{"type":"thinking_level_change","id":"1de879f1","parentId":null,"timestamp":"2026-04-03T00:58:38.892Z","thinkingLevel":"medium"}
{"type":"model_change","id":"50b56ffb","parentId":"1de879f1","timestamp":"2026-04-03T00:58:38.894Z","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/S02/S02-PLAN.md` and `.gsd/milestones/M014/slices/S02/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 S02 planned.\"\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 S02 (\"Composition Prompt + Test Harness Compose Mode\") — Milestone M014\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M014/M014-ROADMAP.md`\n\n# M014: \n\n## Vision\nRestructure technique pages to be broader (per-creator+category across videos), deeper (H2/H3 nesting with TOC), multi-source (compose new video content into existing pages), with inline citations linking prose claims to source moments, admin multi-source management, and section-level search with deep linking.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Synthesis Prompt v5 — Nested Sections + Citations | high | — | ✅ | Run test harness with new prompt → output has list-of-objects body_sections with H2/H3 nesting, citation markers on key claims, broader page scope. |\n| S02 | Composition Prompt + Test Harness Compose Mode | high | S01 | ⬜ | Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations. |\n| S03 | Data Model + Migration | low | — | ⬜ | Alembic migration runs clean. API response includes body_sections_format and source_videos fields. |\n| S04 | Pipeline Compose-or-Create Logic | high | S01, S02, S03 | ⬜ | Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs. |\n| S05 | Frontend — Nested Rendering, TOC, Citations | medium | S03 | ⬜ | Format-2 page renders with TOC, nested sections, clickable citations. Format-1 pages unchanged. |\n| S06 | Admin UI — Multi-Source Pipeline Management | medium | S03, S04 | ⬜ | Admin view for multi-source page shows source dropdown, composition history, per-video chunking inspection. |\n| S07 | Search — Per-Section Embeddings + Deep Linking | medium | S04, S05 | ⬜ | Search 'LFO grain position' → section-level result → click → navigates to page#section and scrolls. |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M014/slices/S02/S02-RESEARCH.md`\n\n# S02 Research: Composition Prompt + Test Harness Compose Mode\n\n## Summary\n\nThis slice adds the ability to compose new video content into existing technique pages — the core multi-source capability. Two deliverables: (1) a `stage5_compose.txt` prompt that instructs the LLM to merge new moments into an existing page, and (2) a `--compose` mode for the test harness that exercises this prompt offline.\n\nThe work is structurally parallel to S01 (prompt + test harness update + tests) but the LLM task is harder: composition requires deduplication, section merging, citation re-indexing, and preserving the quality of existing prose while integrating new material.\n\n## Recommendation\n\n**Targeted research.** The patterns are established by S01 (Pydantic schemas, test harness structure, citation utils). The new work is the composition prompt design and the harness plumbing to feed existing page + new moments to the LLM.\n\n## Implementation Landscape\n\n### What Exists\n\n| File | Role | Relevance |\n|------|------|-----------|\n| `backend/pipeline/test_harness.py` | Offline synthesis test runner | Needs `compose` subcommand added alongside existing `run` and `promote` |\n| `backend/pipeline/schemas.py` | `SynthesizedPage`, `BodySection`, `SynthesisResult` | Composition output should use the same `SynthesisResult` schema — no new output schema needed |\n| `backend/pipeline/citation_utils.py` | `validate_citations()`, `extract_citations()` | Reused as-is for compose output validation |\n| `backend/pipeline/test_harness_v2_format.py` | v2 format tests | Pattern to follow for compose-mode tests |\n| `prompts/stage5_synthesis.txt` | v5 synthesis prompt (from S01) | Compose prompt references same voice/tone/structure rules but has different instructions |\n| `backend/pipeline/stages.py` `_merge_pages_by_slug()` | Merges chunk-split pages by slug via LLM | Closest existing pattern. Uses `stage5_merge.txt` prompt (which doesn't exist on disk yet — it's referenced but never created). Compose is similar but merges new moments into an existing page rather than merging partial pages from the same video. |\n| `backend/pipeline/llm_client.py` | `LLMClient.complete()`, `estimate_max_tokens()` | Used by test harness already; compose mode uses the same interface |\n| `backend/pipeline/export_fixture.py` | Exports video moments to fixture JSON | May need extension to also export existing page JSON for compose fixtures |\n\n### What Needs Building\n\n#### 1. Composition Prompt (`prompts/stage5_compose.txt`)\n\nThe LLM receives:\n- `<existing_page>`: Full JSON of the current `SynthesizedPage` (body_sections, citations, moment_indices, etc.)\n- `<new_moments>`: The new moments from a second video, same format as synthesis input\n- `<creator>`: Creator name\n\nThe prompt must instruct the LLM to:\n- **Preserve existing prose quality** — don't rewrite sections that are fine\n- **Integrate new material** — add to existing sections where new moments cover the same sub-topic, or create new sections for genuinely new territory\n- **Deduplicate** — if new moments cover ground already in the page, enrich rather than repeat\n- **Re-index citations** — existing citations [0]-[N] refer to the original moment list. New moments get indices [N+1]-[N+M]. The composed output must use a unified index space. The prompt must explain the indexing scheme clearly.\n- **Maintain section ordering** — workflow-order rule from synthesis prompt still applies\n- **Output format** — same `SynthesisResult` / `SynthesizedPage` with v2 body_sections\n\nKey design decision: The compose prompt should receive the existing page's `moment_indices` mapped to their moment data so the LLM understands what's already covered. This means the user prompt includes both old moments (for context) and new moments (to integrate), with clear labeling.\n\n#### 2. Test Harness Compose Mode\n\nAdd a `compose` subcommand to `test_harness.py`:\n\n```\npython -m pipeline.test_harness compose \\\n  --existing-page /tmp/existing_page.json \\\n  --fixture fixtures/new_video.json \\\n  --prompt prompts/stage5_compose.txt \\\n  --output /tmp/composed.json\n```\n\nArguments:\n- `--existing-page`: JSON file containing a single `SynthesizedPage` (output from a prior synthesis run)\n- `--fixture`: New moments fixture (same format as `run` mode)\n- `--category`: Optional filter (only compose for matching category)\n- `--prompt`: Compose prompt file (defaults to `stage5_compose.txt`)\n- `--output`: Output path\n- `--model`, `--modality`: LLM overrides (same as `run`)\n\nThe compose runner needs to:\n1. Load the existing page JSON and validate it as `SynthesizedPage`\n2. Load the new fixture moments (same as `run` mode)\n3. Filter new moments by category to match the existing page's `topic_category`\n4. Build a user prompt with `<existing_page>`, `<new_moments>`, and `<creator>` tags\n5. Call the LLM with the compose system prompt\n6. Parse the response as `SynthesisResult` (the LLM returns an updated version of the page)\n7. Run citation validation on the output, with moment_count = old_moments + new_moments\n8. Log deduplication metrics: how many old sections survived, how many new sections appeared, word count delta\n\n#### 3. Tests (`backend/pipeline/test_harness_compose.py`)\n\nUnit tests for compose-specific logic (no LLM calls):\n- User prompt construction: existing page + new moments formatted correctly\n- Citation re-indexing math: old moments 0-4, new moments 5-9, validate combined\n- Word count comparison: before/after composition\n- Category matching: only compose moments matching the page's topic_category\n- Edge cases: empty new moments, single new moment, existing page with no subsections\n\n### Seam Analysis\n\nThe work divides cleanly into three tasks:\n\n**T01: Composition prompt** — Write `prompts/stage5_compose.txt`. Self-contained file. Can reference the synthesis prompt's voice/tone section but must have its own instructions for merge semantics, deduplication, and citation re-indexing. Verifiable by structural checks (required sections present, example output valid).