain compression and how I use it in my productions. So the basic idea is you take the kick drum signal and use it to duck the bass. Most people use a compressor for this but I actually prefer Trackspacer because it gives you frequency-specific ducking. With Trackspacer you can set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end stays right there.\"\n    },\n    {\n      \"start_index\": 3,\n      \"end_index\": 4,\n      \"topic_label\": \"Parallel drum compression with Drum Buss and Valhalla Room\",\n      \"summary\": \"Setting up a parallel compression chain using Ableton's Drum Buss and Valhalla Room reverb for drum processing\",\n      \"transcript_text\": \"Now let me show you another technique — parallel compression on drums. I use Drum Buss with the drive at about 40 percent, then send that to a return track. On the return I put Valhalla Room with a short decay, like 1.2 seconds. Mix it in at minus 12 dB. Your drums just breathe — they get this room sound without getting washy.\"\n    }\n  ]\n}\n"}],"isError":false,"timestamp":1775177090798}}
{"type":"message","id":"dcf6ebf1","parentId":"afb96789","timestamp":"2026-04-03T00:44:54.999Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the stage 5 section of stages.py more carefully — how it stores pages to DB:"},{"type":"toolCall","id":"toolu_01ABufSZxSm127cwB2p8YEwp","name":"read","arguments":{"limit":100,"offset":1300,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":91827,"cacheWrite":483,"totalTokens":92445,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.045913499999999996,"cacheWrite":0.00301875,"total":0.05228724999999999}},"stopReason":"toolUse","timestamp":1775177090799}}
{"type":"message","id":"cd953b3f","parentId":"dcf6ebf1","timestamp":"2026-04-03T00:44:55.005Z","message":{"role":"toolResult","toolCallId":"toolu_01ABufSZxSm127cwB2p8YEwp","toolName":"read","content":[{"type":"text","text":"                    existing = session.execute(\n                        select(TechniquePage).where(\n                            TechniquePage.id.in_(prior_page_ids),\n                            TechniquePage.creator_id == video.creator_id,\n                            func.lower(TechniquePage.topic_category) == func.lower(page_data.topic_category or category),\n                        )\n                    ).scalars().first()\n                    if existing:\n                        logger.info(\n                            \"Stage 5: Matched prior page '%s' (id=%s) by creator+category for video_id=%s\",\n                            existing.slug, existing.id, video_id,\n                        )\n\n                if existing:\n                    # Snapshot existing content before overwriting\n                    try:\n                        sq = existing.source_quality\n                        sq_value = sq.value if hasattr(sq, 'value') else sq\n                        snapshot = {\n                            \"title\": existing.title,\n                            \"slug\": existing.slug,\n                            \"topic_category\": existing.topic_category,\n                            \"topic_tags\": existing.topic_tags,\n                            \"summary\": existing.summary,\n                            \"body_sections\": existing.body_sections,\n                            \"signal_chains\": existing.signal_chains,\n                            \"plugins\": existing.plugins,\n                            \"source_quality\": sq_value,\n                        }\n                        version_count = session.execute(\n                            select(func.count()).where(\n                                TechniquePageVersion.technique_page_id == existing.id\n                            )\n                        ).scalar()\n                        version_number = version_count + 1\n\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                            version_number=version_number,\n                            content_snapshot=snapshot,\n                            pipeline_metadata=_capture_pipeline_metadata(),\n                        )\n                        session.add(version)\n                        logger.info(\n                            \"Version snapshot v%d created for page slug=%s\",\n                            version_number, existing.slug,\n                        )\n                    except Exception as snap_exc:\n                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    page_tags = _compute_page_tags(page_moment_indices, moment_group, all_tags)\n                    existing.topic_tags = page_tags\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=_compute_page_tags(page_moment_indices, moment_group, all_tags),\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n[704 more lines in file. Use offset=1400 to continue.]"}],"isError":false,"timestamp":1775177095002}}
