{"type":"thinking_level_change","id":"88822cc4","parentId":null,"timestamp":"2026-04-01T09:12:11.151Z","thinkingLevel":"medium"}
{"type":"model_change","id":"fd2db2e3","parentId":"88822cc4","timestamp":"2026-04-01T09:12:11.152Z","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\n- R013 — Prompt optimization loop provides automated mechanism to improve prompt templates — generates variants, scores them, and identifies winners\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S04 (\"Expand to Pipeline Stages 2-4\") — 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#### S03 Summary\nSource: `.gsd/milestones/M013/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M013\nmilestone: M013\nprovides:\n  - optimize CLI subcommand for automated prompt A/B testing\n  - PromptVariantGenerator for LLM-powered prompt mutation\n  - OptimizationLoop + OptimizationResult for iterative optimization with full history\n  - Leaderboard table, ASCII trajectory chart, and timestamped JSON result output\nrequires:\n  - slice: S02\n    provides: ScoreRunner, ScoreResult, LLMClient, 5-dimension scoring rubric, VoiceDial pattern\naffects:\n  - S04\nkey_files:\n  - backend/pipeline/quality/variant_generator.py\n  - backend/pipeline/quality/optimizer.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/results/.gitkeep\nkey_decisions:\n  - OptimizationLoop bypasses VoiceDial — owns the full prompt text directly to avoid double-application\n  - Variant validation uses both length diff and line-level symmetric difference to catch trivial mutations\n  - Reporting functions live in __main__.py rather than a separate reporting.py — keeps surface area small\npatterns_established:\n  - Meta-prompt pattern: LLM acts as prompt engineer, receives current prompt + per-dimension scores + rubric summary, outputs a modified variant targeting weakest dimensions\n  - Variant validation gate: min-diff threshold + format marker check before scoring, invalid variants logged and skipped\n  - Optimization history capture: full iteration×variant matrix stored in OptimizationResult for downstream leaderboard/charting/JSON export\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M013/slices/S03/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T09:12:07.490Z\nblocker_discovered: false\n---\n\n# S03: Prompt Variant Generator & Automated A/B Loop\n\n**Automated prompt optimization loop: LLM-powered variant generation, iterative scoring, CLI with leaderboard/trajectory output, and JSON result persistence.**\n\n## What Happened\n\nBuilt two core modules and wired them into the existing quality CLI. `variant_generator.py` provides `PromptVariantGenerator` — given a base prompt and its per-dimension scores, it calls the LLM with a meta-prompt to generate N variants targeting the weakest scoring dimensions. Each variant is validated: must differ from the base by ≥50 chars (via line-level symmetric difference), must preserve JSON format markers (`SynthesisResult`, `\"pages\"`). Invalid variants are logged and skipped.\n\n`optimizer.py` provides `OptimizationLoop` — loads the base stage 5 prompt and fixture data, runs a baseline score, then iterates: generate variants → score each via synthesis + the existing 5-dimension scorer → keep the best → repeat. The loop handles LLM errors gracefully (errored variants are skipped, not fatal). `OptimizationResult` captures the full history (iteration, variant index, prompt text, all scores) for downstream reporting.\n\nThe `optimize` CLI subcommand accepts `--stage`, `--iterations`, `--variants-per-iter`, `--file`, and `--output-dir`. Stage validation restricts to stage 5 (others print a clear message and exit 1). After the loop completes, three reporting functions fire: `print_leaderboard()` shows top 5 variants ranked by composite score with per-dimension breakdown; `print_trajectory()` renders a 15-row ASCII chart of best composite per iteration; `write_results_json()` persists the full result with config metadata to a timestamped JSON file.\n\nOne deviation from the plan: `OptimizationLoop._score_variant()` does its own synthesis call rather than delegating to `ScoreRunner.synthesize_and_score()`, because the loop owns the full prompt text directly and bypassing VoiceDial avoids double-application of voice modifiers.\n\n## Verification\n\nAll slice-level verification checks passed:\n\n1. `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')\"` → exit 0\n2. `python -c \"from pipeline.quality.optimizer import OptimizationLoop, OptimizationResult; print('optimizer ok')\"` → exit 0\n3. `python -m pipeline.quality optimize --help` → shows all 5 args (--stage, --iterations, --variants-per-iter, --file, --output-dir)\n4. `python -m pipeline.quality optimize --stage 3 --iterations 1 --file ...` → prints \"only stage 5 is supported\" and exits 1\n5. `backend/pipeline/quality/results/.gitkeep` exists\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nOptimizationLoop._score_variant() performs its own synthesis call instead of delegating to ScoreRunner.synthesize_and_score(). This avoids double-application of VoiceDial modifiers since the optimization loop owns the full prompt text directly.\n\n## Known Limitations\n\nOnly stage 5 optimization is supported — other stages print an error and exit 1. This is by design; S04 will extend to stages 2-4.\n\n## Follow-ups\n\nS04 extends optimization to pipeline stages 2-4 with stage-appropriate scoring dimensions.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/variant_generator.py` — New module: PromptVariantGenerator with meta-prompt, LLM-powered variant generation, and validation (min-diff + format markers)\n- `backend/pipeline/quality/optimizer.py` — New module: OptimizationLoop (generate→score→select cycles) and OptimizationResult dataclass with full history\n- `backend/pipeline/quality/__main__.py` — Added optimize subparser, print_leaderboard(), print_trajectory(), write_results_json() reporting functions\n- `backend/pipeline/quality/results/.gitkeep` — Created results output directory\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: S04`, `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/S04/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S04 researched.\"","display":false,"id":"e8b14438","parentId":"fd2db2e3","timestamp":"2026-04-01T09:12:11.154Z"}
{"type":"message","id":"86d5d298","parentId":"e8b14438","timestamp":"2026-04-01T09:12:17.904Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research what's needed for S04 — expanding the optimization loop to pipeline stages 2-4. I need to understand the current stage-specific scoring, the existing optimizer code, and what each stage's prompts/outputs look like.","thinkingSignature":"EqQDClkIDBgCKkDgwiEqDsf0iLFRPeFKfST6F5llNUmSPCmtzbFTQ4c4xOP9bNGAi0m3U9dqbk/rcxVxAw+yZWgVBPz+7sJC0N9JMg9jbGF1ZGUtb3B1cy00LTY4ABIMEWamD0lfwIblY9riGgxk/w1322jotDOkrFQiMPGF1E0ORLTUzop72M06uYoBKvTyQlTNMYbFRAnDOD6Gi0X9shy4cXxmR+8ehkS4hyr4ATgKeSXtORJe7lEUBrNM2D8Q2rJWNHFi2fvtE1dgjKoWm5Q6XLp3VeOwHEm9CGld8LL4jgDb+LXaUlwYfBGaJwcQUm0Qqg+5psWQPqI7oT3Z9sk1gHGmAN46jFlHhZjTGSxqhx+4uG6gttl85sAmRYIKarGPiffgBeLkwLZctvjr5llev7wmogKjUUpF5H0u5sH0/vgvYnvRFUc78KT0vFnki/2wL5ZIawNCjKQlkLc+q7fvQJo9YdViuWJdf9FZjdh/IgDJM+v29DC3HvJyKY08KRYZZjlbgz8tCYubZJnXsfuqfpffKf4R6QNlSaz9oUmaeUmeCkJvGAE="},{"type":"text","text":"Let me explore the existing quality infrastructure and pipeline stages to understand what's needed."},{"type":"toolCall","id":"toolu_014LRJypNhQatWax1JaWpU84","name":"bash","arguments":{"command":"find backend/pipeline/quality -type f -name '*.py' | sort"}},{"type":"toolCall","id":"toolu_01SgZeT21rVLZ5Cy5mnHtkoK","name":"bash","arguments":{"command":"ls backend/prompts/"}},{"type":"toolCall","id":"toolu_014tu9AiCDdSWR9kr4bgxSDn","name":"bash","arguments":{"command":"find backend/pipeline -maxdepth 1 -name 'stages*.py' -o -name 'stage_*.py' | sort; ls backend/pipeline/stages/ 2>/dev/null || echo \"no stages dir\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":280,"cacheRead":59647,"cacheWrite":9601,"totalTokens":69531,"cost":{"input":0.000015000000000000002,"output":0.007,"cacheRead":0.0298235,"cacheWrite":0.060006250000000004,"total":0.09684475000000001}},"stopReason":"toolUse","timestamp":1775034731152}}
{"type":"message","id":"edf74519","parentId":"86d5d298","timestamp":"2026-04-01T09:12:17.938Z","message":{"role":"toolResult","toolCallId":"toolu_014LRJypNhQatWax1JaWpU84","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/quality/fitness.py\nbackend/pipeline/quality/fixtures/__init__.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/__main__.py\nbackend/pipeline/quality/optimizer.py\nbackend/pipeline/quality/scorer.py\nbackend/pipeline/quality/variant_generator.py\nbackend/pipeline/quality/voice_dial.py\n"}],"isError":false,"timestamp":1775034737937}}