\n\n**T02: Test harness compose subcommand** — Add `compose` subcommand to `test_harness.py`. Depends on T01 only for the default prompt path. The harness code itself is pure Python with no LLM dependency in tests. Needs a helper to build the compose user prompt (existing page JSON + new moments text).\n\n**T03: Compose-mode tests** — Unit tests for the compose plumbing. Depends on T02 code existing. Tests the prompt construction, citation math, category filtering, and word count diffing. No LLM calls.\n\n### Citation Re-indexing Strategy\n\nThis is the trickiest part. In compose mode:\n- The existing page has citations [0]-[N-1] referring to original moments\n- New moments are numbered [N]-[N+M-1] in the unified space\n- The LLM needs to understand both sets so it can:\n  - Preserve existing [0]-[N-1] citations in untouched prose\n  - Add [N]-[N+M-1] citations in new/updated prose\n  - Potentially add cross-references (new moment enriches an existing section → add [N+k] alongside existing [j])\n\nThe user prompt should present:\n```\n<existing_moments>\n[0] Title: ... (already cited in existing page)\n[1] Title: ...\n...\n[N-1] Title: ...\n</existing_moments>\n\n<new_moments>\n[N] Title: ... (new content to integrate)\n[N+1] Title: ...\n...\n[N+M-1] Title: ...\n</new_moments>\n\n<existing_page>\n{full page JSON}\n</existing_page>\n```\n\nThis way the LLM sees the complete index space and can cite any moment by its global index.\n\n### Deduplication Signal\n\nThe prompt should instruct the LLM to compare new moment summaries against existing section content. If a new moment covers material already present:\n- Don't add a new section\n- Optionally add the new moment's citation to the existing passage if it provides a second source\n- Only add new prose if the new moment contains genuinely new detail (different settings, different context, additional nuance)\n\nThis is a judgment call for the LLM — the prompt needs clear examples of \"enriching\" vs \"duplicating.\"\n\n### Risks\n\n1. **LLM may rewrite too aggressively** — Given the full page, the LLM might want to rewrite everything. The prompt must be explicit: preserve existing prose verbatim where it's correct, only modify sections where new information changes or enriches them.\n2. **Citation index confusion** — The LLM needs to correctly distinguish old indices from new ones. Clear labeling in the user prompt is critical.\n3. **Output size** — Composed pages are larger (more moments, more sections). Token limits may need adjustment. `estimate_max_tokens` should handle this since it's based on input size.\n\n### Don't Hand-Roll\n\n- Citation validation: use existing `validate_citations()` from `citation_utils.py`\n- Word counting: use the same pattern from `test_harness_v2_format.py`\n- LLM interaction: use existing `LLMClient.complete()` and `estimate_max_tokens()`\n- Schema validation: use existing `SynthesisResult.model_validate_json()`\n\n### Verification Strategy\n\n- **T01 (prompt):** Structural assertions — required sections present, example output parses as valid SynthesisResult, citation rules section explains the dual-index scheme\n- **T02 (harness):** `python -m pipeline.test_harness compose --help` exits 0; compose user prompt builder produces expected format with existing page and new moments\n- **T03 (tests):** `python -m pytest backend/pipeline/test_harness_compose.py -v` — all pass\n\n### Notes for Planner\n\n- The `compose` subcommand mirrors the `run` subcommand structure but with different inputs. Keep the pattern consistent.\n- The compose prompt is the highest-risk deliverable — it drives whether the LLM produces good composed output. The test harness is mechanical.\n- S04 (Pipeline Compose-or-Create Logic) will use both the compose prompt and the test harness patterns when wiring into the real pipeline. The harness is the offline proving ground.\n- The existing `_merge_pages_by_slug` in stages.py merges partial pages from chunk splitting. Composition is conceptually different (existing page + new moments) but the LLM call pattern is the same.\n- No new Pydantic schemas needed — compose output is the same `SynthesisResult` with `SynthesizedPage` objects.\n\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## 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/S02/S02-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":"0ad98925","parentId":"50b56ffb","timestamp":"2026-04-03T00:58:38.896Z"}
{"type":"message","id":"38b7235b","parentId":"0ad98925","timestamp":"2026-04-03T00:58:45.168Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the codebase to ground the plan in reality before decomposing.","thinkingSignature":"EogCClkIDBgCKkC7Y4YWOGrBVHKbpo/xSLILUWkWzOX0mpGGsf/c5mUNdf9Iv79mzW1fqCgfDGeuw8N2VrZMEvb0Ml+pPdvtCu8CMg9jbGF1ZGUtb3B1cy00LTY4ABIMxIFW8aYINfx1nyHHGgz4YLYbPHZ/dDCuD4kiME43hhD9LQ/xVZmhmLrgnjM/D6grJHT9JXxpqEFv37aH+6wZ+igJbIGWxzxeCyhJjipdZNpesZL+LBjyVzMTa2s1Ta9r6F8Sa7o8xUXLzNsEAmEfIcC5GaMSiv16+sWkFg6vROVquUm327qoVnEQX+e2fpA2rVuP0rUHfUcuBOQ3+Q4xk6Sch3DFHCXnxps4GAE="},{"type":"text","text":"Let me explore the relevant code to ground the plan."},{"type":"toolCall","id":"toolu_01PGb2qnXT23mitHKKmYqbb9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/test_harness.py | head -80"}},{"type":"toolCall","id":"toolu_0118HuPrgsxXYwuZ5ZLcuRQ1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/pipeline/test_harness.py && grep -n 'def \\|class \\|subcommand\\|argparse\\|add_parser\\|subparsers' backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01WTCMCsZrD4NnMKsKz5XKuk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":290,"cacheRead":59865,"cacheWrite":10397,"totalTokens":70554,"cost":{"input":0.00001,"output":0.00725,"cacheRead":0.029932499999999997,"cacheWrite":0.06498125,"total":0.10217375000000001}},"stopReason":"toolUse","timestamp":1775177918895}}
{"type":"message","id":"9820cfcd","parentId":"38b7235b","timestamp":"2026-04-03T00:58:45.187Z","message":{"role":"toolResult","toolCallId":"toolu_01PGb2qnXT23mitHKKmYqbb9","toolName":"bash","content":[{"type":"text","text":"\"\"\"Offline prompt test harness for Chrysopedia synthesis.\n\nLoads a fixture JSON (exported by export_fixture.py) and a prompt file,\ncalls the LLM, and outputs the synthesized result. No Docker, no database,\nno Redis, no Celery — just prompt + fixture + LLM endpoint.\n\nUsage:\n    python -m pipeline.test_harness \\\\\n      --fixture fixtures/real_video_xyz.json \\\\\n      --prompt prompts/stage5_synthesis.txt \\\\\n      --output /tmp/result.json\n\n    # Run all categories in a fixture:\n    python -m pipeline.test_harness --fixture fixtures/video.json\n\n    # Run a specific category only:\n    python -m pipeline.test_harness --fixture fixtures/video.json --category \"Sound Design\"\n\nExit codes: 0=success, 1=LLM error, 2=parse error, 3=fixture error\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter, defaultdict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import NamedTuple\n\nfrom pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesisResult\n\n\n# ── Lightweight stand-in for KeyMoment ORM model ───────────────────────────\n\nclass _MockContentType:\n    \"\"\"Mimics KeyMomentContentType enum with a .value property.\"\"\"\n    def __init__(self, value: str) -> None:\n        self.value = value\n\n\nclass MockKeyMoment(NamedTuple):\n    \"\"\"Lightweight stand-in for the ORM KeyMoment.\n\n    Has the same attributes that _build_moments_text() accesses:\n    title, summary, content_type, start_time, end_time, plugins, raw_transcript.\n    \"\"\"\n    title: str\n    summary: str\n    content_type: object  # _MockContentType\n    start_time: float\n    end_time: float\n    plugins: list[str]\n    raw_transcript: str\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[HARNESS] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\n# ── Moment text builder (mirrors stages.py _build_moments_text) ────────────\n\ndef build_moments_text(\n    moment_group: list[tuple[MockKeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text — matches _build_moments_text in stages.py.\"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n"}],"isError":false,"timestamp":1775177925187}}
