{"type":"thinking_level_change","id":"6d3eec59","parentId":null,"timestamp":"2026-04-03T00:55:52.024Z","thinkingLevel":"medium"}
{"type":"model_change","id":"63f1a00e","parentId":"6d3eec59","timestamp":"2026-04-03T00:55:52.026Z","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## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S02 (\"Composition Prompt + Test Harness Compose Mode\") — 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 (compact)\nFields: id | when | scope | decision | choice | rationale | revisable | made_by\n\nD024 | M014/S01 | architecture | Content model for sections with subsections | Sections with subsections use empty-string content field; substance lives in subsections | Avoids duplication between section-level content and subsection content. The section heading serves as H2 container; all prose lives in subsection content fields. Sections without subsections use the section content field directly. | 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#### S01 Summary\nSource: `.gsd/milestones/M014/slices/S01/S01-SUMMARY.md`\n\n---\nid: S01\nparent: M014\nmilestone: M014\nprovides:\n  - BodySection/BodySubSection Pydantic models for downstream rendering (S05) and DB storage (S03)\n  - citation_utils.extract_citations() and validate_citations() for pipeline quality checks\n  - stage5_synthesis.txt v5 prompt with v2 output format for pipeline execution (S04)\n  - test_harness v2 format support for composition testing (S02)\nrequires:\n  []\naffects:\n  - S02 — uses updated test harness and prompt for compose mode testing\n  - S04 — pipeline uses the v2 prompt and parses v2 output via BodySection models\n  - S05 — frontend renders list[BodySection] with TOC and citation links\nkey_files:\n  - backend/pipeline/schemas.py\n  - backend/pipeline/citation_utils.py\n  - backend/pipeline/test_citation_utils.py\n  - backend/pipeline/test_harness.py\n  - backend/pipeline/test_harness_v2_format.py\n  - prompts/stage5_synthesis.txt\nkey_decisions:\n  - D024: Citations use 0-based indices matching moment list position\n  - D024: Sections with subsections use empty-string content; substance lives in subsections\n  - Word count includes section.content + subsection.content; heading text excluded\n  - Citation report uses CITE log tag for grep-friendly structured output\npatterns_established:\n  - body_sections v2 format: list[BodySection] with heading/content/subsections, discriminated by body_sections_format='v2'\n  - Citation validation as reusable utility — validate_citations() returns a dict with coverage_pct, invalid_indices, uncited_moments for any consumer\n  - Prompt backup convention: .bak with timestamp suffix before destructive edits\nobservability_surfaces:\n  - CITE log tag in test harness output for per-page citation coverage reporting\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S01/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S01/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T00:55:48.823Z\nblocker_discovered: false\n---\n\n# S01: Synthesis Prompt v5 — Nested Sections + Citations\n\n**Introduced body_sections v2 format (list-of-objects with H2/H3 nesting and inline [N] citation markers), backed by Pydantic schema, citation validation utility, updated synthesis prompt, and v2-aware test harness — all verified with 28 passing tests.**\n\n## What Happened\n\nThis slice replaced the flat dict-based body_sections format with a structured list-of-objects format supporting H2/H3 nesting and inline citation markers.\n\n**T01 — Schema & Citation Utils:** Added BodySection and BodySubSection Pydantic models to schemas.py. BodySection has heading, content, and optional subsections list. Changed SynthesizedPage.body_sections from dict to list[BodySection] and added body_sections_format='v2' discriminator field. Created citation_utils.py with extract_citations() (regex parser for [N] and [N,M] markers) and validate_citations() (returns coverage stats, invalid indices, uncited moments). 15 unit tests cover all edge cases including multi-citation markers, out-of-range indices, subsection citations, and zero-moment edge case.\n\n**T02 — Prompt Rewrite:** Backed up the existing prompt, then made four targeted edits to stage5_synthesis.txt: (1) H3 subsection guidance in the body sections structure section, (2) new Citation rules section defining [N] and [N,M] format with placement conventions, (3) rewrote the Output format JSON example with v2 structure showing both flat and nested sections with inline citations, (4) updated Field rules to describe the new format. All non-output sections (voice, tone, synthesis philosophy) preserved unchanged.\n\n**T03 — Test Harness Update:** Updated two locations in test_harness.py where body_sections was iterated as a dict — now walks BodySection objects for word counting and metadata. Added subsection count reporting and per-page citation coverage logging with a CITE log tag for grep-friendly output. Created test_harness_v2_format.py with 13 tests covering word counting (flat and nested), section counting, citation integration, and SynthesisResult round-trip serialization.\n\n## Verification\n\nAll three task verification commands pass:\n- `python -m pytest backend/pipeline/test_citation_utils.py -v` — 15/15 passed (0.08s)\n- `python -m pytest backend/pipeline/test_harness_v2_format.py -v` — 13/13 passed (0.08s)\n- Prompt structure assertion (body_sections_format, Citation rules, subsections present) — passed\n\nTotal: 28 tests passing, prompt validates structurally.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nThe v2 format is schema-validated and test-harnessed but has not been run against a live LLM yet — that happens when the pipeline compose logic (S04) exercises the prompt end-to-end.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/schemas.py` — Added BodySubSection, BodySection models; changed SynthesizedPage.body_sections to list[BodySection]; added body_sections_format='v2'\n- `backend/pipeline/citation_utils.py` — New file: extract_citations() and validate_citations() functions\n- `backend/pipeline/test_citation_utils.py` — New file: 15 unit tests for citation utilities\n- `backend/pipeline/test_harness.py` — Updated word-count and metadata logic from dict iteration to BodySection object walking; added citation coverage reporting\n- `backend/pipeline/test_harness_v2_format.py` — New file: 13 tests for v2 format word counting, section counting, citation integration, round-trip\n- `prompts/stage5_synthesis.txt` — Added Citation rules section, H3 subsection guidance, rewrote Output format to v2 list-of-objects with citations, updated Field rules\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: S02`, `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/S02/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S02 researched.\"","display":false,"id":"26916c6f","parentId":"63f1a00e","timestamp":"2026-04-03T00:55:52.029Z"}
{"type":"message","id":"869f0793","parentId":"26916c6f","timestamp":"2026-04-03T00:56:00.765Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research what S02 needs. The slice is about \"Composition Prompt + Test Harness Compose Mode\" — creating a prompt and test harness mode that merges new video content into existing technique pages.\n\nLet me start by understanding what exists from S01 and what S02 needs to build.","thinkingSignature":"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"},{"type":"text","text":"S02 is about composition — merging new video content into an existing technique page. Let me trace the existing code to understand what's there and what needs building."},{"type":"toolCall","id":"toolu_01H7EevP97hTSZX4cPaWSGdR","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/pipeline -type f -name '*.py' | head -30 && echo \"---\" && find prompts -type f | head -20"}},{"type":"toolCall","id":"toolu_01HGP2Wv8UAYeBmJ8xRo9TqN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"compose\\|composition\\|merge\\|existing.page\" backend/pipeline/ prompts/ --ignore-case | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":305,"cacheRead":59865,"cacheWrite":6863,"totalTokens":67036,"cost":{"input":0.000015000000000000002,"output":0.007625000000000001,"cacheRead":0.029932499999999997,"cacheWrite":0.04289375,"total":0.08046624999999999}},"stopReason":"toolUse","timestamp":1775177752027}}
{"type":"message","id":"7e325dfc","parentId":"869f0793","timestamp":"2026-04-03T00:56:00.775Z","message":{"role":"toolResult","toolCallId":"toolu_01H7EevP97hTSZX4cPaWSGdR","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/test_citation_utils.py\nbackend/pipeline/llm_client.py\nbackend/pipeline/test_harness.py\nbackend/pipeline/stages.py\nbackend/pipeline/export_fixture.py\nbackend/pipeline/citation_utils.py\nbackend/pipeline/embedding_client.py\nbackend/pipeline/__init__.py\nbackend/pipeline/test_harness_v2_format.py\nbackend/pipeline/qdrant_client.py\nbackend/pipeline/quality/optimizer.py\nbackend/pipeline/quality/__main__.py\nbackend/pipeline/quality/scorer.py\nbackend/pipeline/quality/fitness.py\nbackend/pipeline/quality/variant_generator.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/voice_dial.py\nbackend/pipeline/quality/fixtures/__init__.py\nbackend/pipeline/schemas.py\n---\nprompts/stage3_extraction.txt\nprompts/stage5_synthesis.txt\nprompts/stage2_segmentation.txt\nprompts/README.md\nprompts/stage5_synthesis.20260401_101518.bak\nprompts/stage5_variants/v038.txt\nprompts/stage5_variants/v027.txt\nprompts/stage5_variants/v025.txt\nprompts/stage5_variants/v048.txt\nprompts/stage5_variants/v010.txt\nprompts/stage5_variants/v096.txt\nprompts/stage5_variants/v041.txt\nprompts/stage5_variants/v039.txt\nprompts/stage5_variants/v059.txt\nprompts/stage5_variants/v053.txt\nprompts/stage5_variants/manifest.json\nprompts/stage5_variants/v022.txt\nprompts/stage5_variants/v095.txt\nprompts/stage5_variants/v073.txt\nprompts/stage5_variants/v019.txt\n"}],"isError":false,"timestamp":1775177760775}}
{"type":"message","id":"c8e6cd88","parentId":"7e325dfc","timestamp":"2026-04-03T00:56:00.784Z","message":{"role":"toolResult","toolCallId":"toolu_01HGP2Wv8UAYeBmJ8xRo9TqN","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177760783}}
