{"type":"thinking_level_change","id":"2a00bcde","parentId":null,"timestamp":"2026-04-01T08:46:16.544Z","thinkingLevel":"medium"}
{"type":"model_change","id":"c2de4af0","parentId":"2a00bcde","timestamp":"2026-04-01T08:46:16.545Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S02 (\"Stage 5 Quality Scorer & Voice Preservation Dial\") — Milestone M013\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M013/M013-ROADMAP.md`\n\n# M013: \n\n## Vision\nA fully automated CLI tool that tests FYN-LLM fitness, scores pipeline output across structural, content, voice preservation, readability, and preference dimensions, generates prompt variants, and runs unattended A/B optimization loops — producing reports with winning prompts and score trajectories. Starting with stage 5 synthesis (highest user-visible impact), expanding to all pipeline stages.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | General FYN-LLM Fitness Suite | medium | — | ✅ | Run `python -m pipeline.quality fitness` — outputs pass/fail for Mandelbrot question, JSON compliance, instruction following, and diverse prompt battery against live FYN-LLM |\n| S02 | Stage 5 Quality Scorer & Voice Preservation Dial | high | S01 | ⬜ | Run scorer on a reference article — outputs composite score across 5 dimensions. Run same article at voice_level 0.2 vs 0.8 — voice preservation score differs meaningfully |\n| S03 | Prompt Variant Generator & Automated A/B Loop | high | S02 | ⬜ | Run `python -m pipeline.quality optimize --stage 5 --iterations 10` — generates prompt variants, scores each against reference articles, outputs leaderboard and score trajectory chart |\n| S04 | Expand to Pipeline Stages 2-4 | medium | S03 | ⬜ | Run `python -m pipeline.quality optimize --stage 3 --iterations 5` — optimizes extraction prompts with stage-appropriate scoring |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n| D021 | M011 | scope | Which UI/UX assessment findings to implement in M011 | 12 of 16 findings approved; F01 (beginner paths), F02 (YouTube links), F03 (hide admin), F15 (CTA label) denied | User triaged each finding individually. Denied F01 because audience knows what they want. Denied F02 because no video URLs / don't want to link out. Denied F03 because admin dropdown is fine as-is. Denied F15 as low-value. | Yes | human |\n| D022 |  | requirement | R025 | validated | useDocumentTitle hook called in all 10 pages. Static pages set fixed titles, dynamic pages (SubTopicPage, CreatorDetail, TechniquePage, SearchResults) update title when async data loads. | Yes | agent |\n| D023 | M012/S01 | architecture | Qdrant embedding text enrichment strategy | Prepend creator_name and join topic_tags into embedding text for technique pages and key moments. Batch-resolve creator names at stage 6 start. | Semantic search now surfaces results for creator-name queries and tag-specific queries. Batch resolution avoids N+1 lookups during embedding. Reindex-all endpoint enables one-shot re-embedding after text composition changes. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (7 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic loads a dedicated page with creator-grouped techniques and breadcrumbs.\n**Validation:** Hierarchy renders, filtering works, sub-topics have dedicated pages at /topics/:category/:subtopic with backend filtering and creator grouping. Verified: M010/S01.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n## R016 — Card Hover Animations & Staggered Entrance\n**Status:** validated\n**Description:** All technique/content cards animate on hover (scale + shadow transition). Card grids use staggered fade-in-up entrance animations on page load.\n**Validation:** Cards visually respond to hover with smooth 200ms transition. Grid cards appear sequentially on page load.\n**Primary Owner:** M011/S01\n\n## R017 — Featured Technique Visual Redesign\n**Status:** validated\n**Description:** Featured Technique section on homepage has a visually distinct treatment (gradient border, glow, or full-bleed) that differentiates it from ordinary cards.\n**Validation:** Featured technique is visually prominent and clearly distinct from regular technique cards.\n**Primary Owner:** M011/S01\n\n## R018 — Random Technique Discovery\n**Status:** validated\n**Description:** A \"Random Technique\" button exists that navigates to a randomly selected technique page.\n**Validation:** Clicking the button loads a different technique each time.\n**Primary Owner:** M011/S01\n\n## R019 — Topics Page Default-Collapsed\n**Status:** validated\n**Description:** Topics page loads with all categories collapsed, showing only category cards. Clicking a category expands its sub-topics with a smooth slide animation.\n**Validation:** Page loads collapsed. Click expands with animation. Click again collapses.\n**Primary Owner:** M011/S02\n\n## R020 — Global Search in Navigation\n**Status:** validated\n**Description:** A compact search input in the navigation bar is available on all pages except the homepage. Cmd+K (or /) keyboard shortcut focuses it from any page.\n**Validation:** Search bar visible in nav on Topics, Creators, Technique, About pages. Cmd+K focuses it.\n**Primary Owner:** M011/S03\n\n## R021 — Mobile Hamburger Menu\n**Status:** validated\n**Description:** Navigation uses a hamburger menu on viewports below 768px with generous touch targets (44×44px minimum).\n**Validation:** Mobile viewport shows hamburger icon. Tapping reveals nav items. Touch targets meet minimum size.\n**Primary Owner:** M011/S03\n\n## R022 — Heading Hierarchy Fix\n**Status:** validated\n**Description:** Every page has exactly one H1 element. Heading levels are sequential (no skips from H1 to H3).\n**Validation:** DOM inspection confirms single H1 per page and sequential heading levels.\n**Primary Owner:** M011/S04\n\n## R023 — Skip-to-Content Link\n**Status:** validated\n**Description:** A visually hidden skip link is the first focusable element on every page, visible on keyboard focus.\n**Validation:** Tab from page load shows \"Skip to content\" link. Clicking it jumps to main content area.\n**Primary Owner:** M011/S04\n\n## R024 — Text Contrast AA Compliance\n**Status:** validated\n**Description:** All text meets WCAG AA contrast ratios (4.5:1 for normal text, 3:1 for large text) against the dark background.\n**Validation:** Secondary text color passes 4.5:1 contrast check against page background.\n**Primary Owner:** M011/S04\n\n## R025 — Page-Specific Document Titles\n**Status:** validated\n**Description:** Each route sets a descriptive document title (e.g., \"Bass — Sound Design — Chrysopedia\", \"COPYCATT — Chrysopedia\").\n**Validation:** Browser tab shows distinct title per page. Navigating between pages updates the title.\n**Primary Owner:** M011/S04\n\n## R026 — Creator Stats Topic-Colored Pills\n**Status:** validated\n**Description:** Creator detail page stats line rendered as topic-colored pill badges instead of run-on text.\n**Validation:** Stats show as visually distinct colored pills grouped by topic category.\n**Primary Owner:** M011/S02\n\n## R027 — Tag Overflow Limit on Cards\n**Status:** validated\n**Description:** Technique cards show a maximum of 4 tags, with a \"+N more\" indicator for additional tags.\n**Validation:** Cards with >4 tags show exactly 4 plus a \"+N more\" badge.\n**Primary Owner:** M011/S02\n\n## R028 — Empty Subtopic Handling\n**Status:** validated\n**Description:** Sub-topics with 0 techniques either hidden by default or display a \"Coming soon\" badge.\n**Validation:** Empty subtopics are visually distinguished or hidden. No dead-end clicks.\n**Primary Owner:** M011/S02\n\n## Denied / Out of Scope\n\n## R029 — Beginner Learning Paths\n**Status:** out-of-scope\n**Description:** Curated beginner learning paths with skill-level tags and \"New to Production?\" section.\n**Validation:** n/a\n**Notes:** Denied — audience knows what they're looking for. Assessment F01.\n\n## R030 — YouTube Deep Links on Key Moments\n**Status:** out-of-scope\n**Description:** Key moment timestamps link to source YouTube videos at the correct time.\n**Validation:** n/a\n**Notes:** Denied — no video URLs in data model / don't want to link out. Assessment F02.\n\n## R031 — Hide Admin Dropdown\n**Status:** out-of-scope\n**Description:** Admin dropdown hidden from public visitors behind auth or flag.\n**Validation:** n/a\n**Notes:** Denied — fine as-is. Assessment F03.\n\n## R032 — CTA Label Changes\n**Status:** out-of-scope\n**Description:** Change \"Start Exploring\" to more descriptive CTA text.\n**Validation:** n/a\n**Notes:** Denied — skipped. Assessment F15.\n\n---\n\n[...truncated 6 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what to watch out for.\n\n#### S01 Summary\nSource: `.gsd/milestones/M013/slices/S01/S01-SUMMARY.md`\n\n---\nid: S01\nparent: M013\nmilestone: M013\nprovides:\n  - pipeline.quality package with FitnessRunner and argparse CLI entry point\n  - TestResult dataclass for structured test results\nrequires:\n  []\naffects:\n  - S02\n  - S03\nkey_files:\n  - backend/pipeline/quality/__init__.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/fitness.py\nkey_decisions:\n  - Connectivity probe as first action in run_all() for fast failure before running tests\n  - Mandelbrot test uses thinking modality to exercise both LLM modes\n  - Generous validation thresholds to avoid flaky failures from LLM variance\npatterns_established:\n  - pipeline.quality package structure with argparse subcommands — S02/S03 add score and optimize subcommands to the same CLI\n  - TestResult dataclass as standard return type for all quality tests — carries name, passed, elapsed_seconds, token_count, detail\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S01/tasks/T01-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T08:46:13.884Z\nblocker_discovered: false\n---\n\n# S01: General FYN-LLM Fitness Suite\n\n**Built pipeline.quality package with FitnessRunner CLI — 9 tests across 4 categories (Mandelbrot reasoning, JSON compliance, instruction following, diverse battery), clean connectivity error handling, exits 0/1.**\n\n## What Happened\n\nCreated the `backend/pipeline/quality/` package as the foundation for the M013 quality assurance toolkit. The package has three files: `__init__.py`, `__main__.py` (argparse CLI with `fitness` subcommand), and `fitness.py` (FitnessRunner class).\n\nFitnessRunner implements 9 tests across 4 categories:\n1. **Mandelbrot reasoning** (1 test) — asks about the Mandelbrot set area, checks for key concepts. Uses thinking modality to exercise both LLM modes.\n2. **JSON compliance** (2 tests) — simple and nested JSON with Pydantic validation. Catches malformed/empty responses gracefully.\n3. **Instruction following** (3 tests) — bullet count, keyword inclusion, lowercase constraint. Programmatic compliance checks.\n4. **Diverse battery** (3 tests) — summarization, classification, extraction. Tests practical pipeline-relevant capabilities.\n\nThe CLI runs `python -m pipeline.quality fitness`, prints a formatted pass/fail report with per-test timing and token counts, and exits 0 (all pass) or 1 (any failure or connectivity error). A connectivity pre-check probes the LLM endpoint before running tests, providing a clear error message with the endpoint URL on failure — no tracebacks.\n\nThe argparse structure is designed for extension: S02 and S03 will add `score` and `optimize` subcommands to the same CLI.\n\n## Verification\n\n1. Import check: `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")'` — exits 0, prints \"import ok\".\n2. CLI connectivity error: `cd backend && python -m pipeline.quality fitness` — prints clear \"Cannot reach LLM endpoint\" message with URLs, exits 1, no traceback.\n3. Help output: `python -m pipeline.quality --help` — shows fitness subcommand with description.\n4. No-subcommand: `python -m pipeline.quality` — prints usage, exits 1.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nCannot verify actual test pass/fail behavior without a live FYN-LLM endpoint — only connectivity error path is testable on this machine.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/__init__.py` — Empty package init\n- `backend/pipeline/quality/__main__.py` — Argparse CLI with fitness subcommand, extensible for score/optimize\n- `backend/pipeline/quality/fitness.py` — FitnessRunner class with 9 tests, 4 categories, connectivity pre-check, formatted report output\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M013`, `slice_id: S02`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M013/slices/S02/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S02 researched.\"","display":false,"id":"f431e8fd","parentId":"c2de4af0","timestamp":"2026-04-01T08:46:16.546Z"}