{"type":"message","id":"ba6729aa","parentId":"edf74519","timestamp":"2026-04-01T09:12:17.938Z","message":{"role":"toolResult","toolCallId":"toolu_01SgZeT21rVLZ5Cy5mnHtkoK","toolName":"bash","content":[{"type":"text","text":"ls: cannot access 'backend/prompts/': No such file or directory\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775034737937}}
{"type":"message","id":"b5cdbaa7","parentId":"ba6729aa","timestamp":"2026-04-01T09:12:17.942Z","message":{"role":"toolResult","toolCallId":"toolu_014tu9AiCDdSWR9kr4bgxSDn","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/stages.py\nno stages dir\n"}],"isError":false,"timestamp":1775034737940}}
{"type":"message","id":"0179ed0b","parentId":"b5cdbaa7","timestamp":"2026-04-01T09:12:22.285Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GjWdseAvETYz2biAfqZ8qr","name":"bash","arguments":{"command":"rg -n 'def (stage_|run_stage|run_pipeline)' backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_013MaaL31cWeCiKKpsZtRMvm","name":"bash","arguments":{"command":"rg -rn 'prompts_path|prompt.*path|PROMPT' backend/pipeline/ --include='*.py' | head -20"}},{"type":"toolCall","id":"toolu_01Jut558Hepm1VT3ggs6nUdc","name":"read","arguments":{"path":"backend/pipeline/quality/optimizer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":191,"cacheRead":69248,"cacheWrite":423,"totalTokens":69863,"cost":{"input":0.000005,"output":0.004775000000000001,"cacheRead":0.034623999999999995,"cacheWrite":0.0026437500000000003,"total":0.042047749999999995}},"stopReason":"toolUse","timestamp":1775034737940}}
{"type":"message","id":"b31a4e63","parentId":"0179ed0b","timestamp":"2026-04-01T09:12:22.299Z","message":{"role":"toolResult","toolCallId":"toolu_01GjWdseAvETYz2biAfqZ8qr","toolName":"bash","content":[{"type":"text","text":"1682:def run_pipeline(video_id: str, trigger: str = \"manual\") -> str:\n"}],"isError":false,"timestamp":1775034742298}}
{"type":"message","id":"818952bd","parentId":"b31a4e63","timestamp":"2026-04-01T09:12:22.299Z","message":{"role":"toolResult","toolCallId":"toolu_013MaaL31cWeCiKKpsZtRMvm","toolName":"bash","content":[{"type":"text","text":"rg: unrecognized flag --include\n\nsimilar flags that are available: --include-zero\n"}],"isError":false,"timestamp":1775034742299}}
{"type":"message","id":"3ae853da","parentId":"818952bd","timestamp":"2026-04-01T09:12:22.303Z","message":{"role":"toolResult","toolCallId":"toolu_01Jut558Hepm1VT3ggs6nUdc","toolName":"read","content":[{"type":"text","text":"\"\"\"Automated prompt optimization loop for Stage 5 synthesis.\n\nOrchestrates a generate→score→select cycle:\n1. Score the current best prompt against reference fixtures\n2. Generate N variants targeting weak dimensions\n3. Score each variant\n4. Keep the best scorer as the new baseline\n5. Repeat for K iterations\n\nUsage (via CLI):\n    python -m pipeline.quality optimize --stage 5 --iterations 10\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, ScoreResult, ScoreRunner\nfrom pipeline.quality.variant_generator import PromptVariantGenerator\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass OptimizationResult:\n    \"\"\"Full result of an optimization run.\"\"\"\n\n    best_prompt: str = \"\"\n    best_score: ScoreResult = field(default_factory=ScoreResult)\n    history: list[dict] = field(default_factory=list)\n    elapsed_seconds: float = 0.0\n\n\nclass OptimizationLoop:\n    \"\"\"Runs iterative prompt optimization for a pipeline stage.\n\n    Each iteration generates *variants_per_iter* prompt mutations,\n    scores each against reference fixture data, and keeps the\n    highest-composite-scoring variant as the new baseline.\n\n    Parameters\n    ----------\n    client:\n        LLMClient instance for LLM calls (synthesis + scoring + variant gen).\n    stage:\n        Pipeline stage number (currently only 5 is supported).\n    fixture_path:\n        Path to a JSON fixture file containing ``creator_name`` and ``moments``.\n    iterations:\n        Number of generate→score→select cycles.\n    variants_per_iter:\n        Number of variant prompts to generate per iteration.\n    \"\"\"\n\n    def __init__(\n        self,\n        client: LLMClient,\n        stage: int,\n        fixture_path: str,\n        iterations: int = 5,\n        variants_per_iter: int = 2,\n    ) -> None:\n        self.client = client\n        self.stage = stage\n        self.fixture_path = fixture_path\n        self.iterations = iterations\n        self.variants_per_iter = variants_per_iter\n\n        self.scorer = ScoreRunner(client)\n        self.generator = PromptVariantGenerator(client)\n\n    def run(self) -> OptimizationResult:\n        \"\"\"Execute the full optimization loop.\n\n        Returns\n        -------\n        OptimizationResult\n            Contains the best prompt, its scores, full iteration history,\n            and wall-clock elapsed time.\n        \"\"\"\n        from pipeline.stages import _load_prompt\n\n        t0 = time.monotonic()\n\n        # Load base prompt\n        prompt_file = f\"stage{self.stage}_synthesis.txt\"\n        try:\n            base_prompt = _load_prompt(prompt_file)\n        except FileNotFoundError:\n            logger.error(\"Prompt file not found: %s\", prompt_file)\n            return OptimizationResult(\n                best_prompt=\"\",\n                best_score=ScoreResult(error=f\"Prompt file not found: {prompt_file}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        # Load fixture data\n        try:\n            fixture = self._load_fixture()\n        except (FileNotFoundError, json.JSONDecodeError, KeyError) as exc:\n            logger.error(\"Failed to load fixture: %s\", exc)\n            return OptimizationResult(\n                best_prompt=base_prompt,\n                best_score=ScoreResult(error=f\"Fixture load error: {exc}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        moments = fixture[\"moments\"]\n        creator_name = fixture[\"creator_name\"]\n        history: list[dict] = []\n\n        # Score the baseline\n        print(f\"\\n{'='*60}\")\n        print(f\"  PROMPT OPTIMIZATION — Stage {self.stage}\")\n        print(f\"  Iterations: {self.iterations}, Variants/iter: {self.variants_per_iter}\")\n        print(f\"{'='*60}\\n\")\n\n        print(\"  Scoring baseline prompt...\")\n        best_score = self.scorer.synthesize_and_score(\n            moments=moments,\n            creator_name=creator_name,\n            voice_level=0.5,\n        )\n        best_prompt = base_prompt\n\n        history.append({\n            \"iteration\": 0,\n            \"variant_index\": 0,\n            \"prompt_text\": base_prompt[:200] + \"...\" if len(base_prompt) > 200 else base_prompt,\n            \"prompt_length\": len(base_prompt),\n            \"composite\": best_score.composite,\n            \"scores\": {d: getattr(best_score, d) for d in DIMENSIONS},\n            \"error\": best_score.error,\n            \"label\": \"baseline\",\n        })\n\n        if best_score.error:\n            print(f\"  ✗ Baseline scoring failed: {best_score.error}\")\n            print(\"  Aborting optimization — fix the baseline first.\\n\")\n            return OptimizationResult(\n                best_prompt=best_prompt,\n                best_score=best_score,\n                history=history,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        self._print_iteration_summary(0, best_score, is_baseline=True)\n\n        # Iterate\n        for iteration in range(1, self.iterations + 1):\n            print(f\"\\n  ── Iteration {iteration}/{self.iterations} ──\")\n\n            # Generate variants\n            variants = self.generator.generate(\n                base_prompt=best_prompt,\n                scores=best_score,\n                n=self.variants_per_iter,\n            )\n\n            if not variants:\n                print(\"  ⚠ No valid variants generated — skipping iteration\")\n                continue\n\n            # Score each variant\n            iteration_best_score = best_score\n            iteration_best_prompt = best_prompt\n\n            for vi, variant_prompt in enumerate(variants):\n                print(f\"  Scoring variant {vi + 1}/{len(variants)}...\")\n\n                # Temporarily replace the base prompt with the variant for synthesis\n                score = self._score_variant(\n                    variant_prompt, moments, creator_name,\n                )\n\n                history.append({\n                    \"iteration\": iteration,\n                    \"variant_index\": vi + 1,\n                    \"prompt_text\": variant_prompt[:200] + \"...