{"type":"message","id":"20289afb","parentId":"c8e6cd88","timestamp":"2026-04-03T00:56:05.270Z","message":{"role":"assistant","content":[{"type":"text","text":"No compose/merge references yet. Let me read the key files — the test harness, the current synthesis prompt, and schemas."},{"type":"toolCall","id":"toolu_01HyveeGMroTa1QX5zTVi9ge","name":"read","arguments":{"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01DVUfwgG92R3xwCQSpLYCg4","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":128,"cacheRead":66728,"cacheWrite":740,"totalTokens":67597,"cost":{"input":0.000005,"output":0.0032,"cacheRead":0.033364,"cacheWrite":0.004625000000000001,"total":0.041193999999999995}},"stopReason":"toolUse","timestamp":1775177760783}}
{"type":"message","id":"1eafe432","parentId":"20289afb","timestamp":"2026-04-03T00:56:05.275Z","message":{"role":"toolResult","toolCallId":"toolu_01HyveeGMroTa1QX5zTVi9ge","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.citation_utils import validate_citations\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            section_count = len(sections)\n            subsection_count = sum(len(s.subsections) for s in sections)\n            word_count = sum(\n                len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n                for s in sections\n            )\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{section_count} sections ({subsection_count} subsections), \"\n                f\"{word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            # Citation coverage reporting\n            cit = validate_citations(sections, len(page.moment_indices))\n            _log(\n                \"CITE\",\n                f\"    Citations: {cit['total_citations']}/{len(page.moment_indices)} moments cited \"\n                f\"({cit['coverage_pct']}% coverage)\"\n                + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n                + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\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(\n                    len(s.get(\"content\", \"\").split())\n                    + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n                    for s in p.get(\"body_sections\", [])\n                )\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":1775177765273}}
{"type":"message","id":"f130ad29","parentId":"1eafe432","timestamp":"2026-04-03T00:56:05.279Z","message":{"role":"toolResult","toolCallId":"toolu_01DVUfwgG92R3xwCQSpLYCg4","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 BodySubSection(BaseModel):\n    \"\"\"An H3-level subsection within a body section.\"\"\"\n\n    heading: str = Field(description=\"H3 subsection heading\")\n    content: str = Field(description=\"Subsection body text (may contain [N] citation markers)\")\n\n\nclass BodySection(BaseModel):\n    \"\"\"An H2-level section of a technique page body.\"\"\"\n\n    heading: str = Field(description=\"H2 section heading\")\n    content: str = Field(description=\"Section body text (may contain [N] citation markers)\")\n    subsections: list[BodySubSection] = Field(\n        default_factory=list,\n        description=\"Optional H3-level subsections\",\n    )\n\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: list[BodySection] = Field(\n        default_factory=list,\n        description=\"Structured body content as H2 sections with optional H3 subsections\",\n    )\n    body_sections_format: str = Field(\n        default=\"v2\",\n        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\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":1775177765273}}
{"type":"message","id":"0ef7107e","parentId":"f130ad29","timestamp":"2026-04-03T00:56:12.044Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the current synthesis prompt and the stages.py to understand how synthesis is called in production."},{"type":"toolCall","id":"toolu_01LZVv1zAXCPfX8CoeSBRWoZ","name":"read","arguments":{"path":"prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01QCSMYq5zjpyjH9fvtk9ooz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"synthesize|_build_moments|stage5|synthesis\" backend/pipeline/stages.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":163,"cacheRead":67468,"cacheWrite":7136,"totalTokens":74768,"cost":{"input":0.000005,"output":0.0040750000000000005,"cacheRead":0.033734,"cacheWrite":0.0446,"total":0.082414}},"stopReason":"toolUse","timestamp":1775177765276}}
{"type":"message","id":"67d27721","parentId":"0ef7107e","timestamp":"2026-04-03T00:56:12.056Z","message":{"role":"toolResult","toolCallId":"toolu_01LZVv1zAXCPfX8CoeSBRWoZ","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### H3 subsections\n\nSections can contain 2-3 subsections when a topic has distinct sub-aspects that benefit from their own heading. Subsections are optional — flat sections (no subsections, just prose in the section's content) are the default and perfectly fine when the topic doesn't warrant splitting. Use subsections when a section covers distinct sub-steps or when breaking it up improves scannability. Each subsection follows the same prose quality rules as sections (substantive, specific, no filler). A section with subsections should have minimal top-level content (1-2 introductory sentences at most) — the substance lives in the subsections.\n\n## Citation rules\n\nPlace inline citation markers to trace factual claims back to specific source moments:\n\n- **Format**: `[N]` where N is the zero-indexed position of the moment in the input array. Use `[N,M]` when a claim synthesizes information from multiple moments.\n- **Placement**: At the end of the sentence, before the period: \"Set the LFO to 3/16th timing [2].\" For multi-citation: \"The parallel routing preserves transient clarity while adding harmonic content [1,3].\"\n- **What to cite**: Specific factual claims — settings, values, frequencies, routing decisions, plugin choices, ratios, time values. Every section must have at least one citation.\n- **What NOT to cite**: General observations, orienting sentences, writing transitions, or statements that don't trace to a specific moment. Don't cite common production knowledge that isn't from the source.\n- **Reuse**: A moment can be cited multiple times across different sections when its content is relevant in multiple contexts.\n- **Coverage**: Aim to cite every input moment at least once. If a moment contributed to the page's content, cite it where its information appears.\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 [0,1]. The signature crunch comes from parallel soft-clip saturation with a pre-delay that preserves the clean transient [3].\",\n      \"body_sections_format\": \"v2\",\n      \"body_sections\": [\n        {\n          \"heading\": \"Three-layer snare construction\",\n          \"content\": \"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) [0]. Vital's noise oscillator works well for the click layer, sometimes with a bandpass around 2-4kHz to control the character [1].\\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 [1] — 'compression adds sustain as a side effect while a transient shaper gives you direct independent control of both.'\",\n          \"subsections\": []\n        },\n        {\n          \"heading\": \"Parallel saturation for crunch\",\n          \"content\": \"\",\n          \"subsections\": [\n            {\n              \"heading\": \"Send routing and pre-delay\",\n              \"content\": \"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 [3]. The key detail: add a pre-delay of approximately 5ms on the saturation send [3]. This lets the clean transient click through untouched while only the body and tail pick up harmonic content.\"\n            },\n            {\n              \"heading\": \"Protecting the transient\",\n              \"content\": \"Avoid saturating the transient directly — it 'smears the snap into mush' and you lose the precision that makes the snare cut through a dense mix [3]. If the saturation send is still coloring the attack, shorten the pre-delay by 1-2ms increments until the click sits cleanly above the harmonic layer.\"\n            }\n          ]\n        },\n        {\n          \"heading\": \"Bus processing in dense arrangements\",\n          \"content\": \"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 [4]. 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 [2]. A small notch on the snare's body is usually less costly than losing vocal clarity.\",\n          \"subsections\": []\n        }\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\nNote the structure changes from v1:\n- `body_sections` is now a **list of objects**, not a dict. Each object has `heading` (string), `content` (string), and `subsections` (list of sub-objects with `heading` and `content`).\n- Sections without subsections use an empty `subsections` array and put all prose in `content`.\n- Sections WITH subsections put minimal introductory text (or empty string) in `content` and the substance in `subsections`.\n- `body_sections_format` must be `\"v2\"` — this signals the parser to expect the list-of-objects format.\n- Inline `[N]` citation markers appear in prose content (both section and subsection level).\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**: List of section objects. Each object has `heading` (string), `content` (string with inline `[N]` citation markers), and `subsections` (list of sub-objects with `heading` and `content`, or empty array). Section headings derived from content (never generic). Each section 2-5 substantive paragraphs across its content and subsections.\n- **body_sections_format**: Must be `\"v2\"`. Signals the parser to expect the list-of-objects body_sections format.\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":1775177772053}}
