{"type":"thinking_level_change","id":"d62db8f3","parentId":null,"timestamp":"2026-04-03T01:24:04.903Z","thinkingLevel":"medium"}
{"type":"model_change","id":"28fe5f89","parentId":"d62db8f3","timestamp":"2026-04-03T01:24:04.904Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/S04-PLAN.md` and `.gsd/milestones/M014/slices/S04/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S04 planned.\"\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\n- R012 — Composition prompt and harness provide the offline-testable merge mechanism for updating existing technique pages with new video content\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S04 (\"Pipeline Compose-or-Create Logic\") — Milestone M014\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M014/M014-ROADMAP.md`\n\n# M014: \n\n## Vision\nRestructure technique pages to be broader (per-creator+category across videos), deeper (H2/H3 nesting with TOC), multi-source (compose new video content into existing pages), with inline citations linking prose claims to source moments, admin multi-source management, and section-level search with deep linking.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Synthesis Prompt v5 — Nested Sections + Citations | high | — | ✅ | Run test harness with new prompt → output has list-of-objects body_sections with H2/H3 nesting, citation markers on key claims, broader page scope. |\n| S02 | Composition Prompt + Test Harness Compose Mode | high | S01 | ✅ | Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations. |\n| S03 | Data Model + Migration | low | — | ✅ | Alembic migration runs clean. API response includes body_sections_format and source_videos fields. |\n| S04 | Pipeline Compose-or-Create Logic | high | S01, S02, S03 | ⬜ | Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs. |\n| S05 | Frontend — Nested Rendering, TOC, Citations | medium | S03 | ⬜ | Format-2 page renders with TOC, nested sections, clickable citations. Format-1 pages unchanged. |\n| S06 | Admin UI — Multi-Source Pipeline Management | medium | S03, S04 | ⬜ | Admin view for multi-source page shows source dropdown, composition history, per-video chunking inspection. |\n| S07 | Search — Per-Section Embeddings + Deep Linking | medium | S04, S05 | ⬜ | Search 'LFO grain position' → section-level result → click → navigates to page#section and scrolls. |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M014/slices/S04/S04-RESEARCH.md`\n\n# S04 Research: Pipeline Compose-or-Create Logic\n\n## Summary\n\nThis slice modifies stage 5 (synthesis) in `backend/pipeline/stages.py` to:\n1. Detect when a technique page already exists for the same creator+category (from a prior video)\n2. Use the compose prompt (`stage5_compose.txt`) instead of synthesis prompt when composing\n3. Store the v2 `body_sections_format` on newly created/updated pages\n4. Populate the `technique_page_videos` association table so pages track all contributing videos\n\nThe compose prompt, Pydantic schemas, and test harness are all done (S01, S02). The data model and migration are done (S03). This slice wires them together in the live pipeline.\n\n## Recommendation\n\nTargeted research. The pattern is clear from reading the existing code — the compose-or-create decision is a conditional branch inside the per-category loop in `stage5_synthesis()`. The main risk is getting the LLM call plumbing right (same shape as `_synthesize_chunk` but using compose prompt and different user prompt format). No new technology. No ambiguous requirements.\n\n## Implementation Landscape\n\n### File: `backend/pipeline/stages.py` (2102 lines)\n\n**Current flow (stage5_synthesis, line 1127):**\n1. Load video, moments, creator, classification data\n2. Group moments by topic_category\n3. For each category group: synthesize via `_synthesize_chunk()` (or chunk+merge for large groups)\n4. Persist pages: check for existing page by slug or by prior_page_ids (from Redis snapshot), create or update\n5. Link moments to pages, set `processing_status = complete`\n\n**What changes:**\n- After grouping moments, before calling LLM: query DB for existing `TechniquePage` with same `creator_id` + `topic_category`\n- If found → compose path: load existing page's body_sections + moments, build compose prompt via logic similar to `build_compose_prompt()` from test_harness.py, call LLM with `stage5_compose.txt` system prompt, parse result as `SynthesisResult`\n- If not found → create path: existing synthesis flow (unchanged)\n- After persisting page: INSERT into `technique_page_videos` (upsert pattern due to unique constraint)\n- Set `body_sections_format = 'v2'` on all newly created/updated pages (both compose and create paths)\n\n**Key helpers to reuse:**\n- `_build_moments_text()` (line 957) — formats moments for prompt, returns (text, tags)\n- `_synthesize_chunk()` (line 983) — single-category synthesis LLM call\n- `_safe_parse_llm_response()` (line 325) — parse + truncation detection + retry\n- `_load_prompt()` (line 244) — loads from prompts/ directory\n- `estimate_max_tokens()` — from `pipeline.llm_client`\n- `_make_llm_callback()` (line 137) — observability callback\n- `_build_request_params()` (line 189) — LLM request config\n- `_capture_pipeline_metadata()` (line 884) — for version snapshots\n\n**New import needed:** `TechniquePageVideo` from `models`\n\n### File: `backend/pipeline/test_harness.py`\n\nContains `build_compose_prompt()` (line 332) which is the reference implementation for building the compose user prompt. The pipeline stage will need its own version that works with real `KeyMoment` ORM objects instead of `MockKeyMoment` test doubles. The structure is identical — XML tags with `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>`.\n\n### Compose prompt inputs needed:\n1. **existing_page** — JSON of the existing TechniquePage's SynthesizedPage-compatible dict (title, slug, topic_category, summary, body_sections, signal_chains, plugins, source_quality)\n2. **existing_moments** — formatted text of key moments already linked to this page (from previous video(s))\n3. **new_moments** — formatted text of moments from the current video, with offset indices starting at N\n4. **creator** — creator name\n\n### The compose-or-create decision point:\n\nFor each `(creator_id, topic_category)` group in the current video's moments:\n```\nexisting_page = SELECT FROM technique_pages \n  WHERE creator_id = ? AND LOWER(topic_category) = LOWER(?)\n  LIMIT 1\n```\n- If `existing_page` exists → compose path\n- If not → create path (current synthesis flow)\n\nThe existing code already does slug-based and prior_page_ids-based matching at *persist* time (line ~1320). The compose decision needs to happen *before* the LLM call, not after. This is the key architectural change — the detection moves earlier in the flow.