\" if len(variant_prompt) > 200 else variant_prompt,\n                    \"prompt_length\": len(variant_prompt),\n                    \"composite\": score.composite,\n                    \"scores\": {d: getattr(score, d) for d in DIMENSIONS},\n                    \"error\": score.error,\n                    \"label\": f\"iter{iteration}_v{vi+1}\",\n                })\n\n                if score.error:\n                    print(f\"    ✗ Variant {vi + 1} errored: {score.error}\")\n                    continue\n\n                if score.composite > iteration_best_score.composite:\n                    iteration_best_score = score\n                    iteration_best_prompt = variant_prompt\n                    print(f\"    ✓ New best: {score.composite:.3f} (was {best_score.composite:.3f})\")\n                else:\n                    print(f\"    · Score {score.composite:.3f} ≤ current best {iteration_best_score.composite:.3f}\")\n\n            # Update global best if this iteration improved\n            if iteration_best_score.composite > best_score.composite:\n                best_score = iteration_best_score\n                best_prompt = iteration_best_prompt\n                print(f\"  ★ Iteration {iteration} improved: {best_score.composite:.3f}\")\n            else:\n                print(f\"  · No improvement in iteration {iteration}\")\n\n            self._print_iteration_summary(iteration, best_score)\n\n        # Final report\n        elapsed = round(time.monotonic() - t0, 2)\n        self._print_final_report(best_score, history, elapsed)\n\n        return OptimizationResult(\n            best_prompt=best_prompt,\n            best_score=best_score,\n            history=history,\n            elapsed_seconds=elapsed,\n        )\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _load_fixture(self) -> dict:\n        \"\"\"Load and validate the fixture JSON file.\"\"\"\n        path = Path(self.fixture_path)\n        if not path.exists():\n            raise FileNotFoundError(f\"Fixture not found: {path}\")\n        data = json.loads(path.read_text(encoding=\"utf-8\"))\n\n        if \"moments\" not in data:\n            raise KeyError(\"Fixture must contain 'moments' key\")\n        if \"creator_name\" not in data:\n            raise KeyError(\"Fixture must contain 'creator_name' key\")\n\n        return data\n\n    def _score_variant(\n        self,\n        variant_prompt: str,\n        moments: list[dict],\n        creator_name: str,\n    ) -> ScoreResult:\n        \"\"\"Score a variant prompt by running synthesis + scoring.\n\n        Uses the variant as a direct system prompt for synthesis, bypassing\n        VoiceDial (the optimization loop owns the full prompt text).\n        \"\"\"\n        from pipeline.schemas import SynthesisResult\n        from pipeline.stages import _get_stage_config\n\n        import json as _json\n        import openai as _openai\n\n        model_override, modality = _get_stage_config(self.stage)\n\n        moments_json = _json.dumps(moments, indent=2)\n        user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=variant_prompt,\n                user_prompt=user_prompt,\n                response_model=SynthesisResult,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (_openai.APIConnectionError, _openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Synthesis LLM error: {exc}\",\n            )\n        except Exception as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            logger.exception(\"Unexpected error during variant synthesis\")\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Unexpected synthesis error: {exc}\",\n            )\n\n        # Parse synthesis\n        raw_text = str(raw).strip()\n        try:\n            synthesis = self.client.parse_response(raw_text, SynthesisResult)\n        except Exception as exc:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Variant synthesis parse error: {exc}\",\n            )\n\n        if not synthesis.pages:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=\"Variant synthesis returned no pages\",\n            )\n\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        result = self.scorer.score_page(page_json, moments)\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def _print_iteration_summary(\n        self,\n        iteration: int,\n        score: ScoreResult,\n        is_baseline: bool = False,\n    ) -> None:\n        \"\"\"Print a compact one-line summary of the current best scores.\"\"\"\n        label = \"BASELINE\" if is_baseline else f\"ITER {iteration}\"\n        dims = \"  \".join(\n            f\"{d[:4]}={getattr(score, d):.2f}\" for d in DIMENSIONS\n        )\n        print(f\"  [{label}] composite={score.composite:.3f}  {dims}\")\n\n    def _print_final_report(\n        self,\n        best_score: ScoreResult,\n        history: list[dict],\n        elapsed: float,\n    ) -> None:\n        \"\"\"Print the final optimization summary.\"\"\"\n        print(f\"\\n{'='*60}\")\n        print(\"  OPTIMIZATION COMPLETE\")\n        print(f\"{'='*60}\")\n        print(f\"  Total time: {elapsed}s\")\n        print(f\"  Iterations: {self.iterations}\")\n        print(f\"  Variants scored: {len(history) - 1}\")  # minus baseline\n\n        baseline_composite = history[0][\"composite\"] if history else 0.0\n        improvement = best_score.composite - baseline_composite\n\n        print(f\"\\n  Baseline composite: {baseline_composite:.3f}\")\n        print(f\"  Best composite:     {best_score.composite:.3f}\")\n        if improvement > 0:\n            print(f\"  Improvement:        +{improvement:.3f}\")\n        else:\n            print(f\"  Improvement:        {improvement:.3f} (no gain)\")\n\n        print(f\"\\n  Per-dimension best scores:\")\n        for d in DIMENSIONS:\n            val = getattr(best_score, d)\n            bar = \"█\" * int(val * 20) + \"░\" * (20 - int(val * 20))\n            print(f\"    {d.replace('_', ' ').title():25s} {val:.2f}  {bar}\")\n\n        errored = sum(1 for h in history if h.get(\"error\"))\n        if errored:\n            print(f\"\\n  ⚠ {errored} variant(s) errored during scoring\")\n\n        print(f\"{'='*60}\\n\")\n"}],"isError":false,"timestamp":1775034742299}}
{"type":"message","id":"a0c6f002","parentId":"3ae853da","timestamp":"2026-04-01T09:12:26.245Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me understand the stage structure and scoring dimensions:"},{"type":"toolCall","id":"toolu_018e3kgwZX84ji8VTRLJy8Mx","name":"read","arguments":{"path":"backend/pipeline/quality/scorer.py"}},{"type":"toolCall","id":"toolu_01Xidcfvs9tqapgrRYEmKwQn","name":"bash","arguments":{"command":"rg -n 'def _load_prompt|def _get_stage_config|stage.*_config' backend/pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":141,"cacheRead":69671,"cacheWrite":3833,"totalTokens":73646,"cost":{"input":0.000005,"output":0.0035250000000000004,"cacheRead":0.0348355,"cacheWrite":0.023956250000000002,"total":0.062321749999999995}},"stopReason":"toolUse","timestamp":1775034742300}}
{"type":"message","id":"02c1f9b8","parentId":"a0c6f002","timestamp":"2026-04-01T09:12:26.258Z","message":{"role":"toolResult","toolCallId":"toolu_018e3kgwZX84ji8VTRLJy8Mx","toolName":"read","content":[{"type":"text","text":"\"\"\"Stage 5 quality scorer — LLM-as-judge evaluation across 5 dimensions.\n\nEvaluates a synthesized technique page against source moments on:\n1. Structural quality — section naming, count, paragraph depth\n2. Content specificity — concrete details vs vague generalities\n3. Voice preservation — direct quotes, attributed opinions, personality\n4. Readability / flow — synthesis quality, logical ordering, no redundancy\n5. Factual fidelity — no hallucinated specifics, grounded in source moments\n\nRun via: python -m pipeline.quality score --file <path>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport sys\nimport time\nfrom dataclasses import dataclass, field\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.voice_dial import VoiceDial\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Scoring rubric (hardcoded for iteration speed) ───────────────────────────\n\nSCORING_RUBRIC = \"\"\"\\\nYou are an expert evaluator of synthesized technique articles for music production education.