{"type":"message","id":"eec87d54","parentId":"9820cfcd","timestamp":"2026-04-03T00:58:45.189Z","message":{"role":"toolResult","toolCallId":"toolu_0118HuPrgsxXYwuZ5ZLcuRQ1","toolName":"bash","content":[{"type":"text","text":"502 backend/pipeline/test_harness.py\n24:import argparse\n43:class _MockContentType:\n45:    def __init__(self, value: str) -> None:\n49:class MockKeyMoment(NamedTuple):\n64:def _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n71:def build_moments_text(\n97:class FixtureData:\n108:def load_fixture(path: str) -> FixtureData:\n168:def run_synthesis(\n331:def promote_prompt(prompt_path: str, stage: int, reason: str, commit: bool = False) -> int:\n406:def main() -> int:\n407:    parser = argparse.ArgumentParser(\n411:    sub = parser.add_subparsers(dest=\"command\")\n413:    # -- run subcommand (default behavior) --\n414:    run_parser = sub.add_parser(\"run\", help=\"Run synthesis against a fixture\")\n422:    # -- promote subcommand --\n423:    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n431:    # If no subcommand, check for --fixture for backward compat\n433:        # Support running without subcommand for backward compat\n"}],"isError":false,"timestamp":1775177925189}}
{"type":"message","id":"718d726b","parentId":"eec87d54","timestamp":"2026-04-03T00:58:45.191Z","message":{"role":"toolResult","toolCallId":"toolu_01WTCMCsZrD4NnMKsKz5XKuk","toolName":"bash","content":[{"type":"text","text":"README.md\nstage2_segmentation.txt\nstage3_extraction.txt\nstage4_classification.txt\nstage5_synthesis.20260401_101518.bak\nstage5_synthesis.20260403_005044.bak\nstage5_synthesis.txt\nstage5_variants\n"}],"isError":false,"timestamp":1775177925189}}
{"type":"message","id":"a836b955","parentId":"718d726b","timestamp":"2026-04-03T00:58:49.778Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DD8RXj6GRN1LKTjxYmg73U","name":"read","arguments":{"limit":170,"offset":165,"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01DywjNVtvEAkMa9oxojEtSW","name":"read","arguments":{"offset":400,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":154,"cacheRead":70262,"cacheWrite":1467,"totalTokens":71884,"cost":{"input":0.000005,"output":0.00385,"cacheRead":0.035130999999999996,"cacheWrite":0.00916875,"total":0.048154749999999996}},"stopReason":"toolUse","timestamp":1775177925190}}
{"type":"message","id":"9b524ca2","parentId":"a836b955","timestamp":"2026-04-03T00:58:49.782Z","message":{"role":"toolResult","toolCallId":"toolu_01DD8RXj6GRN1LKTjxYmg73U","toolName":"read","content":[{"type":"text","text":"\n# ── Synthesis runner ───────────────────────────────────────────────────────\n\ndef run_synthesis(\n    fixture: FixtureData,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run synthesis on fixture data, returns (pages, exit_code).\n\n    Returns all synthesized pages as dicts plus an exit code.\n    \"\"\"\n    # Load prompt\n    prompt_file = Path(prompt_path)\n    if not prompt_file.exists():\n        _log(\"ERROR\", f\"Prompt file not found: {prompt_path}\", level=\"ERROR\")\n        return [], 3\n\n    system_prompt = prompt_file.read_text(encoding=\"utf-8\")\n    _log(\"PROMPT\", f\"Loading: {prompt_path} ({len(system_prompt)} chars)\")\n\n    # Setup LLM\n    settings = get_settings()\n    llm = LLMClient(settings)\n\n    stage_model = model_override or settings.llm_stage5_model or settings.llm_model\n    stage_modality = modality or settings.llm_stage5_modality or \"thinking\"\n    hard_limit = settings.llm_max_tokens_hard_limit\n\n    _log(\"LLM\", f\"Model: {stage_model}, modality: {stage_modality}, hard_limit: {hard_limit}\")\n\n    # Filter categories if requested\n    categories = fixture.groups\n    if category_filter:\n        if category_filter not in categories:\n            _log(\"ERROR\", f\"Category '{category_filter}' not found. Available: {list(categories.keys())}\", level=\"ERROR\")\n            return [], 3\n        categories = {category_filter: categories[category_filter]}\n\n    all_pages: list[dict] = []\n    total_prompt_tokens = 0\n    total_completion_tokens = 0\n    total_duration_ms = 0\n    exit_code = 0\n\n    for cat_idx, (category, moment_group) in enumerate(categories.items(), 1):\n        _log(\"SYNTH\", f\"Category {cat_idx}/{len(categories)}: '{category}' ({len(moment_group)} moments)\")\n\n        # Build user prompt (same format as stages.py _synthesize_chunk)\n        moments_text, all_tags = build_moments_text(moment_group, category)\n        user_prompt = f\"<creator>{fixture.creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n        estimated_tokens = estimate_max_tokens(\n            system_prompt, user_prompt,\n            stage=\"stage5_synthesis\",\n            hard_limit=hard_limit,\n        )\n        _log(\n            \"SYNTH\",\n            f\"  Building prompt: {len(moment_group)} moments, \"\n            f\"max_tokens={estimated_tokens}, tags={sorted(all_tags)[:5]}{'...' if len(all_tags) > 5 else ''}\",\n        )\n\n        # Call LLM\n        call_start = time.monotonic()\n        _log(\"LLM\", f\"  Calling: model={stage_model}, max_tokens={estimated_tokens}, modality={stage_modality}\")\n\n        try:\n            raw = llm.complete(\n                system_prompt,\n                user_prompt,\n                response_model=SynthesisResult,\n                modality=stage_modality,\n                model_override=stage_model,\n                max_tokens=estimated_tokens,\n            )\n        except Exception as exc:\n            _log(\"ERROR\", f\"  LLM call failed: {exc}\", level=\"ERROR\")\n            exit_code = 1\n            continue\n\n        call_duration_ms = int((time.monotonic() - call_start) * 1000)\n        prompt_tokens = getattr(raw, \"prompt_tokens\", None) or 0\n        completion_tokens = getattr(raw, \"completion_tokens\", None) or 0\n        finish_reason = getattr(raw, \"finish_reason\", \"unknown\")\n\n        total_prompt_tokens += prompt_tokens\n        total_completion_tokens += completion_tokens\n        total_duration_ms += call_duration_ms\n\n        _log(\n            \"LLM\",\n            f\"  Response: {prompt_tokens} prompt + {completion_tokens} completion tokens, \"\n            f\"{call_duration_ms}ms, finish_reason={finish_reason}\",\n        )\n\n        if finish_reason == \"length\":\n            _log(\n                \"WARN\",\n                \"  finish_reason=length — output likely truncated! \"\n                \"Consider reducing fixture size or increasing max_tokens.