{"type":"message","id":"ecac9cc5","parentId":"67d27721","timestamp":"2026-04-03T00:56:12.060Z","message":{"role":"toolResult","toolCallId":"toolu_01QCSMYq5zjpyjH9fvtk9ooz","toolName":"bash","content":[{"type":"text","text":"899:        \"stage5_synthesis.txt\",\n919:            \"stage5\": settings.llm_stage5_model,\n926:            \"stage5\": settings.llm_stage5_modality,\n957:def _build_moments_text(\n983:def _synthesize_chunk(\n996:    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n1000:    moments_text, _ = _build_moments_text(chunk, category)\n1003:    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n1012:            video_id, \"stage5_synthesis\",\n1077:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n1082:            stage=\"stage5_synthesis\", hard_limit=hard_limit,\n1093:                video_id, \"stage5_synthesis\",\n1127:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n1130:    Groups moments by (creator, topic_category), calls the LLM to synthesize\n1134:    For large category groups (exceeding synthesis_chunk_size), moments are\n1135:    split into chronological chunks, synthesized independently, then pages\n1143:    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n1144:    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n1147:    chunk_size = settings.synthesis_chunk_size\n1188:        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n1201:            # ── Chunked synthesis with truncation recovery ─────────\n1205:                    result = _synthesize_chunk(\n1210:                    synthesized_pages = list(result.pages)\n1213:                        category, len(moment_group), len(synthesized_pages),\n1227:                        sub_result = _synthesize_chunk(\n1237:                    synthesized_pages = chunk_pages\n1240:                        category, len(synthesized_pages),\n1243:                # Large group — split into chunks, synthesize each, then merge\n1257:                    result = _synthesize_chunk(\n1273:                synthesized_pages = _merge_pages_by_slug(\n"}],"isError":false,"timestamp":1775177772058}}
{"type":"message","id":"b8c18f90","parentId":"ecac9cc5","timestamp":"2026-04-03T00:56:19.010Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me read the relevant section of stages.py around the synthesis and merge logic, and check if there's already a stage5_merge prompt or any compose-related infrastructure."},{"type":"toolCall","id":"toolu_01S9kj7awUCdX2jbwwjKPoJk","name":"read","arguments":{"limit":100,"offset":1060,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_017iCnotBJr23aKzRtAJ3Mfz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/stage5_merge* 2>/dev/null; ls prompts/stage5_compos* 2>/dev/null; echo \"---\"; rg -l \"merge_pages\\|_merge_pages_by_slug\" backend/pipeline/ | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":244,"cacheRead":74604,"cacheWrite":6665,"totalTokens":81514,"cost":{"input":0.000005,"output":0.0061,"cacheRead":0.037301999999999995,"cacheWrite":0.04165625,"total":0.08506324999999999}},"stopReason":"toolUse","timestamp":1775177772059}}
{"type":"message","id":"5269f53c","parentId":"b8c18f90","timestamp":"2026-04-03T00:56:19.020Z","message":{"role":"toolResult","toolCallId":"toolu_01S9kj7awUCdX2jbwwjKPoJk","toolName":"read","content":[{"type":"text","text":"        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        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n        merge_user_prompt = f\"<creator>{creator_name}</creator>\\n<pages>\\n{pages_json}\\n</pages>\"\n\n        max_tokens = estimate_max_tokens(\n            merge_system_prompt, merge_user_prompt,\n            stage=\"stage5_synthesis\", hard_limit=hard_limit,\n        )\n        logger.info(\n            \"Stage 5: Merge call for slug '%s' — %d partial pages, max_tokens=%d\",\n            slug, len(pages_group), max_tokens,\n        )\n\n        raw = llm.complete(\n            merge_system_prompt, merge_user_prompt,\n            response_model=SynthesisResult,\n            on_complete=_make_llm_callback(\n                video_id, \"stage5_synthesis\",\n                system_prompt=merge_system_prompt,\n                user_prompt=merge_user_prompt,\n                run_id=run_id, context_label=f\"merge:{slug}\",\n                request_params=_build_request_params(max_tokens, model_override, modality, \"SynthesisResult\", hard_limit),\n            ),\n            modality=modality, model_override=model_override,\n            max_tokens=max_tokens,\n        )\n        merge_result = _safe_parse_llm_response(\n            raw, SynthesisResult, llm,\n            merge_system_prompt, merge_user_prompt,\n            modality=modality, model_override=model_override,\n            max_tokens=max_tokens,\n        )\n\n        if merge_result.pages:\n            final_pages.extend(merge_result.pages)\n            logger.info(\n                \"Stage 5: Merge produced %d page(s) for slug '%s'\",\n                len(merge_result.pages), slug,\n            )\n        else:\n            # Merge returned nothing — fall back to keeping the partials\n            logger.warning(\n                \"Stage 5: Merge returned 0 pages for slug '%s', keeping %d partials\",\n                slug, len(pages_group),\n            )\n            final_pages.extend(pages_group)\n\n    return final_pages\n\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    For large category groups (exceeding synthesis_chunk_size), moments are\n    split into chronological chunks, synthesized independently, then pages\n    with matching slugs are merged via a dedicated merge LLM call.\n\n    Sets processing_status to 'complete'.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n\n    settings = get_settings()\n    chunk_size = settings.synthesis_chunk_size\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n\n[944 more lines in file. Use offset=1160 to continue.]"}],"isError":false,"timestamp":1775177779019}}
{"type":"message","id":"09edd810","parentId":"5269f53c","timestamp":"2026-04-03T00:56:19.037Z","message":{"role":"toolResult","toolCallId":"toolu_017iCnotBJr23aKzRtAJ3Mfz","toolName":"bash","content":[{"type":"text","text":"---\n"}],"isError":false,"timestamp":1775177779034}}
{"type":"message","id":"091b7713","parentId":"09edd810","timestamp":"2026-04-03T00:56:24.361Z","message":{"role":"assistant","content":[{"type":"text","text":"No stage5_merge.txt or compose prompt exists yet. Let me check what the merge prompt looks like and how existing pages are stored/fetched in the DB."},{"type":"toolCall","id":"toolu_018ytJxQnb6SM59S6o26R9yk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find prompts -name \"*merge*\" -o -name \"*compose*\" 2>/dev/null; echo \"---\"; rg \"stage5_merge\" prompts/ backend/pipeline/ 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01Ay7qVz8EFwkc7mVb4ETBcj","name":"read","arguments":{"limit":70,"offset":1160,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":232,"cacheRead":81269,"cacheWrite":1419,"totalTokens":82921,"cost":{"input":0.000005,"output":0.0058000000000000005,"cacheRead":0.0406345,"cacheWrite":0.00886875,"total":0.055308249999999996}},"stopReason":"toolUse","timestamp":1775177779035}}
{"type":"message","id":"ca7f1d51","parentId":"091b7713","timestamp":"2026-04-03T00:56:24.380Z","message":{"role":"toolResult","toolCallId":"toolu_018ytJxQnb6SM59S6o26R9yk","toolName":"bash","content":[{"type":"text","text":"---\nbackend/pipeline/stages.py:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n"}],"isError":false,"timestamp":1775177784380}}
{"type":"message","id":"40602bba","parentId":"ca7f1d51","timestamp":"2026-04-03T00:56:24.382Z","message":{"role":"toolResult","toolCallId":"toolu_01Ay7qVz8EFwkc7mVb4ETBcj","toolName":"read","content":[{"type":"text","text":"            )\n            .scalars()\n            .all()\n        )\n\n        # Resolve creator name for the LLM prompt\n        creator = session.execute(\n            select(Creator).where(Creator.id == video.creator_id)\n        ).scalar_one_or_none()\n        creator_name = creator.name if creator else \"Unknown\"\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        # Normalize category casing to prevent near-duplicate groups\n        # (e.g., \"Sound design\" vs \"Sound Design\")\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\").strip().title()\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        hard_limit = settings.llm_max_tokens_hard_limit\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n\n        for category, moment_group in groups.items():\n            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:\n                # Small group — try single LLM call first\n                try:\n                    result = _synthesize_chunk(\n                        moment_group, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, f\"category:{category}\",\n                    )\n                    synthesized_pages = list(result.pages)\n                    logger.info(\n                        \"Stage 5: category '%s' — %d moments, %d page(s) from single call\",\n                        category, len(moment_group), len(synthesized_pages),\n                    )\n                except LLMTruncationError:\n                    # Output too large for model context — split in half and retry\n                    logger.warning(\n                        \"Stage 5: category '%s' truncated with %d moments. \"\n                        \"Splitting into smaller chunks and retrying.\",\n                        category, len(moment_group),\n                    )\n                    half = max(1, len(moment_group) // 2)\n                    chunk_pages = []\n                    for sub_start in range(0, len(moment_group), half):\n                        sub_chunk = moment_group[sub_start:sub_start + half]\n                        sub_label = f\"category:{category} recovery-chunk:{sub_start // half + 1}\"\n                        sub_result = _synthesize_chunk(\n                            sub_chunk, category, creator_name,\n                            system_prompt, llm, model_override, modality, hard_limit,\n\n[874 more lines in file. Use offset=1230 to continue.]"}],"isError":false,"timestamp":1775177784380}}