\n\nYou will be given:\n1. A synthesized technique page (JSON with title, summary, body_sections)\n2. The source key moments (transcript excerpts, summaries, tags) used to create it\n\nEvaluate the page across these 5 dimensions, scoring each 0.0 to 1.0:\n\n**structural** — Section naming and organization\n- 0.9-1.0: Well-named specific sections (not generic \"Overview\"/\"Tips\"), appropriate count (3-6), 2-5 paragraphs per section\n- 0.5-0.7: Acceptable structure but some generic section names or uneven depth\n- 0.0-0.3: Poor structure — too few/many sections, generic names, single-paragraph sections\n\n**content_specificity** — Concrete technical details\n- 0.9-1.0: Rich in frequencies (Hz), time values (ms), ratios, plugin names, specific settings, dB values\n- 0.5-0.7: Some specific details but padded with vague statements (\"adjust to taste\", \"experiment with settings\")\n- 0.0-0.3: Mostly vague generalities with few concrete values from the source material\n\n**voice_preservation** — Creator's authentic voice\n- 0.9-1.0: Direct quotes preserved, opinions attributed to creator by name, personality and strong views retained\n- 0.5-0.7: Some paraphrased references to creator's views but few direct quotes\n- 0.0-0.3: Encyclopedia style — creator's voice completely smoothed out, no attribution\n\n**readability** — Synthesis quality and flow\n- 0.9-1.0: Reads as a cohesive article, related info merged, logical flow, no redundancy or contradiction\n- 0.5-0.7: Generally readable but some awkward transitions or minor repetition\n- 0.0-0.3: Feels like concatenated bullet points, disjointed, redundant passages\n\n**factual_fidelity** — Grounded in source material\n- 0.9-1.0: Every claim traceable to source moments, no invented plugin names/settings/techniques\n- 0.5-0.7: Mostly grounded but 1-2 details seem embellished or not directly from sources\n- 0.0-0.3: Contains hallucinated specifics — plugin names, settings, or techniques not in sources\n\nReturn ONLY a JSON object with this exact structure:\n{\n  \"structural\": <float 0.0-1.0>,\n  \"content_specificity\": <float 0.0-1.0>,\n  \"voice_preservation\": <float 0.0-1.0>,\n  \"readability\": <float 0.0-1.0>,\n  \"factual_fidelity\": <float 0.0-1.0>,\n  \"justifications\": {\n    \"structural\": \"<1-2 sentence justification>\",\n    \"content_specificity\": \"<1-2 sentence justification>\",\n    \"voice_preservation\": \"<1-2 sentence justification>\",\n    \"readability\": \"<1-2 sentence justification>\",\n    \"factual_fidelity\": \"<1-2 sentence justification>\"\n  }\n}\n\"\"\"\n\nDIMENSIONS = [\n    \"structural\",\n    \"content_specificity\",\n    \"voice_preservation\",\n    \"readability\",\n    \"factual_fidelity\",\n]\n\n\n# ── Result type ──────────────────────────────────────────────────────────────\n\n@dataclass\nclass ScoreResult:\n    \"\"\"Outcome of scoring a technique page across 5 quality dimensions.\"\"\"\n\n    structural: float = 0.0\n    content_specificity: float = 0.0\n    voice_preservation: float = 0.0\n    readability: float = 0.0\n    factual_fidelity: float = 0.0\n    composite: float = 0.0\n    justifications: dict[str, str] = field(default_factory=dict)\n    elapsed_seconds: float = 0.0\n    error: str | None = None\n\n\n# ── Runner ───────────────────────────────────────────────────────────────────\n\nclass ScoreRunner:\n    \"\"\"Scores a Stage 5 technique page using LLM-as-judge evaluation.\"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    def score_page(\n        self,\n        page_json: dict,\n        moments: list[dict],\n    ) -> ScoreResult:\n        \"\"\"Evaluate a technique page against source moments.\n\n        Parameters\n        ----------\n        page_json:\n            Synthesized page dict (title, summary, body_sections).\n        moments:\n            Source key moments with transcript_excerpt, summary, etc.\n\n        Returns\n        -------\n        ScoreResult with per-dimension scores and justifications.\n        \"\"\"\n        # Build the user prompt with the page and source moments\n        user_prompt = (\n            \"## Synthesized Technique Page\\n\\n\"\n            f\"```json\\n{json.dumps(page_json, indent=2)}\\n```\\n\\n\"\n            \"## Source Key Moments\\n\\n\"\n            f\"```json\\n{json.dumps(moments, indent=2)}\\n```\\n\\n\"\n            \"Score this page across all 5 dimensions.\"\n        )\n\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=SCORING_RUBRIC,\n                user_prompt=user_prompt,\n                response_model=BaseModel,  # triggers JSON mode\n                modality=\"chat\",\n            )\n            elapsed = round(time.monotonic() - t0, 2)\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            elapsed = round(time.monotonic() - t0, 2)\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            return ScoreResult(\n                elapsed_seconds=elapsed,\n                error=(\n                    f\"Cannot reach LLM endpoint at {url} (fallback {fallback}). \"\n                    f\"Error: {exc}\"\n                ),\n            )\n\n        # Parse the LLM judge response\n        raw_text = str(resp).strip()\n        try:\n            parsed = json.loads(raw_text)\n        except json.JSONDecodeError:\n            logger.error(\"Malformed judge response (not JSON): %.300s\", raw_text)\n            return ScoreResult(\n                elapsed_seconds=elapsed,\n                error=f\"Malformed judge response (not valid JSON). Raw excerpt: {raw_text[:200]}\",\n            )\n\n        return self._parse_scores(parsed, elapsed)\n\n    def _parse_scores(self, parsed: dict, elapsed: float) -> ScoreResult:\n        \"\"\"Extract and validate scores from parsed JSON response.\"\"\"\n        scores: dict[str, float] = {}\n        justifications: dict[str, str] = {}\n\n        raw_justifications = parsed.get(\"justifications\", {})\n        if not isinstance(raw_justifications, dict):\n            raw_justifications = {}\n\n        for dim in DIMENSIONS:\n            raw = parsed.get(dim)\n            if raw is None:\n                logger.warning(\"Missing dimension '%s' in judge response\", dim)\n                scores[dim] = 0.0\n                justifications[dim] = \"(missing from judge response)\"\n                continue\n\n            try:\n                val = float(raw)\n                scores[dim] = max(0.0, min(1.0, val))  # clamp\n            except (TypeError, ValueError):\n                logger.warning(\"Invalid value for '%s': %r\", dim, raw)\n                scores[dim] = 0.0\n                justifications[dim] = f\"(invalid value: {raw!r})\"\n                continue\n\n            justifications[dim] = str(raw_justifications.get(dim, \"\"))\n\n        composite = sum(scores.values()) / len(DIMENSIONS)\n\n        return ScoreResult(\n            structural=scores[\"structural\"],\n            content_specificity=scores[\"content_specificity\"],\n            voice_preservation=scores[\"voice_preservation\"],\n            readability=scores[\"readability\"],\n            factual_fidelity=scores[\"factual_fidelity\"],\n            composite=round(composite, 3),\n            justifications=justifications,\n            elapsed_seconds=elapsed,\n        )\n\n    def synthesize_and_score(\n        self,\n        moments: list[dict],\n        creator_name: str,\n        voice_level: float,\n    ) -> ScoreResult:\n        \"\"\"Re-synthesize from source moments with a voice-dialed prompt, then score.\n\n        Loads the stage 5 synthesis prompt from disk, applies the VoiceDial\n        modifier at the given voice_level, calls the LLM to produce a\n        SynthesisResult, then scores the first page.\n\n        Parameters\n        ----------\n        moments:\n            Source key moments (dicts with summary, transcript_excerpt, etc.)\n        creator_name:\n            Creator name to inject into the synthesis prompt.\n        voice_level:\n            Float 0.0–1.0 controlling voice preservation intensity.\n\n        Returns\n        -------\n        ScoreResult with per-dimension scores after voice-dialed re-synthesis.\n        \"\"\"\n        from pipeline.schemas import SynthesisResult\n        from pipeline.stages import _get_stage_config, _load_prompt\n\n        # Load and modify the stage 5 system prompt\n        try:\n            base_prompt = _load_prompt(\"stage5_synthesis.txt\")\n        except FileNotFoundError as exc:\n            return ScoreResult(error=f\"Prompt file not found: {exc}\")\n\n        dial = VoiceDial(base_prompt)\n        modified_prompt = dial.modify(voice_level)\n        band = dial.band_name(voice_level)\n\n        # Build user prompt in the same format as _synthesize_chunk\n        moments_json = json.dumps(moments, indent=2)\n        user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        model_override, modality = _get_stage_config(5)\n\n        print(f\"  Re-synthesizing at voice_level={voice_level} (band={band})...