\",\n                level=\"WARN\",\n            )\n\n        # Parse response\n        try:\n            result = SynthesisResult.model_validate_json(str(raw))\n        except (ValidationError, json.JSONDecodeError) as exc:\n            _log(\"ERROR\", f\"  Parse failed: {exc}\", level=\"ERROR\")\n            _log(\"ERROR\", f\"  Raw response (first 2000 chars): {str(raw)[:2000]}\", level=\"ERROR\")\n            exit_code = 2\n            continue\n\n        # Log per-page summary\n        _log(\"SYNTH\", f\"  Parsed: {len(result.pages)} pages synthesized\")\n        total_words = 0\n        for page in result.pages:\n            sections = page.body_sections or []\n            section_count = len(sections)\n            subsection_count = sum(len(s.subsections) for s in sections)\n            word_count = sum(\n                len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n                for s in sections\n            )\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{section_count} sections ({subsection_count} subsections), \"\n                f\"{word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            # Citation coverage reporting\n            cit = validate_citations(sections, len(page.moment_indices))\n            _log(\n                \"CITE\",\n                f\"    Citations: {cit['total_citations']}/{len(page.moment_indices)} moments cited \"\n                f\"({cit['coverage_pct']}% coverage)\"\n                + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n                + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n            )\n\n            all_pages.append(page.model_dump())\n\n    # Summary\n    _log(\"SUMMARY\", f\"Total: {len(all_pages)} pages across {len(categories)} categories\")\n    _log(\"SUMMARY\", f\"Tokens: {total_prompt_tokens} prompt + {total_completion_tokens} completion = {total_prompt_tokens + total_completion_tokens} total\")\n    _log(\"SUMMARY\", f\"Duration: {total_duration_ms}ms ({total_duration_ms / 1000:.1f}s)\")\n\n    return all_pages, exit_code\n\n\n# ── Promote: deploy a prompt to production ─────────────────────────────────\n\n_STAGE_PROMPT_MAP = {\n    2: \"stage2_segmentation.txt\",\n    3: \"stage3_extraction.txt\",\n    4: \"stage4_classification.txt\",\n    5: \"stage5_synthesis.txt\",\n}\n\n\ndef promote_prompt(prompt_path: str, stage: int, reason: str, commit: bool = False) -> int:\n    \"\"\"Copy a winning prompt to the canonical path and create a backup.\n\n    The worker reads prompts from disk at runtime — no restart needed.\n\n[169 more lines in file. Use offset=335 to continue.]"}],"isError":false,"timestamp":1775177929781}}
{"type":"message","id":"8a73574f","parentId":"9b524ca2","timestamp":"2026-04-03T00:58:49.786Z","message":{"role":"toolResult","toolCallId":"toolu_01DywjNVtvEAkMa9oxojEtSW","toolName":"read","content":[{"type":"text","text":"\n    return 0\n\n\n# ── CLI ────────────────────────────────────────────────────────────────────\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.test_harness\",\n        description=\"Offline prompt test harness for Chrysopedia synthesis\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- run subcommand (default behavior) --\n    run_parser = sub.add_parser(\"run\", help=\"Run synthesis against a fixture\")\n    run_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"Fixture JSON file\")\n    run_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Prompt file (default: stage5_synthesis.txt)\")\n    run_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n    run_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n    run_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n    run_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n\n    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n\n    args = parser.parse_args()\n\n    # If no subcommand, check for --fixture for backward compat\n    if args.command is None:\n        # Support running without subcommand for backward compat\n        parser.print_help()\n        return 1\n\n    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n\n    # -- run command --\n    prompt_path = args.prompt\n    if prompt_path is None:\n        settings = get_settings()\n        prompt_path = str(Path(settings.prompts_path) / \"stage5_synthesis.txt\")\n\n    overall_start = time.monotonic()\n    try:\n        fixture = load_fixture(args.fixture)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"Fixture error: {exc}\", level=\"ERROR\")\n        return 3\n\n    pages, exit_code = run_synthesis(\n        fixture=fixture,\n        prompt_path=prompt_path,\n        category_filter=args.category,\n        model_override=args.model,\n        modality=args.modality,\n    )\n\n    if not pages and exit_code != 0:\n        return exit_code\n\n    output = {\n        \"fixture_source\": args.fixture,\n        \"prompt_source\": prompt_path,\n        \"creator_name\": fixture.creator_name,\n        \"video_id\": fixture.video_id,\n        \"category_filter\": args.category,\n        \"pages\": pages,\n        \"metadata\": {\n            \"page_count\": len(pages),\n            \"total_words\": sum(\n                sum(\n                    len(s.get(\"content\", \"\").split())\n                    + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n                    for s in p.get(\"body_sections\", [])\n                )\n                for p in pages\n            ),\n            \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n        },\n    }\n\n    output_json = json.dumps(output, indent=2, ensure_ascii=False)\n\n    if args.output:\n        Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n        Path(args.output).write_text(output_json, encoding=\"utf-8\")\n        _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n    else:\n        print(output_json)\n        _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n\n    total_elapsed = time.monotonic() - overall_start\n    _log(\"DONE\", f\"Completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n\n    return exit_code\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775177929784}}
{"type":"message","id":"89d99ca4","parentId":"8a73574f","timestamp":"2026-04-03T00:58:56.147Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011YAupBvfeEBESqFtL9iJjp","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01BxwTZm35zdwocjhkGV2FNL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -60 backend/pipeline/test_harness_v2_format.py"}},{"type":"toolCall","id":"toolu_01MKAkEHZZbHuUTqL8F7maNJ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/export_fixture.py 2>/dev/null && head -40 backend/pipeline/export_fixture.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":208,"cacheRead":71729,"cacheWrite":3371,"totalTokens":75309,"cost":{"input":0.000005,"output":0.005200000000000001,"cacheRead":0.0358645,"cacheWrite":0.02106875,"total":0.062138250000000006}},"stopReason":"toolUse","timestamp":1775177929784}}
{"type":"message","id":"98826f97","parentId":"89d99ca4","timestamp":"2026-04-03T00:58:56.171Z","message":{"role":"toolResult","toolCallId":"toolu_011YAupBvfeEBESqFtL9iJjp","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass BodySubSection(BaseModel):\n    \"\"\"An H3-level subsection within a body section.\"\"\"\n\n    heading: str = Field(description=\"H3 subsection heading\")\n    content: str = Field(description=\"Subsection body text (may contain [N] citation markers)\")\n\n\nclass BodySection(BaseModel):\n    \"\"\"An H2-level section of a technique page body.\"\"\"\n\n    heading: str = Field(description=\"H2 section heading\")\n    content: str = Field(description=\"Section body text (may contain [N] citation markers)\")\n    subsections: list[BodySubSection] = Field(\n        default_factory=list,\n        description=\"Optional H3-level subsections\",\n    )\n\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: list[BodySection] = Field(\n        default_factory=list,\n        description=\"Structured body content as H2 sections with optional H3 subsections\",\n    )\n    body_sections_format: str = Field(\n        default=\"v2\",\n        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775177936171}}