{"type":"message","id":"b2ca5528","parentId":"40602bba","timestamp":"2026-04-03T00:56:27.874Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HF6jmGHJvFpxu83JGqs1bd","name":"read","arguments":{"limit":100,"offset":1230,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":82688,"cacheWrite":1258,"totalTokens":84039,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.041344,"cacheWrite":0.0078625,"total":0.0515115}},"stopReason":"toolUse","timestamp":1775177784380}}
{"type":"message","id":"1345d590","parentId":"b2ca5528","timestamp":"2026-04-03T00:56:27.880Z","message":{"role":"toolResult","toolCallId":"toolu_01HF6jmGHJvFpxu83JGqs1bd","toolName":"read","content":[{"type":"text","text":"                            video_id, run_id, sub_label,\n                        )\n                        # Reindex moment_indices to global offsets\n                        for p in sub_result.pages:\n                            if p.moment_indices:\n                                p.moment_indices = [idx + sub_start for idx in p.moment_indices]\n                        chunk_pages.extend(sub_result.pages)\n                    synthesized_pages = chunk_pages\n                    logger.info(\n                        \"Stage 5: category '%s' — %d page(s) from recovery split\",\n                        category, len(synthesized_pages),\n                    )\n            else:\n                # Large group — split into chunks, synthesize each, then merge\n                num_chunks = (len(moment_group) + chunk_size - 1) // chunk_size\n                logger.info(\n                    \"Stage 5: category '%s' has %d moments — splitting into %d chunks of ≤%d\",\n                    category, len(moment_group), num_chunks, chunk_size,\n                )\n\n                chunk_pages = []\n                for chunk_idx in range(num_chunks):\n                    chunk_start = chunk_idx * chunk_size\n                    chunk_end = min(chunk_start + chunk_size, len(moment_group))\n                    chunk = moment_group[chunk_start:chunk_end]\n                    chunk_label = f\"category:{category} chunk:{chunk_idx + 1}/{num_chunks}\"\n\n                    result = _synthesize_chunk(\n                        chunk, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, chunk_label,\n                    )\n                    chunk_pages.extend(result.pages)\n                    logger.info(\n                        \"Stage 5: %s produced %d page(s)\",\n                        chunk_label, len(result.pages),\n                    )\n\n                # Merge pages with matching slugs across chunks\n                logger.info(\n                    \"Stage 5: category '%s' — %d total pages from %d chunks, checking for merges\",\n                    category, len(chunk_pages), num_chunks,\n                )\n                synthesized_pages = _merge_pages_by_slug(\n                    chunk_pages, creator_name,\n                    llm, model_override, modality, hard_limit,\n                    video_id, run_id,\n                )\n                logger.info(\n                    \"Stage 5: category '%s' — %d final page(s) after merge\",\n                    category, len(synthesized_pages),\n                )\n\n            # ── Persist pages to DB ──────────────────────────────────\n            # Load prior pages from this video (snapshot taken before pipeline reset)\n            prior_page_ids = _load_prior_pages(video_id)\n\n            for page_data in synthesized_pages:\n                page_moment_indices = getattr(page_data, \"moment_indices\", None) or []\n                existing = None\n\n                # First: check by slug (most specific match)\n                if existing is None:\n                    existing = session.execute(\n                        select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n                    ).scalar_one_or_none()\n\n                # Fallback: check prior pages from this video by creator + category\n                # Use .first() since multiple pages may share a category\n                if existing is None and prior_page_ids:\n                    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\n[774 more lines in file. Use offset=1330 to continue.]"}],"isError":false,"timestamp":1775177787877}}
{"type":"message","id":"3c68d65b","parentId":"1345d590","timestamp":"2026-04-03T00:56:35.745Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I can see the existing merge pattern. Now let me understand the full picture. The roadmap says S02 needs:\n1. A **composition prompt** (for merging new moments into existing page)\n2. A **test harness `--compose` mode** that takes an existing page + new moments → merged output\n\nLet me check the existing fixture format and test harness v2 format tests to understand what the compose mode needs."},{"type":"toolCall","id":"toolu_013ESDcPJ5teSRD5NifePmjb","name":"read","arguments":{"path":"backend/pipeline/test_harness_v2_format.py"}},{"type":"toolCall","id":"toolu_014FS1JKsG6Q9FnSz1N8cq4P","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls fixtures/ 2>/dev/null; find . -name \"*.json\" -path \"*/fixtures/*\" | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":241,"cacheRead":83946,"cacheWrite":1307,"totalTokens":85495,"cost":{"input":0.000005,"output":0.006025,"cacheRead":0.041972999999999996,"cacheWrite":0.00816875,"total":0.05617175}},"stopReason":"toolUse","timestamp":1775177787878}}
{"type":"message","id":"2a3b196a","parentId":"3c68d65b","timestamp":"2026-04-03T00:56:35.753Z","message":{"role":"toolResult","toolCallId":"toolu_013ESDcPJ5teSRD5NifePmjb","toolName":"read","content":[{"type":"text","text":"\"\"\"Tests for test_harness compatibility with v2 body_sections format.\n\nValidates that word-counting and citation integration work correctly\nwith the list[BodySection] structure (v2) instead of the old dict format.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport pytest\n\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.schemas import BodySection, BodySubSection, SynthesizedPage, SynthesisResult\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n\ndef _make_page(\n    body_sections: list[BodySection],\n    moment_indices: list[int] | None = None,\n    title: str = \"Test Page\",\n    slug: str = \"test-page\",\n) -> SynthesizedPage:\n    return SynthesizedPage(\n        title=title,\n        slug=slug,\n        topic_category=\"Testing\",\n        summary=\"A test page.\",\n        body_sections=body_sections,\n        moment_indices=moment_indices or [],\n    )\n\n\ndef _count_words_v2(sections: list[BodySection]) -> int:\n    \"\"\"Replicate the word-counting logic from the updated test_harness.\"\"\"\n    return sum(\n        len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n        for s in sections\n    )\n\n\ndef _count_words_metadata(pages_dicts: list[dict]) -> int:\n    \"\"\"Replicate the metadata total_words logic (operates on dicts after model_dump).\"\"\"\n    return sum(\n        sum(\n            len(s.get(\"content\", \"\").split())\n            + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n            for s in p.get(\"body_sections\", [])\n        )\n        for p in pages_dicts\n    )\n\n\n# ── Word counting tests ─────────────────────────────────────────────────────\n\n\nclass TestWordCounting:\n    def test_flat_sections_no_subsections(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"one two three\"),\n            BodySection(heading=\"Details\", content=\"four five\"),\n        ]\n        assert _count_words_v2(sections) == 5\n\n    def test_sections_with_subsections(self):\n        sections = [\n            BodySection(\n                heading=\"Main\",\n                content=\"alpha beta\",  # 2 words\n                subsections=[\n                    BodySubSection(heading=\"Sub A\", content=\"gamma delta epsilon\"),  # 3 words\n                    BodySubSection(heading=\"Sub B\", content=\"zeta\"),  # 1 word\n                ],\n            ),\n        ]\n        assert _count_words_v2(sections) == 6\n\n    def test_empty_sections_list(self):\n        assert _count_words_v2([]) == 0\n\n    def test_section_with_empty_content(self):\n        sections = [\n            BodySection(heading=\"Empty\", content=\"\"),\n        ]\n        # \"\".split() returns [], len([]) == 0\n        assert _count_words_v2(sections) == 0\n\n    def test_metadata_word_count_matches(self):\n        \"\"\"Metadata total_words (from model_dump dicts) matches Pydantic object counting.\"\"\"\n        sections = [\n            BodySection(\n                heading=\"H2\",\n                content=\"one two three\",\n                subsections=[\n                    BodySubSection(heading=\"H3\", content=\"four five six seven\"),\n                ],\n            ),\n            BodySection(heading=\"Another\", content=\"eight nine\"),\n        ]\n        page = _make_page(sections, moment_indices=[0, 1])\n        pages_dicts = [page.model_dump()]\n\n        assert _count_words_v2(sections) == 9\n        assert _count_words_metadata(pages_dicts) == 9\n\n\n# ── Section/subsection counting ─────────────────────────────────────────────\n\n\nclass TestSectionCounting:\n    def test_section_and_subsection_counts(self):\n        sections = [\n            BodySection(heading=\"A\", content=\"text\", subsections=[\n                BodySubSection(heading=\"A.1\", content=\"sub text\"),\n            ]),\n            BodySection(heading=\"B\", content=\"more text\"),\n            BodySection(heading=\"C\", content=\"even more\", subsections=[\n                BodySubSection(heading=\"C.1\", content=\"sub1\"),\n                BodySubSection(heading=\"C.2\", content=\"sub2\"),\n            ]),\n        ]\n        section_count = len(sections)\n        subsection_count = sum(len(s.subsections) for s in sections)\n        assert section_count == 3\n        assert subsection_count == 3\n\n\n# ── Citation integration ─────────────────────────────────────────────────────\n\n\nclass TestCitationIntegration:\n    def test_full_coverage(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"First point [0]. Second point [1].\"),\n            BodySection(heading=\"Details\", content=\"More on [0] and [2].