\")\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=modified_prompt,\n                user_prompt=user_prompt,\n                response_model=SynthesisResult,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=(\n                    f\"Cannot reach LLM endpoint at {url} (fallback {fallback}). \"\n                    f\"Error: {exc}\"\n                ),\n            )\n\n        # Parse synthesis response\n        raw_text = str(raw).strip()\n        try:\n            synthesis = self.client.parse_response(raw_text, SynthesisResult)\n        except (json.JSONDecodeError, ValueError, Exception) as exc:\n            logger.error(\"Malformed synthesis response: %.300s\", raw_text)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Malformed synthesis response: {exc}. Raw excerpt: {raw_text[:200]}\",\n            )\n\n        if not synthesis.pages:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=\"Synthesis returned no pages.\",\n            )\n\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        print(f\"  Synthesis complete ({elapsed_synth}s). Scoring...\")\n        result = self.score_page(page_json, moments)\n        # Include synthesis time in total\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def print_report(self, result: ScoreResult) -> None:\n        \"\"\"Print a formatted scoring report to stdout.\"\"\"\n        print(\"\\n\" + \"=\" * 60)\n        print(\"  STAGE 5 QUALITY SCORE REPORT\")\n        print(\"=\" * 60)\n\n        if result.error:\n            print(f\"\\n  ✗ Error: {result.error}\\n\")\n            print(\"=\" * 60 + \"\\n\")\n            return\n\n        for dim in DIMENSIONS:\n            score = getattr(result, dim)\n            bar = self._score_bar(score)\n            justification = result.justifications.get(dim, \"\")\n            print(f\"\\n  {dim.replace('_', ' ').title()}\")\n            print(f\"    Score: {score:.2f}  {bar}\")\n            if justification:\n                # Wrap justification at ~60 chars\n                for line in self._wrap(justification, 56):\n                    print(f\"    {line}\")\n\n        print(\"\\n\" + \"-\" * 60)\n        print(f\"  Composite: {result.composite:.3f}\")\n        print(f\"  Time: {result.elapsed_seconds}s\")\n        print(\"=\" * 60 + \"\\n\")\n\n    @staticmethod\n    def _score_bar(score: float, width: int = 20) -> str:\n        \"\"\"Render a visual bar for a 0-1 score.\"\"\"\n        filled = int(score * width)\n        return \"█\" * filled + \"░\" * (width - filled)\n\n    @staticmethod\n    def _wrap(text: str, width: int) -> list[str]:\n        \"\"\"Simple word wrap.\"\"\"\n        words = text.split()\n        lines: list[str] = []\n        current = \"\"\n        for word in words:\n            if current and len(current) + len(word) + 1 > width:\n                lines.append(current)\n                current = word\n            else:\n                current = f\"{current} {word}\" if current else word\n        if current:\n            lines.append(current)\n        return lines\n"}],"isError":false,"timestamp":1775034746256}}
{"type":"message","id":"50013218","parentId":"02c1f9b8","timestamp":"2026-04-01T09:12:26.271Z","message":{"role":"toolResult","toolCallId":"toolu_01Xidcfvs9tqapgrRYEmKwQn","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str) -> str:\n262:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n405:        model_override, modality = _get_stage_config(2)\n482:        model_override, modality = _get_stage_config(3)\n655:        model_override, modality = _get_stage_config(4)\n1085:        model_override, modality = _get_stage_config(5)\n"}],"isError":false,"timestamp":1775034746268}}
{"type":"message","id":"ce899172","parentId":"50013218","timestamp":"2026-04-01T09:12:31.162Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me see the stages, prompt loading, and what stages 2-4 actually do:"},{"type":"toolCall","id":"toolu_01JKqHcSjfMzJXuYuFUDnJDb","name":"read","arguments":{"limit":50,"offset":240,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01XAgqXbvXzyGMiJHEF9FYsJ","name":"bash","arguments":{"command":"rg -n 'def .*stage.*2|def.*segment|def.*_segment' backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":73504,"cacheWrite":4163,"totalTokens":77848,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.036752,"cacheWrite":0.02601875,"total":0.06727575}},"stopReason":"toolUse","timestamp":1775034746269}}
{"type":"message","id":"974e80d4","parentId":"ce899172","timestamp":"2026-04-01T09:12:31.172Z","message":{"role":"toolResult","toolCallId":"toolu_01JKqHcSjfMzJXuYuFUDnJDb","toolName":"read","content":[{"type":"text","text":"        _SessionLocal = sessionmaker(bind=_get_sync_engine())\n    return _SessionLocal()\n\n\ndef _load_prompt(template_name: str) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    Raises FileNotFoundError if the template does not exist.\n    \"\"\"\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n    if not path.exists():\n        logger.error(\"Prompt template not found: %s\", path)\n        raise FileNotFoundError(f\"Prompt template not found: {path}\")\n    return path.read_text(encoding=\"utf-8\")\n\n\ndef _get_llm_client() -> LLMClient:\n    \"\"\"Return an LLMClient configured from settings.\"\"\"\n    return LLMClient(get_settings())\n\n\ndef _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n    \"\"\"Return (model_override, modality) for a pipeline stage.\n\n    Reads stage-specific config from Settings. If the stage-specific model\n    is None/empty, returns None (LLMClient will use its default). If the\n    stage-specific modality is unset, defaults to \"chat\".\n    \"\"\"\n    settings = get_settings()\n    model = getattr(settings, f\"llm_stage{stage_num}_model\", None) or None\n    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n    return model, modality\n\n\ndef _load_canonical_tags() -> dict:\n    \"\"\"Load canonical tag taxonomy from config/canonical_tags.yaml.\"\"\"\n    # Walk up from backend/ to find config/\n    candidates = [\n        Path(\"config/canonical_tags.yaml\"),\n        Path(\"../config/canonical_tags.yaml\"),\n    ]\n    for candidate in candidates:\n        if candidate.exists():\n            with open(candidate, encoding=\"utf-8\") as f:\n                return yaml.safe_load(f)\n    raise FileNotFoundError(\n        \"canonical_tags.yaml not found. Searched: \" + \", \".join(str(c) for c in candidates)\n    )\n\n\n[1490 more lines in file. Use offset=290 to continue.]"}],"isError":false,"timestamp":1775034751172}}
{"type":"message","id":"47e0bb60","parentId":"974e80d4","timestamp":"2026-04-01T09:12:31.182Z","message":{"role":"toolResult","toolCallId":"toolu_01XAgqXbvXzyGMiJHEF9FYsJ","toolName":"bash","content":[{"type":"text","text":"363:def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\n"}],"isError":false,"timestamp":1775034751180}}
{"type":"message","id":"fc79040e","parentId":"47e0bb60","timestamp":"2026-04-01T09:12:34.933Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014Ds1GfcbVBWb6YqtqeTY82","name":"read","arguments":{"limit":150,"offset":363,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01AKvkQqffQSxxZu2zNtfjEx","name":"bash","arguments":{"command":"rg -n 'def stage3|def stage4' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":146,"cacheRead":77667,"cacheWrite":808,"totalTokens":78622,"cost":{"input":0.000005,"output":0.00365,"cacheRead":0.0388335,"cacheWrite":0.005050000000000001,"total":0.0475385}},"stopReason":"toolUse","timestamp":1775034751180}}