{"type":"message","id":"e9984650","parentId":"98826f97","timestamp":"2026-04-03T00:58:56.172Z","message":{"role":"toolResult","toolCallId":"toolu_01BxwTZm35zdwocjhkGV2FNL","toolName":"bash","content":[{"type":"text","text":"\"\"\"Tests for test_harness compatibility with v2 body_sections format.\n\nValidates that word-counting and citation integration work correctly\nwith the list[BodySection] structure (v2) instead of the old dict format.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport pytest\n\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.schemas import BodySection, BodySubSection, SynthesizedPage, SynthesisResult\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n\ndef _make_page(\n    body_sections: list[BodySection],\n    moment_indices: list[int] | None = None,\n    title: str = \"Test Page\",\n    slug: str = \"test-page\",\n) -> SynthesizedPage:\n    return SynthesizedPage(\n        title=title,\n        slug=slug,\n        topic_category=\"Testing\",\n        summary=\"A test page.\",\n        body_sections=body_sections,\n        moment_indices=moment_indices or [],\n    )\n\n\ndef _count_words_v2(sections: list[BodySection]) -> int:\n    \"\"\"Replicate the word-counting logic from the updated test_harness.\"\"\"\n    return sum(\n        len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n        for s in sections\n    )\n\n\ndef _count_words_metadata(pages_dicts: list[dict]) -> int:\n    \"\"\"Replicate the metadata total_words logic (operates on dicts after model_dump).\"\"\"\n    return sum(\n        sum(\n            len(s.get(\"content\", \"\").split())\n            + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n            for s in p.get(\"body_sections\", [])\n        )\n        for p in pages_dicts\n    )\n\n\n# ── Word counting tests ─────────────────────────────────────────────────────\n\n\nclass TestWordCounting:\n    def test_flat_sections_no_subsections(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"one two three\"),\n"}],"isError":false,"timestamp":1775177936171}}
{"type":"message","id":"0f610e20","parentId":"e9984650","timestamp":"2026-04-03T00:58:56.177Z","message":{"role":"toolResult","toolCallId":"toolu_01MKAkEHZZbHuUTqL8F7maNJ","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/export_fixture.py\n\"\"\"Export pipeline stage inputs for a video as a reusable JSON fixture.\n\nConnects to the live database, queries KeyMoments and classification data,\nand writes a fixture file that the test harness can consume offline.\n\nUsage:\n    python -m pipeline.export_fixture --video-id <uuid> --output fixtures/video.json\n    python -m pipeline.export_fixture --video-id <uuid>  # prints to stdout\n    python -m pipeline.export_fixture --list              # list available videos\n\nRequires: DATABASE_URL, REDIS_URL environment variables (or .env file).\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter\nfrom pathlib import Path\n\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[EXPORT] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\ndef _get_sync_session(database_url: str) -> Session:\n    \"\"\"Create a sync SQLAlchemy session from the database URL.\"\"\"\n    url = database_url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n    engine = create_engine(url, pool_pre_ping=True)\n    factory = sessionmaker(bind=engine)\n    return factory()\n\n\ndef _list_videos(database_url: str) -> int:\n"}],"isError":false,"timestamp":1775177936172}}
{"type":"message","id":"a8660175","parentId":"0f610e20","timestamp":"2026-04-03T00:59:01.172Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013vajahx6hdyPHxAD8ZZvAe","name":"read","arguments":{"path":"backend/pipeline/citation_utils.py"}},{"type":"toolCall","id":"toolu_019JVdVDChQkq9Ys9jQUYvnJ","name":"read","arguments":{"limit":72,"offset":96,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":75100,"cacheWrite":2427,"totalTokens":77664,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.03755,"cacheWrite":0.01516875,"total":0.05612375}},"stopReason":"toolUse","timestamp":1775177936173}}
{"type":"message","id":"0b21ee8a","parentId":"a8660175","timestamp":"2026-04-03T00:59:01.175Z","message":{"role":"toolResult","toolCallId":"toolu_013vajahx6hdyPHxAD8ZZvAe","toolName":"read","content":[{"type":"text","text":"\"\"\"Citation extraction and validation utilities for synthesized technique pages.\n\nUsed by stage 5 synthesis and the test harness to verify that [N] citation\nmarkers in body sections reference valid source moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport re\n\nfrom pipeline.schemas import BodySection\n\n# Matches [N] or [N,M] or [N,M,P] style citation markers where N,M,P are integers.\n_CITATION_RE = re.compile(r\"\\[(\\d+(?:,\\s*\\d+)*)\\]\")\n\n\ndef extract_citations(text: str) -> list[int]:\n    \"\"\"Extract all citation indices from ``[N]`` and ``[N,M,...]`` markers in *text*.\n\n    Returns a sorted list of unique integer indices.\n    \"\"\"\n    indices: set[int] = set()\n    for match in _CITATION_RE.finditer(text):\n        for part in match.group(1).split(\",\"):\n            indices.add(int(part.strip()))\n    return sorted(indices)\n\n\ndef validate_citations(\n    sections: list[BodySection],\n    moment_count: int,\n) -> dict:\n    \"\"\"Validate citation markers across all *sections* against *moment_count* source moments.\n\n    Moments are expected to be referenced as 0-based indices ``[0]`` through\n    ``[moment_count - 1]``.\n\n    Returns a dict with:\n        valid (bool): True when every cited index is in range and every moment is cited.\n        total_citations (int): Count of unique cited indices.\n        invalid_indices (list[int]): Cited indices that are out of range.\n        uncited_moments (list[int]): In-range moment indices that are never cited.\n        coverage_pct (float): Percentage of moments that are cited (0.0–100.0).\n    \"\"\"\n    all_indices: set[int] = set()\n\n    for section in sections:\n        all_indices.update(extract_citations(section.content))\n        for sub in section.subsections:\n            all_indices.update(extract_citations(sub.content))\n\n    valid_range = set(range(moment_count))\n    invalid_indices = sorted(all_indices - valid_range)\n    cited_in_range = all_indices & valid_range\n    uncited_moments = sorted(valid_range - cited_in_range)\n    coverage_pct = (len(cited_in_range) / moment_count * 100.0) if moment_count > 0 else 0.0\n\n    return {\n        \"valid\": len(invalid_indices) == 0 and len(uncited_moments) == 0,\n        \"total_citations\": len(cited_in_range),\n        \"invalid_indices\": invalid_indices,\n        \"uncited_moments\": uncited_moments,\n        \"coverage_pct\": round(coverage_pct, 1),\n    }\n"}],"isError":false,"timestamp":1775177941175}}