\n\n### TechniquePageVideo population:\n\nAfter creating or updating a `TechniquePage`, insert a row:\n```python\nfrom sqlalchemy.dialects.postgresql import insert as pg_insert\n\nstmt = pg_insert(TechniquePageVideo).values(\n    technique_page_id=page.id,\n    source_video_id=video_id,\n).on_conflict_do_nothing()\nsession.execute(stmt)\n```\n\nThe `on_conflict_do_nothing` handles the unique constraint gracefully for reprocessing scenarios.\n\n### Loading existing moments for compose prompt:\n\nWhen composing, we need the existing page's linked moments:\n```python\nexisting_moments = session.execute(\n    select(KeyMoment)\n    .where(KeyMoment.technique_page_id == existing_page.id)\n    .order_by(KeyMoment.start_time)\n).scalars().all()\n```\n\nThese get formatted as indices [0]-[N-1]. The new video's moments for this category get indices [N]-[N+M-1].\n\n### body_sections_format tracking:\n\n- New pages created via synthesis → `body_sections_format = 'v2'` (the v5 prompt always outputs v2)\n- Pages updated via compose → `body_sections_format = 'v2'` (compose prompt also outputs v2)\n- Existing v1 pages NOT processed in this pipeline run → unchanged (backward compatible)\n\nThe column already has `default='v1'` and `server_default='v1'` from S03 migration.\n\n## Seams / Task Decomposition\n\n### T01: Compose detection + compose LLM call helper\n- Add `_build_compose_user_prompt()` helper (similar to test harness's `build_compose_prompt()` but using real ORM objects)\n- Add `_compose_into_existing()` helper that takes an existing page + existing moments + new moments, calls LLM with compose system prompt, returns `SynthesisResult`\n- This is the riskiest piece — get it testable before wiring into the main flow\n\n### T02: Wire compose-or-create into stage5_synthesis + TechniquePageVideo\n- Modify the per-category loop: before calling `_synthesize_chunk()`, check for existing page\n- If found and has moments → call `_compose_into_existing()`\n- If not → existing synthesis path\n- After persisting page: insert `TechniquePageVideo` row\n- Set `body_sections_format = 'v2'` on all pages touched\n- Add `TechniquePageVideo` to imports\n\n### T03: Integration test / verification\n- Test with mock LLM to verify compose path is triggered correctly\n- Verify TechniquePageVideo rows are created\n- Verify body_sections_format is set\n- The roadmap demo says \"Process two COPYCATT videos\" — this is an end-to-end verification on ub01, not something we can unit test here\n\n## Constraints\n\n1. **Sync SQLAlchemy only** — stages.py uses sync sessions (Celery is sync). No async.\n2. **stage5_merge.txt doesn't exist** — `_merge_pages_by_slug` references it but the file is missing. This is pre-existing; don't fix it in this slice.\n3. **Redis classification data** — stage 4 stores classification in Redis with 24h TTL. If the second video is processed >24h after the first, classification data for the first video may be gone. The compose flow loads existing *moments from the DB*, not classification data — so this isn't a blocker.\n4. **Existing page detection must be case-insensitive** — KNOWLEDGE.md notes \"LLM-generated topic categories have inconsistent casing\". Use `func.lower()` for category matching.\n5. **The compose prompt** (`stage5_compose.txt`) is self-contained — no runtime import from synthesis prompt needed (per S02 decision).\n6. **Existing _load_prior_pages Redis snapshot** — currently used for reprocessing the *same* video. The compose flow is for a *different* video contributing to the same page. These are separate mechanisms — compose uses DB query, reprocess uses Redis snapshot. Don't conflate them.\n\n## Risks\n\n1. **Compose LLM output quality** — First time the compose prompt runs against real LLM. May need prompt tuning. Mitigated by: the prompt was carefully designed in S02, and `_safe_parse_llm_response` handles parse failures with retry.\n2. **Multiple pages per category** — If a creator has 2+ pages in the same category (from chunked synthesis), the compose detection query returns only one. The current code handles this with `LIMIT 1` / `.first()`. Worth logging when >1 exists but proceeding with the first match.\n3. **Moment count growth** — Composing many moments into a single page may exceed context limits. The existing truncation recovery (split-in-half retry) doesn't apply to compose since we can't split the existing page. Mitigated by: compose prompts are typically shorter than synthesis (existing page is already summarized).\n\n## Verification Strategy\n\n1. **Unit tests** for `_build_compose_user_prompt()` — XML structure, citation offset math (same pattern as test_harness_compose.py tests)\n2. **Unit test** for compose-or-create branching logic — mock the DB query result\n3. **Integration check** on ub01: process a video, verify page created with format v2 and TechniquePageVideo row. Then process a second video for same creator — verify compose path triggered, page updated, second TechniquePageVideo row added.\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 3 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n#### 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#### S02 Summary\nSource: `.gsd/milestones/M014/slices/S02/S02-SUMMARY.md`\n\n---\nid: S02\nparent: M014\nmilestone: M014\nprovides:\n  - stage5_compose.txt prompt for S04 pipeline compose-or-create logic\n  - build_compose_prompt() function for S04 to call from pipeline stages\n  - compose CLI subcommand for offline testing of composition before pipeline integration\nrequires:\n  - slice: S01\n    provides: v2 SynthesisResult schema and body_sections format used by composition output\naffects:\n  - S04\nkey_files:\n  - prompts/stage5_compose.txt\n  - backend/pipeline/test_harness.py\n  - backend/pipeline/test_harness_compose.py\n  - conftest.py\nkey_decisions:\n  - Composition prompt is self-contained — carries its own writing standards rather than importing from synthesis prompt at runtime\n  - Offset-based citation scheme: existing keep [0]-[N-1], new get [N]-[N+M-1], no renumbering of existing citations\n  - Compose accepts both harness output (with .pages[]) and raw SynthesizedPage JSON\n  - Root conftest.py bootstraps backend/ onto sys.path for project-root test discovery via pipeline symlink\npatterns_established:\n  - Compose prompt pattern: XML-tagged inputs with offset-based citation indexing for multi-source content merging\n  - Test harness subcommand pattern: build_*_prompt() pure function + run_*() orchestrator + CLI wiring\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M014/slices/S02/tasks/T03-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:10:16.667Z\nblocker_discovered: false\n---\n\n# S02: Composition Prompt + Test Harness Compose Mode\n\n**Composition prompt, test harness compose subcommand, and 16 unit tests enable offline testing of merging new video moments into existing technique pages with correct citation re-indexing.