{"type":"message","id":"f27dddae","parentId":"f431e8fd","timestamp":"2026-04-01T08:46:27.250Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research what's needed for S02: \"Stage 5 Quality Scorer & Voice Preservation Dial\". I need to understand:\n\n1. What Stage 5 does (technique page synthesis)\n2. The existing quality package from S01\n3. What scoring dimensions are needed\n4. What \"voice preservation dial\" means\n5. How to verify\n\nLet me explore the relevant code.","thinkingSignature":"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"},{"type":"text","text":"Let me explore the relevant code — Stage 5 synthesis, the quality package from S01, and the prompt templates."},{"type":"toolCall","id":"toolu_017hM86Hgaa9G5ok51ZEwTMW","name":"bash","arguments":{"command":"find backend/pipeline/quality -type f | head -20 && echo \"---\" && find backend/pipeline/stages* -type f 2>/dev/null | head -10 && echo \"---\" && find backend/prompts -type f 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01NoSF8CiBJhF6QPJyxzKmUP","name":"bash","arguments":{"command":"rg -l \"stage.?5\\|synthesis\\|technique.page\" backend/pipeline/ --type py 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":313,"cacheRead":59647,"cacheWrite":9163,"totalTokens":69126,"cost":{"input":0.000015000000000000002,"output":0.007825,"cacheRead":0.0298235,"cacheWrite":0.05726875,"total":0.09493225}},"stopReason":"toolUse","timestamp":1775033176545}}
{"type":"message","id":"3e651419","parentId":"f27dddae","timestamp":"2026-04-01T08:46:27.270Z","message":{"role":"toolResult","toolCallId":"toolu_017hM86Hgaa9G5ok51ZEwTMW","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/quality/__main__.py\nbackend/pipeline/quality/fitness.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/__pycache__/fitness.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/__init__.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/__main__.cpython-312.pyc\n---\nbackend/pipeline/stages.py\n---\n"}],"isError":false,"timestamp":1775033187270}}
{"type":"message","id":"10d287d4","parentId":"3e651419","timestamp":"2026-04-01T08:46:27.279Z","message":{"role":"toolResult","toolCallId":"toolu_01NoSF8CiBJhF6QPJyxzKmUP","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033187277}}
{"type":"message","id":"172d2ac8","parentId":"10d287d4","timestamp":"2026-04-01T08:46:31.570Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KuwGLQJdxiAGFVuUS6HeNs","name":"bash","arguments":{"command":"rg -l \"stage.?5\\|synthesis\\|technique.page\\|synthesize\" backend/ --type py 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_014MzFkkwXGMCrsFSji3csAH","name":"bash","arguments":{"command":"ls backend/prompts/ 2>/dev/null || echo \"no prompts dir\" && find backend/ -name \"*.txt\" -path \"*/prompt*\" 2>/dev/null | head -10 && find backend/ -name \"*.yaml\" -path \"*/prompt*\" 2>/dev/null | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":197,"cacheRead":68810,"cacheWrite":381,"totalTokens":69389,"cost":{"input":0.000005,"output":0.0049250000000000006,"cacheRead":0.034405,"cacheWrite":0.00238125,"total":0.041716249999999996}},"stopReason":"toolUse","timestamp":1775033187277}}
{"type":"message","id":"aee425f2","parentId":"172d2ac8","timestamp":"2026-04-01T08:46:31.601Z","message":{"role":"toolResult","toolCallId":"toolu_01KuwGLQJdxiAGFVuUS6HeNs","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033191599}}
{"type":"message","id":"b17d2974","parentId":"aee425f2","timestamp":"2026-04-01T08:46:31.603Z","message":{"role":"toolResult","toolCallId":"toolu_014MzFkkwXGMCrsFSji3csAH","toolName":"bash","content":[{"type":"text","text":"no prompts dir\n"}],"isError":false,"timestamp":1775033191599}}
{"type":"message","id":"4b9ba290","parentId":"b17d2974","timestamp":"2026-04-01T08:46:35.246Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FSe6SeDonGdFgFhyYTb65m","name":"bash","arguments":{"command":"find backend/ -name \"prompts\" -type d 2>/dev/null && find . -name \"prompts\" -type d 2>/dev/null | head -5"}},{"type":"toolCall","id":"toolu_01H9makeYUYazb4e1cDGJxAo","name":"bash","arguments":{"command":"wc -l backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":69191,"cacheWrite":265,"totalTokens":69593,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0345955,"cacheWrite":0.0016562500000000002,"total":0.03965675}},"stopReason":"toolUse","timestamp":1775033191601}}
{"type":"message","id":"39a4db3e","parentId":"4b9ba290","timestamp":"2026-04-01T08:46:35.278Z","message":{"role":"toolResult","toolCallId":"toolu_01FSe6SeDonGdFgFhyYTb65m","toolName":"bash","content":[{"type":"text","text":"./prompts\n"}],"isError":false,"timestamp":1775033195278}}
{"type":"message","id":"c463d8d2","parentId":"39a4db3e","timestamp":"2026-04-01T08:46:35.280Z","message":{"role":"toolResult","toolCallId":"toolu_01H9makeYUYazb4e1cDGJxAo","toolName":"bash","content":[{"type":"text","text":"1778 backend/pipeline/stages.py\n"}],"isError":false,"timestamp":1775033195278}}
{"type":"message","id":"b25d51d3","parentId":"c463d8d2","timestamp":"2026-04-01T08:46:38.789Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Ga6mDqtbrpG8KreMXCfakf","name":"bash","arguments":{"command":"ls prompts/"}},{"type":"toolCall","id":"toolu_016sMZTpY93ht9YtnzZh1weA","name":"bash","arguments":{"command":"rg -n \"def.*stage.?5\\|def.*synth\\|def.*technique_page\\|Stage 5\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":126,"cacheRead":69456,"cacheWrite":212,"totalTokens":69795,"cost":{"input":0.000005,"output":0.00315,"cacheRead":0.034727999999999995,"cacheWrite":0.001325,"total":0.03920799999999999}},"stopReason":"toolUse","timestamp":1775033195279}}
{"type":"message","id":"3df6c87e","parentId":"b25d51d3","timestamp":"2026-04-01T08:46:38.802Z","message":{"role":"toolResult","toolCallId":"toolu_01Ga6mDqtbrpG8KreMXCfakf","toolName":"bash","content":[{"type":"text","text":"README.md\nstage2_segmentation.txt\nstage3_extraction.txt\nstage4_classification.txt\nstage5_synthesis.txt\n"}],"isError":false,"timestamp":1775033198802}}
{"type":"message","id":"854fa058","parentId":"3df6c87e","timestamp":"2026-04-01T08:46:38.809Z","message":{"role":"toolResult","toolCallId":"toolu_016sMZTpY93ht9YtnzZh1weA","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775033198807}}
{"type":"message","id":"1304d07d","parentId":"854fa058","timestamp":"2026-04-01T08:46:41.897Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013YDJ2nZ79xJqHGK4v2QAbM","name":"bash","arguments":{"command":"rg -n \"def \" backend/pipeline/stages.py | head -30"}},{"type":"toolCall","id":"toolu_01WGzoqmtUBVSuey2T6g4EMR","name":"read","arguments":{"path":"prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":69668,"cacheWrite":231,"totalTokens":70011,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.034834,"cacheWrite":0.0014437500000000002,"total":0.039057749999999995}},"stopReason":"toolUse","timestamp":1775033198808}}
{"type":"message","id":"f7c99cc9","parentId":"1304d07d","timestamp":"2026-04-01T08:46:41.908Z","message":{"role":"toolResult","toolCallId":"toolu_013YDJ2nZ79xJqHGK4v2QAbM","toolName":"bash","content":[{"type":"text","text":"59:def _set_error_status(video_id: str, stage_name: str, error: Exception) -> None:\n79:def _emit_event(\n122:def _is_debug_mode() -> bool:\n137:def _make_llm_callback(\n160:    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n189:def _build_request_params(\n224:def _get_sync_engine():\n236:def _get_sync_session() -> Session:\n244:def _load_prompt(template_name: str) -> str:\n257:def _get_llm_client() -> LLMClient:\n262:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n275:def _load_canonical_tags() -> dict:\n291:def _format_taxonomy_for_prompt(tags_data: dict) -> str:\n302:def _safe_parse_llm_response(\n363:def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\n445:def stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\n556:def _classify_moment_batch(\n613:def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n718:def _store_classification_data(video_id: str, data: list[dict]) -> None:\n728:def _load_classification_data(video_id: str) -> list[dict]:\n742:def _get_git_commit_sha() -> str:\n779:def _capture_pipeline_metadata() -> dict:\n830:def _compute_page_tags(\n852:def _build_moments_text(\n878:def _synthesize_chunk(\n920:def _slug_base(slug: str) -> str:\n929:def _merge_pages_by_slug(\n1022:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n1322:def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n1497:def _snapshot_prior_pages(video_id: str) -> None:\n"}],"isError":false,"timestamp":1775033201907}}
{"type":"message","id":"99429426","parentId":"f7c99cc9","timestamp":"2026-04-01T08:46:41.911Z","message":{"role":"toolResult","toolCallId":"toolu_01WGzoqmtUBVSuey2T6g4EMR","toolName":"read","content":[{"type":"text","text":"You are an expert technical writer specializing in music production education. Your task is to synthesize a set of related key moments from the same creator into a single, high-quality technique page that serves as a definitive reference on the topic.\n\n## What you are creating\n\nA Chrysopedia technique page is NOT a generic article or wiki entry. It is a focused reference document that a music producer will consult mid-session when they need to understand and apply a specific technique. The reader is Alt+Tabbing from their DAW, looking for actionable knowledge, and wants to absorb the key insight and get back to work in under 2 minutes.\n\nThe page has two complementary sections:\n\n1. **Study guide prose** — rich, detailed paragraphs organized by sub-aspect of the technique. This is for learning and deep understanding. It reads like notes from an expert mentor, not a textbook.\n2. **Key moments index** — a compact list of the individual source moments that contributed to this page, each with a descriptive title that enables quick scanning.\n\nBoth sections are essential. The prose synthesizes and explains; the moment index lets readers quickly locate the specific insight they need.\n\n## Voice and tone\n\nWrite as if you are a knowledgeable colleague explaining what you learned from watching this creator's content. The tone should be:\n\n- **Direct and confident** — state what the creator does, not \"the creator appears to\" or \"it seems like they\"\n- **Technical but accessible** — use production terminology naturally, but explain non-obvious concepts when the creator's explanation adds value\n- **Preserving the creator's voice** — when the creator uses a memorable phrase, vivid metaphor, or strong opinion, quote them directly with quotation marks. These are often the most valuable parts. Examples: 'He warns against using OTT on snares — says it \"smears the snap into mush.