\"),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is True\n        assert result[\"coverage_pct\"] == 100.0\n        assert result[\"invalid_indices\"] == []\n        assert result[\"uncited_moments\"] == []\n\n    def test_partial_coverage(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"Only cites [0].\"),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is False\n        assert result[\"coverage_pct\"] == pytest.approx(33.3, abs=0.1)\n        assert result[\"uncited_moments\"] == [1, 2]\n\n    def test_invalid_index(self):\n        sections = [\n            BodySection(heading=\"Bad\", content=\"Cites [0] and [99].\"),\n        ]\n        result = validate_citations(sections, moment_count=2)\n        assert result[\"invalid_indices\"] == [99]\n\n    def test_citations_in_subsections(self):\n        sections = [\n            BodySection(\n                heading=\"Main\",\n                content=\"See [0].\",\n                subsections=[\n                    BodySubSection(heading=\"Sub\", content=\"Also [1] and [2].\"),\n                ],\n            ),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is True\n        assert result[\"total_citations\"] == 3\n\n    def test_multi_citation_markers(self):\n        sections = [\n            BodySection(heading=\"X\", content=\"Both sources agree [0,1].\"),\n        ]\n        result = validate_citations(sections, moment_count=2)\n        assert result[\"valid\"] is True\n        assert result[\"total_citations\"] == 2\n\n    def test_no_sections(self):\n        result = validate_citations([], moment_count=0)\n        assert result[\"valid\"] is True\n        assert result[\"coverage_pct\"] == 0.0\n\n\n# ── End-to-end: SynthesisResult with v2 body_sections ───────────────────────\n\n\nclass TestSynthesisResultV2:\n    def test_round_trip_model_dump(self):\n        \"\"\"SynthesisResult with v2 body_sections round-trips through model_dump/validate.\"\"\"\n        sections = [\n            BodySection(\n                heading=\"Overview\",\n                content=\"This technique [0] is fundamental.\",\n                subsections=[\n                    BodySubSection(heading=\"Key Concept\", content=\"Detail [1].\"),\n                ],\n            ),\n        ]\n        page = _make_page(sections, moment_indices=[0, 1])\n        result = SynthesisResult(pages=[page])\n\n        dumped = result.model_dump()\n        restored = SynthesisResult.model_validate(dumped)\n\n        assert len(restored.pages) == 1\n        restored_page = restored.pages[0]\n        assert len(restored_page.body_sections) == 1\n        assert restored_page.body_sections[0].heading == \"Overview\"\n        assert len(restored_page.body_sections[0].subsections) == 1\n        assert restored_page.body_sections_format == \"v2\"\n"}],"isError":false,"timestamp":1775177795752}}
{"type":"message","id":"ff629923","parentId":"2a3b196a","timestamp":"2026-04-03T00:56:35.798Z","message":{"role":"toolResult","toolCallId":"toolu_014FS1JKsG6Q9FnSz1N8cq4P","toolName":"bash","content":[{"type":"text","text":"./tests/fixtures/sample_transcript.json\n./backend/pipeline/quality/fixtures/sample_classifications.json\n./backend/pipeline/quality/fixtures/sample_segments.json\n./backend/pipeline/quality/fixtures/sample_moments.json\n./backend/pipeline/quality/fixtures/sample_topic_group.json\n./backend/tests/fixtures/sample_transcript.json\n"}],"isError":false,"timestamp":1775177795795}}
{"type":"message","id":"9d00d0cc","parentId":"ff629923","timestamp":"2026-04-03T00:56:41.825Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019TMCkfKJYLQZH9Bwr6Wzrm","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python3 -c \"import json; d=json.load(open('backend/pipeline/quality/fixtures/sample_topic_group.json')); print(json.dumps(list(d.keys()), indent=2)); print('---'); print(json.dumps(d.get('moments', d.get('moment_group', []))[:1], indent=2)[:2000])\" 2>/dev/null || echo \"FAILED\""}},{"type":"toolCall","id":"toolu_017FC3xyJfPYns2sNoMi8gxE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"export_fixture\\|fixture\" backend/pipeline/export_fixture.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":245,"cacheRead":85253,"cacheWrite":2444,"totalTokens":87943,"cost":{"input":0.000005,"output":0.006125,"cacheRead":0.0426265,"cacheWrite":0.015275,"total":0.06403149999999999}},"stopReason":"toolUse","timestamp":1775177795796}}
{"type":"message","id":"f3aa016e","parentId":"9d00d0cc","timestamp":"2026-04-03T00:56:41.854Z","message":{"role":"toolResult","toolCallId":"toolu_019TMCkfKJYLQZH9Bwr6Wzrm","toolName":"bash","content":[{"type":"text","text":"[\n  \"topic_segments\"\n]\n---\n[]\n"}],"isError":false,"timestamp":1775177801853}}
{"type":"message","id":"4c0c01b1","parentId":"f3aa016e","timestamp":"2026-04-03T00:56:41.856Z","message":{"role":"toolResult","toolCallId":"toolu_017FC3xyJfPYns2sNoMi8gxE","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177801853}}
{"type":"message","id":"613b634f","parentId":"4c0c01b1","timestamp":"2026-04-03T00:56:44.706Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HG9CKWsNioCpbkAX6hM91C","name":"read","arguments":{"path":"backend/pipeline/export_fixture.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":59,"cacheRead":87697,"cacheWrite":322,"totalTokens":88079,"cost":{"input":0.000005,"output":0.001475,"cacheRead":0.0438485,"cacheWrite":0.0020125,"total":0.047341}},"stopReason":"toolUse","timestamp":1775177801854}}
{"type":"message","id":"a27d0080","parentId":"613b634f","timestamp":"2026-04-03T00:56:44.712Z","message":{"role":"toolResult","toolCallId":"toolu_01HG9CKWsNioCpbkAX6hM91C","toolName":"read","content":[{"type":"text","text":"\"\"\"Export pipeline stage inputs for a video as a reusable JSON fixture.\n\nConnects to the live database, queries KeyMoments and classification data,\nand writes a fixture file that the test harness can consume offline.\n\nUsage:\n    python -m pipeline.export_fixture --video-id <uuid> --output fixtures/video.json\n    python -m pipeline.export_fixture --video-id <uuid>  # prints to stdout\n    python -m pipeline.export_fixture --list              # list available videos\n\nRequires: DATABASE_URL, REDIS_URL environment variables (or .env file).\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter\nfrom pathlib import Path\n\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[EXPORT] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\ndef _get_sync_session(database_url: str) -> Session:\n    \"\"\"Create a sync SQLAlchemy session from the database URL.\"\"\"\n    url = database_url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n    engine = create_engine(url, pool_pre_ping=True)\n    factory = sessionmaker(bind=engine)\n    return factory()\n\n\ndef _list_videos(database_url: str) -> int:\n    \"\"\"List all videos with their processing status and moment counts.\"\"\"\n    from models import Creator, KeyMoment, SourceVideo\n\n    session = _get_sync_session(database_url)\n    try:\n        videos = (\n            session.execute(\n                select(SourceVideo).order_by(SourceVideo.created_at.desc())\n            )\n            .scalars()\n            .all()\n        )\n        if not videos:\n            _log(\"LIST\", \"No videos found in database\")\n            return 0\n\n        print(f\"\\n{'ID':<38s} {'Status':<14s} {'Moments':>7s}  {'Creator':<20s}  {'Filename'}\", file=sys.stderr)\n        print(f\"{'─'*38} {'─'*14} {'─'*7}  {'─'*20}  {'─'*40}\", file=sys.stderr)\n\n        for video in videos:\n            moment_count = (\n                session.execute(\n                    select(KeyMoment.id).where(KeyMoment.source_video_id == video.id)\n                )\n                .scalars()\n                .all()\n            )\n            creator = session.execute(\n                select(Creator).where(Creator.id == video.creator_id)\n            ).scalar_one_or_none()\n            creator_name = creator.name if creator else \"?\"\n\n            print(\n                f\"{str(video.id):<38s} {video.processing_status.value:<14s} \"\n                f\"{len(moment_count):>7d}  {creator_name:<20s}  {video.filename}\",\n                file=sys.stderr,\n            )\n\n        print(f\"\\nTotal: {len(videos)} videos\\n\", file=sys.stderr)\n        return 0\n    finally:\n        session.close()\n\n\ndef export_fixture(\n    database_url: str,\n    redis_url: str,\n    video_id: str,\n    output_path: str | None = None,\n) -> int:\n    \"\"\"Export stage 5 inputs for a video as a JSON fixture.\n\n    Returns exit code: 0 = success, 1 = error.\n    \"\"\"\n    from models import Creator, KeyMoment, SourceVideo\n\n    start = time.monotonic()\n    _log(\"CONNECT\", \"Connecting to database...\")\n\n    session = _get_sync_session(database_url)\n    try:\n        # ── Load video ──────────────────────────────────────────────────\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n\n        if video is None:\n            _log(\"ERROR\", f\"Video not found: {video_id}\", level=\"ERROR\")\n            return 1\n\n        creator = session.execute(\n            select(Creator).where(Creator.id == video.creator_id)\n        ).scalar_one_or_none()\n        creator_name = creator.name if creator else \"Unknown\"\n\n        _log(\n            \"VIDEO\",\n            f\"Found: {video.filename} by {creator_name} \"\n            f\"({video.duration_seconds or '?'