**\n\n## What Happened\n\nThree tasks delivered the composition pipeline's offline testing surface:\n\nT01 wrote `prompts/stage5_compose.txt` — a self-contained LLM prompt for merging new video moments into existing technique pages. It defines four XML input tags (`<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>`), offset-based citation re-indexing (existing moments keep [0]-[N-1], new moments get [N]-[N+M-1]), merge/enrich/dedup rules with concrete examples, workflow-ordered section placement, and v2 SynthesisResult output format with two validated JSON examples.\n\nT02 added `build_compose_prompt()` and `run_compose()` to the test harness, plus a `compose` CLI subcommand. The prompt builder constructs the XML-tagged user prompt with correct citation index offsets. `run_compose()` loads an existing page JSON + two fixture files (original moments and new moments), filters new moments by category, calls the LLM with the compose system prompt, validates the output as SynthesisResult, and logs compose-specific metrics (word count before/after, sections before/after).\n\nT03 created 16 unit tests in `test_harness_compose.py` covering prompt XML structure (6 tests), citation offset arithmetic (4 tests), category filtering (2 tests), and edge cases (4 tests including empty moments, single moment, no subsections, large offsets). All pass in 0.4s.\n\nA root-level `conftest.py` was added to bootstrap `backend/` onto `sys.path`, fixing test discovery when running from the project root via the `pipeline` symlink.\n\n## Verification\n\nAll three verification gates pass:\n1. `python -m pytest pipeline/test_harness_compose.py -v` — 16/16 passed (0.40s) from project root\n2. `cd backend && python -m pipeline.test_harness compose --help` — exits 0, correct arg listing\n3. Structural validation of `prompts/stage5_compose.txt` — 13053 chars, all required sections present, JSON examples parse valid\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nAdded root-level conftest.py to fix sys.path for project-root test execution — not in original plan but required for the verification command to work without cd backend prefix.\n\n## Known Limitations\n\nrun_compose() requires an LLM endpoint to actually test composition end-to-end. The unit tests validate prompt construction and plumbing only.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `prompts/stage5_compose.txt` — New composition prompt with merge rules, citation re-indexing, dedup guidance, two v2 JSON examples\n- `backend/pipeline/test_harness.py` — Added build_compose_prompt(), run_compose(), and compose CLI subcommand\n- `backend/pipeline/test_harness_compose.py` — 16 unit tests for compose prompt construction, citation math, category filtering, edge cases\n- `conftest.py` — Root-level sys.path bootstrap for project-root test discovery via pipeline symlink\n\n#### S03 Summary\nSource: `.gsd/milestones/M014/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M014\nmilestone: M014\nprovides:\n  - body_sections_format column on technique_pages (default 'v1')\n  - technique_page_videos association table\n  - SourceVideoSummary schema\n  - source_videos field on TechniquePageDetail API response\n  - body_sections accepts list | dict | None\nrequires:\n  []\naffects:\n  - S04\n  - S05\n  - S06\nkey_files:\n  - alembic/versions/012_multi_source_format.py\n  - backend/models.py\n  - backend/schemas.py\n  - backend/routers/techniques.py\nkey_decisions:\n  - Used TIMESTAMP (not WITH TIME ZONE) for added_at to stay consistent with existing schema convention\npatterns_established:\n  - Association table pattern with dual CASCADE FKs and unique constraint for many-to-many with metadata (added_at)\n  - body_sections_format discriminator column for handling multiple content formats in the same table\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S03/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:20:27.897Z\nblocker_discovered: false\n---\n\n# S03: Data Model + Migration\n\n**Added body_sections_format column, technique_page_videos association table, and wired both into the API response for multi-source technique pages.**\n\n## What Happened\n\nThis slice laid the data foundation for M014's multi-source, nested-section technique pages. Two tasks delivered three changes:\n\n**T01 — Schema + Migration:** Created Alembic migration 012 adding `body_sections_format` (VARCHAR(20), NOT NULL, default 'v1') to technique_pages and a new `technique_page_videos` association table with dual CASCADE foreign keys and a unique constraint on (technique_page_id, source_video_id). Updated SQLAlchemy models with the new `TechniquePageVideo` class and `body_sections_format` column on `TechniquePage`. Widened the Pydantic `body_sections` type from `dict | None` to `list | dict | None` to support both v1 (dict) and v2 (list-of-objects) formats. Added `SourceVideoSummary` schema and `source_videos` field to `TechniquePageDetail`.\n\n**T02 — API Wiring:** Updated `get_technique()` to eagerly load `source_video_links` → `source_video` via chained `selectinload`. Builds the `source_videos` list from association table rows. Ran migration on ub01 and verified the API returns both new fields with correct defaults (`body_sections_format: \"v1\"`, `source_videos: []`).\n\nAll existing technique pages continue to work unchanged — the v1 default ensures backward compatibility.\n\n## Verification\n\nAll slice-level checks passed:\n1. `from models import TechniquePageVideo, TechniquePage; assert hasattr(TechniquePage, 'body_sections_format')` → OK\n2. `from schemas import SourceVideoSummary, TechniquePageDetail` → OK\n3. `alembic upgrade head` on ub01 Docker → clean (migration 012 applied)\n4. `curl` to live API for existing technique → `body_sections_format: \"v1\"` and `source_videos: []` present in response\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nNone.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `alembic/versions/012_multi_source_format.py` — New migration: body_sections_format column + technique_page_videos table\n- `backend/models.py` — Added TechniquePageVideo model, body_sections_format column, source_video_links relationship\n- `backend/schemas.py` — Widened body_sections type, added SourceVideoSummary, added source_videos to TechniquePageDetail\n- `backend/routers/techniques.py` — Eager-load source_video_links, build source_videos list in technique detail response\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Project**: `.gsd/PROJECT.md`\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M014/M014-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M014/slices/S04/S04-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"bba67971","parentId":"28fe5f89","timestamp":"2026-04-03T01:24:04.908Z"}