\"' or 'Her reasoning: \"every bus you add is another place you'll be tempted to put a compressor that doesn't need to be there.\"'\n- **Specific over general** — always prefer concrete details (frequencies, ratios, ms values, plugin names, specific settings) over vague descriptions. \"Uses compression\" is never acceptable if the source moments contain specifics.\n\n## Body sections structure\n\nDo NOT use generic section names like \"Overview,\" \"Step-by-Step Process,\" \"Key Settings,\" or \"Tips and Variations.\" These produce lifeless, formulaic output.\n\nInstead, derive section names from the actual content. Each section should cover one sub-aspect of the technique. Use descriptive names that tell the reader exactly what they'll learn:\n\nGood section names (examples):\n- \"Layer construction\" / \"Saturation and the crunch character\" / \"Mix context and bus processing\"\n- \"Resampling loop\" / \"Preserving transient information\" / \"Wavetable import settings\"\n- \"Overall philosophy\" / \"Bus structure\" / \"Gain staging mindset\"\n- \"Oscillator setup and FM routing\" / \"Effects chain per-layer\" / \"Automating movement\"\n\nBad section names (never use these):\n- \"Overview\" / \"Introduction\" / \"Step-by-Step Process\" / \"Key Settings\" / \"Tips and Variations\" / \"Conclusion\" / \"Summary\"\n\nEach section should be 2-5 paragraphs of substantive prose. A section with only 1-2 sentences is too thin — either merge it with another section or expand it with the detail available in the source moments.\n\n## Signal chains\n\nWhen the source moments describe a signal routing chain (oscillator → effects → processing → bus), represent it as a structured signal chain object. Signal chains are only included when the creator explicitly walks through routing — do not infer chains from casual plugin mentions.\n\nFormat signal chain steps to include the role of each stage, not just the plugin name:\n- Good: [\"Noise osc (Vital)\", \"Transient Shaper (Kilohearts, attack +6dB)\", \"EQ (Pro-Q 3, shelf -3dB @ 12kHz)\", \"Send → Trash 2 (tape algo, 35% wet)\"]\n- Bad: [\"Vital\", \"Kilohearts\", \"EQ\", \"Trash 2\"]\n\n## Plugin detail rule\n\nInclude specific plugin names, settings, and parameters ONLY when the creator was teaching that setting — spending time explaining why they chose it, what it does, or how to configure it. If a plugin is merely visible or briefly mentioned without explanation, include it in the plugins list but do not feature it in the body prose.\n\nThis distinction is critical for page quality. A page that lists every plugin the creator happened to have open reads like a gear list. A page that explains the plugins the creator intentionally demonstrated reads like education.\n\n## Synthesis, not concatenation\n\nYou are synthesizing knowledge, not summarizing a video. This means:\n\n- **Merge related information**: If the creator discusses snare transient shaping at timestamp 1:42:00 and then returns to refine the point at 2:15:00, these should be woven into one coherent section, not presented as two separate observations.\n- **Build a logical flow**: Organize sections in the order a producer would naturally encounter these decisions (e.g., sound source → processing → mixing context), even if the creator covered them in a different order.\n- **Resolve redundancy**: If two moments say essentially the same thing, combine them into one clear statement. Don't repeat yourself.\n- **Note contradictions**: If the creator says contradictory things in different moments (e.g., recommends different settings for the same parameter), note both and provide the context for each (\"In dense arrangements, he pulls the sustain back further; for sparse sections, he leaves more room for the tail\").\n\n## Source quality assessment\n\nAssess source_quality based on the nature of the input moments:\n- **structured**: Moments come from a planned tutorial with clear instructional flow. Most details are explicitly taught.\n- **mixed**: Some moments are well-structured, others are scattered or conversational. Common for track breakdowns.\n- **unstructured**: Moments are extracted from livestreams, Q&A sessions, or very informal content. Insights were scattered across a long session.\n\n## Input format\n\nThe creator name is provided in a <creator> tag. Key moments are provided inside <moments> tags as a JSON array, enriched with classification metadata (topic_category, topic_tags). All moments are from the same creator and related topic area. ALWAYS use the creator name from the <creator> tag in titles, slugs, and prose — never invent or guess a creator name from transcript content.\n\n## Output format\n\nReturn a JSON object with a single key \"pages\" containing a list of synthesized pages. Most inputs produce a single page, but if the moments clearly cover two distinctly separate techniques (e.g., moments about both \"kick design\" and \"hi-hat design\" that happen to share a topic_category), split them into separate pages. When splitting, you MUST assign each moment to exactly one page via the moment_indices field — every input moment index must appear in exactly one page's moment_indices array.\n\n```json\n{\n  \"pages\": [\n    {\n      \"title\": \"Snare Design by ExampleCreator\",\n      \"slug\": \"snare-design-examplecreator\",\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"drums\", \"snare\", \"layering\", \"saturation\", \"transient shaping\"],\n      \"summary\": \"ExampleCreator builds snares as three independent layers — transient click, tonal body, and noise tail — with each shaped by a transient shaper before any bus processing. The signature crunch comes from parallel soft-clip saturation with a pre-delay that preserves the clean transient. In dense mixes, he uses HP sidechaining on the snare bus to maintain punch without competing with sub content.\",\n      \"body_sections\": {\n        \"Layer construction\": \"ExampleCreator builds snares as three independent layers, each shaped before they are summed. The transient click is a short noise burst (2-5ms decay) — he uses Vital's noise oscillator for this, sometimes with a bandpass around 2-4kHz to control the character. The tonal body is a pitched sine or triangle wave around 180-220Hz, tuned to complement the key of the track. The tail is filtered white noise with a fast exponential decay.\\n\\nThe critical insight: he shapes each layer's transient independently before any bus processing. He uses Kilohearts Transient Shaper (attack +4 to +6dB, sustain -6 to -8dB) rather than compression for this, because \\\"compression adds sustain as a side effect while a transient shaper gives you direct independent control of both.\\\"\",\n        \"Saturation and the crunch character\": \"The signature ExampleCreator snare crunch comes from parallel saturation — not inline. He routes the summed snare to a send with Trash 2 using the tape algorithm at 30-40% wet. The key detail: he puts a pre-delay of approximately 5ms on the saturation send, which lets the clean transient click through untouched while only the body and tail pick up harmonic content.\\n\\nHe explicitly warns against saturating the transient directly — says it \\\"smears the snap into mush\\\" and you lose the precision that makes the snare cut through.\",\n        \"Mix context and bus processing\": \"In dense arrangements, ExampleCreator prioritizes punch over sustain. On the snare bus compressor, he uses a high-pass sidechain filter (around 200-300Hz) so low-end energy from the body layer does not trigger gain reduction. This keeps the snare's ability to cut through the mix independent of whatever the sub bass is doing.\\n\\nHe also checks the snare against the lead or vocal bus specifically, not just soloed — because the 2-4kHz presence range is where both elements compete, and he would rather notch the snare's body slightly than lose vocal clarity.\"\n      },\n      \"signal_chains\": [\n        {\n          \"name\": \"Snare layer processing\",\n          \"steps\": [\n            \"Noise osc (Vital) → Transient Shaper (Kilohearts, attack +6dB, sustain -8dB) → EQ (Pro-Q 3, shelf -3dB @ 12kHz)\",\n            \"Dry path → snare bus\",\n            \"Send → Pre-delay (5ms) → Trash 2 (tape algorithm, 35% wet) → snare bus\"\n          ]\n        }\n      ],\n      \"plugins\": [\"Vital\", \"Kilohearts Transient Shaper\", \"FabFilter Pro-Q 3\", \"iZotope Trash 2\"],\n      \"source_quality\": \"structured\",\n      \"moment_indices\": [0, 1, 2, 3, 4]\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **title**: The technique or concept name followed by \"by {name from <creator> tag}\" — concise and search-friendly. Examples: \"Snare Design by Break\", \"Bass Resampling Workflow by KOAN Sound\", \"Mid-Side EQ for Width by Mr. Bill\". Use title case.\n- **slug**: URL-safe, lowercase, hyphenated version of the title including creator name. Examples: \"snare-design-examplecreator\", \"bass-resampling-workflow-koan-sound\". The creator name in the slug prevents collisions when multiple creators teach the same technique.\n- **topic_category**: The primary category. Must match the taxonomy.\n- **topic_tags**: All relevant tags aggregated from the classified moments. Deduplicated.\n- **summary**: 2-4 sentences that capture the essence of the entire technique page. This summary appears as the page header and in search results, so it must be information-dense and compelling. A reader should understand the core approach from this summary alone.\n- **body_sections**: Dictionary of section_name → prose content. Section names are derived from content, not generic templates. Prose follows all voice, tone, and quality guidelines above. Use \\n\\n for paragraph breaks within a section.\n- **signal_chains**: Array of signal chain objects. Each has a \"name\" (what this chain is for) and \"steps\" (ordered list of stages with plugin names, settings, and roles). Only include when explicitly demonstrated by the creator. Empty array if not applicable.\n- **plugins**: Deduplicated array of all plugins, instruments, and specific tools mentioned across the moments. Use \"<Manufacturer> <PluginName>\" format consistently (e.g., \"FabFilter Pro-Q 3\" not \"Pro-Q\", \"Xfer Serum\" not just \"Serum\", \"Valhalla VintageVerb\" not \"Valhalla reverb\", \"Kilohearts Disperser\" not \"Disperser\"). Always include the manufacturer name for disambiguation.\n- **source_quality**: One of \"structured\", \"mixed\", \"unstructured\".\n- **moment_indices**: Array of integer indices from the input moments list that this page covers. Every moment index must appear in exactly one page. If you produce a single page, include all indices. If you split into multiple pages, partition the indices so each moment is assigned to the page it most closely relates to. This field is required.\n\n## Critical rules\n\n- Never produce generic filler prose. Every sentence should contain specific, actionable information or meaningful creator reasoning. If you find yourself writing \"This technique is useful for...\" or \"This is an important aspect of production...\" — delete it and write something specific instead.\n- Never invent information. If the source moments don't specify a value, don't make one up. Say \"he adjusts the attack\" not \"he sets the attack to 2ms\" if the specific value wasn't mentioned.\n- Preserve the creator's actual opinions and warnings. These are often the most valuable content. Quote them directly when they are memorable or forceful.\n- If the source moments are thin (only 1-2 moments with brief summaries), produce a proportionally shorter page. A 2-section page with genuine substance is better than a 5-section page padded with filler.\n- Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1775033201907}}