{"type":"message","id":"bf5c92c9","parentId":"cd953b3f","timestamp":"2026-04-03T00:44:59.921Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I now have a thorough understanding of the current state. Let me also quickly check what the quality scorer/optimizer expects:"},{"type":"toolCall","id":"toolu_019dgasJymUyVA8Xkf9pKEPC","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/ --glob \"*.py\" | head -20"}},{"type":"toolCall","id":"toolu_01QT48ha4mgqtg2gnEqRPT32","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/pipeline/export_fixture.py 2>/dev/null | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":198,"cacheRead":91827,"cacheWrite":1799,"totalTokens":93825,"cost":{"input":0.000005,"output":0.00495,"cacheRead":0.045913499999999996,"cacheWrite":0.01124375,"total":0.062112249999999994}},"stopReason":"toolUse","timestamp":1775177095003}}
{"type":"message","id":"351c4d6d","parentId":"bf5c92c9","timestamp":"2026-04-03T00:44:59.941Z","message":{"role":"toolResult","toolCallId":"toolu_019dgasJymUyVA8Xkf9pKEPC","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:200:1. A synthesized technique page (JSON with title, summary, body_sections)\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:283:        format_markers=[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"],\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:552:            \"body_sections\": [\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py:554:                for heading, content in page.body_sections.items()\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/__main__.py:337:        \"body_sections\": [\n/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/variant_generator.py:84:_FORMAT_MARKERS = [\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]\n"}],"isError":false,"timestamp":1775177099940}}
{"type":"message","id":"02a0263b","parentId":"351c4d6d","timestamp":"2026-04-03T00:44:59.944Z","message":{"role":"toolResult","toolCallId":"toolu_01QT48ha4mgqtg2gnEqRPT32","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177099940}}
{"type":"message","id":"15998f42","parentId":"02a0263b","timestamp":"2026-04-03T00:45:03.834Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check how the scorer iterates body_sections — it currently does `.items()` which assumes a flat dict:"},{"type":"toolCall","id":"toolu_015ffgWwZMXNMSLjb6YGBUX7","name":"read","arguments":{"limit":25,"offset":545,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/scorer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":93626,"cacheWrite":537,"totalTokens":94299,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.046813,"cacheWrite":0.0033562500000000003,"total":0.05354925}},"stopReason":"toolUse","timestamp":1775177099942}}
{"type":"message","id":"580721f6","parentId":"15998f42","timestamp":"2026-04-03T00:45:03.839Z","message":{"role":"toolResult","toolCallId":"toolu_015ffgWwZMXNMSLjb6YGBUX7","toolName":"read","content":[{"type":"text","text":"\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        print(f\"  Synthesis complete ({elapsed_synth}s). Scoring...\")\n        result = self.score_page(page_json, moments)\n        # Include synthesis time in total\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def print_report(self, result: ScoreResult, stage: int = 5) -> None:\n        \"\"\"Print a formatted scoring report to stdout.\"\"\"\n        dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else list(result.scores.keys())\n        stage_label = f\"STAGE {stage}\" if stage in STAGE_CONFIGS else \"QUALITY\"\n\n        print(\"\\n\" + \"=\" * 60)\n\n[46 more lines in file. Use offset=570 to continue.]"}],"isError":false,"timestamp":1775177103836}}
{"type":"message","id":"0bdf075b","parentId":"580721f6","timestamp":"2026-04-03T00:45:07.432Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the embedding stage to understand what it reads from body_sections:"},{"type":"toolCall","id":"toolu_012hoqMkNWK5BLyGSAWU11V9","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":94163,"cacheWrite":436,"totalTokens":94705,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.0470815,"cacheWrite":0.002725,"total":0.0524365}},"stopReason":"toolUse","timestamp":1775177103837}}
{"type":"message","id":"78d6dc7c","parentId":"0bdf075b","timestamp":"2026-04-03T00:45:07.446Z","message":{"role":"toolResult","toolCallId":"toolu_012hoqMkNWK5BLyGSAWU11V9","toolName":"bash","content":[{"type":"text","text":"1324:                            \"body_sections\": existing.body_sections,\n1357:                    existing.body_sections = page_data.body_sections\n1372:                        body_sections=page_data.body_sections,\n"}],"isError":false,"timestamp":1775177107444}}