{"type":"message","id":"6ce493fd","parentId":"fc79040e","timestamp":"2026-04-01T09:12:34.943Z","message":{"role":"toolResult","toolCallId":"toolu_014Ds1GfcbVBWb6YqtqeTY82","toolName":"read","content":[{"type":"text","text":"def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Analyze transcript segments and identify topic boundaries.\n\n    Loads all TranscriptSegment rows for the video, sends them to the LLM\n    for topic boundary detection, and updates topic_label on each segment.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 2 (segmentation) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage2_segmentation\", \"start\", run_id=run_id)\n\n    session = _get_sync_session()\n    try:\n        # Load segments ordered by index\n        segments = (\n            session.execute(\n                select(TranscriptSegment)\n                .where(TranscriptSegment.source_video_id == video_id)\n                .order_by(TranscriptSegment.segment_index)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not segments:\n            logger.info(\"Stage 2: No segments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Build transcript text with indices for the LLM\n        transcript_lines = []\n        for seg in segments:\n            transcript_lines.append(\n                f\"[{seg.segment_index}] ({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n            )\n        transcript_text = \"\\n\".join(transcript_lines)\n\n        # Load prompt and call LLM\n        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(2)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage2_segmentation\", hard_limit=hard_limit)\n        logger.info(\"Stage 2 using model=%s, modality=%s, max_tokens=%d\", model_override or \"default\", modality, max_tokens)\n        _s2_request_params = _build_request_params(max_tokens, model_override, modality, \"SegmentationResult\", hard_limit)\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt, run_id=run_id, request_params=_s2_request_params),\n                           modality=modality, model_override=model_override, max_tokens=max_tokens)\n        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override, max_tokens=max_tokens)\n\n        # Update topic_label on each segment row\n        seg_by_index = {s.segment_index: s for s in segments}\n        for topic_seg in result.segments:\n            for idx in range(topic_seg.start_index, topic_seg.end_index + 1):\n                if idx in seg_by_index:\n                    seg_by_index[idx].topic_label = topic_seg.topic_label\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage2_segmentation\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 2 (segmentation) completed for video_id=%s in %.1fs — %d topic groups found\",\n            video_id, elapsed, len(result.segments),\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise  # Don't retry missing prompt files\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage2_segmentation\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 2 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Extract key moments from each topic segment group.\n\n    Groups segments by topic_label, calls the LLM for each group to extract\n    moments, creates KeyMoment rows, and sets processing_status=extracted.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 3 (extraction) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage3_extraction\", \"start\", run_id=run_id)\n\n    session = _get_sync_session()\n    try:\n        # Load segments with topic labels\n        segments = (\n            session.execute(\n                select(TranscriptSegment)\n                .where(TranscriptSegment.source_video_id == video_id)\n                .order_by(TranscriptSegment.segment_index)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not segments:\n            logger.info(\"Stage 3: No segments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Group segments by topic_label\n        groups: dict[str, list[TranscriptSegment]] = defaultdict(list)\n        for seg in segments:\n            label = seg.topic_label or \"unlabeled\"\n            groups[label].append(seg)\n\n        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(3)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        logger.info(\"Stage 3 using model=%s, modality=%s\", model_override or \"default\", modality)\n        total_moments = 0\n\n        for topic_label, group_segs in groups.items():\n            # Build segment text for this group\n            seg_lines = []\n            for seg in group_segs:\n                seg_lines.append(\n                    f\"({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n                )\n            segment_text = \"\\n\".join(seg_lines)\n\n            user_prompt = (\n                f\"Topic: {topic_label}\\n\\n\"\n                f\"<segment>\\n{segment_text}\\n</segment>\"\n            )\n\n            max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage3_extraction\", hard_limit=hard_limit)\n            _s3_request_params = _build_request_params(max_tokens, model_override, modality, \"ExtractionResult\", hard_limit)\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\", system_prompt=system_prompt, user_prompt=user_prompt, run_id=run_id, context_label=topic_label, request_params=_s3_request_params),\n                               modality=modality, model_override=model_override, max_tokens=max_tokens)\n            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override, max_tokens=max_tokens)\n\n            # Create KeyMoment rows\n            for moment in result.moments:\n                # Validate content_type against enum\n                try:\n                    ct = KeyMomentContentType(moment.content_type)\n\n[1267 more lines in file. Use offset=513 to continue.]"}],"isError":false,"timestamp":1775034754943}}
{"type":"message","id":"bd424507","parentId":"6ce493fd","timestamp":"2026-04-01T09:12:34.949Z","message":{"role":"toolResult","toolCallId":"toolu_01AKvkQqffQSxxZu2zNtfjEx","toolName":"bash","content":[{"type":"text","text":"445:def stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\n613:def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n"}],"isError":false,"timestamp":1775034754947}}
{"type":"message","id":"fd5eb1c9","parentId":"bd424507","timestamp":"2026-04-01T09:12:38.438Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Y7igYx8PCi6VMuWbYMnRM1","name":"read","arguments":{"limit":100,"offset":613,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01Q1yYNi4qxpKYakykB6xk5N","name":"bash","arguments":{"command":"ls backend/prompts/ 2>/dev/null || find backend -name 'stage*_*.txt' -type f 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":163,"cacheRead":78475,"cacheWrite":2342,"totalTokens":80981,"cost":{"input":0.000005,"output":0.0040750000000000005,"cacheRead":0.0392375,"cacheWrite":0.014637500000000001,"total":0.057955000000000007}},"stopReason":"toolUse","timestamp":1775034754948}}
{"type":"message","id":"a00bd1ab","parentId":"fd5eb1c9","timestamp":"2026-04-01T09:12:38.445Z","message":{"role":"toolResult","toolCallId":"toolu_01Y7igYx8PCi6VMuWbYMnRM1","toolName":"read","content":[{"type":"text","text":"def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Classify key moments against the canonical tag taxonomy.\n\n    Loads all KeyMoment rows for the video, sends them to the LLM with the\n    canonical taxonomy, and stores classification results in Redis for\n    stage 5 consumption. Updates content_type if the classifier overrides it.\n\n    For large moment sets, automatically batches into groups of\n    _STAGE4_BATCH_SIZE to stay within model context window limits.\n\n    Stage 4 does NOT change processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 4 (classification) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage4_classification\", \"start\", run_id=run_id)\n\n    session = _get_sync_session()\n    try:\n        # Load key moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not moments:\n            logger.info(\"Stage 4: No moments found for video_id=%s, skipping.\", video_id)\n            _store_classification_data(video_id, [])\n            return video_id\n\n        # Load canonical tags\n        tags_data = _load_canonical_tags()\n        taxonomy_text = _format_taxonomy_for_prompt(tags_data)\n\n        system_prompt = _load_prompt(\"stage4_classification.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(4)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n\n        # Batch moments for classification\n        all_classifications = []\n        for batch_start in range(0, len(moments), _STAGE4_BATCH_SIZE):\n            batch = moments[batch_start:batch_start + _STAGE4_BATCH_SIZE]\n            result = _classify_moment_batch(\n                batch, batch_start, taxonomy_text, system_prompt,\n                llm, model_override, modality, hard_limit,\n                video_id, run_id,\n            )\n            # Reindex: batch uses 0-based indices, remap to global indices\n            for cls in result.classifications:\n                cls.moment_index += batch_start\n            all_classifications.extend(result.classifications)\n\n        # Apply content_type overrides and prepare classification data for stage 5\n        classification_data = []\n\n        for cls in all_classifications:\n            if 0 <= cls.moment_index < len(moments):\n                moment = moments[cls.moment_index]\n\n                # Apply content_type override if provided\n                if cls.content_type_override:\n                    try:\n                        moment.content_type = KeyMomentContentType(cls.content_type_override)\n                    except ValueError:\n                        pass\n\n                classification_data.append({\n                    \"moment_id\": str(moment.id),\n                    \"topic_category\": cls.topic_category.strip().title(),\n                    \"topic_tags\": cls.topic_tags,\n                })\n\n        session.commit()\n\n        # Store classification data in Redis for stage 5\n        _store_classification_data(video_id, classification_data)\n\n        elapsed = time.monotonic() - start\n        num_batches = (len(moments) + _STAGE4_BATCH_SIZE - 1) // _STAGE4_BATCH_SIZE\n        _emit_event(video_id, \"stage4_classification\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 4 (classification) completed for video_id=%s in %.1fs — \"\n            \"%d moments classified in %d batch(es)\",\n            video_id, elapsed, len(classification_data), num_batches,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage4_classification\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 4 failed for video_id=%s: %s\", video_id, exc)\n\n[1067 more lines in file. Use offset=713 to continue.]"}],"isError":false,"timestamp":1775034758445}}