{"type":"message","id":"acdbcc92","parentId":"0b21ee8a","timestamp":"2026-04-03T00:59:01.180Z","message":{"role":"toolResult","toolCallId":"toolu_019JVdVDChQkq9Ys9jQUYvnJ","toolName":"read","content":[{"type":"text","text":"@dataclass\nclass FixtureData:\n    \"\"\"Parsed fixture with moments grouped by category.\"\"\"\n    creator_name: str\n    video_id: str\n    content_type: str\n    filename: str\n    # Groups: category -> list of (MockKeyMoment, cls_info_dict)\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]]\n    total_moments: int\n\n\ndef load_fixture(path: str) -> FixtureData:\n    \"\"\"Load and parse a fixture JSON file into grouped moments.\"\"\"\n    fixture_path = Path(path)\n    if not fixture_path.exists():\n        raise FileNotFoundError(f\"Fixture not found: {path}\")\n\n    raw = fixture_path.read_text(encoding=\"utf-8\")\n    size_kb = len(raw.encode(\"utf-8\")) / 1024\n    data = json.loads(raw)\n\n    moments_raw = data.get(\"moments\", [])\n    if not moments_raw:\n        raise ValueError(f\"Fixture has no moments: {path}\")\n\n    _log(\"FIXTURE\", f\"Loading: {path} ({size_kb:.1f} KB, {len(moments_raw)} moments)\")\n\n    # Build MockKeyMoment objects and group by category\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]] = defaultdict(list)\n\n    for m in moments_raw:\n        cls = m.get(\"classification\", {})\n        category = cls.get(\"topic_category\", m.get(\"topic_category\", \"Uncategorized\"))\n        tags = cls.get(\"topic_tags\", m.get(\"topic_tags\", []))\n\n        mock = MockKeyMoment(\n            title=m.get(\"title\", m.get(\"summary\", \"\")[:80]),\n            summary=m.get(\"summary\", \"\"),\n            content_type=_MockContentType(m.get(\"content_type\", \"technique\")),\n            start_time=m.get(\"start_time\", 0.0),\n            end_time=m.get(\"end_time\", 0.0),\n            plugins=m.get(\"plugins\", []),\n            raw_transcript=m.get(\"raw_transcript\", m.get(\"transcript_excerpt\", \"\")),\n        )\n        cls_info = {\"topic_category\": category, \"topic_tags\": tags}\n        groups[category].append((mock, cls_info))\n\n    # Log breakdown\n    cat_counts = {cat: len(moms) for cat, moms in groups.items()}\n    counts = list(cat_counts.values())\n    _log(\n        \"FIXTURE\",\n        f\"Breakdown: {len(groups)} categories, \"\n        f\"moments per category: min={min(counts)}, max={max(counts)}, \"\n        f\"avg={sum(counts)/len(counts):.1f}\",\n    )\n    for cat, count in sorted(cat_counts.items(), key=lambda x: -x[1]):\n        _log(\"FIXTURE\", f\"  {cat}: {count} moments\")\n\n    return FixtureData(\n        creator_name=data.get(\"creator_name\", \"Unknown\"),\n        video_id=data.get(\"video_id\", \"unknown\"),\n        content_type=data.get(\"content_type\", \"tutorial\"),\n        filename=data.get(\"filename\", \"unknown\"),\n        groups=dict(groups),\n        total_moments=len(moments_raw),\n    )\n\n\n# ── Synthesis runner ───────────────────────────────────────────────────────\n\n\n[336 more lines in file. Use offset=168 to continue.]"}],"isError":false,"timestamp":1775177941178}}
{"type":"message","id":"df03ee47","parentId":"acdbcc92","timestamp":"2026-04-03T00:59:06.427Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the templates."},{"type":"toolCall","id":"toolu_01QPp8YcPZVWkEWfQqW1Fh77","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01Y8c2Bqetxdzccq1QUM2WxP","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":127,"cacheRead":77527,"cacheWrite":1755,"totalTokens":79410,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.0387635,"cacheWrite":0.010968750000000001,"total":0.05291225}},"stopReason":"toolUse","timestamp":1775177941178}}
{"type":"message","id":"8ead7cbe","parentId":"df03ee47","timestamp":"2026-04-03T00:59:06.429Z","message":{"role":"toolResult","toolCallId":"toolu_01QPp8YcPZVWkEWfQqW1Fh77","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":1775177946429}}
{"type":"message","id":"4e29f47d","parentId":"8ead7cbe","timestamp":"2026-04-03T00:59:06.432Z","message":{"role":"toolResult","toolCallId":"toolu_01Y8c2Bqetxdzccq1QUM2WxP","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":1775177946430}}
{"type":"message","id":"57869562","parentId":"4e29f47d","timestamp":"2026-04-03T00:59:21.811Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me plan S02.\n\nThe slice needs:\n1. A composition prompt (`prompts/stage5_compose.txt`)\n2. A `compose` subcommand in `test_harness.py`\n3. Tests for compose-mode logic\n\nThe research doc already has a clean 3-task decomposition. Let me verify it makes sense and then plan.\n\nKey observations:\n- The test harness has `run` and `promote` subcommands. Adding `compose` follows the same pattern.\n- The compose prompt is the high-risk deliverable — it needs to handle deduplication, citation re-indexing, section merging.\n\n\n- The existing schemas and citation utilities are already set up, so I can reuse them without adding anything new.\n\nI'll break this into three tasks: write the composition prompt, add the compose subcommand with CLI args and LLM integration, then write unit tests for the compose logic. Keeping them separate makes sense since the subcommand work is substantial on its own, even though the tests are tightly coupled.\n\nNow I need to check the fixture format to see what the compose operation actually needs to work with.","thinkingSignature":"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"},{"type":"text","text":"Good picture. The research doc is thorough and the 3-task decomposition is well-justified. The compose prompt is the high-risk piece; the harness and tests are mechanical but substantial. Let me verify one more thing about fixture format before planning."},{"type":"toolCall","id":"toolu_012JqWczvkCwp3VeKFeBWk98","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls fixtures/ 2>/dev/null | head -5"}},{"type":"toolCall","id":"toolu_01HgBMNJvxW1jfi8Rwz6pg21","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -30 prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":535,"cacheRead":79282,"cacheWrite":2735,"totalTokens":82553,"cost":{"input":0.000005,"output":0.013375000000000001,"cacheRead":0.039640999999999996,"cacheWrite":0.01709375,"total":0.07011475}},"stopReason":"toolUse","timestamp":1775177946431}}
{"type":"message","id":"608090a5","parentId":"57869562","timestamp":"2026-04-03T00:59:21.823Z","message":{"role":"toolResult","toolCallId":"toolu_012JqWczvkCwp3VeKFeBWk98","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775177961823}}
{"type":"message","id":"84c9dbd6","parentId":"608090a5","timestamp":"2026-04-03T00:59:21.833Z","message":{"role":"toolResult","toolCallId":"toolu_01HgBMNJvxW1jfi8Rwz6pg21","toolName":"bash","content":[{"type":"text","text":"You are writing a technique reference page for music producers. Your job is to teach. You watched this creator work, absorbed their methods, and now you're writing the clear, instructive guide that helps another producer apply these techniques in their own sessions. Your tone is direct and knowledgeable — a senior colleague walking someone through an approach they've validated in practice.\n\n## What you are creating\n\nA Chrysopedia technique page teaches a specific production method. Not a video summary. Not a blog post. A clear, instructive reference that a producer can follow in their DAW.\n\nA producer should be able to read this page and apply the technique. If they can't, the page failed.\n\nThe page contains:\n1. **Instructive prose** — substantive paragraphs that teach the technique step by step. Every paragraph earns its place with at least one specific, actionable detail. If a paragraph could be deleted without losing anything concrete, delete it.\n2. **Key moments index** — reference list of the source moments for readers who want to trace information back to the original content.\n\n## Voice and tone\n\nYour tone is instructive and confident. You're teaching a technique, not narrating someone's session. The creator is the source — they discovered it, they validated it — but the page teaches it as method.\n\n- **Instructive, not narrative** — write \"Route the effect at 100% wet on a parallel lane, then control level via gain\" rather than \"He routes the effect at 100% wet.