{"type":"message","id":"c880ba39","parentId":"78d6dc7c","timestamp":"2026-04-03T00:45:10.953Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XGuPb2CZeRodq5yoRzs5QU","name":"bash","arguments":{"command":"rg -n \"body_sections\" /home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":87,"cacheRead":94599,"cacheWrite":171,"totalTokens":94858,"cost":{"input":0.000005,"output":0.0021750000000000003,"cacheRead":0.0472995,"cacheWrite":0.00106875,"total":0.05054825}},"stopReason":"toolUse","timestamp":1775177107445}}
{"type":"message","id":"c20e19a5","parentId":"c880ba39","timestamp":"2026-04-03T00:45:10.970Z","message":{"role":"toolResult","toolCallId":"toolu_01XGuPb2CZeRodq5yoRzs5QU","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775177110967}}
{"type":"message","id":"9dcfe03e","parentId":"c20e19a5","timestamp":"2026-04-03T00:45:14.736Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GzefssRGgayW2Np27YTaZa","name":"bash","arguments":{"command":"rg -n \"body_sections\\|sections\\|\\.body\" /home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":94599,"cacheWrite":289,"totalTokens":94987,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.0472995,"cacheWrite":0.0018062500000000001,"total":0.05156075}},"stopReason":"toolUse","timestamp":1775177110968}}
{"type":"message","id":"b5a8f9c9","parentId":"9dcfe03e","timestamp":"2026-04-03T00:45:14.754Z","message":{"role":"toolResult","toolCallId":"toolu_01GzefssRGgayW2Np27YTaZa","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177114751}}
{"type":"message","id":"e5fb0b57","parentId":"b5a8f9c9","timestamp":"2026-04-03T00:45:18.380Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RipU7zJSmqYrySMTMgdqfU","name":"bash","arguments":{"command":"rg -n \"technique.*text\\|embed.*technique\\|upsert_technique\" /home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":94770,"cacheWrite":230,"totalTokens":95104,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.047385,"cacheWrite":0.0014375,"total":0.0514025}},"stopReason":"toolUse","timestamp":1775177114752}}
{"type":"message","id":"969aae66","parentId":"e5fb0b57","timestamp":"2026-04-03T00:45:18.400Z","message":{"role":"toolResult","toolCallId":"toolu_01RipU7zJSmqYrySMTMgdqfU","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177118397}}
{"type":"message","id":"5c270469","parentId":"969aae66","timestamp":"2026-04-03T00:45:21.532Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015rXjx78uC2gnmJF8t3P6zE","name":"bash","arguments":{"command":"rg -n \"def \" /home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":95000,"cacheWrite":117,"totalTokens":95207,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.0475,"cacheWrite":0.00073125,"total":0.050461250000000006}},"stopReason":"toolUse","timestamp":1775177118398}}
{"type":"message","id":"bae362af","parentId":"5c270469","timestamp":"2026-04-03T00:45:21.550Z","message":{"role":"toolResult","toolCallId":"toolu_015rXjx78uC2gnmJF8t3P6zE","toolName":"bash","content":[{"type":"text","text":"28:    def __init__(self, settings: Settings) -> None:\n35:    def ensure_collection(self) -> None:\n71:    def delete_by_video_id(self, video_id: str) -> int:\n110:    def upsert_points(self, points: list[PointStruct]) -> None:\n140:    def upsert_technique_pages(\n188:    def upsert_key_moments(\n"}],"isError":false,"timestamp":1775177121547}}
{"type":"message","id":"728ed692","parentId":"bae362af","timestamp":"2026-04-03T00:45:25.182Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WUx6TacNWmEJAe1nYm4VHk","name":"read","arguments":{"limit":60,"offset":140,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":95000,"cacheWrite":324,"totalTokens":95436,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.0475,"cacheWrite":0.002025,"total":0.052305}},"stopReason":"toolUse","timestamp":1775177121548}}
{"type":"message","id":"a081e87b","parentId":"728ed692","timestamp":"2026-04-03T00:45:25.187Z","message":{"role":"toolResult","toolCallId":"toolu_01WUx6TacNWmEJAe1nYm4VHk","toolName":"read","content":[{"type":"text","text":"    def upsert_technique_pages(\n        self,\n        pages: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for technique pages.\n\n        Each page dict must contain:\n            page_id, creator_id, title, topic_category, topic_tags, summary\n\n        Parameters\n        ----------\n        pages:\n            Metadata dicts, one per technique page.\n        vectors:\n            Corresponding embedding vectors (same order as pages).\n        \"\"\"\n        if len(pages) != len(vectors):\n            logger.warning(\n                \"Technique-page count (%d) != vector count (%d). Skipping upsert.