{"type":"message","id":"cd1586e8","parentId":"99429426","timestamp":"2026-04-01T08:46:46.437Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — the stage 5 prompt is substantial. Now let me read the stage 5 synthesis function and the quality package's existing structure."},{"type":"toolCall","id":"toolu_016UNdAKeNoX3S93JZTZ9RZh","name":"read","arguments":{"limit":300,"offset":1022,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01HiHkopHXJTjsHbToaniVeN","name":"read","arguments":{"path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":165,"cacheRead":69899,"cacheWrite":3987,"totalTokens":74052,"cost":{"input":0.000005,"output":0.004125,"cacheRead":0.0349495,"cacheWrite":0.02491875,"total":0.06399825}},"stopReason":"toolUse","timestamp":1775033201908}}
{"type":"message","id":"71e3795f","parentId":"cd1586e8","timestamp":"2026-04-01T08:46:46.440Z","message":{"role":"toolResult","toolCallId":"toolu_016UNdAKeNoX3S93JZTZ9RZh","toolName":"read","content":[{"type":"text","text":"def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    For large category groups (exceeding synthesis_chunk_size), moments are\n    split into chronological chunks, synthesized independently, then pages\n    with matching slugs are merged via a dedicated merge LLM call.\n\n    Sets processing_status to 'complete'.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n\n    settings = get_settings()\n    chunk_size = settings.synthesis_chunk_size\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Resolve creator name for the LLM prompt\n        creator = session.execute(\n            select(Creator).where(Creator.id == video.creator_id)\n        ).scalar_one_or_none()\n        creator_name = creator.name if creator else \"Unknown\"\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        # Normalize category casing to prevent near-duplicate groups\n        # (e.g., \"Sound design\" vs \"Sound Design\")\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\").strip().title()\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        hard_limit = settings.llm_max_tokens_hard_limit\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n\n        for category, moment_group in groups.items():\n            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:\n                # Small group — try single LLM call first\n                try:\n                    result = _synthesize_chunk(\n                        moment_group, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, f\"category:{category}\",\n                    )\n                    synthesized_pages = list(result.pages)\n                    logger.info(\n                        \"Stage 5: category '%s' — %d moments, %d page(s) from single call\",\n                        category, len(moment_group), len(synthesized_pages),\n                    )\n                except LLMTruncationError:\n                    # Output too large for model context — split in half and retry\n                    logger.warning(\n                        \"Stage 5: category '%s' truncated with %d moments. \"\n                        \"Splitting into smaller chunks and retrying.\",\n                        category, len(moment_group),\n                    )\n                    half = max(1, len(moment_group) // 2)\n                    chunk_pages = []\n                    for sub_start in range(0, len(moment_group), half):\n                        sub_chunk = moment_group[sub_start:sub_start + half]\n                        sub_label = f\"category:{category} recovery-chunk:{sub_start // half + 1}\"\n                        sub_result = _synthesize_chunk(\n                            sub_chunk, category, creator_name,\n                            system_prompt, llm, model_override, modality, hard_limit,\n                            video_id, run_id, sub_label,\n                        )\n                        # Reindex moment_indices to global offsets\n                        for p in sub_result.pages:\n                            if p.moment_indices:\n                                p.moment_indices = [idx + sub_start for idx in p.moment_indices]\n                        chunk_pages.extend(sub_result.pages)\n                    synthesized_pages = chunk_pages\n                    logger.info(\n                        \"Stage 5: category '%s' — %d page(s) from recovery split\",\n                        category, len(synthesized_pages),\n                    )\n            else:\n                # Large group — split into chunks, synthesize each, then merge\n                num_chunks = (len(moment_group) + chunk_size - 1) // chunk_size\n                logger.info(\n                    \"Stage 5: category '%s' has %d moments — splitting into %d chunks of ≤%d\",\n                    category, len(moment_group), num_chunks, chunk_size,\n                )\n\n                chunk_pages = []\n                for chunk_idx in range(num_chunks):\n                    chunk_start = chunk_idx * chunk_size\n                    chunk_end = min(chunk_start + chunk_size, len(moment_group))\n                    chunk = moment_group[chunk_start:chunk_end]\n                    chunk_label = f\"category:{category} chunk:{chunk_idx + 1}/{num_chunks}\"\n\n                    result = _synthesize_chunk(\n                        chunk, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, chunk_label,\n                    )\n                    chunk_pages.extend(result.pages)\n                    logger.info(\n                        \"Stage 5: %s produced %d page(s)\",\n                        chunk_label, len(result.pages),\n                    )\n\n                # Merge pages with matching slugs across chunks\n                logger.info(\n                    \"Stage 5: category '%s' — %d total pages from %d chunks, checking for merges\",\n                    category, len(chunk_pages), num_chunks,\n                )\n                synthesized_pages = _merge_pages_by_slug(\n                    chunk_pages, creator_name,\n                    llm, model_override, modality, hard_limit,\n                    video_id, run_id,\n                )\n                logger.info(\n                    \"Stage 5: category '%s' — %d final page(s) after merge\",\n                    category, len(synthesized_pages),\n                )\n\n            # ── Persist pages to DB ──────────────────────────────────\n            # Load prior pages from this video (snapshot taken before pipeline reset)\n            prior_page_ids = _load_prior_pages(video_id)\n\n            for page_data in synthesized_pages:\n                page_moment_indices = getattr(page_data, \"moment_indices\", None) or []\n                existing = None\n\n                # First: check by slug (most specific match)\n                if existing is None:\n                    existing = session.execute(\n                        select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n                    ).scalar_one_or_none()\n\n                # Fallback: check prior pages from this video by creator + category\n                # Use .first() since multiple pages may share a category\n                if existing is None and prior_page_ids:\n                    existing = session.execute(\n                        select(TechniquePage).where(\n                            TechniquePage.id.in_(prior_page_ids),\n                            TechniquePage.creator_id == video.creator_id,\n                            func.lower(TechniquePage.topic_category) == func.lower(page_data.topic_category or category),\n                        )\n                    ).scalars().first()\n                    if existing:\n                        logger.info(\n                            \"Stage 5: Matched prior page '%s' (id=%s) by creator+category for video_id=%s\",\n                            existing.slug, existing.id, video_id,\n                        )\n\n                if existing:\n                    # Snapshot existing content before overwriting\n                    try:\n                        sq = existing.source_quality\n                        sq_value = sq.value if hasattr(sq, 'value') else sq\n                        snapshot = {\n                            \"title\": existing.title,\n                            \"slug\": existing.slug,\n                            \"topic_category\": existing.topic_category,\n                            \"topic_tags\": existing.topic_tags,\n                            \"summary\": existing.summary,\n                            \"body_sections\": existing.body_sections,\n                            \"signal_chains\": existing.signal_chains,\n                            \"plugins\": existing.plugins,\n                            \"source_quality\": sq_value,\n                        }\n                        version_count = session.execute(\n                            select(func.count()).where(\n                                TechniquePageVersion.technique_page_id == existing.id\n                            )\n                        ).scalar()\n                        version_number = version_count + 1\n\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                            version_number=version_number,\n                            content_snapshot=snapshot,\n                            pipeline_metadata=_capture_pipeline_metadata(),\n                        )\n                        session.add(version)\n                        logger.info(\n                            \"Version snapshot v%d created for page slug=%s\",\n                            version_number, existing.slug,\n                        )\n                    except Exception as snap_exc:\n                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    page_tags = _compute_page_tags(page_moment_indices, moment_group, all_tags)\n                    existing.topic_tags = page_tags\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=_compute_page_tags(page_moment_indices, moment_group, all_tags),\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n        # Update processing_status\n        video.processing_status = ProcessingStatus.complete\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage5_synthesis\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage5_synthesis\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 6: Embed & Index ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=0)\n\n[458 more lines in file. Use offset=1322 to continue.]"}],"isError":false,"timestamp":1775033206439}}
{"type":"message","id":"02f1f906","parentId":"71e3795f","timestamp":"2026-04-01T08:46:46.445Z","message":{"role":"toolResult","toolCallId":"toolu_01HiHkopHXJTjsHbToaniVeN","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM fitness test suite.\n\nRun with: python -m pipeline.quality fitness\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    args = parser.parse_args()\n\n    if args.command is None:\n        parser.print_help()\n        return 1\n\n    if args.command == \"fitness\":\n        settings = get_settings()\n        client = LLMClient(settings)\n        runner = FitnessRunner(client)\n        return runner.run_all()\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775033206443}}