\" The reader is here to learn a technique, not to read about what someone did. Use imperative/instructive phrasing (\"set\", \"route\", \"use\", \"check\") for methods. The page reads like a lesson, not a biography.\n- **No hesitation** — state techniques as established method. \"Set the LFO to 3/16th timing\" not \"you might want to try setting the LFO to around 3/16th.\"\n- **Direct quotes for personality** — pull 2-3 direct quotes per page where the creator's phrasing is vivid or memorable. These give the page life. Introduce them with a brief attribution (\"as he puts it,\" or \"in his words,\") then let the quote do the work. Outside of quotes, the prose teaches — no attribution needed.\n- **Specific and grounded** — every technical claim needs concrete backing: plugin names, settings, frequencies, ratios, time values, routing decisions. \"Use some compression\" is worthless. \"OTT at 30% depth on the bus\" is useful.\n- **Match the energy of the source** — if the creator is hyped about a technique, let that land. If they're cautionary, make it feel urgent.\n- **Efficiency** — say it once, say it well. If a sentence doesn't teach something specific or make the next specific thing clearer, cut it.\n\n### Creator name usage — hard rule\n\nThe body sections are written as instructional content, not a biography. The creator's name appears in the **title** and **summary** for attribution — that's where the reader learns whose technique this is. The body sections then TEACH the technique directly, like a lesson book or a well-written blog post.\n\nThe creator's name should appear **at most once** in the entire body_sections content — and only when introducing a direct quote for the first time. Otherwise, the prose is instructive: \"do this\", \"set this to X\", \"route the signal through Y.\" Not \"he does this\", \"she sets this to X.\"\n\nBAD — third-person narration in every section:\n"}],"isError":false,"timestamp":1775177961827}}
{"type":"message","id":"392100f0","parentId":"84c9dbd6","timestamp":"2026-04-03T00:59:24.983Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01P2AvJfy6969kDbr4pVj3V7","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":82017,"cacheWrite":1051,"totalTokens":83150,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.041008499999999996,"cacheWrite":0.00656875,"total":0.04960724999999999}},"stopReason":"toolUse","timestamp":1775177961829}}
{"type":"message","id":"900e47c9","parentId":"392100f0","timestamp":"2026-04-03T00:59:24.991Z","message":{"role":"toolResult","toolCallId":"toolu_01P2AvJfy6969kDbr4pVj3V7","toolName":"bash","content":[{"type":"text","text":"251 prompts/stage5_synthesis.txt\n"}],"isError":false,"timestamp":1775177964990}}
{"type":"message","id":"d85f0d93","parentId":"900e47c9","timestamp":"2026-04-03T01:00:28.300Z","message":{"role":"assistant","content":[{"type":"text","text":"I have a solid picture now. Three tasks as the research suggested: prompt, harness compose subcommand, tests. The prompt is the highest-risk piece and should come first so T02 and T03 can reference it. T02 adds the `compose` subcommand to the test harness. T03 writes unit tests for the compose plumbing.\n\nNo requirements from REQUIREMENTS.md are owned by this slice — M014 is adding new capabilities that aren't yet tracked as requirements."},{"type":"toolCall","id":"toolu_01CnFhKmiUGMmEwoqi5Di8mf","name":"gsd_plan_slice","arguments":{"goal":"Composition prompt (stage5_compose.txt) and test harness --compose mode enable offline testing of merging new video moments into existing technique pages, producing unified v2 output with correct citation re-indexing and deduplication.","integrationClosure":"Upstream: `backend/pipeline/schemas.py` (SynthesisResult, SynthesizedPage, BodySection), `backend/pipeline/citation_utils.py` (validate_citations), `backend/pipeline/test_harness.py` (existing harness patterns, build_moments_text, load_fixture), `prompts/stage5_synthesis.txt` (voice/tone reference). New wiring: compose subcommand registered in test_harness.py CLI, stage5_compose.txt prompt file. What remains: S04 wires compose prompt into real pipeline; S04 calls compose from production stage code.","milestoneId":"M014","observabilityImpact":"Compose subcommand logs structured output with COMPOSE tag (parallel to existing SYNTH/CITE tags) — word count delta, section merge stats, citation coverage for composed output.","proofLevel":"Contract — compose prompt structure validated statically, harness compose subcommand verified via CLI + unit tests. No live LLM call required (that happens in S04).","sliceId":"S02","successCriteria":"- `prompts/stage5_compose.txt` exists with merge/dedup/citation-reindex instructions and valid v2 output example\n- `python -m pipeline.test_harness compose --help` exits 0 with documented args\n- Compose user prompt builder produces correctly formatted output with existing page + new moments + unified citation index space\n- `python -m pytest backend/pipeline/test_harness_compose.py -v` — all tests pass covering prompt construction, citation re-indexing math, category filtering, and edge cases","tasks":[{"description":"Create the composition prompt that instructs the LLM to merge new video moments into an existing technique page. The prompt receives three inputs: existing page JSON, new moments list, and creator name. Must define merge semantics (preserve existing prose, integrate new material into existing sections or create new ones), deduplication rules (enrich rather than repeat), citation re-indexing scheme (existing [0]-[N-1], new [N]-[N+M-1]), and output format (same SynthesisResult/SynthesizedPage v2 schema as synthesis).\n\nSteps:\n1. Read `prompts/stage5_synthesis.txt` to understand voice/tone rules and v2 output format — the compose prompt must reference the same writing standards but NOT duplicate them (the compose prompt is self-contained).\n2. Write `prompts/stage5_compose.txt` with these sections:\n   - Role/context preamble explaining the composition task\n   - Input format: `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>` XML tags\n   - Merge rules: preserve existing prose verbatim where correct; add new sections for genuinely new territory; enrich existing sections with new detail where topics overlap\n   - Deduplication guidance with examples of 'enriching' vs 'duplicating'\n   - Citation re-indexing rules: existing moments keep indices [0]-[N-1], new moments get [N]-[N+M-1], composed output uses the unified index space\n   - Section ordering: maintain workflow-order rule from synthesis\n   - Output format: same SynthesisResult JSON with v2 body_sections (list[BodySection])\n   - Include a concrete JSON example showing composed output with mixed old/new citations\n3. Validate the prompt structurally: check that the JSON example parses as valid SynthesisResult, citation rules section mentions the dual-index scheme, merge rules section is present.","estimate":"45m","expectedOutput":["`prompts/stage5_compose.txt` — complete composition prompt with merge rules, citation re-indexing, dedup guidance, and v2 output example"],"files":["prompts/stage5_compose.txt","prompts/stage5_synthesis.txt"],"inputs":["`prompts/stage5_synthesis.txt` — voice/tone rules and v2 output format to reference","`backend/pipeline/schemas.py` — SynthesisResult/SynthesizedPage/BodySection schema definitions"],"taskId":"T01","title":"Write stage5_compose.txt composition prompt","verify":"python3 -c \"\nimport json, pathlib, re\np = pathlib.Path('prompts/stage5_compose.txt')\nassert p.exists(), 'prompt file missing'\ntxt = p.read_text()\nassert len(txt) > 2000, f'prompt too short: {len(txt)} chars'\nfor section in ['existing_page', 'new_moments', 'existing_moments', 'creator', 'Citation', 'dedup', 'Merge']:\n    assert any(s.lower() in txt.lower() for s in [section]), f'missing section: {section}'\n# Check JSON example is valid\njson_match = re.search(r'```json\\n(.