\",\n                len(pages),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for page, vector in zip(pages, vectors):\n            # Deterministic UUID: same page always gets the same point ID\n            point_id = str(uuid.uuid5(_QDRANT_NAMESPACE, f\"tp:{page['page_id']}\"))\n            point = PointStruct(\n                id=point_id,\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"creator_name\": page.get(\"creator_name\", \"\"),\n                    \"title\": page[\"title\"],\n                    \"slug\": page.get(\"slug\", \"\"),\n                    \"topic_category\": page[\"topic_category\"],\n                    \"topic_tags\": page.get(\"topic_tags\") or [],\n                    \"summary\": page.get(\"summary\") or \"\",\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n    def upsert_key_moments(\n        self,\n        moments: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for key moments.\n\n        Each moment dict must contain:\n            moment_id, source_video_id, title, start_time, end_time, content_type\n\n        Parameters\n        ----------\n\n[37 more lines in file. Use offset=200 to continue.]"}],"isError":false,"timestamp":1775177125184}}
{"type":"message","id":"d8d72d81","parentId":"a081e87b","timestamp":"2026-04-03T00:45:29.556Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the embedding text is built for technique pages in stage 6:"},{"type":"toolCall","id":"toolu_01RfDmWV9vAkRnADWdADmQiV","name":"read","arguments":{"limit":80,"offset":1427,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":127,"cacheRead":95324,"cacheWrite":669,"totalTokens":96121,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.047661999999999996,"cacheWrite":0.00418125,"total":0.055023249999999996}},"stopReason":"toolUse","timestamp":1775177125185}}
{"type":"message","id":"1a89c8c9","parentId":"d8d72d81","timestamp":"2026-04-03T00:45:29.564Z","message":{"role":"toolResult","toolCallId":"toolu_01RfDmWV9vAkRnADWdADmQiV","toolName":"read","content":[{"type":"text","text":"def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load technique pages created for this video's moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Get unique technique page IDs from moments\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n        pages = []\n        if page_ids:\n            pages = (\n                session.execute(\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                )\n                .scalars()\n                .all()\n            )\n\n        if not moments and not pages:\n            logger.info(\"Stage 6: No moments or pages for video_id=%s, skipping.\", video_id)\n            if run_id:\n                _finish_run(run_id, \"complete\")\n            return video_id\n\n        # Resolve creator names for enriched embedding text\n        creator_ids = {p.creator_id for p in pages}\n        creator_map: dict[str, str] = {}\n        if creator_ids:\n            creators = (\n                session.execute(\n                    select(Creator).where(Creator.id.in_(creator_ids))\n                )\n                .scalars()\n                .all()\n            )\n            creator_map = {str(c.id): c.name for c in creators}\n\n        # Resolve creator name for key moments via source_video → creator\n        video_ids = {m.source_video_id for m in moments}\n        video_creator_map: dict[str, str] = {}\n        if video_ids:\n            rows = session.execute(\n                select(SourceVideo.id, Creator.name)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .where(SourceVideo.id.in_(video_ids))\n            ).all()\n            video_creator_map = {str(r[0]): r[1] for r in rows}\n\n        embed_client = EmbeddingClient(settings)\n        qdrant = QdrantManager(settings)\n\n        # Ensure collection exists before upserting\n        qdrant.ensure_collection()\n\n        # ── Embed & upsert technique pages ───────────────────────────────\n        if pages:\n            page_texts = []\n            page_dicts = []\n            for p in pages:\n                creator_name = creator_map.get(str(p.creator_id), \"\")\n\n[597 more lines in file. Use offset=1507 to continue.]"}],"isError":false,"timestamp":1775177129560}}
{"type":"message","id":"99e3f2d5","parentId":"1a89c8c9","timestamp":"2026-04-03T00:45:32.556Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013H3dKToxbYKq4BYUK2FzQM","name":"read","arguments":{"limit":30,"offset":1507,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":108,"cacheRead":95993,"cacheWrite":965,"totalTokens":97067,"cost":{"input":0.000005,"output":0.0027,"cacheRead":0.0479965,"cacheWrite":0.00603125,"total":0.05673275}},"stopReason":"toolUse","timestamp":1775177129562}}