{"type":"message","id":"50013ff9","parentId":"02f1f906","timestamp":"2026-04-01T08:46:51.357Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the `_synthesize_chunk` function and the data models to understand the output structure."},{"type":"toolCall","id":"toolu_01FyYF3YdN6HwQ2ik74Z6Uk1","name":"read","arguments":{"limit":50,"offset":878,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01Tztu16AFYBsef2DLcgNxRH","name":"bash","arguments":{"command":"rg -n \"class TechniquePage\\b\" backend/models/ 2>/dev/null || rg -n \"class TechniquePage\\b\" backend/ --type py 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":198,"cacheRead":73886,"cacheWrite":4044,"totalTokens":78129,"cost":{"input":0.000005,"output":0.00495,"cacheRead":0.036943,"cacheWrite":0.025275000000000002,"total":0.067173}},"stopReason":"toolUse","timestamp":1775033206444}}
{"type":"message","id":"3dccc6e5","parentId":"50013ff9","timestamp":"2026-04-01T08:46:51.367Z","message":{"role":"toolResult","toolCallId":"toolu_01FyYF3YdN6HwQ2ik74Z6Uk1","toolName":"read","content":[{"type":"text","text":"def _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n    E.g. 'wavetable-sound-design-copycatt' → 'wavetable-sound-design'\n    Also normalizes casing.\n    \"\"\"\n    return slug.lower().strip()\n\n\n[852 more lines in file. Use offset=928 to continue.]"}],"isError":false,"timestamp":1775033211366}}
{"type":"message","id":"2611db05","parentId":"3dccc6e5","timestamp":"2026-04-01T08:46:51.378Z","message":{"role":"toolResult","toolCallId":"toolu_01Tztu16AFYBsef2DLcgNxRH","toolName":"bash","content":[{"type":"text","text":"backend/models.py:205:class TechniquePage(Base):\n"}],"isError":false,"timestamp":1775033211376}}
{"type":"message","id":"d59e7ef2","parentId":"2611db05","timestamp":"2026-04-01T08:46:55.309Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01N2fBjXx3TjSvRrLTkMaYVE","name":"bash","arguments":{"command":"rg -n \"class SynthesisResult\\|class SynthesizedPage\\|class KeyMoment\" backend/ --type py 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01Lgi1FTopRem6fbMEfqZfQL","name":"read","arguments":{"limit":40,"offset":205,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":165,"cacheRead":77930,"cacheWrite":868,"totalTokens":78964,"cost":{"input":0.000005,"output":0.004125,"cacheRead":0.038965,"cacheWrite":0.005425,"total":0.04852}},"stopReason":"toolUse","timestamp":1775033211377}}
{"type":"message","id":"0f81c113","parentId":"d59e7ef2","timestamp":"2026-04-01T08:46:55.332Z","message":{"role":"toolResult","toolCallId":"toolu_01N2fBjXx3TjSvRrLTkMaYVE","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033215331}}
{"type":"message","id":"0af44c24","parentId":"0f81c113","timestamp":"2026-04-01T08:46:55.335Z","message":{"role":"toolResult","toolCallId":"toolu_01Lgi1FTopRem6fbMEfqZfQL","toolName":"read","content":[{"type":"text","text":"class TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n\n[220 more lines in file. Use offset=245 to continue.]"}],"isError":false,"timestamp":1775033215331}}
{"type":"message","id":"7f72b49d","parentId":"0af44c24","timestamp":"2026-04-01T08:46:58.847Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012R3MGmRPqLXtDUaQy3gkfy","name":"bash","arguments":{"command":"rg -n \"class SynthesisResult\\|class SynthesizedPage\" backend/ --type py -r 2>/dev/null; rg -rn \"SynthesisResult\" backend/pipeline/ --type py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":108,"cacheRead":78798,"cacheWrite":916,"totalTokens":79823,"cost":{"input":0.000005,"output":0.0027,"cacheRead":0.039398999999999997,"cacheWrite":0.005725,"total":0.047828999999999997}},"stopReason":"toolUse","timestamp":1775033215333}}
{"type":"message","id":"1b6c8616","parentId":"7f72b49d","timestamp":"2026-04-01T08:46:58.875Z","message":{"role":"toolResult","toolCallId":"toolu_012R3MGmRPqLXtDUaQy3gkfy","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/schemas.py:class n(BaseModel):\nbackend/pipeline/stages.py:    n,\nbackend/pipeline/stages.py:) -> n:\nbackend/pipeline/stages.py:    Returns the parsed n.\nbackend/pipeline/stages.py:        system_prompt, user_prompt, response_model=n,\nbackend/pipeline/stages.py:            request_params=_build_request_params(estimated_input, model_override, modality, \"n\", hard_limit),\nbackend/pipeline/stages.py:        raw, n, llm, system_prompt, user_prompt,\nbackend/pipeline/stages.py:            response_model=n,\nbackend/pipeline/stages.py:                request_params=_build_request_params(max_tokens, model_override, modality, \"n\", hard_limit),\nbackend/pipeline/stages.py:            raw, n, llm,\n"}],"isError":false,"timestamp":1775033218871}}
{"type":"message","id":"ceed9476","parentId":"1b6c8616","timestamp":"2026-04-01T08:47:01.773Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TEBBGdWA6MWB6JG77UpVSh","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":57,"cacheRead":79714,"cacheWrite":335,"totalTokens":80107,"cost":{"input":0.000005,"output":0.001425,"cacheRead":0.039857,"cacheWrite":0.00209375,"total":0.043380749999999996}},"stopReason":"toolUse","timestamp":1775033218873}}
{"type":"message","id":"1fe7109e","parentId":"ceed9476","timestamp":"2026-04-01T08:47:01.778Z","message":{"role":"toolResult","toolCallId":"toolu_01TEBBGdWA6MWB6JG77UpVSh","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775033221776}}
{"type":"message","id":"4bc8d441","parentId":"1fe7109e","timestamp":"2026-04-01T08:47:06.638Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture of the data model and stage 5 output. Let me check what the fitness runner looks like to understand the pattern, and check for existing config settings relevant to quality/voice."},{"type":"toolCall","id":"toolu_01QzjstZrRs13PxgP7VibtGS","name":"read","arguments":{"path":"backend/pipeline/quality/fitness.py"}},{"type":"toolCall","id":"toolu_01FXAFgFaFP3x7fBTH8XwofU","name":"bash","arguments":{"command":"rg -n \"voice\\|voice_level\\|voice_preservation\\|voice_dial\" backend/ --type py 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":174,"cacheRead":80049,"cacheWrite":1090,"totalTokens":81314,"cost":{"input":0.000005,"output":0.004350000000000001,"cacheRead":0.0400245,"cacheWrite":0.0068125,"total":0.051191999999999994}},"stopReason":"toolUse","timestamp":1775033221777}}
{"type":"message","id":"248a2ffe","parentId":"4bc8d441","timestamp":"2026-04-01T08:47:06.645Z","message":{"role":"toolResult","toolCallId":"toolu_01QzjstZrRs13PxgP7VibtGS","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM fitness test runner.\n\nTests four categories:\n1. Mandelbrot reasoning — factual knowledge / reasoning depth\n2. JSON compliance — simple and nested structured output\n3. Instruction following — bullet count, keyword inclusion, casing\n4. Diverse prompt battery — summarization, classification, extraction\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom pipeline.llm_client import LLMClient\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Result types ─────────────────────────────────────────────────────────────\n\n@dataclass\nclass TestResult:\n    \"\"\"Outcome of a single fitness test.\"\"\"\n\n    name: str\n    passed: bool\n    elapsed_seconds: float\n    token_count: int | None = None\n    detail: str = \"\"\n\n\n@dataclass\nclass CategoryReport:\n    \"\"\"Results for one test category.\"\"\"\n\n    category: str\n    results: list[TestResult] = field(default_factory=list)\n\n    @property\n    def all_passed(self) -> bool:\n        return all(r.passed for r in self.results)\n\n\n# ── Pydantic models for JSON compliance tests ────────────────────────────────\n\nclass SimpleItem(BaseModel):\n    name: str\n    count: int\n\n\nclass Address(BaseModel):\n    street: str\n    city: str\n    zip_code: str\n\n\nclass PersonWithAddress(BaseModel):\n    name: str\n    age: int\n    address: Address\n\n\n# ── Runner ───────────────────────────────────────────────────────────────────\n\nclass FitnessRunner:\n    \"\"\"Runs all fitness tests against the configured LLM endpoint.\"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    # ── Public entry point ───────────────────────────────────────────────\n\n    def run_all(self) -> int:\n        \"\"\"Run all fitness tests, print report, return exit code (0=pass, 1=fail).\"\"\"\n        categories: list[CategoryReport] = []\n\n        # Connectivity pre-check — fail fast with a clear message\n        try:\n            self._probe_connectivity()\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            print(\n                f\"\\n✗ Cannot reach LLM endpoint at {url} (fallback {fallback})\\n\"\n                f\"  Error: {exc}\\n\"\n            )\n            return 1\n\n        categories.append(self._run_mandelbrot())\n        categories.append(self._run_json_compliance())\n        categories.append(self._run_instruction_following())\n        categories.append(self._run_diverse_battery())\n\n        self._print_report(categories)\n\n        return 0 if all(c.all_passed for c in categories) else 1\n\n    # ── Connectivity probe ───────────────────────────────────────────────\n\n    def _probe_connectivity(self) -> None:\n        \"\"\"Quick completion to verify the endpoint is reachable.\"\"\"\n        self.client.complete(\n            system_prompt=\"You are a test probe.\",\n            user_prompt=\"Respond with the single word: ok\",\n        )\n\n    # ── Category 1: Mandelbrot reasoning ─────────────────────────────────\n\n    def _run_mandelbrot(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Mandelbrot Reasoning\")\n        cat.results.append(self._test_mandelbrot())\n        return cat\n\n    def _test_mandelbrot(self) -> TestResult:\n        name = \"mandelbrot_area_knowledge\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a mathematics expert. Answer precisely and concisely.\",\n                user_prompt=(\n                    \"What is the approximate area of the Mandelbrot set? \"\n                    \"Include the numerical value and mention whether the exact area is known.\"\n                ),\n                modality=\"thinking\",\n            )\n            elapsed = time.monotonic() - t0\n            text = resp.lower()\n            # Check for key concepts\n            has_area = any(kw in text for kw in [\"1.506\", \"1.507\", \"1.50659\"])\n            has_uncertainty = any(\n                kw in text\n                for kw in [\"not exactly known\", \"not known exactly\", \"approximate\", \"estimated\", \"conjecture\"]\n            )\n            passed = has_area and has_uncertainty\n            detail = \"\" if passed else f\"Missing: area={has_area}, uncertainty={has_uncertainty}. Response: {resp[:200]}\"\n            return TestResult(\n                name=name,\n                passed=passed,\n                elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Category 2: JSON compliance ──────────────────────────────────────\n\n    def _run_json_compliance(self) -> CategoryReport:\n        cat = CategoryReport(category=\"JSON Compliance\")\n        cat.results.append(self._test_json_simple())\n        cat.results.append(self._test_json_nested())\n        return cat\n\n    def _test_json_simple(self) -> TestResult:\n        name = \"json_simple_object\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a JSON generator. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    'Generate a JSON object with exactly two keys: \"name\" (a string) '\n                    'and \"count\" (an integer). Example structure: {\"name\": \"...\", \"count\": N}'\n                ),\n                response_model=SimpleItem,\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            return self._validate_json(name, resp, SimpleItem, elapsed)\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_json_nested(self) -> TestResult:\n        name = \"json_nested_object\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a JSON generator. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    'Generate a JSON object with keys \"name\" (string), \"age\" (integer), '\n                    'and \"address\" (object with \"street\", \"city\", \"zip_code\" string fields).'\n                ),\n                response_model=PersonWithAddress,\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            return self._validate_json(name, resp, PersonWithAddress, elapsed)\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _validate_json(\n        self,\n        name: str,\n        resp: str,\n        model: type[BaseModel],\n        elapsed: float,\n    ) -> TestResult:\n        \"\"\"Parse response as JSON, validate against Pydantic model.\"\"\"\n        text = str(resp).strip()\n        if not text:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=\"Empty response from LLM\",\n            )\n        try:\n            parsed = json.loads(text)\n        except json.JSONDecodeError as exc:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=f\"Invalid JSON: {exc}. Raw: {text[:200]}\",\n            )\n        try:\n            model.model_validate(parsed)\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=f\"Schema validation failed: {exc}\",\n            )\n        return TestResult(\n            name=name, passed=True, elapsed_seconds=round(elapsed, 2),\n            token_count=getattr(resp, \"completion_tokens\", None),\n        )\n\n    # ── Category 3: Instruction following ────────────────────────────────\n\n    def _run_instruction_following(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Instruction Following\")\n        cat.results.append(self._test_bullet_count())\n        cat.results.append(self._test_keyword_inclusion())\n        cat.results.append(self._test_lowercase_only())\n        return cat\n\n    def _test_bullet_count(self) -> TestResult:\n        name = \"instruction_bullet_count\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=\"List exactly 3 benefits of exercise. Use bullet points starting with '- '.\",\n            )\n            elapsed = time.monotonic() - t0\n            lines = [l.strip() for l in str(resp).strip().splitlines() if l.strip().startswith(\"- \")]\n            passed = len(lines) == 3\n            detail = \"\" if passed else f\"Expected 3 bullets, got {len(lines)}: {str(resp)[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_keyword_inclusion(self) -> TestResult:\n        name = \"instruction_keyword_inclusion\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=(\n                    \"Write one sentence about the weather. \"\n                    'You MUST include the word \"elephant\" somewhere in your sentence.'\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            passed = \"elephant\" in str(resp).lower()\n            detail = \"\" if passed else f\"Missing keyword 'elephant'. Response: {str(resp)[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_lowercase_only(self) -> TestResult:\n        name = \"instruction_lowercase_only\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=(\n                    \"Write a short sentence about the ocean. \"\n                    \"Use ONLY lowercase letters — no uppercase at all, not even at the start.\"\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            # Allow non-alpha chars (punctuation, spaces, numbers) but no uppercase letters\n            has_upper = any(c.isupper() for c in text)\n            passed = not has_upper and len(text) > 5\n            detail = \"\" if passed else f\"Contains uppercase or too short. Response: {text[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Category 4: Diverse prompt battery ───────────────────────────────\n\n    def _run_diverse_battery(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Diverse Prompt Battery\")\n        cat.results.append(self._test_summarization())\n        cat.results.append(self._test_classification())\n        cat.results.append(self._test_extraction())\n        return cat\n\n    def _test_summarization(self) -> TestResult:\n        name = \"battery_summarization\"\n        paragraph = (\n            \"The James Webb Space Telescope (JWST) is the largest optical telescope in space. \"\n            \"Launched in December 2021, it is designed to conduct infrared astronomy. Its high \"\n            \"resolution and sensitivity allow it to view objects too old and distant for the Hubble \"\n            \"Space Telescope. Among its goals are observing the first stars and the formation of \"\n            \"the first galaxies, and detailed atmospheric characterization of exoplanets.\"\n        )\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a concise summarizer.\",\n                user_prompt=f\"Summarize the following in exactly 2 sentences:\\n\\n{paragraph}\",\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            # Rough sentence count: split on period followed by space or end\n            sentences = [s.strip() for s in text.replace(\"! \", \". \").split(\". \") if s.strip()]\n            # Be generous: 1-3 sentences is acceptable\n            passed = 1 <= len(sentences) <= 3 and len(text) > 20\n            detail = \"\" if passed else f\"Expected ~2 sentences, got {len(sentences)}. Response: {text[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_classification(self) -> TestResult:\n        name = \"battery_classification\"\n        categories = [\"technology\", \"sports\", \"politics\", \"science\", \"entertainment\"]\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=(\n                    \"You are a text classifier. Respond with ONLY one word from the given categories.\"\n                ),\n                user_prompt=(\n                    f\"Classify the following text into one of these categories: {', '.join(categories)}\\n\\n\"\n                    \"Text: \\\"NASA's Perseverance rover has discovered organic molecules on Mars, \"\n                    \"suggesting the planet may have once harbored microbial life.\\\"\\n\\n\"\n                    \"Category:\"\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            answer = str(resp).strip().lower().rstrip(\".\")\n            passed = answer in categories\n            detail = \"\" if passed else f\"Response '{answer}' not in {categories}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_extraction(self) -> TestResult:\n        name = \"battery_extraction\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a data extractor. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    \"Extract the following fields as a JSON object: \"\n                    '\"event_name\", \"date\", \"location\"\\n\\n'\n                    \"Text: \\\"The annual Tech Summit 2026 will be held on March 15, 2026 \"\n                    'in San Francisco, California.\"\\n\\n'\n                    \"JSON:\"\n                ),\n                response_model=BaseModel,  # triggers json mode\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            if not text:\n                return TestResult(\n                    name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                    token_count=getattr(resp, \"completion_tokens\", None),\n                    detail=\"Empty response from LLM\",\n                )\n            try:\n                parsed = json.loads(text)\n            except json.JSONDecodeError as exc:\n                return TestResult(\n                    name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                    token_count=getattr(resp, \"completion_tokens\", None),\n                    detail=f\"Invalid JSON: {exc}. Raw: {text[:200]}\",\n                )\n            required_keys = {\"event_name\", \"date\", \"location\"}\n            present = set(parsed.keys()) & required_keys\n            passed = present == required_keys\n            detail = \"\" if passed else f\"Missing keys: {required_keys - present}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Report formatting ────────────────────────────────────────────────\n\n    def _print_report(self, categories: list[CategoryReport]) -> None:\n        \"\"\"Print a formatted pass/fail report to stdout.\"\"\"\n        total = 0\n        passed_count = 0\n\n        print(\"\\n\" + \"=\" * 60)\n        print(\"  FYN-LLM FITNESS REPORT\")\n        print(\"=\" * 60)\n\n        for cat in categories:\n            status = \"✓ PASS\" if cat.all_passed else \"✗ FAIL\"\n            print(f\"\\n  [{status}] {cat.category}\")\n            for r in cat.results:\n                total += 1\n                icon = \"✓\" if r.passed else \"✗\"\n                tokens = f\" ({r.token_count} tok)\" if r.token_count else \"\"\n                print(f\"    {icon} {r.name}  [{r.elapsed_seconds}s{tokens}]\")\n                if r.detail:\n                    # Indent detail lines\n                    for line in r.detail.splitlines():\n                        print(f\"      {line}\")\n                if r.passed:\n                    passed_count += 1\n\n        print(\"\\n\" + \"-\" * 60)\n        print(f\"  Total: {passed_count}/{total} passed\")\n        if passed_count == total:\n            print(\"  Result: ✓ ALL PASS\")\n        else:\n            print(f\"  Result: ✗ {total - passed_count} FAILED\")\n        print(\"=\" * 60 + \"\\n\")\n"}],"isError":false,"timestamp":1775033226644}}
{"type":"message","id":"019eb006","parentId":"248a2ffe","timestamp":"2026-04-01T08:47:06.652Z","message":{"role":"toolResult","toolCallId":"toolu_01FXAFgFaFP3x7fBTH8XwofU","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033226651}}
{"type":"message","id":"4b53639d","parentId":"019eb006","timestamp":"2026-04-01T08:47:11.564Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the LLM client and config for any voice-level or scoring-related settings."},{"type":"toolCall","id":"toolu_01XBpfwCNJRKRnSvAgk6aaa9","name":"bash","arguments":{"command":"rg -n \"voice_level\\|voice_preservation\\|synthesis_chunk\\|llm_max_tokens\\|llm_api_url\\|llm_fallback\\|quality_scorer\\|scoring\" backend/config.py | head -20"}},{"type":"toolCall","id":"toolu_017SWBvSquLjLoKYKUSpkA4X","name":"bash","arguments":{"command":"rg -n \"def complete\" backend/pipeline/llm_client.py | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":189,"cacheRead":81139,"cacheWrite":5248,"totalTokens":86577,"cost":{"input":0.000005,"output":0.004725,"cacheRead":0.0405695,"cacheWrite":0.0328,"total":0.0780995}},"stopReason":"toolUse","timestamp":1775033226651}}
{"type":"message","id":"310b533e","parentId":"4b53639d","timestamp":"2026-04-01T08:47:11.582Z","message":{"role":"toolResult","toolCallId":"toolu_01XBpfwCNJRKRnSvAgk6aaa9","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033231582}}
{"type":"message","id":"fde7a0fe","parentId":"310b533e","timestamp":"2026-04-01T08:47:11.589Z","message":{"role":"toolResult","toolCallId":"toolu_017SWBvSquLjLoKYKUSpkA4X","toolName":"bash","content":[{"type":"text","text":"179:    def complete(\n"}],"isError":false,"timestamp":1775033231587}}
{"type":"message","id":"06b21d79","parentId":"fde7a0fe","timestamp":"2026-04-01T08:47:15.976Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LMXnqbkvC5ryW6qWcuJczM","name":"read","arguments":{"limit":60,"offset":179,"path":"backend/pipeline/llm_client.py"}},{"type":"toolCall","id":"toolu_0124X2yJmV5xu3iR7HemAtfi","name":"bash","arguments":{"command":"rg -n \"synthesis_chunk_size\\|voice\\|scoring\" backend/config.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":154,"cacheRead":86387,"cacheWrite":259,"totalTokens":86801,"cost":{"input":0.000005,"output":0.00385,"cacheRead":0.043193499999999996,"cacheWrite":0.0016187500000000002,"total":0.048667249999999995}},"stopReason":"toolUse","timestamp":1775033231588}}