}s, {video.content_type.value}, \"\n            f\"status={video.processing_status.value})\",\n        )\n\n        # ── Load key 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        if not moments:\n            _log(\"ERROR\", f\"No key moments found for video_id={video_id}\", level=\"ERROR\")\n            _log(\"HINT\", \"Pipeline stages 2-3 must complete before export is possible\", level=\"ERROR\")\n            return 1\n\n        time_min = min(m.start_time for m in moments)\n        time_max = max(m.end_time for m in moments)\n        _log(\"MOMENTS\", f\"Loaded {len(moments)} key moments (time range: {time_min:.1f}s - {time_max:.1f}s)\")\n\n        # ── Load classification data ────────────────────────────────────\n        classification_data: list[dict] = []\n        cls_source = \"missing\"\n\n        # Try Redis first\n        try:\n            import redis as redis_lib\n\n            r = redis_lib.Redis.from_url(redis_url)\n            key = f\"chrysopedia:classification:{video_id}\"\n            raw = r.get(key)\n            if raw is not None:\n                classification_data = json.loads(raw)\n                cls_source = \"redis\"\n                ttl = r.ttl(key)\n                _log(\"CLASSIFY\", f\"Source: redis ({len(classification_data)} entries, TTL={ttl}s)\")\n        except Exception as exc:\n            _log(\"CLASSIFY\", f\"Redis unavailable: {exc}\", level=\"WARN\")\n\n        # Fallback: check SourceVideo.classification_data column (Phase 2 addition)\n        if not classification_data:\n            video_cls = getattr(video, \"classification_data\", None)\n            if video_cls:\n                classification_data = video_cls\n                cls_source = \"postgresql\"\n                _log(\"CLASSIFY\", f\"Source: postgresql ({len(classification_data)} entries)\")\n\n        if not classification_data:\n            _log(\"CLASSIFY\", \"No classification data found in Redis or PostgreSQL\", level=\"WARN\")\n            _log(\"HINT\", \"Pipeline stage 4 must complete before classification data is available\", level=\"WARN\")\n            cls_source = \"missing\"\n\n        # Build classification lookup by moment_id\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Count moments without classification\n        unclassified = sum(1 for m in moments if str(m.id) not in cls_by_moment_id)\n        if unclassified > 0:\n            _log(\"CLASSIFY\", f\"WARNING: {unclassified}/{len(moments)} moments have no classification data\", level=\"WARN\")\n\n        # ── Build fixture ───────────────────────────────────────────────\n        fixture_moments = []\n        category_counts: Counter[str] = Counter()\n\n        for m in moments:\n            cls_info = cls_by_moment_id.get(str(m.id), {})\n            topic_category = cls_info.get(\"topic_category\", \"Uncategorized\")\n            topic_tags = cls_info.get(\"topic_tags\", [])\n            category_counts[topic_category] += 1\n\n            fixture_moments.append({\n                \"moment_id\": str(m.id),\n                \"title\": m.title,\n                \"summary\": m.summary,\n                \"content_type\": m.content_type.value,\n                \"start_time\": m.start_time,\n                \"end_time\": m.end_time,\n                \"plugins\": m.plugins or [],\n                \"raw_transcript\": m.raw_transcript or \"\",\n                # Classification data (stage 4 output)\n                \"classification\": {\n                    \"topic_category\": topic_category,\n                    \"topic_tags\": topic_tags,\n                },\n                # Compatibility fields for existing quality/scorer format\n                \"transcript_excerpt\": (m.raw_transcript or \"\")[:500],\n                \"topic_tags\": topic_tags,\n                \"topic_category\": topic_category,\n            })\n\n        fixture = {\n            \"video_id\": str(video.id),\n            \"creator_name\": creator_name,\n            \"content_type\": video.content_type.value,\n            \"filename\": video.filename,\n            \"duration_seconds\": video.duration_seconds,\n            \"classification_source\": cls_source,\n            \"export_timestamp\": time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime()),\n            \"moments\": fixture_moments,\n        }\n\n        fixture_json = json.dumps(fixture, indent=2, ensure_ascii=False)\n        fixture_size_kb = len(fixture_json.encode(\"utf-8\")) / 1024\n\n        # ── Output ──────────────────────────────────────────────────────\n        if output_path:\n            Path(output_path).parent.mkdir(parents=True, exist_ok=True)\n            Path(output_path).write_text(fixture_json, encoding=\"utf-8\")\n            _log(\n                \"OUTPUT\",\n                f\"Wrote fixture: {output_path} ({fixture_size_kb:.1f} KB, \"\n                f\"{len(fixture_moments)} moments, {len(category_counts)} categories)\",\n            )\n        else:\n            # Print fixture JSON to stdout\n            print(fixture_json)\n            _log(\n                \"OUTPUT\",\n                f\"Printed fixture to stdout ({fixture_size_kb:.1f} KB, \"\n                f\"{len(fixture_moments)} moments, {len(category_counts)} categories)\",\n            )\n\n        # Category breakdown\n        for cat, count in category_counts.most_common():\n            _log(\"CATEGORY\", f\"  {cat}: {count} moments\")\n\n        elapsed = time.monotonic() - start\n        _log(\"DONE\", f\"Export completed in {elapsed:.1f}s\")\n        return 0\n\n    except Exception as exc:\n        _log(\"ERROR\", f\"Export failed: {exc}\", level=\"ERROR\")\n        import traceback\n        traceback.print_exc(file=sys.stderr)\n        return 1\n    finally:\n        session.close()\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.export_fixture\",\n        description=\"Export pipeline stage inputs for a video as a reusable JSON fixture\",\n    )\n    parser.add_argument(\n        \"--video-id\",\n        type=str,\n        help=\"UUID of the video to export\",\n    )\n    parser.add_argument(\n        \"--output\", \"-o\",\n        type=str,\n        default=None,\n        help=\"Output file path (default: print to stdout)\",\n    )\n    parser.add_argument(\n        \"--list\",\n        action=\"store_true\",\n        default=False,\n        help=\"List all videos with status and moment counts\",\n    )\n\n    args = parser.parse_args()\n\n    # Load settings\n    from config import get_settings\n    settings = get_settings()\n\n    if args.list:\n        return _list_videos(settings.database_url)\n\n    if not args.video_id:\n        parser.error(\"--video-id is required (or use --list to see available videos)\")\n\n    return export_fixture(\n        database_url=settings.database_url,\n        redis_url=settings.redis_url,\n        video_id=args.video_id,\n        output_path=args.output,\n    )\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775177804709}}
{"type":"message","id":"2ea242d5","parentId":"a27d0080","timestamp":"2026-04-03T00:56:50.053Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how existing TechniquePages are stored in the DB model, since compose mode needs to load an existing page."},{"type":"toolCall","id":"toolu_01C1NeZZthXy1jrRuudym2Wb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class TechniquePage\" backend/models.py | head -5"}},{"type":"toolCall","id":"toolu_01SBUeUqNDtY7aoLGjgt4Ywv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"body_sections|body_sections_format\" backend/models.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":192,"cacheRead":87697,"cacheWrite":3446,"totalTokens":91336,"cost":{"input":0.000005,"output":0.0048000000000000004,"cacheRead":0.0438485,"cacheWrite":0.0215375,"total":0.070191}},"stopReason":"toolUse","timestamp":1775177804710}}
{"type":"message","id":"fc42e410","parentId":"2ea242d5","timestamp":"2026-04-03T00:56:50.073Z","message":{"role":"toolResult","toolCallId":"toolu_01C1NeZZthXy1jrRuudym2Wb","toolName":"bash","content":[{"type":"text","text":"206:class TechniquePage(Base):\n276:class TechniquePageVersion(Base):\n"}],"isError":false,"timestamp":1775177810073}}
{"type":"message","id":"dfef9e26","parentId":"fc42e410","timestamp":"2026-04-03T00:56:50.081Z","message":{"role":"toolResult","toolCallId":"toolu_01SBUeUqNDtY7aoLGjgt4Ywv","toolName":"bash","content":[{"type":"text","text":"218:    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n"}],"isError":false,"timestamp":1775177810078}}
{"type":"message","id":"ef9374e2","parentId":"dfef9e26","timestamp":"2026-04-03T00:56:53.811Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KGNDzrjDTnBxqDAKKq6ufa","name":"read","arguments":{"limit":80,"offset":206,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":91143,"cacheWrite":305,"totalTokens":91538,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.0455715,"cacheWrite":0.0019062500000000002,"total":0.04970775}},"stopReason":"toolUse","timestamp":1775177810079}}
{"type":"message","id":"11baac60","parentId":"ef9374e2","timestamp":"2026-04-03T00:56:53.816Z","message":{"role":"toolResult","toolCallId":"toolu_01KGNDzrjDTnBxqDAKKq6ufa","toolName":"read","content":[{"type":"text","text":"class TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass TechniquePageVersion(Base):\n    \"\"\"Snapshot of a TechniquePage before a pipeline re-synthesis overwrites it.\"\"\"\n    __tablename__ = \"technique_page_versions\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    version_number: Mapped[int] = mapped_column(Integer, nullable=False)\n    content_snapshot: Mapped[dict] = mapped_column(JSONB, nullable=False)\n\n[181 more lines in file. Use offset=286 to continue.]"}],"isError":false,"timestamp":1775177813813}}
{"type":"message","id":"3e3b2a36","parentId":"11baac60","timestamp":"2026-04-03T00:56:58.234Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me also quickly check the LLM client interface and how `estimate_max_tokens` works, plus the `_load_prompt` pattern."},{"type":"toolCall","id":"toolu_01Uko1AMjWp2H9VPyfrXPeE4","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def complete\\|def estimate_max_tokens\\|def _load_prompt\" backend/pipeline/llm_client.py backend/pipeline/stages.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":152,"cacheRead":91448,"cacheWrite":1260,"totalTokens":92861,"cost":{"input":0.000005,"output":0.0038,"cacheRead":0.045724,"cacheWrite":0.007875,"total":0.057404000000000004}},"stopReason":"toolUse","timestamp":1775177813815}}
{"type":"message","id":"01f054e5","parentId":"3e3b2a36","timestamp":"2026-04-03T00:56:58.248Z","message":{"role":"toolResult","toolCallId":"toolu_01Uko1AMjWp2H9VPyfrXPeE4","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177818246}}