{"type":"message","id":"479f782d","parentId":"bba67971","timestamp":"2026-04-03T01:24:12.599Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand the slice scope. S04 is \"Pipeline Compose-or-Create Logic\" — it wires the compose prompt (from S02) into the live pipeline's stage 5 synthesis, using the data model from S03. The research doc is thorough. Let me verify the current state of the code before planning.","thinkingSignature":"EtkDClkIDBgCKkA/cCgSZf186j7vGdCa7uHzcLoplrYwKFfbsiSRhZxDenx9bUsFcU2+x14HEOSlyBHuAJ1G+5U9MGTLD/bNLp2HMg9jbGF1ZGUtb3B1cy00LTY4ABIMLxTQvRCsyY8hfH5FGgwL3/5Rtzyy2ffwH2YiMARH9OEY32xhVl2Je98lQo4UxJt2OIg3tRLJeq6cHfq4VaQ97/E6dIWJeEUnFzFKyCqtAqu6LQNEVvE/ar2jPnYEvxMG5WtUnuEqvXbNvTzfdsSiEV8wwLf/9wDN0FGIqusFpT9qin+zbrKXh3LLSK7jQCu5YT66zOqOrBryIih+06KWUDOzyQfA1l7qFxLbfToahGS2EZtQiiQuUMihEVm8A7X0QmNGTiF1jsXxefRVJRmEyXfylNzKfbGZSstWD/9FebMeoHc7fNocnfUlMDB1iLdZY4jHSijzzMH/CUBzhmzzlhJFh6vZ4ym4Av8JkNbeBsibaL3R7lMCaeMIKx3aTPUDjnI5BWlRqISn6pou66x7lyzaSCm52lBvrpcHGLYL5KoSdfpRD1SxfJ/HqDD/5vNMpfOJMAkjXU3RWghc3e3XiJ6VWg+sv+W1WYIL9tKAmyVLrSBoSSugJ56FVegYAQ=="},{"type":"text","text":"Let me explore the key code areas to ground the plan in reality."},{"type":"toolCall","id":"toolu_01X8VRiQN4snk725JhUFZyyR","name":"read","arguments":{"limit":80,"offset":1120,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01NEStA1WFaVTqHDJ3ZC7XLF","name":"read","arguments":{"limit":80,"offset":950,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01JtQtekZNXMC4mBCvLqXgf4","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":318,"cacheRead":59865,"cacheWrite":12720,"totalTokens":72905,"cost":{"input":0.00001,"output":0.00795,"cacheRead":0.029932499999999997,"cacheWrite":0.0795,"total":0.1173925}},"stopReason":"toolUse","timestamp":1775179444906}}
{"type":"message","id":"95f6f130","parentId":"479f782d","timestamp":"2026-04-03T01:24:12.603Z","message":{"role":"toolResult","toolCallId":"toolu_01X8VRiQN4snk725JhUFZyyR","toolName":"read","content":[{"type":"text","text":"            )\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            .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[904 more lines in file. Use offset=1200 to continue.]"}],"isError":false,"timestamp":1775179452603}}
{"type":"message","id":"88642cab","parentId":"95f6f130","timestamp":"2026-04-03T01:24:12.606Z","message":{"role":"toolResult","toolCallId":"toolu_01NEStA1WFaVTqHDJ3ZC7XLF","toolName":"read","content":[{"type":"text","text":"        if 0 <= idx < len(moment_group):\n            _, cls_info = moment_group[idx]\n            page_tags.update(cls_info.get(\"topic_tags\", []))\n\n    return list(page_tags) if page_tags else None\n\n\ndef _build_moments_text(\n    moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text and collect all tags for a group of moments.\n\n    Returns (moments_text, all_tags).\n    \"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n    E.g. 'wavetable-sound-design-copycatt' → 'wavetable-sound-design'\n    Also normalizes casing.\n\n[1074 more lines in file. Use offset=1030 to continue.]"}],"isError":false,"timestamp":1775179452605}}
{"type":"message","id":"a4d126f4","parentId":"88642cab","timestamp":"2026-04-03T01:24:12.609Z","message":{"role":"toolResult","toolCallId":"toolu_01JtQtekZNXMC4mBCvLqXgf4","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1775179452606}}
{"type":"message","id":"65b1114d","parentId":"a4d126f4","timestamp":"2026-04-03T01:24:16.186Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WdQcpfSu5LfcQFxv2WJNem","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}},{"type":"toolCall","id":"toolu_019wiJRaCe7kr52GpZyWtuku","name":"read","arguments":{"limit":120,"offset":1200,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":144,"cacheRead":72585,"cacheWrite":3939,"totalTokens":76669,"cost":{"input":0.000005,"output":0.0036000000000000003,"cacheRead":0.0362925,"cacheWrite":0.024618750000000002,"total":0.06451625}},"stopReason":"toolUse","timestamp":1775179452607}}
{"type":"message","id":"1032c2d9","parentId":"65b1114d","timestamp":"2026-04-03T01:24:16.189Z","message":{"role":"toolResult","toolCallId":"toolu_01WdQcpfSu5LfcQFxv2WJNem","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1775179456188}}
{"type":"message","id":"34cf478a","parentId":"1032c2d9","timestamp":"2026-04-03T01:24:16.193Z","message":{"role":"toolResult","toolCallId":"toolu_019wiJRaCe7kr52GpZyWtuku","toolName":"read","content":[{"type":"text","text":"\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                            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\n[784 more lines in file. Use offset=1320 to continue.]"}],"isError":false,"timestamp":1775179456191}}
{"type":"message","id":"9e27fc08","parentId":"34cf478a","timestamp":"2026-04-03T01:24:20.625Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016chJM7WaUNoMv4TR2hji5K","name":"read","arguments":{"limit":120,"offset":1320,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01FiEhGRrJGUUmDCqnPFPmpd","name":"read","arguments":{"limit":80,"offset":320,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":169,"cacheRead":76524,"cacheWrite":2545,"totalTokens":79239,"cost":{"input":0.000005,"output":0.0042250000000000005,"cacheRead":0.038262,"cacheWrite":0.01590625,"total":0.05839825}},"stopReason":"toolUse","timestamp":1775179456191}}
{"type":"message","id":"1008e224","parentId":"9e27fc08","timestamp":"2026-04-03T01:24:20.628Z","message":{"role":"toolResult","toolCallId":"toolu_016chJM7WaUNoMv4TR2hji5K","toolName":"read","content":[{"type":"text","text":"                            \"slug\": existing.slug,\n                            \"topic_category\": existing.topic_category,\n                            \"topic_tags\": existing.topic_tags,\n                            \"summary\": existing.summary,\n                            \"body_sections\": existing.body_sections,\n                            \"signal_chains\": existing.signal_chains,\n                            \"plugins\": existing.plugins,\n                            \"source_quality\": sq_value,\n                        }\n                        version_count = session.execute(\n                            select(func.count()).where(\n                                TechniquePageVersion.technique_page_id == existing.id\n                            )\n                        ).scalar()\n                        version_number = version_count + 1\n\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                            