{"type":"message","id":"5cf100e4","parentId":"a00bd1ab","timestamp":"2026-04-01T09:12:38.450Z","message":{"role":"toolResult","toolCallId":"toolu_01Q1yYNi4qxpKYakykB6xk5N","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775034758448}}
{"type":"message","id":"f2181c89","parentId":"5cf100e4","timestamp":"2026-04-01T09:12:42.617Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Dw5BMKLFAa8c9LsMMEm7qE","name":"bash","arguments":{"command":"rg -n 'prompts_path' backend/config.py | head -5"}},{"type":"toolCall","id":"toolu_01Jgif8oWEmL9BqCnhhfP71W","name":"bash","arguments":{"command":"find backend -name '*.txt' -path '*prompt*' -o -name '*.txt' -path '*stage*' 2>/dev/null | head -20; find . -path '*/prompts/*' -name '*.txt' 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":170,"cacheRead":80817,"cacheWrite":1334,"totalTokens":82322,"cost":{"input":0.000005,"output":0.00425,"cacheRead":0.0404085,"cacheWrite":0.008337500000000001,"total":0.053001000000000006}},"stopReason":"toolUse","timestamp":1775034758449}}
{"type":"message","id":"e93ba568","parentId":"f2181c89","timestamp":"2026-04-01T09:12:42.629Z","message":{"role":"toolResult","toolCallId":"toolu_01Dw5BMKLFAa8c9LsMMEm7qE","toolName":"bash","content":[{"type":"text","text":"64:    prompts_path: str = \"./prompts\"\n"}],"isError":false,"timestamp":1775034762629}}
{"type":"message","id":"a50b8457","parentId":"e93ba568","timestamp":"2026-04-01T09:12:42.656Z","message":{"role":"toolResult","toolCallId":"toolu_01Jgif8oWEmL9BqCnhhfP71W","toolName":"bash","content":[{"type":"text","text":"./prompts/stage3_extraction.txt\n./prompts/stage5_synthesis.txt\n./prompts/stage2_segmentation.txt\n./prompts/stage4_classification.txt\n"}],"isError":false,"timestamp":1775034762654}}
{"type":"message","id":"520bce31","parentId":"a50b8457","timestamp":"2026-04-01T09:12:46.303Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the existing __main__.py to understand the CLI and stage gating, and the variant generator:"},{"type":"toolCall","id":"toolu_014qHUu7mWsjN2N7U6okAhhB","name":"read","arguments":{"path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":85,"cacheRead":82151,"cacheWrite":291,"totalTokens":82528,"cost":{"input":0.000005,"output":0.002125,"cacheRead":0.0410755,"cacheWrite":0.00181875,"total":0.04502425}},"stopReason":"toolUse","timestamp":1775034762655}}
{"type":"message","id":"0400130e","parentId":"520bce31","timestamp":"2026-04-01T09:12:46.310Z","message":{"role":"toolResult","toolCallId":"toolu_014qHUu7mWsjN2N7U6okAhhB","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness   — Run LLM fitness tests across four categories\n  score     — Score a Stage 5 technique page across 5 quality dimensions\n  optimize  — Automated prompt optimization loop with leaderboard output\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .optimizer import OptimizationLoop, OptimizationResult\nfrom .scorer import DIMENSIONS, ScoreRunner\n\n\n# ── Reporting helpers ────────────────────────────────────────────────────────\n\n\ndef print_leaderboard(result: OptimizationResult) -> None:\n    \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n    # Filter to entries that actually scored (no errors)\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if not scored:\n        print(\"\\n  No successfully scored variants to rank.\\n\")\n        return\n\n    ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n\n    print(f\"\\n{'='*72}\")\n    print(\"  LEADERBOARD — Top 5 Variants by Composite Score\")\n    print(f\"{'='*72}\")\n\n    # Header\n    dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in DIMENSIONS)\n    print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n    print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}\")\n\n    for i, entry in enumerate(ranked, 1):\n        label = entry.get(\"label\", \"?\")[:16]\n        comp = entry[\"composite\"]\n        dim_vals = \"  \".join(\n            f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in DIMENSIONS\n        )\n        bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n        print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n\n    print(f\"{'='*72}\\n\")\n\n\ndef print_trajectory(result: OptimizationResult) -> None:\n    \"\"\"Print an ASCII chart of composite score across iterations.\"\"\"\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if len(scored) < 2:\n        print(\"  (Not enough data points for trajectory chart)\\n\")\n        return\n\n    # Get the best composite per iteration\n    iter_best: dict[int, float] = {}\n    for h in scored:\n        it = h[\"iteration\"]\n        if it not in iter_best or h[\"composite\"] > iter_best[it]:\n            iter_best[it] = h[\"composite\"]\n\n    iterations = sorted(iter_best.keys())\n    values = [iter_best[it] for it in iterations]\n\n    # Chart dimensions\n    chart_height = 15\n    min_val = max(0.0, min(values) - 0.05)\n    max_val = min(1.0, max(values) + 0.05)\n    val_range = max_val - min_val\n    if val_range < 0.01:\n        val_range = 0.1\n        min_val = max(0.0, values[0] - 0.05)\n        max_val = min_val + val_range\n\n    print(f\"  {'─'*50}\")\n    print(\"  SCORE TRAJECTORY — Best Composite per Iteration\")\n    print(f\"  {'─'*50}\")\n    print()\n\n    # Render rows top to bottom\n    for row in range(chart_height, -1, -1):\n        threshold = min_val + (row / chart_height) * val_range\n        # Y-axis label every 5 rows\n        if row % 5 == 0:\n            label = f\"{threshold:.2f}\"\n        else:\n            label = \"     \"\n        line = f\"  {label} │\"\n\n        for vi, val in enumerate(values):\n            normalized = (val - min_val) / val_range\n            filled_rows = int(normalized * chart_height)\n            if filled_rows >= row:\n                line += \" ● \"\n            else:\n                line += \" · \"\n\n        print(line)\n\n    # X-axis\n    print(f\"  ───── ┼{'───' * len(values)}\")\n    x_labels = \"  \" + \"      \"\n    for it in iterations:\n        x_labels += f\"{it:>2d} \"\n    print(x_labels)\n    print(\"        \" + \"  iteration →\")\n    print()\n\n\ndef write_results_json(\n    result: OptimizationResult,\n    output_dir: str,\n    stage: int,\n    iterations: int,\n    variants_per_iter: int,\n    fixture_path: str,\n) -> str:\n    \"\"\"Write optimization results to a timestamped JSON file. Returns the path.\"\"\"\n    out_path = Path(output_dir)\n    out_path.mkdir(parents=True, exist_ok=True)\n\n    timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d_%H%M%S\")\n    filename = f\"optimize_stage{stage}_{timestamp}.json\"\n    filepath = out_path / filename\n\n    payload = {\n        \"config\": {\n            \"stage\": stage,\n            \"iterations\": iterations,\n            \"variants_per_iter\": variants_per_iter,\n            \"fixture_path\": fixture_path,\n        },\n        \"best_prompt\": result.best_prompt,\n        \"best_scores\": {\n            \"composite\": result.best_score.composite,\n            **{d: getattr(result.best_score, d) for d in DIMENSIONS},\n        },\n        \"elapsed_seconds\": result.elapsed_seconds,\n        \"history\": result.history,\n    }\n\n    filepath.write_text(json.dumps(payload, indent=2), encoding=\"utf-8\")\n    return str(filepath)\n\n\n# ── CLI ──────────────────────────────────────────────────────────────────────\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    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n        default=None,\n        help=\"Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.