{"type":"message","id":"e1c0e04e","parentId":"01f054e5","timestamp":"2026-04-03T00:57:02.601Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013W76Zt2Y6Hd9Vs4dQ9ieNF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def complete|def estimate_max|def _load_prompt|class LLMClient\" backend/pipeline/llm_client.py backend/pipeline/stages.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":114,"cacheRead":92708,"cacheWrite":166,"totalTokens":92989,"cost":{"input":0.000005,"output":0.00285,"cacheRead":0.046354,"cacheWrite":0.0010375,"total":0.0502465}},"stopReason":"toolUse","timestamp":1775177818247}}
{"type":"message","id":"49c46fb6","parentId":"e1c0e04e","timestamp":"2026-04-03T00:57:02.622Z","message":{"role":"toolResult","toolCallId":"toolu_013W76Zt2Y6Hd9Vs4dQ9ieNF","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/llm_client.py:118:def estimate_max_tokens(\nbackend/pipeline/llm_client.py:163:class LLMClient:\nbackend/pipeline/llm_client.py:179:    def complete(\nbackend/pipeline/stages.py:244:def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n"}],"isError":false,"timestamp":1775177822619}}
{"type":"message","id":"030d5fa8","parentId":"49c46fb6","timestamp":"2026-04-03T00:57:06.165Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Y5iGtPmaRGuo5XC5hQoBV3","name":"read","arguments":{"limit":30,"offset":244,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":92874,"cacheWrite":221,"totalTokens":93187,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.046437,"cacheWrite":0.00138125,"total":0.05009825}},"stopReason":"toolUse","timestamp":1775177822620}}
{"type":"message","id":"b469469b","parentId":"030d5fa8","timestamp":"2026-04-03T00:57:06.171Z","message":{"role":"toolResult","toolCallId":"toolu_01Y5iGtPmaRGuo5XC5hQoBV3","toolName":"read","content":[{"type":"text","text":"def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    If ``video_id`` is provided, checks Redis for a per-video prompt override\n    (key: ``chrysopedia:prompt_override:{video_id}:{template_name}``) before\n    falling back to the on-disk template. Overrides are set by\n    ``run_single_stage`` for single-stage re-runs with custom prompts.\n\n    Raises FileNotFoundError if no override exists and the template is missing.\n    \"\"\"\n    # Check for per-video prompt override in Redis\n    if video_id:\n        try:\n            import redis\n            settings = get_settings()\n            r = redis.Redis.from_url(settings.redis_url)\n            override_key = f\"chrysopedia:prompt_override:{video_id}:{template_name}\"\n            override = r.get(override_key)\n            if override:\n                prompt_text = override.decode(\"utf-8\")\n                logger.info(\n                    \"[PROMPT] Using override from Redis: video_id=%s, template=%s (%d chars)\",\n                    video_id, template_name, len(prompt_text),\n                )\n                return prompt_text\n        except Exception as exc:\n            logger.warning(\"[PROMPT] Redis override check failed: %s\", exc)\n\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n\n[1830 more lines in file. Use offset=274 to continue.]"}],"isError":false,"timestamp":1775177826168}}
{"type":"message","id":"dbb98bf8","parentId":"b469469b","timestamp":"2026-04-03T00:57:15.592Z","message":{"role":"assistant","content":[{"type":"text","text":"I have everything I need. The compose mode needs:\n\n1. **A composition prompt** (`stage5_compose.txt`) — system prompt that instructs the LLM to merge new moments into an existing page's body_sections, handle deduplication, add new sections, and update citations\n2. **A new schema** — possibly a `CompositionResult` that extends or mirrors `SynthesisResult` but accounts for the merge semantics  \n3. **Test harness `--compose` mode** — CLI that accepts `--existing-page` (JSON of an existing SynthesizedPage) + `--fixture` (new moments) and runs the composition prompt\n4. **Tests** — verify compose mode word counting, citation re-indexing, deduplication detection\n\nLet me check if there's an M014 context or research doc with more detail on what compose means."},{"type":"toolCall","id":"toolu_01Y8yqjyEj89mwHZrF5RgvC9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls .gsd/milestones/M014/M014-*.md 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":284,"cacheRead":93095,"cacheWrite":481,"totalTokens":93861,"cost":{"input":0.000005,"output":0.0071,"cacheRead":0.0465475,"cacheWrite":0.0030062500000000002,"total":0.05665875}},"stopReason":"toolUse","timestamp":1775177826169}}
{"type":"message","id":"8f36207e","parentId":"dbb98bf8","timestamp":"2026-04-03T00:57:15.606Z","message":{"role":"toolResult","toolCallId":"toolu_01Y8yqjyEj89mwHZrF5RgvC9","toolName":"bash","content":[{"type":"text","text":".gsd/milestones/M014/M014-ROADMAP.md\n"}],"isError":false,"timestamp":1775177835604}}
{"type":"message","id":"32cac8e7","parentId":"8f36207e","timestamp":"2026-04-03T00:57:19.203Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CoyEWHyiFyHaGhz49feoXY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"compose\\|composition\\|dedup\\|existing.page\\|multi.source\" .gsd/milestones/M014/ --ignore-case 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":117,"cacheRead":93576,"cacheWrite":315,"totalTokens":94009,"cost":{"input":0.000005,"output":0.002925,"cacheRead":0.046787999999999996,"cacheWrite":0.00196875,"total":0.05168675}},"stopReason":"toolUse","timestamp":1775177835605}}
{"type":"message","id":"dc2d9ebd","parentId":"32cac8e7","timestamp":"2026-04-03T00:57:19.229Z","message":{"role":"toolResult","toolCallId":"toolu_01CoyEWHyiFyHaGhz49feoXY","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177839225}}
{"type":"message","id":"489fcb0a","parentId":"dc2d9ebd","timestamp":"2026-04-03T00:57:24.367Z","message":{"role":"assistant","content":[{"type":"text","text":"The roadmap's slice description is the primary spec. Let me also check if there are any existing result JSON files from S01's test runs to understand what an \"existing page\" looks like in practice."},{"type":"toolCall","id":"toolu_01KNm79AupdaCnAkJMQt3GFA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"result*.json\" -o -name \"*synthesis_output*\" -o -name \"*harness_output*\" 2>/dev/null | head -10; echo \"---\"; find fixtures -type f 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":171,"cacheRead":93891,"cacheWrite":131,"totalTokens":94194,"cost":{"input":0.000005,"output":0.004275,"cacheRead":0.0469455,"cacheWrite":0.00081875,"total":0.05204425}},"stopReason":"toolUse","timestamp":1775177839227}}
{"type":"message","id":"712d1774","parentId":"489fcb0a","timestamp":"2026-04-03T00:57:24.415Z","message":{"role":"toolResult","toolCallId":"toolu_01KNm79AupdaCnAkJMQt3GFA","toolName":"bash","content":[{"type":"text","text":"---\n"}],"isError":false,"timestamp":1775177844413}}
{"type":"message","id":"811ebd99","parentId":"712d1774","timestamp":"2026-04-03T00:57:30.415Z","message":{"role":"assistant","content":[{"type":"text","text":"No existing fixtures directory at project root. The test harness expects them to be created via `export_fixture.py`. For compose mode testing, we'll need a mock existing page fixture.\n\nNow let me check the existing `_merge_pages_by_slug` function since that's the closest existing pattern to composition."},{"type":"toolCall","id":"toolu_01QsasshP4xUFyZswbLugHVd","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def _merge_pages_by_slug\" backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_0134hud23fGGqfo38bNPrbQf","name":"read","arguments":{"limit":60,"offset":1020,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":233,"cacheRead":94022,"cacheWrite":185,"totalTokens":94441,"cost":{"input":0.000005,"output":0.005825,"cacheRead":0.047011,"cacheWrite":0.0011562500000000002,"total":0.05399725}},"stopReason":"toolUse","timestamp":1775177844414}}
{"type":"message","id":"63e902ef","parentId":"811ebd99","timestamp":"2026-04-03T00:57:30.431Z","message":{"role":"toolResult","toolCallId":"toolu_01QsasshP4xUFyZswbLugHVd","toolName":"bash","content":[{"type":"text","text":"1034:def _merge_pages_by_slug(\n"}],"isError":false,"timestamp":1775177850430}}
{"type":"message","id":"209cdad4","parentId":"63e902ef","timestamp":"2026-04-03T00:57:30.435Z","message":{"role":"toolResult","toolCallId":"toolu_0134hud23fGGqfo38bNPrbQf","toolName":"read","content":[{"type":"text","text":"        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        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n        merge_user_prompt = f\"<creator>{creator_name}</creator>\\n<pages>\\n{pages_json}\\n</pages>\"\n\n\n[1024 more lines in file. Use offset=1080 to continue.]"}],"isError":false,"timestamp":1775177850430}}