version_number=version_number,\n                            content_snapshot=snapshot,\n                            pipeline_metadata=_capture_pipeline_metadata(),\n                        )\n                        session.add(version)\n                        logger.info(\n                            \"Version snapshot v%d created for page slug=%s\",\n                            version_number, existing.slug,\n                        )\n                    except Exception as snap_exc:\n                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    page_tags = _compute_page_tags(page_moment_indices, moment_group, all_tags)\n                    existing.topic_tags = page_tags\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=_compute_page_tags(page_moment_indices, moment_group, all_tags),\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n        # Update processing_status\n        video.processing_status = ProcessingStatus.complete\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage5_synthesis\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage5_synthesis\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 6: Embed & Index ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=0)\ndef stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n\n[664 more lines in file. Use offset=1440 to continue.]"}],"isError":false,"timestamp":1775179460628}}
{"type":"message","id":"0ccd0f58","parentId":"1008e224","timestamp":"2026-04-03T01:24:20.631Z","message":{"role":"toolResult","toolCallId":"toolu_01FiEhGRrJGUUmDCqnPFPmpd","toolName":"read","content":[{"type":"text","text":"\n# ── Compose: merge new moments into existing page ──────────────────────────\n\ndef _count_page_words(page_dict: dict) -> int:\n    \"\"\"Count total words in a page's body sections.\"\"\"\n    return sum(\n        len(s.get(\"content\", \"\").split())\n        + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n        for s in page_dict.get(\"body_sections\", [])\n    )\n\n\ndef build_compose_prompt(\n    existing_page: dict,\n    existing_moments: list[tuple[MockKeyMoment, dict]],\n    new_moments: list[tuple[MockKeyMoment, dict]],\n    creator_name: str,\n) -> str:\n    \"\"\"Build the user prompt for composition (merging new moments into an existing page).\n\n    Existing moments keep indices [0]-[N-1].\n    New moments get indices [N]-[N+M-1].\n    Uses build_moments_text() for formatting, with index offsets applied for new moments.\n    \"\"\"\n    category = existing_page.get(\"topic_category\", \"Uncategorized\")\n\n    # Format existing moments [0]-[N-1]\n    existing_text, _ = build_moments_text(existing_moments, category)\n\n    # Format new moments with offset indices [N]-[N+M-1]\n    n = len(existing_moments)\n    new_lines = []\n    for i, (m, cls_info) in enumerate(new_moments):\n        tags = cls_info.get(\"topic_tags\", [])\n        new_lines.append(\n            f\"[{n + 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    new_text = \"\\n\\n\".join(new_lines)\n\n    page_json = json.dumps(existing_page, indent=2, ensure_ascii=False)\n\n    return (\n        f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n        f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n        f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n        f\"<creator>{creator_name}</creator>\"\n    )\n\n\ndef run_compose(\n    existing_page_path: str,\n    existing_fixture_path: str,\n    new_fixture_path: str,\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 composition: merge new fixture moments into an existing page.\n\n    Returns (pages, exit_code) — same shape as run_synthesis().\n    \"\"\"\n    # Load existing page JSON\n    existing_page_file = Path(existing_page_path)\n    if not existing_page_file.exists():\n        _log(\"ERROR\", f\"Existing page not found: {existing_page_path}\", level=\"ERROR\")\n        return [], 3\n\n    try:\n        existing_raw = json.loads(existing_page_file.read_text(encoding=\"utf-8\"))\n    except json.JSONDecodeError as exc:\n        _log(\"ERROR\", f\"Invalid JSON in existing page: {exc}\", level=\"ERROR\")\n        return [], 3\n\n[432 more lines in file. Use offset=400 to continue.]"}],"isError":false,"timestamp":1775179460629}}
{"type":"message","id":"5493b8a8","parentId":"0ccd0f58","timestamp":"2026-04-03T01:24:26.229Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LJdybrd3wxRhKdHkhafcaS","name":"read","arguments":{"limit":30,"offset":130,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01BeWfBjSH5s4spwmaUcsbm9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"from models import\\|from models \" backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":165,"cacheRead":79069,"cacheWrite":2579,"totalTokens":81814,"cost":{"input":0.000005,"output":0.004125,"cacheRead":0.0395345,"cacheWrite":0.01611875,"total":0.05978325}},"stopReason":"toolUse","timestamp":1775179460630}}
{"type":"message","id":"a3bf3792","parentId":"5493b8a8","timestamp":"2026-04-03T01:24:26.241Z","message":{"role":"toolResult","toolCallId":"toolu_01LJdybrd3wxRhKdHkhafcaS","toolName":"read","content":[{"type":"text","text":"        if val is not None:\n            return val.decode().lower() == \"true\"\n    except Exception:\n        pass\n    return getattr(get_settings(), \"debug_mode\", False)\n\n\ndef _make_llm_callback(\n    video_id: str,\n    stage: str,\n    system_prompt: str | None = None,\n    user_prompt: str | None = None,\n    run_id: str | None = None,\n    context_label: str | None = None,\n    request_params: dict | None = None,\n):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\n\n    When debug mode is enabled, captures full system prompt, user prompt,\n    and response text on each llm_call event.\n\n    Parameters\n    ----------\n    request_params:\n        Dict of LLM request parameters (max_tokens, model_override, modality,\n        response_model, temperature, etc.) to store in the event payload for\n        debugging which parameters were actually sent to the API.\n    \"\"\"\n    debug = _is_debug_mode()\n\n\n[1944 more lines in file. Use offset=160 to continue.]"}],"isError":false,"timestamp":1775179466240}}
{"type":"message","id":"12e12ee4","parentId":"a3bf3792","timestamp":"2026-04-03T01:24:26.250Z","message":{"role":"toolResult","toolCallId":"toolu_01BeWfBjSH5s4spwmaUcsbm9","toolName":"bash","content":[{"type":"text","text":"27:from models import (\n1683:        from models import PipelineRun\n1736:    from models import PipelineRun, PipelineRunTrigger\n1761:    from models import PipelineRun, PipelineRunStatus, _now\n"}],"isError":false,"timestamp":1775179466248}}