{"type":"message","id":"1aa87bf0","parentId":"06b21d79","timestamp":"2026-04-01T08:47:15.989Z","message":{"role":"toolResult","toolCallId":"toolu_01LMXnqbkvC5ryW6qWcuJczM","toolName":"read","content":[{"type":"text","text":"    def complete(\n        self,\n        system_prompt: str,\n        user_prompt: str,\n        response_model: type[BaseModel] | None = None,\n        modality: str = \"chat\",\n        model_override: str | None = None,\n        on_complete: \"Callable | None\" = None,\n        max_tokens: int | None = None,\n    ) -> \"LLMResponse\":\n        \"\"\"Send a chat completion request, falling back on connection/timeout errors.\n\n        Parameters\n        ----------\n        system_prompt:\n            System message content.\n        user_prompt:\n            User message content.\n        response_model:\n            If provided and modality is \"chat\", ``response_format`` is set to\n            ``{\"type\": \"json_object\"}``. For \"thinking\" modality, JSON\n            instructions are appended to the system prompt instead.\n        modality:\n            Either \"chat\" (default) or \"thinking\". Thinking modality skips\n            response_format and strips ``<think>`` tags from output.\n        model_override:\n            Model name to use instead of the default. If None, uses the\n            configured default for the endpoint.\n        max_tokens:\n            Override for max_tokens on this call. If None, falls back to\n            the configured ``llm_max_tokens`` from settings.\n\n        Returns\n        -------\n        LLMResponse\n            Raw completion text (str subclass) with finish_reason metadata.\n        \"\"\"\n        kwargs: dict = {}\n        effective_system = system_prompt\n\n        if modality == \"thinking\":\n            # Thinking models often don't support response_format: json_object.\n            # Instead, append explicit JSON instructions to the system prompt.\n            if response_model is not None:\n                json_schema_hint = (\n                    \"\\n\\nYou MUST respond with ONLY valid JSON. \"\n                    \"No markdown code fences, no explanation, no preamble — \"\n                    \"just the raw JSON object.\"\n                )\n                effective_system = system_prompt + json_schema_hint\n        else:\n            # Chat modality — use standard JSON mode\n            if response_model is not None:\n                kwargs[\"response_format\"] = {\"type\": \"json_object\"}\n\n        messages = [\n            {\"role\": \"system\", \"content\": effective_system},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n\n[143 more lines in file. Use offset=239 to continue.]"}],"isError":false,"timestamp":1775033235988}}
{"type":"message","id":"ec96a415","parentId":"1aa87bf0","timestamp":"2026-04-01T08:47:15.994Z","message":{"role":"toolResult","toolCallId":"toolu_0124X2yJmV5xu3iR7HemAtfi","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033235992}}
{"type":"message","id":"aff573d3","parentId":"ec96a415","timestamp":"2026-04-01T08:47:19.112Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01M6FYL57XJrBBe2QcX5DyeG","name":"bash","arguments":{"command":"rg -n \"synthesis_chunk\\|chunk_size\\|llm_max_tokens\\|llm_api_url\\|llm_fallback_url\" backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":86646,"cacheWrite":799,"totalTokens":87541,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.043323,"cacheWrite":0.00499375,"total":0.05069675}},"stopReason":"toolUse","timestamp":1775033235993}}
{"type":"message","id":"8e8662b3","parentId":"aff573d3","timestamp":"2026-04-01T08:47:19.127Z","message":{"role":"toolResult","toolCallId":"toolu_01M6FYL57XJrBBe2QcX5DyeG","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775033239124}}
{"type":"message","id":"a713d5ac","parentId":"8e8662b3","timestamp":"2026-04-01T08:47:22.823Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QpMv4e9EMdoF7HK9DH6vvt","name":"bash","arguments":{"command":"rg -n \"class Settings\\|synthesis\\|llm_\\|chunk\" backend/config.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":79,"cacheRead":87445,"cacheWrite":126,"totalTokens":87651,"cost":{"input":0.000005,"output":0.001975,"cacheRead":0.0437225,"cacheWrite":0.0007875,"total":0.046490000000000004}},"stopReason":"toolUse","timestamp":1775033239125}}
{"type":"message","id":"4fa848a2","parentId":"a713d5ac","timestamp":"2026-04-01T08:47:22.842Z","message":{"role":"toolResult","toolCallId":"toolu_01QpMv4e9EMdoF7HK9DH6vvt","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033242839}}
{"type":"message","id":"255d6076","parentId":"4fa848a2","timestamp":"2026-04-01T08:47:25.613Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014ciBRoYPXLcEcYH3TbDyMq","name":"bash","arguments":{"command":"find backend/ -name \"config.py\" -type f"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":64,"cacheRead":87571,"cacheWrite":93,"totalTokens":87729,"cost":{"input":0.000005,"output":0.0016,"cacheRead":0.0437855,"cacheWrite":0.0005812500000000001,"total":0.04597175}},"stopReason":"toolUse","timestamp":1775033242840}}
{"type":"message","id":"bebd32ab","parentId":"255d6076","timestamp":"2026-04-01T08:47:25.630Z","message":{"role":"toolResult","toolCallId":"toolu_014ciBRoYPXLcEcYH3TbDyMq","toolName":"bash","content":[{"type":"text","text":"backend/config.py\n"}],"isError":false,"timestamp":1775033245627}}
{"type":"message","id":"43fbdc44","parentId":"bebd32ab","timestamp":"2026-04-01T08:47:28.499Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CNp3qXZW5HuFUvw2dzv2oM","name":"bash","arguments":{"command":"rg -n \"class\\|synthesis\\|llm\\|chunk\\|voice\\|prompt\" backend/config.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":83,"cacheRead":87664,"cacheWrite":82,"totalTokens":87830,"cost":{"input":0.000005,"output":0.002075,"cacheRead":0.043831999999999996,"cacheWrite":0.0005125,"total":0.046424499999999994}},"stopReason":"toolUse","timestamp":1775033245628}}
{"type":"message","id":"04bb9ed9","parentId":"43fbdc44","timestamp":"2026-04-01T08:47:28.517Z","message":{"role":"toolResult","toolCallId":"toolu_01CNp3qXZW5HuFUvw2dzv2oM","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033248514}}
{"type":"message","id":"121eee7d","parentId":"04bb9ed9","timestamp":"2026-04-01T08:47:31.344Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019EdXg3so1c76DXgFzMFR2H","name":"bash","arguments":{"command":"head -5 backend/config.py && wc -l backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":70,"cacheRead":87746,"cacheWrite":97,"totalTokens":87914,"cost":{"input":0.000005,"output":0.00175,"cacheRead":0.043872999999999995,"cacheWrite":0.00060625,"total":0.04623425}},"stopReason":"toolUse","timestamp":1775033248515}}
{"type":"message","id":"07a55892","parentId":"121eee7d","timestamp":"2026-04-01T08:47:31.356Z","message":{"role":"toolResult","toolCallId":"toolu_019EdXg3so1c76DXgFzMFR2H","toolName":"bash","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n86 backend/config.py\n"}],"isError":false,"timestamp":1775033251354}}
{"type":"message","id":"5a1c4725","parentId":"07a55892","timestamp":"2026-04-01T08:47:33.931Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019tkYpRRjNNxXQuW7jaA588","name":"read","arguments":{"path":"backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":55,"cacheRead":87843,"cacheWrite":118,"totalTokens":88017,"cost":{"input":0.000005,"output":0.0013750000000000001,"cacheRead":0.043921499999999995,"cacheWrite":0.0007375,"total":0.046039}},"stopReason":"toolUse","timestamp":1775033251354}}
{"type":"message","id":"5d80d06c","parentId":"5a1c4725","timestamp":"2026-04-01T08:47:33.937Z","message":{"role":"toolResult","toolCallId":"toolu_019tkYpRRjNNxXQuW7jaA588","toolName":"read","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"fyn-llm-agent-chat\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"fyn-llm-agent-chat\"\n\n    # Per-stage model overrides (optional — falls back to llm_model / \"chat\")\n    llm_stage2_model: str | None = \"fyn-llm-agent-chat\"   # segmentation — mechanical, fast chat\n    llm_stage2_modality: str = \"chat\"\n    llm_stage3_model: str | None = \"fyn-llm-agent-think\"  # extraction — reasoning\n    llm_stage3_modality: str = \"thinking\"\n    llm_stage4_model: str | None = \"fyn-llm-agent-chat\"   # classification — mechanical, fast chat\n    llm_stage4_modality: str = \"chat\"\n    llm_stage5_model: str | None = \"fyn-llm-agent-think\"  # synthesis — reasoning\n    llm_stage5_modality: str = \"thinking\"\n\n    # Dynamic token estimation — each stage calculates max_tokens from input size\n    llm_max_tokens_hard_limit: int = 32768   # Hard ceiling for dynamic estimator\n    llm_max_tokens: int = 32768              # Fallback when no estimate is provided (must not exceed hard_limit)\n    llm_temperature: float = 0.0             # Deterministic output for structured JSON extraction\n\n    # Stage 5 synthesis chunking — max moments per LLM call before splitting\n    synthesis_chunk_size: int = 30\n\n    # Embedding endpoint\n    embedding_api_url: str = \"http://localhost:11434/v1\"\n    embedding_model: str = \"nomic-embed-text\"\n    embedding_dimensions: int = 768\n\n    # Qdrant\n    qdrant_url: str = \"http://localhost:6333\"\n    qdrant_collection: str = \"chrysopedia\"\n\n    # Prompt templates\n    prompts_path: str = \"./prompts\"\n\n    # Debug mode — when True, pipeline captures full LLM prompts and responses\n    debug_mode: bool = False\n\n    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\n\n    # Git commit SHA (set at Docker build time or via env var)\n    git_commit_sha: str = \"unknown\"\n\n    model_config = {\n        \"env_file\": \".env\",\n        \"env_file_encoding\": \"utf-8\",\n        \"case_sensitive\": False,\n    }\n\n\n@lru_cache\ndef get_settings() -> Settings:\n    \"\"\"Return cached application settings (singleton).\"\"\"\n    return Settings()\n"}],"isError":false,"timestamp":1775033253933}}
{"type":"message","id":"1cc7bd6b","parentId":"5d80d06c","timestamp":"2026-04-01T08:47:38.608Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what existing technique pages look like in the DB to understand real data patterns, and look at the `_build_moments_text` function."},{"type":"toolCall","id":"toolu_01R4tS2S69sK43a2ay1vkTRb","name":"read","arguments":{"limit":30,"offset":852,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":125,"cacheRead":87961,"cacheWrite":1011,"totalTokens":89098,"cost":{"input":0.000005,"output":0.003125,"cacheRead":0.0439805,"cacheWrite":0.006318750000000001,"total":0.05342925}},"stopReason":"toolUse","timestamp":1775033253935}}
{"type":"message","id":"329e8d28","parentId":"1cc7bd6b","timestamp":"2026-04-01T08:47:38.614Z","message":{"role":"toolResult","toolCallId":"toolu_01R4tS2S69sK43a2ay1vkTRb","toolName":"read","content":[{"type":"text","text":"def _build_moments_text(\n    moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text and collect all tags for a group of moments.\n\n    Returns (moments_text, all_tags).\n    \"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n\n[898 more lines in file. Use offset=882 to continue.]"}],"isError":false,"timestamp":1775033258611}}
{"type":"message","id":"4e98bb86","parentId":"329e8d28","timestamp":"2026-04-01T08:47:43.188Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have a thorough understanding of the codebase. Let me check the roadmap's success criteria for S02 more precisely."},{"type":"toolCall","id":"toolu_01LwEFCFMNdZ77tBoYM8w786","name":"bash","arguments":{"command":"rg -A 10 \"S02.*Stage 5 Quality Scorer\" .gsd/milestones/M013/M013-ROADMAP.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":119,"cacheRead":88972,"cacheWrite":533,"totalTokens":89625,"cost":{"input":0.000005,"output":0.002975,"cacheRead":0.044486,"cacheWrite":0.00333125,"total":0.050797249999999995}},"stopReason":"toolUse","timestamp":1775033258612}}