{"type":"message","id":"1567bf3c","parentId":"209cdad4","timestamp":"2026-04-03T00:58:35.558Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. The merge pattern already exists — it merges same-slug pages from chunk splitting within a single video. Composition is different: it merges *new moments from a new video* into an *existing page from a previous video*. This is the core multi-source feature.\n\nI have everything I need for the research. Let me write it up."},{"type":"toolCall","id":"toolu_01GD1PH2yoQ5PAuPGuADLGzx","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S02 Research: Composition Prompt + Test Harness Compose Mode\n\n## Summary\n\nThis slice adds the ability to compose new video content into existing technique pages — the core multi-source capability. Two deliverables: (1) a `stage5_compose.txt` prompt that instructs the LLM to merge new moments into an existing page, and (2) a `--compose` mode for the test harness that exercises this prompt offline.\n\nThe work is structurally parallel to S01 (prompt + test harness update + tests) but the LLM task is harder: composition requires deduplication, section merging, citation re-indexing, and preserving the quality of existing prose while integrating new material.\n\n## Recommendation\n\n**Targeted research.** The patterns are established by S01 (Pydantic schemas, test harness structure, citation utils). The new work is the composition prompt design and the harness plumbing to feed existing page + new moments to the LLM.\n\n## Implementation Landscape\n\n### What Exists\n\n| File | Role | Relevance |\n|------|------|-----------|\n| `backend/pipeline/test_harness.py` | Offline synthesis test runner | Needs `compose` subcommand added alongside existing `run` and `promote` |\n| `backend/pipeline/schemas.py` | `SynthesizedPage`, `BodySection`, `SynthesisResult` | Composition output should use the same `SynthesisResult` schema — no new output schema needed |\n| `backend/pipeline/citation_utils.py` | `validate_citations()`, `extract_citations()` | Reused as-is for compose output validation |\n| `backend/pipeline/test_harness_v2_format.py` | v2 format tests | Pattern to follow for compose-mode tests |\n| `prompts/stage5_synthesis.txt` | v5 synthesis prompt (from S01) | Compose prompt references same voice/tone/structure rules but has different instructions |\n| `backend/pipeline/stages.py` `_merge_pages_by_slug()` | Merges chunk-split pages by slug via LLM | Closest existing pattern. Uses `stage5_merge.txt` prompt (which doesn't exist on disk yet — it's referenced but never created). Compose is similar but merges new moments into an existing page rather than merging partial pages from the same video. |\n| `backend/pipeline/llm_client.py` | `LLMClient.complete()`, `estimate_max_tokens()` | Used by test harness already; compose mode uses the same interface |\n| `backend/pipeline/export_fixture.py` | Exports video moments to fixture JSON | May need extension to also export existing page JSON for compose fixtures |\n\n### What Needs Building\n\n#### 1. Composition Prompt (`prompts/stage5_compose.txt`)\n\nThe LLM receives:\n- `<existing_page>`: Full JSON of the current `SynthesizedPage` (body_sections, citations, moment_indices, etc.)\n- `<new_moments>`: The new moments from a second video, same format as synthesis input\n- `<creator>`: Creator name\n\nThe prompt must instruct the LLM to:\n- **Preserve existing prose quality** — don't rewrite sections that are fine\n- **Integrate new material** — add to existing sections where new moments cover the same sub-topic, or create new sections for genuinely new territory\n- **Deduplicate** — if new moments cover ground already in the page, enrich rather than repeat\n- **Re-index citations** — existing citations [0]-[N] refer to the original moment list. New moments get indices [N+1]-[N+M]. The composed output must use a unified index space. The prompt must explain the indexing scheme clearly.\n- **Maintain section ordering** — workflow-order rule from synthesis prompt still applies\n- **Output format** — same `SynthesisResult` / `SynthesizedPage` with v2 body_sections\n\nKey design decision: The compose prompt should receive the existing page's `moment_indices` mapped to their moment data so the LLM understands what's already covered. This means the user prompt includes both old moments (for context) and new moments (to integrate), with clear labeling.\n\n#### 2. Test Harness Compose Mode\n\nAdd a `compose` subcommand to `test_harness.py`:\n\n```\npython -m pipeline.test_harness compose \\\n  --existing-page /tmp/existing_page.json \\\n  --fixture fixtures/new_video.json \\\n  --prompt prompts/stage5_compose.txt \\\n  --output /tmp/composed.json\n```\n\nArguments:\n- `--existing-page`: JSON file containing a single `SynthesizedPage` (output from a prior synthesis run)\n- `--fixture`: New moments fixture (same format as `run` mode)\n- `--category`: Optional filter (only compose for matching category)\n- `--prompt`: Compose prompt file (defaults to `stage5_compose.txt`)\n- `--output`: Output path\n- `--model`, `--modality`: LLM overrides (same as `run`)\n\nThe compose runner needs to:\n1. Load the existing page JSON and validate it as `SynthesizedPage`\n2. Load the new fixture moments (same as `run` mode)\n3. Filter new moments by category to match the existing page's `topic_category`\n4. Build a user prompt with `<existing_page>`, `<new_moments>`, and `<creator>` tags\n5. Call the LLM with the compose system prompt\n6. Parse the response as `SynthesisResult` (the LLM returns an updated version of the page)\n7. Run citation validation on the output, with moment_count = old_moments + new_moments\n8. Log deduplication metrics: how many old sections survived, how many new sections appeared, word count delta\n\n#### 3. Tests (`backend/pipeline/test_harness_compose.py`)\n\nUnit tests for compose-specific logic (no LLM calls):\n- User prompt construction: existing page + new moments formatted correctly\n- Citation re-indexing math: old moments 0-4, new moments 5-9, validate combined\n- Word count comparison: before/after composition\n- Category matching: only compose moments matching the page's topic_category\n- Edge cases: empty new moments, single new moment, existing page with no subsections\n\n### Seam Analysis\n\nThe work divides cleanly into three tasks:\n\n**T01: Composition prompt** — Write `prompts/stage5_compose.txt`. Self-contained file. Can reference the synthesis prompt's voice/tone section but must have its own instructions for merge semantics, deduplication, and citation re-indexing. Verifiable by structural checks (required sections present, example output valid).\n\n**T02: Test harness compose subcommand** — Add `compose` subcommand to `test_harness.py`. Depends on T01 only for the default prompt path. The harness code itself is pure Python with no LLM dependency in tests. Needs a helper to build the compose user prompt (existing page JSON + new moments text).\n\n**T03: Compose-mode tests** — Unit tests for the compose plumbing. Depends on T02 code existing. Tests the prompt construction, citation math, category filtering, and word count diffing. No LLM calls.\n\n### Citation Re-indexing Strategy\n\nThis is the trickiest part. In compose mode:\n- The existing page has citations [0]-[N-1] referring to original moments\n- New moments are numbered [N]-[N+M-1] in the unified space\n- The LLM needs to understand both sets so it can:\n  - Preserve existing [0]-[N-1] citations in untouched prose\n  - Add [N]-[N+M-1] citations in new/updated prose\n  - Potentially add cross-references (new moment enriches an existing section → add [N+k] alongside existing [j])\n\nThe user prompt should present:\n```\n<existing_moments>\n[0] Title: ... (already cited in existing page)\n[1] Title: ...\n...\n[N-1] Title: ...\n</existing_moments>\n\n<new_moments>\n[N] Title: ... (new content to integrate)\n[N+1] Title: ...\n...\n[N+M-1] Title: ...\n</new_moments>\n\n<existing_page>\n{full page JSON}\n</existing_page>\n```\n\nThis way the LLM sees the complete index space and can cite any moment by its global index.\n\n### Deduplication Signal\n\nThe prompt should instruct the LLM to compare new moment summaries against existing section content. If a new moment covers material already present:\n- Don't add a new section\n- Optionally add the new moment's citation to the existing passage if it provides a second source\n- Only add new prose if the new moment contains genuinely new detail (different settings, different context, additional nuance)\n\nThis is a judgment call for the LLM — the prompt needs clear examples of \"enriching\" vs \"duplicating.\"\n\n### Risks\n\n1. **LLM may rewrite too aggressively** — Given the full page, the LLM might want to rewrite everything. The prompt must be explicit: preserve existing prose verbatim where it's correct, only modify sections where new information changes or enriches them.\n2. **Citation index confusion** — The LLM needs to correctly distinguish old indices from new ones. Clear labeling in the user prompt is critical.\n3. **Output size** — Composed pages are larger (more moments, more sections). Token limits may need adjustment. `estimate_max_tokens` should handle this since it's based on input size.\n\n### Don't Hand-Roll\n\n- Citation validation: use existing `validate_citations()` from `citation_utils.py`\n- Word counting: use the same pattern from `test_harness_v2_format.py`\n- LLM interaction: use existing `LLMClient.complete()` and `estimate_max_tokens()`\n- Schema validation: use existing `SynthesisResult.model_validate_json()`\n\n### Verification Strategy\n\n- **T01 (prompt):** Structural assertions — required sections present, example output parses as valid SynthesisResult, citation rules section explains the dual-index scheme\n- **T02 (harness):** `python -m pipeline.test_harness compose --help` exits 0; compose user prompt builder produces expected format with existing page and new moments\n- **T03 (tests):** `python -m pytest backend/pipeline/test_harness_compose.py -v` — all pass\n\n### Notes for Planner\n\n- The `compose` subcommand mirrors the `run` subcommand structure but with different inputs. Keep the pattern consistent.\n- The compose prompt is the highest-risk deliverable — it drives whether the LLM produces good composed output. The test harness is mechanical.\n- S04 (Pipeline Compose-or-Create Logic) will use both the compose prompt and the test harness patterns when wiring into the real pipeline. The harness is the offline proving ground.\n- The existing `_merge_pages_by_slug` in stages.py merges partial pages from chunk splitting. Composition is conceptually different (existing page + new moments) but the LLM call pattern is the same.\n- No new Pydantic schemas needed — compose output is the same `SynthesisResult` with `SynthesizedPage` objects.\n","milestone_id":"M014","slice_id":"S02"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2863,"cacheRead":94207,"cacheWrite":916,"totalTokens":97987,"cost":{"input":0.000005,"output":0.071575,"cacheRead":0.0471035,"cacheWrite":0.005725,"total":0.12440849999999999}},"stopReason":"toolUse","timestamp":1775177850431}}
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{"type":"message","id":"acdb1951","parentId":"b8e3fba2","timestamp":"2026-04-03T00:58:38.530Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S02 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":95123,"cacheWrite":2901,"totalTokens":98036,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.0475615,"cacheWrite":0.01813125,"total":0.06597275}},"stopReason":"stop","timestamp":1775177915562}}