{"type":"message","id":"823f18f0","parentId":"12e12ee4","timestamp":"2026-04-03T01:24:30.723Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TX89QJDKSXQchPuyZU43qo","name":"read","arguments":{"limit":20,"offset":27,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01P63fjftDrue5YvhpGrM9X2","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"TechniquePageVideo\\|technique_page_videos\\|body_sections_format\" backend/models.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":176,"cacheRead":81648,"cacheWrite":565,"totalTokens":82390,"cost":{"input":0.000005,"output":0.0044,"cacheRead":0.040824,"cacheWrite":0.00353125,"total":0.04876025}},"stopReason":"toolUse","timestamp":1775179466248}}
{"type":"message","id":"ce9f3ede","parentId":"823f18f0","timestamp":"2026-04-03T01:24:30.729Z","message":{"role":"toolResult","toolCallId":"toolu_01TX89QJDKSXQchPuyZU43qo","toolName":"read","content":[{"type":"text","text":"from models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient, LLMResponse, estimate_max_tokens\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\n\n[2057 more lines in file. Use offset=47 to continue.]"}],"isError":false,"timestamp":1775179470729}}
{"type":"message","id":"649567c6","parentId":"ce9f3ede","timestamp":"2026-04-03T01:24:30.733Z","message":{"role":"toolResult","toolCallId":"toolu_01P63fjftDrue5YvhpGrM9X2","toolName":"bash","content":[{"type":"text","text":"219:    body_sections_format: Mapped[str] = mapped_column(\n250:    source_video_links: Mapped[list[TechniquePageVideo]] = sa_relationship(\n312:class TechniquePageVideo(Base):\n314:    __tablename__ = \"technique_page_videos\"\n"}],"isError":false,"timestamp":1775179470731}}
{"type":"message","id":"2599a5fe","parentId":"649567c6","timestamp":"2026-04-03T01:24:48.479Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I have a clear picture now. The existing `stage5_synthesis` loop does: group moments by category → synthesize → persist. The compose-or-create decision needs to be injected before the LLM call. Let me check the test harness's `build_compose_prompt` more closely and confirm what the pipeline import needs to look like.\n\nThe research doc is solid and matches the code. Let me plan this as two tasks:\n\n1. **T01**: Add the compose helpers and detection logic into `stages.py` + write unit tests\n2. **T02**: Wire TechniquePageVideo + body_sections_format into the persist section + integration test\n\nActually, looking more carefully — the compose detection, LLM call, *and* the persist changes (TechniquePageVideo, body_sections_format) are all tightly coupled within the same function. The compose path changes both how synthesis happens and how persistence happens. Splitting by \"helpers\" vs \"wiring\" would create artificial seams since the compose result feeds directly into the persist block.\n\nBetter split: T01 builds the compose helpers + modifies `stage5_synthesis` for compose-or-create + adds `TechniquePageVideo` population + `body_sections_format` setting. T02 writes the tests. But T01 would be huge (10+ steps).\n\nLet me reconsider. The research suggests:\n- T01: Compose detection + compose LLM helper functions (testable in isolation)\n- T02: Wire into stage5_synthesis + TechniquePageVideo + body_sections_format\n- T03: Tests\n\nT01 and T02 are tightly coupled — T02 can't be verified without T01. And T02 is the riskiest part. Let me keep it at 2 tasks:\n\n**T01**: Add `_build_compose_user_prompt()` and `_compose_into_existing()` helpers, modify `stage5_synthesis` to detect existing pages and branch to compose path, add `TechniquePageVideo` population and `body_sections_format = 'v2'` on all pages. This is one cohesive change.\n\n**T02**: Write unit tests covering compose prompt construction, compose-or-create branching, TechniquePageVideo insertion, and body_sections_format setting.\n\nThis keeps T01 focused on implementation and T02 on verification, with T02 having full context of what T01 built."},{"type":"toolCall","id":"toolu_01UtQn4TL1XeKGGJ3V9c111z","name":"read","arguments":{"limit":30,"offset":240,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":647,"cacheRead":82213,"cacheWrite":488,"totalTokens":83349,"cost":{"input":0.000005,"output":0.016175000000000002,"cacheRead":0.0411065,"cacheWrite":0.00305,"total":0.0603365}},"stopReason":"toolUse","timestamp":1775179470732}}
{"type":"message","id":"d9fabbd9","parentId":"2599a5fe","timestamp":"2026-04-03T01:24:48.484Z","message":{"role":"toolResult","toolCallId":"toolu_01UtQn4TL1XeKGGJ3V9c111z","toolName":"read","content":[{"type":"text","text":"        _SessionLocal = sessionmaker(bind=_get_sync_engine())\n    return _SessionLocal()\n\n\ndef _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\n[1834 more lines in file. Use offset=270 to continue.]"}],"isError":false,"timestamp":1775179488482}}
{"type":"message","id":"ce9e4fc3","parentId":"d9fabbd9","timestamp":"2026-04-03T01:26:02.057Z","message":{"role":"assistant","content":[{"type":"text","text":"I have all the context I need. Let me now build the plan."},{"type":"toolCall","id":"toolu_01WUPHZbGGdSFMojG2pSGyep","name":"gsd_plan_slice","arguments":{"goal":"Stage 5 detects existing technique pages by creator+category and uses the compose prompt to merge new video content into them. All pages get body_sections_format='v2' and technique_page_videos rows tracking contributing videos.","integrationClosure":"Upstream: stage5_compose.txt prompt (S02), SynthesisResult/BodySection schemas (S01), TechniquePageVideo model + body_sections_format column (S03). New wiring: compose-or-create branch in stage5_synthesis, TechniquePageVideo population. Remaining: S05 frontend, S06 admin, S07 search.","milestoneId":"M014","observabilityImpact":"INFO log on compose path trigger with page slug + moment counts. WARNING log when multiple pages match creator+category. TechniquePageVideo rows and body_sections_format column inspectable via DB queries.","proofLevel":"integration","sliceId":"S04","successCriteria":"**Goal:** Stage 5 detects existing technique pages by creator+category and uses the compose prompt to merge new video content into them. All pages get body_sections_format='v2' and technique_page_videos rows tracking contributing videos.\n\n**Demo:** Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs.\n\n## Must-Haves\n\n- Compose-or-create decision: before LLM call, query DB for existing TechniquePage by creator_id + LOWER(topic_category)\n- If existing page found with moments → call compose prompt (stage5_compose.txt) with existing page JSON, existing moments, new offset-indexed moments\n- If no existing page → existing synthesis path (unchanged)\n- `_build_compose_user_prompt()` helper using real ORM KeyMoment objects (not MockKeyMoment)\n- `_compose_into_existing()` helper calling LLM with compose system prompt, returning SynthesisResult\n- All created/updated pages set `body_sections_format = 'v2'`\n- After persisting each page, INSERT into technique_page_videos with on_conflict_do_nothing\n- TechniquePageVideo import added to stages.py\n- Unit tests for compose prompt construction, branching logic, TechniquePageVideo insertion\n\n## Threat Surface\n\n- **Abuse**: N/A — pipeline is backend-only, triggered by admin. No user-facing input path.