{"type":"message","id":"e128529e","parentId":"99e3f2d5","timestamp":"2026-04-03T00:45:32.562Z","message":{"role":"toolResult","toolCallId":"toolu_013H3dKToxbYKq4BYUK2FzQM","toolName":"read","content":[{"type":"text","text":"                tags_joined = \" \".join(p.topic_tags) if p.topic_tags else \"\"\n                text = f\"{creator_name} {p.title} {p.topic_category or ''} {tags_joined} {p.summary or ''}\"\n                page_texts.append(text.strip())\n                page_dicts.append({\n                    \"page_id\": str(p.id),\n                    \"creator_id\": str(p.creator_id),\n                    \"creator_name\": creator_name,\n                    \"title\": p.title,\n                    \"slug\": p.slug,\n                    \"topic_category\": p.topic_category or \"\",\n                    \"topic_tags\": p.topic_tags or [],\n                    \"summary\": p.summary or \"\",\n                })\n\n            page_vectors = embed_client.embed(page_texts)\n            if page_vectors:\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d technique page vectors for video_id=%s\",\n                    len(page_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d technique pages (video_id=%s). \"\n                    \"Skipping page upsert.\",\n                    len(page_texts), video_id,\n                )\n\n        # ── Embed & upsert key moments ───────────────────────────────────\n        if moments:\n\n[567 more lines in file. Use offset=1537 to continue.]"}],"isError":false,"timestamp":1775177132559}}
{"type":"message","id":"3d0af38b","parentId":"e128529e","timestamp":"2026-04-03T00:46:40.055Z","message":{"role":"assistant","content":[{"type":"text","text":"I have all the information I need. Let me now understand what this slice needs to deliver, summarize the key findings, and write the research doc.\n\nKey findings:\n\n1. **Current format**: `body_sections` is a flat `dict[str, str]` (section_name → prose). Stored as JSONB in PostgreSQL.\n\n2. **Target format**: list-of-objects with H2/H3 nesting and citation markers. The roadmap says \"list-of-objects body_sections with H2/H3 nesting, citation markers on key claims.\"\n\n3. **Scope of S01**: Only the prompt + schema + test harness validation. No DB migration, no frontend changes (those are S03 and S05).\n\n4. **Files that need changing**:\n   - `prompts/stage5_synthesis.txt` — rewrite output format to list-of-objects with nesting + citations\n   - `backend/pipeline/schemas.py` — update `SynthesizedPage.body_sections` type from `dict` to `list[dict]` (or a nested model)\n   - `backend/pipeline/test_harness.py` — update word-count/section-count logic for new structure\n\n5. **Downstream consumers** (NOT changed in S01 but must be aware):\n   - `stages.py` lines 1324, 1357, 1372 — stores body_sections to DB\n   - `quality/scorer.py` line 554 — iterates `.items()` on body_sections\n   - `quality/__main__.py` line 337 — same\n   - Frontend `TechniquePage.tsx` — iterates `Object.entries()`\n   - Stage 6 embedding — only uses `summary`, not body_sections\n\n6. **Citation design**: Moments are indexed `[0]`, `[1]`, etc. in the prompt input. Citations can reference these indices inline: `[0]`, `[0,2]`. This lets the frontend link claims to specific key moments.\n\n7. **Nesting design**: H2 sections contain H3 subsections. A natural representation: `[{heading: str, level: 2, content: str, subsections: [{heading: str, level: 3, content: str}]}]`\n\n8. **Risk**: The LLM must reliably produce the new JSON structure. This is the highest risk — prompt engineering to get consistent nested output with citations from a general-purpose LLM."},{"type":"toolCall","id":"toolu_014gaUr8Mq8Lbcdz32uCMaTa","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# 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","milestone_id":"M014","slice_id":"S01"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2945,"cacheRead":96958,"cacheWrite":507,"totalTokens":100411,"cost":{"input":0.000005,"output":0.07362500000000001,"cacheRead":0.048479,"cacheWrite":0.00316875,"total":0.12527775000000002}},"stopReason":"toolUse","timestamp":1775177132560}}
{"type":"message","id":"dca1d2ee","parentId":"3d0af38b","timestamp":"2026-04-03T00:46:40.065Z","message":{"role":"toolResult","toolCallId":"toolu_014gaUr8Mq8Lbcdz32uCMaTa","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M014/slices/S01/S01-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M014/slices/S01/S01-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775177200062}}
{"type":"message","id":"cad9f57b","parentId":"dca1d2ee","timestamp":"2026-04-03T00:46:43.102Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S01 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":97465,"cacheWrite":2983,"totalTokens":100460,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.0487325,"cacheWrite":0.01864375,"total":0.06765625}},"stopReason":"stop","timestamp":1775177200063}}