*?)```', txt, re.DOTALL)\nassert json_match, 'no JSON example found'\njson.loads(json_match.group(1))\nprint('All structural checks passed')\n\""},{"description":"Add a `compose` subcommand to `backend/pipeline/test_harness.py` that loads an existing page JSON + new moments fixture, builds a compose user prompt, calls the LLM via the compose prompt, and validates the output. Also extract the compose user-prompt builder as a testable function.\n\nSteps:\n1. Read `backend/pipeline/test_harness.py` fully to understand the existing `run` and `promote` subcommand patterns.\n2. Add a `build_compose_prompt()` function that takes:\n   - `existing_page: dict` (SynthesizedPage as dict, from prior synthesis output)\n   - `existing_moments: list[tuple[MockKeyMoment, dict]]` (the original moments the page was built from)\n   - `new_moments: list[tuple[MockKeyMoment, dict]]` (new video's moments to compose in)\n   - `creator_name: str`\n   Returns: the user prompt string with `<existing_page>`, `<existing_moments>`, `<new_moments>`, and `<creator>` XML tags. Existing moments get indices [0]-[N-1], new moments get [N]-[N+M-1]. Uses `build_moments_text()` for formatting moment lists.\n3. Add a `run_compose()` function (parallel to `run_synthesis()`) that:\n   - Loads the existing page JSON and validates as SynthesizedPage\n   - Loads the new fixture via existing `load_fixture()`\n   - Filters new moments by category to match `existing_page.topic_category`\n   - Needs the existing moments for citation context — accept them as a separate fixture or extract from the existing page's moment_indices + a moments fixture\n   - Calls `build_compose_prompt()` to build the user prompt\n   - Calls LLM with compose system prompt\n   - Parses response as SynthesisResult\n   - Runs citation validation with moment_count = len(existing_moments) + len(new_moments)\n   - Logs compose-specific metrics: word count before/after, sections before/after, new sections added\n4. Register `compose` subcommand in the `main()` argparse with args: `--existing-page` (required), `--fixture` (required), `--existing-fixture` (required, original moments fixture for citation context), `--prompt`, `--output`, `--category`, `--model`, `--modality`.\n5. Wire the subcommand handler to call `run_compose()` and write output JSON.\n6. Verify: `cd backend && python -m pipeline.test_harness compose --help` exits 0.","estimate":"1h","expectedOutput":["`backend/pipeline/test_harness.py` — updated with compose subcommand, build_compose_prompt() function, run_compose() function"],"files":["backend/pipeline/test_harness.py"],"inputs":["`backend/pipeline/test_harness.py` — existing harness with run/promote subcommands","`backend/pipeline/schemas.py` — SynthesizedPage, SynthesisResult for validation","`backend/pipeline/citation_utils.py` — validate_citations() for output checking","`prompts/stage5_compose.txt` — default compose prompt path (from T01)"],"taskId":"T02","title":"Add compose subcommand to test harness","verify":"cd backend && python -m pipeline.test_harness compose --help"},{"description":"Write unit tests for the compose harness plumbing — no LLM calls. Tests cover prompt construction, citation re-indexing math, category filtering, word count comparison, and edge cases.\n\nSteps:\n1. Read the updated `backend/pipeline/test_harness.py` (from T02) to understand `build_compose_prompt()` signature and `run_compose()` interface.\n2. Read `backend/pipeline/test_harness_v2_format.py` for test patterns (helper functions, BodySection construction, etc.).\n3. Create `backend/pipeline/test_harness_compose.py` with these test classes:\n   - `TestBuildComposePrompt`: \n     - Existing page + 3 old moments + 2 new moments → prompt contains all XML tags, old moments indexed [0]-[2], new moments indexed [3]-[4]\n     - Creator name appears in `<creator>` tag\n     - Existing page JSON appears in `<existing_page>` tag and is valid JSON\n     - Moment text uses same format as `build_moments_text()`\n   - `TestCitationReindexing`:\n     - 5 old + 3 new moments → valid citation range is [0]-[7]\n     - validate_citations() with moment_count=8 accepts citations in [0]-[7]\n     - validate_citations() with moment_count=8 rejects citation [8]\n   - `TestCategoryFiltering`:\n     - Build fixture with moments in 2 categories → compose with existing page of category A → only category A new moments used\n   - `TestEdgeCases`:\n     - Empty new moments list → prompt still valid, just no new_moments content\n     - Single new moment → prompt has one entry at index [N]\n     - Existing page with no subsections → handled correctly\n4. Run tests: `cd backend && python -m pytest pipeline/test_harness_compose.py -v`","estimate":"45m","expectedOutput":["`backend/pipeline/test_harness_compose.py` — unit tests for compose prompt construction, citation re-indexing, category filtering, edge cases"],"files":["backend/pipeline/test_harness_compose.py"],"inputs":["`backend/pipeline/test_harness.py` — build_compose_prompt() and run_compose() from T02","`backend/pipeline/schemas.py` — SynthesizedPage, BodySection for test fixtures","`backend/pipeline/citation_utils.py` — validate_citations() for citation math tests","`backend/pipeline/test_harness_v2_format.py` — test patterns and helpers to follow"],"taskId":"T03","title":"Unit tests for compose-mode prompt building and validation","verify":"cd backend && python -m pytest pipeline/test_harness_compose.py -v"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3154,"cacheRead":83068,"cacheWrite":105,"totalTokens":86328,"cost":{"input":0.000005,"output":0.07885,"cacheRead":0.041534,"cacheWrite":0.00065625,"total":0.12104525000000001}},"stopReason":"toolUse","timestamp":1775177964990}}
{"type":"message","id":"96f18c56","parentId":"d85f0d93","timestamp":"2026-04-03T01:00:28.360Z","message":{"role":"toolResult","toolCallId":"toolu_01CnFhKmiUGMmEwoqi5Di8mf","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S02 (M014)"}],"details":{"operation":"plan_slice","milestoneId":"M014","sliceId":"S02","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/S02-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/tasks/T02-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/tasks/T03-PLAN.md"]},"isError":false,"timestamp":1775178028357}}
{"type":"message","id":"6833362c","parentId":"96f18c56","timestamp":"2026-04-03T01:00:37.892Z","message":{"role":"assistant","content":[{"type":"text","text":"The self-audit checks out:\n\n- **Completion semantics:** T01 creates the prompt, T02 wires the compose subcommand (which loads it), T03 proves the plumbing works. After all three, the demo — \"run test harness --compose mode with existing page + new moments → merged output\" — is achievable.\n- **Requirement coverage:** No active requirements owned by this slice; it's new M014 capability.\n- **Task completeness:** All tasks have steps, must-haves (implicit in description), verification commands, concrete file paths in inputs/outputs.\n- **Dependency correctness:** T01 → T02 → T03 is sequential. T02 reads the prompt from T01; T03 tests the functions from T02.\n- **Scope:** T01 is 1 file, T02 is 1 file with ~3 functions, T03 is 1 new test file. Well within single-context-window bounds.\n\nSlice S02 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":234,"cacheRead":83173,"cacheWrite":3153,"totalTokens":86561,"cost":{"input":0.000005,"output":0.00585,"cacheRead":0.0415865,"cacheWrite":0.01970625,"total":0.06714775}},"stopReason":"stop","timestamp":1775178028358}}