\n- **Data exposure**: None — LLM prompts contain only extracted moments (no PII/secrets).\n- **Input trust**: LLM output is already validated via _safe_parse_llm_response + SynthesisResult schema.\n\n## Requirement Impact\n\n- **Requirements touched**: R012 (Incremental Content Addition) — this slice delivers the compose mechanism for updating existing pages with new video content\n- **Re-verify**: Existing single-video synthesis still produces correct output (no regression)\n- **Decisions revisited**: D024 (body_sections format) — now enforced at pipeline level\n\n## Proof Level\n\n- This slice proves: integration (compose path wired end-to-end in pipeline stage)\n- Real runtime required: yes (LLM + DB on ub01 for demo verification)\n- Human/UAT required: no (automated tests + manual ub01 demo)\n\n## Verification\n\n- `cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_compose_pipeline.py -v` — all tests pass\n- Compose prompt builder produces correct XML structure with offset indices\n- Compose-or-create branch triggered correctly based on DB query result\n- body_sections_format set to 'v2' on both new and updated pages\n- TechniquePageVideo rows created with on_conflict_do_nothing\n\n## Observability / Diagnostics\n\n- Runtime signals: INFO log when compose path triggered (existing page slug + moment counts), WARNING log when >1 page found for creator+category\n- Inspection surfaces: technique_page_videos table rows, body_sections_format column value\n- Failure visibility: compose LLM errors surfaced through existing _safe_parse_llm_response retry + logging\n- Redaction constraints: none\n\n## Integration Closure\n\n- Upstream surfaces consumed: `prompts/stage5_compose.txt` (S02), `backend/pipeline/schemas.py` SynthesisResult/BodySection (S01), `backend/models.py` TechniquePageVideo/body_sections_format (S03)\n- New wiring introduced: compose-or-create branch in stage5_synthesis per-category loop, TechniquePageVideo INSERT after page persist\n- What remains before milestone is truly usable end-to-end: S05 (frontend rendering), S06 (admin UI), S07 (search)","tasks":[{"description":"Add two helper functions to stages.py and modify the stage5_synthesis per-category loop to detect existing pages and branch to the compose path.\n\n## Steps\n\n1. Add `TechniquePageVideo` to the imports from `models` at line ~27.\n\n2. Add `_build_compose_user_prompt(existing_page, existing_moments, new_moments, creator_name)` helper function:\n   - Takes an existing `TechniquePage` ORM object, a list of `KeyMoment` ORM objects (existing), a list of `(KeyMoment, dict)` tuples (new moments with classification), and creator name string\n   - Serialize existing page to dict matching SynthesizedPage shape: title, slug, topic_category, summary, body_sections, signal_chains, plugins, source_quality\n   - Format existing moments as `[0]-[N-1]` using `_build_moments_text()` pattern but from plain KeyMoment objects (not tuples with cls_info — existing moments don't have classification data, use empty dict)\n   - Format new moments as `[N]-[N+M-1]` using `_build_moments_text()` with offset indices applied\n   - Build XML-tagged user prompt: `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>` tags (same structure as test_harness.py's `build_compose_prompt()`)\n   - Return the user prompt string\n\n3. Add `_compose_into_existing(existing_page, existing_moments, new_moment_group, category, creator_name, system_prompt, llm, model_override, modality, hard_limit, video_id, run_id)` helper function:\n   - Load compose system prompt via `_load_prompt('stage5_compose.txt', video_id=video_id)`\n   - Call `_build_compose_user_prompt()` to build user prompt\n   - Estimate tokens via `estimate_max_tokens()`\n   - Call `llm.complete()` with compose system prompt, response_model=SynthesisResult, same callback/param pattern as `_synthesize_chunk()`\n   - Parse via `_safe_parse_llm_response()` and return SynthesisResult\n\n4. Modify the per-category loop in `stage5_synthesis()` (around line 1200):\n   - **Before** the existing chunked synthesis block, add compose detection:\n     ```\n     existing_page = session.execute(\n         select(TechniquePage).where(\n             TechniquePage.creator_id == video.creator_id,\n             func.lower(TechniquePage.topic_category) == func.lower(category),\n         )\n     ).scalars().first()\n     ```\n   - If `existing_page` is found, load its linked moments:\n     ```\n     existing_moments = session.execute(\n         select(KeyMoment)\n         .where(KeyMoment.technique_page_id == existing_page.id)\n         .order_by(KeyMoment.start_time)\n     ).scalars().all()\n     ```\n   - If existing_page AND existing_moments → compose path: call `_compose_into_existing()`, use result.pages as synthesized_pages\n   - Log INFO: 'Stage 5: Composing into existing page \\'%s\\' (%d existing moments + %d new moments)'\n   - If >1 page matches, log WARNING about multiple matches and proceed with first\n   - If no existing_page → fall through to existing synthesis block (unchanged)\n   - Wrap in `else` so existing chunked synthesis only runs when not composing\n\n5. In the persist block (around line 1380), after the `if existing:` / `else:` branch that creates/updates the page:\n   - Set `page.body_sections_format = 'v2'` on every page (both new and updated)\n   - Add TechniquePageVideo INSERT:\n     ```python\n     from sqlalchemy.dialects.postgresql import insert as pg_insert\n     stmt = pg_insert(TechniquePageVideo.