{"type":"message","id":"60ad450b","parentId":"4e98bb86","timestamp":"2026-04-01T08:47:43.205Z","message":{"role":"toolResult","toolCallId":"toolu_01LwEFCFMNdZ77tBoYM8w786","toolName":"bash","content":[{"type":"text","text":"| S02 | Stage 5 Quality Scorer & Voice Preservation Dial | high | S01 | ⬜ | Run scorer on a reference article — outputs composite score across 5 dimensions. Run same article at voice_level 0.2 vs 0.8 — voice preservation score differs meaningfully |\n| S03 | Prompt Variant Generator & Automated A/B Loop | high | S02 | ⬜ | Run `python -m pipeline.quality optimize --stage 5 --iterations 10` — generates prompt variants, scores each against reference articles, outputs leaderboard and score trajectory chart |\n| S04 | Expand to Pipeline Stages 2-4 | medium | S03 | ⬜ | Run `python -m pipeline.quality optimize --stage 3 --iterations 5` — optimizes extraction prompts with stage-appropriate scoring |\n"}],"isError":false,"timestamp":1775033263203}}
{"type":"message","id":"66598afc","parentId":"60ad450b","timestamp":"2026-04-01T08:48:43.724Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have the full picture. Let me write the research artifact."},{"type":"toolCall","id":"toolu_01RV65Zz3T23aKmTweMT3etb","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S02 Research: Stage 5 Quality Scorer & Voice Preservation Dial\n\n## Summary\n\nThis slice adds a `score` subcommand to the existing `pipeline.quality` CLI that evaluates Stage 5 synthesis output (technique pages) across 5 dimensions, plus a `voice_level` parameter that modifies the stage 5 prompt to dial voice preservation up or down — with the scorer proving the dial works by producing measurably different voice preservation scores.\n\nThe work is **targeted research** — known patterns (LLM-as-judge scoring, prompt interpolation), applied to well-understood codebase structures.\n\n## Recommendation\n\nBuild a `scorer.py` module in `backend/pipeline/quality/` alongside the existing `fitness.py`. The scorer:\n\n1. Takes a reference technique page (either from DB by slug, or from a JSON file) and scores it across 5 dimensions using LLM-as-judge evaluation.\n2. Optionally re-synthesizes the page with a modified prompt (voice_level dial injected into the stage 5 system prompt) before scoring.\n3. Outputs a structured report with per-dimension scores and a composite.\n\nThe voice_level dial modifies the stage 5 synthesis prompt by interpolating a voice-emphasis instruction. At 0.0 (\"clinical\"), the prompt suppresses direct quotes and creator voice. At 1.0 (\"maximum voice\"), it amplifies quote preservation, creator opinions, and personality. The existing prompt (stage5_synthesis.txt) already has strong voice preservation language — that's roughly voice_level 0.6-0.7 baseline.\n\n## Implementation Landscape\n\n### Existing code to build on\n\n| File | What it provides | How S02 uses it |\n|------|-----------------|-----------------|\n| `backend/pipeline/quality/__main__.py` | argparse CLI with subcommand pattern | Add `score` subcommand with `--slug`, `--file`, `--voice-level` args |\n| `backend/pipeline/quality/fitness.py` | `TestResult` dataclass, `FitnessRunner` pattern | Follow same pattern: `ScoreRunner` class, `ScoreResult` dataclass |\n| `backend/pipeline/stages.py` → `_synthesize_chunk()` | Calls LLM with stage5 prompt + moments | Scorer can reuse `_load_prompt()`, `_get_llm_client()`, `_get_stage_config()` |\n| `backend/pipeline/schemas.py` → `SynthesizedPage`, `SynthesisResult` | Pydantic models for stage 5 output | Scorer validates re-synthesized output against these |\n| `backend/config.py` → `Settings` | All LLM endpoint config | Scorer uses same LLM client config |\n| `prompts/stage5_synthesis.txt` | The full synthesis prompt (~4KB) | Base prompt for voice_level interpolation |\n| `backend/pipeline/llm_client.py` → `LLMClient.complete()` | LLM call with fallback, modality support | Used for both re-synthesis and scoring judge calls |\n\n### The 5 scoring dimensions\n\nBased on the stage 5 prompt's quality guidelines, these are the natural scoring dimensions:\n\n1. **Structural quality** — Does the page have well-named sections (not generic \"Overview\"/\"Tips\"), appropriate section count, 2-5 paragraphs per section, proper body_sections structure?\n2. **Content specificity** — Does the prose contain concrete details (frequencies, ratios, ms values, plugin settings) vs vague generalities? Ratio of specific claims to filler sentences.\n3. **Voice preservation** — Does the page preserve the creator's actual words, opinions, warnings? Presence of direct quotes, attributed opinions, personality.\n4. **Readability / flow** — Does the page read as synthesis (not concatenation)? Logical section ordering, merged related info, no redundancy, contradiction handling.\n5. **Factual fidelity** — Does the page avoid inventing information not in the source moments? No phantom specifics, no hallucinated plugin names or settings.\n\nEach dimension scored 0.0–1.0 by the LLM judge. Composite = weighted average (weights configurable, default equal).\n\n### Voice level dial implementation\n\nThe dial works by interpolating a voice-emphasis modifier into the stage 5 system prompt before the LLM call. This is NOT a new prompt file — it's a runtime prefix/suffix injected into the existing prompt.\n\n**voice_level 0.0 (\"clinical\"):** Append instruction to suppress direct quotes, write in neutral third-person encyclopedia style, avoid attributing opinions.\n\n**voice_level 0.5 (\"balanced\"):** No modification — use the base prompt as-is. The existing prompt already asks for moderate voice preservation.\n\n**voice_level 1.0 (\"maximum voice\"):** Append instruction to maximize direct quotes from transcript, preserve every memorable phrase, prioritize creator's exact words over paraphrase, include personality and strong opinions even if tangential.\n\nLinear interpolation between these anchor points selects which modifier text to inject and how strongly. In practice, 3 bands (low/mid/high) with blended instructions is simpler and more reliable than continuous interpolation with an LLM.\n\n### Scoring approach: LLM-as-judge\n\nThe scorer sends the technique page JSON + the source moments to the LLM with a scoring rubric prompt. The rubric prompt is a new file (`prompts/scoring_rubric.txt` or hardcoded in scorer.py — hardcoded is simpler for iteration, can extract later).\n\nThe judge LLM call returns a JSON object with per-dimension scores and brief justifications. Using the same LLM that generated the content is acceptable for relative scoring (comparing voice_level 0.2 vs 0.8 on the same content) even though it's not ideal for absolute quality assessment.\n\n### Reference article sourcing\n\nThe scorer needs a technique page to evaluate. Two modes:\n- `--slug <slug>`: Load from DB (requires DB connection). Loads the TechniquePage + its KeyMoments + classification data.\n- `--file <path>`: Load from a JSON file (no DB needed). The file contains the synthesized page JSON + the source moments array. This mode is useful for testing without a running stack.\n\nFor the voice_level comparison demo, the scorer needs to re-synthesize from source moments, not just score an existing page. So the flow is:\n1. Load reference moments (from DB or file)\n2. Synthesize at voice_level X using modified prompt\n3. Score the result\n4. Repeat at voice_level Y\n5. Compare scores\n\n### Data needed for reference test\n\nThe scorer needs at least one set of real key moments to re-synthesize. Options:\n- Connect to the production DB on ub01 and pull a technique page + its moments\n- Create a fixture JSON file with sample moments for offline testing\n\nA fixture file is better for the CLI demo — no DB dependency. The fixture should contain a realistic set of 5-8 moments with actual transcript excerpts, plugin mentions, and classification data.\n\n### File structure after S02\n\n```\nbackend/pipeline/quality/\n  __init__.py          (existing)\n  __main__.py          (modified — add 'score' subcommand)\n  fitness.py           (existing, untouched)\n  scorer.py            (NEW — ScoreRunner, ScoreResult, scoring logic)\n  voice_dial.py        (NEW — voice level prompt modifier)\n  fixtures/            (NEW — sample moments JSON for offline testing)\n    sample_moments.json\n```\n\n### Natural task seams\n\n1. **T01: ScoreResult dataclass + scoring rubric + ScoreRunner skeleton** — Define the 5-dimension scoring schema, write the LLM judge rubric prompt, implement `ScoreRunner.score_page()` that sends a page to the judge and returns structured scores. Wire into CLI as `score` subcommand with `--slug` and `--file` args.\n\n2. **T02: Voice dial implementation + re-synthesis flow** — Implement `voice_dial.py` with prompt modifier logic (3 bands). Add `--voice-level` arg to the `score` subcommand. When voice_level is provided, re-synthesize the page from source moments using the modified prompt before scoring. Build the sample fixture file.\n\n3. **T03: Integration verification + report formatting** — End-to-end test: score a reference article, run at voice_level 0.2 vs 0.8, verify scores differ meaningfully on the voice preservation dimension. Format the output report (similar to fitness report style). Verify CLI help text.\n\n### Risks\n\n| Risk | Likelihood | Mitigation |\n|------|-----------|------------|\n| LLM judge scores don't differentiate voice levels | Medium | The scoring rubric must be specific about what constitutes voice preservation (direct quotes present, attributed opinions, personality markers). If the judge is too coarse, sharpen the rubric with examples. |\n| No live LLM endpoint accessible from dev machine | High | Same as S01 — we can build and verify structure/imports locally, but actual scoring requires FYN-LLM. The fixture file allows verifying the data flow without a live endpoint. |\n| Re-synthesis is expensive (full stage 5 LLM call per voice level) | Low | Expected — this is a quality tool, not a hot path. Each run takes ~30-60s per synthesis. Acceptable for a CLI benchmarking tool. |\n\n### Skills check\n\nNo external skills needed. This is pure Python (dataclasses, Pydantic, argparse) using the existing LLM client. No new libraries required.\n","milestone_id":"M013","slice_id":"S02"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2368,"cacheRead":89505,"cacheWrite":336,"totalTokens":92210,"cost":{"input":0.000005,"output":0.0592,"cacheRead":0.0447525,"cacheWrite":0.0021000000000000003,"total":0.10605750000000001}},"stopReason":"toolUse","timestamp":1775033263204}}
{"type":"message","id":"ddc02178","parentId":"66598afc","timestamp":"2026-04-01T08:48:43.728Z","message":{"role":"toolResult","toolCallId":"toolu_01RV65Zz3T23aKmTweMT3etb","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M013/slices/S02/S02-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M013/slices/S02/S02-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775033323726}}
{"type":"message","id":"46407d08","parentId":"ddc02178","timestamp":"2026-04-01T08:48:46.206Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S02 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":89841,"cacheWrite":2406,"totalTokens":92259,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.044920499999999995,"cacheWrite":0.0150375,"total":0.060238}},"stopReason":"stop","timestamp":1775033323727}}