__table__).values(\n         technique_page_id=page.id,\n         source_video_id=video.id,\n     ).on_conflict_do_nothing()\n     session.execute(stmt)\n     ```\n\n## Must-Haves\n\n- [ ] `_build_compose_user_prompt()` produces XML-tagged prompt with correct offset indices\n- [ ] `_compose_into_existing()` calls LLM with compose system prompt and returns SynthesisResult\n- [ ] Compose-or-create decision queries DB by creator_id + LOWER(topic_category)\n- [ ] Existing synthesis path unchanged when no existing page found\n- [ ] body_sections_format = 'v2' set on all pages\n- [ ] TechniquePageVideo row inserted for every page+video combination\n- [ ] Case-insensitive category matching (func.lower)\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM (compose) | _safe_parse_llm_response retries once, then raises (existing retry mechanism) | Celery task retry (max_retries=3) | SynthesisResult validation rejects, retry with feedback |\n| DB (existing page query) | Exception propagates to stage-level handler, triggers retry | Same | N/A |\n\n## Negative Tests\n\n- No existing page for creator+category → falls through to standard synthesis (no compose)\n- Existing page found but zero linked moments → should still compose (empty existing_moments list)\n- Multiple pages match creator+category → uses first, logs warning","estimate":"1.5h","expectedOutput":["`backend/pipeline/stages.py` — modified with _build_compose_user_prompt(), _compose_into_existing(), compose-or-create branch, TechniquePageVideo INSERT, body_sections_format='v2'"],"files":["backend/pipeline/stages.py"],"inputs":["`backend/pipeline/stages.py` — existing stage5_synthesis function to modify","`backend/pipeline/test_harness.py` — reference implementation of build_compose_prompt() for prompt structure","`backend/models.py` — TechniquePageVideo model, body_sections_format column","`prompts/stage5_compose.txt` — compose system prompt loaded by _compose_into_existing()","`backend/pipeline/schemas.py` — SynthesisResult schema for LLM response parsing"],"taskId":"T01","title":"Add compose helpers and wire compose-or-create logic into stage5_synthesis","verify":"cd /home/aux/projects/content-to-kb-automator && python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\" && grep -q 'body_sections_format' backend/pipeline/stages.py && grep -q 'TechniquePageVideo' backend/pipeline/stages.py && grep -q 'stage5_compose' backend/pipeline/stages.py"},{"description":"Create test_compose_pipeline.py covering compose prompt construction, compose-or-create branching, TechniquePageVideo insertion, and body_sections_format setting.\n\n## Steps\n\n1. Create `backend/pipeline/test_compose_pipeline.py`.\n\n2. Write test fixtures:\n   - Mock KeyMoment objects (using simple namedtuples or dataclasses with .title, .summary, .content_type, .start_time, .end_time, .plugins, .raw_transcript, .id, .technique_page_id, .source_video_id)\n   - Mock TechniquePage object with .id, .title, .slug, .topic_category, .summary, .body_sections, .signal_chains, .plugins, .source_quality, .creator_id\n   - Use `unittest.mock` for DB session and LLM client\n\n3. Test `_build_compose_user_prompt()`:\n   - **test_compose_prompt_xml_structure**: verify output contains `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>` tags\n   - **test_compose_prompt_offset_indices**: with 3 existing moments and 2 new moments, verify existing use [0]-[2] and new use [3]-[4]\n   - **test_compose_prompt_empty_existing_moments**: 0 existing, N new → new moments start at [0]\n   - **test_compose_prompt_page_json**: verify existing page serialized as JSON within `<existing_page>` tags\n\n4. Test compose-or-create branching:\n   - **test_compose_branch_triggered**: mock session.execute to return an existing page + moments for the same creator+category → verify `_compose_into_existing` is called (patch it)\n   - **test_create_branch_no_existing**: mock session.execute to return None for existing page query → verify `_synthesize_chunk` is called instead\n   - **test_category_case_insensitive**: verify query uses func.lower for category matching (inspect the query or test with mixed-case input)\n\n5. Test TechniquePageVideo and body_sections_format:\n   - **test_body_sections_format_v2**: verify pages created by both compose and create paths have body_sections_format='v2'\n   - **test_technique_page_video_inserted**: verify INSERT with on_conflict_do_nothing is executed after page persist\n\n## Must-Haves\n\n- [ ] At least 4 tests for _build_compose_user_prompt covering XML structure, offset math, empty existing, page JSON\n- [ ] At least 2 tests for branching logic (compose triggered vs create fallback)\n- [ ] At least 1 test for body_sections_format = 'v2'\n- [ ] At least 1 test for TechniquePageVideo insertion\n- [ ] All tests pass with `python -m pytest backend/pipeline/test_compose_pipeline.py -v`","estimate":"1h","expectedOutput":["`backend/pipeline/test_compose_pipeline.py` — unit tests for compose prompt construction, branching, TechniquePageVideo, body_sections_format"],"files":["backend/pipeline/test_compose_pipeline.py"],"inputs":["`backend/pipeline/stages.py` — modified by T01 with compose helpers and wiring","`backend/pipeline/test_harness_compose.py` — reference for compose prompt test patterns","`backend/models.py` — TechniquePageVideo model shape for mock construction"],"taskId":"T02","title":"Write unit tests for compose pipeline logic","verify":"cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_compose_pipeline.py -v"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":4046,"cacheRead":82701,"cacheWrite":1018,"totalTokens":87766,"cost":{"input":0.000005,"output":0.10115,"cacheRead":0.0413505,"cacheWrite":0.0063625,"total":0.148868}},"stopReason":"toolUse","timestamp":1775179488482}}
{"type":"message","id":"345bcd52","parentId":"ce9e4fc3","timestamp":"2026-04-03T01:26:02.110Z","message":{"role":"toolResult","toolCallId":"toolu_01WUPHZbGGdSFMojG2pSGyep","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S04 (M014)"}],"details":{"operation":"plan_slice","milestoneId":"M014","sliceId":"S04","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/S04-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1775179562108}}
{"type":"message","id":"5dd9ac78","parentId":"345bcd52","timestamp":"2026-04-03T01:26:10.571Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n- **Completion semantics**: T01 implements all production logic (compose helpers, branch, TechniquePageVideo, body_sections_format). T02 tests it. Together they satisfy the goal and demo.\n- **Requirement coverage**: R012 (incremental content addition) is advanced by T01's compose-or-create branch. All must-haves map to tasks.\n- **Task completeness**: Both tasks have steps, must-haves, verification, concrete input/output file paths.\n- **Dependency correctness**: T02 depends on T01's output — sequential order is correct.\n- **Scope sanity**: T01 has 5 steps touching 1 file. T02 has 5 steps touching 1 file. Both well within limits.\n- **Feature completeness**: T01 delivers the pipeline change, T02 proves it works.\n- **Quality gates**: Threat surface, requirement impact, failure modes, and negative tests all filled.\n\nSlice S04 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":235,"cacheRead":83719,"cacheWrite":4064,"totalTokens":88019,"cost":{"input":0.000005,"output":0.005875,"cacheRead":0.0418595,"cacheWrite":0.025400000000000002,"total":0.07313950000000001}},"stopReason":"stop","timestamp":1775179562109}}