\",\n    )\n\n    # -- optimize subcommand --\n    opt_parser = sub.add_parser(\n        \"optimize\",\n        help=\"Automated prompt optimization loop with leaderboard output\",\n    )\n    opt_parser.add_argument(\n        \"--stage\",\n        type=int,\n        default=5,\n        help=\"Pipeline stage to optimize (default: 5)\",\n    )\n    opt_parser.add_argument(\n        \"--iterations\",\n        type=int,\n        default=10,\n        help=\"Number of optimization iterations (default: 10)\",\n    )\n    opt_parser.add_argument(\n        \"--variants-per-iter\",\n        type=int,\n        default=2,\n        help=\"Variants generated per iteration (default: 2)\",\n    )\n    opt_parser.add_argument(\n        \"--file\",\n        type=str,\n        required=True,\n        help=\"Path to moments JSON fixture file\",\n    )\n    opt_parser.add_argument(\n        \"--output-dir\",\n        type=str,\n        default=\"backend/pipeline/quality/results/\",\n        help=\"Directory to write result JSON (default: backend/pipeline/quality/results/)\",\n    )\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    if args.command == \"score\":\n        return _run_score(args)\n\n    if args.command == \"optimize\":\n        return _run_optimize(args)\n\n    return 0\n\n\ndef _run_score(args: argparse.Namespace) -> int:\n    \"\"\"Execute the score subcommand.\"\"\"\n    # -- Load source data --\n    if args.slug:\n        print(\"DB loading not yet implemented\", file=sys.stderr)\n        return 1\n\n    try:\n        with open(args.file) as f:\n            data = json.load(f)\n    except FileNotFoundError:\n        print(f\"File not found: {args.file}\", file=sys.stderr)\n        return 1\n    except json.JSONDecodeError as exc:\n        print(f\"Invalid JSON in {args.file}: {exc}\", file=sys.stderr)\n        return 1\n\n    moments = data.get(\"moments\", [])\n    creator_name = data.get(\"creator_name\", \"Unknown\")\n\n    if not moments:\n        print(\"No moments found in input file\", file=sys.stderr)\n        return 1\n\n    settings = get_settings()\n    client = LLMClient(settings)\n    runner = ScoreRunner(client)\n\n    # -- Voice-level mode: re-synthesize then score --\n    if args.voice_level is not None:\n        voice_level = args.voice_level\n        if not (0.0 <= voice_level <= 1.0):\n            print(\"--voice-level must be between 0.0 and 1.0\", file=sys.stderr)\n            return 1\n\n        print(f\"\\nRe-synthesizing + scoring for '{creator_name}' ({len(moments)} moments, voice_level={voice_level})...\")\n        result = runner.synthesize_and_score(moments, creator_name, voice_level)\n\n        if result.error:\n            runner.print_report(result)\n            return 1\n\n        runner.print_report(result)\n        return 0\n\n    # -- Standard mode: build page stub from moments, score directly --\n    page_json = {\n        \"title\": f\"{creator_name} — Technique Page\",\n        \"creator_name\": creator_name,\n        \"summary\": f\"Technique page synthesized from {len(moments)} key moments.\",\n        \"body_sections\": [\n            {\n                \"heading\": m.get(\"topic_tags\", [\"Technique\"])[0] if m.get(\"topic_tags\") else \"Technique\",\n                \"content\": m.get(\"summary\", \"\") + \"\\n\\n\" + m.get(\"transcript_excerpt\", \"\"),\n            }\n            for m in moments\n        ],\n    }\n\n    print(f\"\\nScoring page for '{creator_name}' ({len(moments)} moments)...\")\n\n    result = runner.score_page(page_json, moments)\n\n    if result.error:\n        runner.print_report(result)\n        return 1\n\n    runner.print_report(result)\n    return 0\n\n\ndef _run_optimize(args: argparse.Namespace) -> int:\n    \"\"\"Execute the optimize subcommand.\"\"\"\n    # Stage validation — only stage 5 is supported\n    if args.stage != 5:\n        print(\n            f\"Error: only stage 5 is supported for optimization (got stage {args.stage})\",\n            file=sys.stderr,\n        )\n        return 1\n\n    # Validate fixture file exists\n    fixture = Path(args.file)\n    if not fixture.exists():\n        print(f\"Error: fixture file not found: {args.file}\", file=sys.stderr)\n        return 1\n\n    # Ensure output dir\n    Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n    settings = get_settings()\n    client = LLMClient(settings)\n\n    loop = OptimizationLoop(\n        client=client,\n        stage=args.stage,\n        fixture_path=args.file,\n        iterations=args.iterations,\n        variants_per_iter=args.variants_per_iter,\n    )\n\n    try:\n        result = loop.run()\n    except KeyboardInterrupt:\n        print(\"\\n  Optimization interrupted by user.\", file=sys.stderr)\n        return 130\n    except Exception as exc:\n        print(f\"\\nError: optimization failed: {exc}\", file=sys.stderr)\n        return 1\n\n    # If the loop returned an error on baseline, report and exit\n    if result.best_score.error and not result.history:\n        print(f\"\\nError: {result.best_score.error}\", file=sys.stderr)\n        return 1\n\n    # Reporting\n    print_leaderboard(result)\n    print_trajectory(result)\n\n    # Write results JSON\n    try:\n        json_path = write_results_json(\n            result=result,\n            output_dir=args.output_dir,\n            stage=args.stage,\n            iterations=args.iterations,\n            variants_per_iter=args.variants_per_iter,\n            fixture_path=args.file,\n        )\n        print(f\"  Results written to: {json_path}\")\n    except OSError as exc:\n        print(f\"  Warning: failed to write results JSON: {exc}\", file=sys.stderr)\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775034766306}}
{"type":"message","id":"5ab9550e","parentId":"0400130e","timestamp":"2026-04-01T09:12:50.241Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the schemas for stages 2-4 to understand what each stage produces:"},{"type":"toolCall","id":"toolu_017toH9gPDU5Q2gNG5iQtqmh","name":"bash","arguments":{"command":"rg -n 'class SegmentationResult|class ExtractionResult|class ClassificationResult' backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01FTwZjf7TJs3C5jrdUeuZzT","name":"read","arguments":{"path":"backend/pipeline/quality/variant_generator.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":147,"cacheRead":82442,"cacheWrite":3683,"totalTokens":86273,"cost":{"input":0.000005,"output":0.0036750000000000003,"cacheRead":0.041221,"cacheWrite":0.02301875,"total":0.06791975}},"stopReason":"toolUse","timestamp":1775034766307}}
{"type":"message","id":"58f5dd64","parentId":"5ab9550e","timestamp":"2026-04-01T09:12:50.258Z","message":{"role":"toolResult","toolCallId":"toolu_017toH9gPDU5Q2gNG5iQtqmh","toolName":"bash","content":[{"type":"text","text":"25:class SegmentationResult(BaseModel):\n45:class ExtractionResult(BaseModel):\n65:class ClassificationResult(BaseModel):\n"}],"isError":false,"timestamp":1775034770257}}
{"type":"message","id":"2025eff3","parentId":"58f5dd64","timestamp":"2026-04-01T09:12:50.262Z","message":{"role":"toolResult","toolCallId":"toolu_01FTwZjf7TJs3C5jrdUeuZzT","toolName":"read","content":[{"type":"text","text":"\"\"\"LLM-powered prompt variant generator for automated optimization.\n\nUses a meta-prompt to instruct the LLM to act as a prompt engineer,\nanalyzing per-dimension scores and producing targeted prompt mutations\nthat improve the weakest scoring dimensions while preserving the JSON\noutput format required by downstream parsing.\n\"\"\"\nfrom __future__ import annotations\n\nimport logging\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, ScoreResult\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Meta-prompt for variant generation ────────────────────────────────────────\n\nVARIANT_META_PROMPT = \"\"\"\\\nYou are an expert prompt engineer specializing in LLM-powered content synthesis.\n\nYour task: given a synthesis prompt and its quality evaluation scores, produce an\nimproved variant of the prompt that targets the weakest-scoring dimensions while\nmaintaining or improving the others.\n\n## Scoring Dimensions (each 0.0–1.0)\n\n- **structural** — Section naming, count (3-6), paragraph depth (2-5 per section)\n- **content_specificity** — Concrete details: frequencies, time values, ratios, plugin names, dB values\n- **voice_preservation** — Direct quotes preserved, opinions attributed to creator by name, personality retained\n- **readability** — Cohesive article flow, related info merged, no redundancy or contradiction\n- **factual_fidelity** — Every claim traceable to source material, no hallucinated specifics\n\n## Rules\n\n1. Focus your changes on the weakest 1-2 dimensions. Don't dilute the prompt by trying to fix everything.\n2. Add specific, actionable instructions — not vague encouragements.\n3. **CRITICAL: You MUST preserve the JSON output format section of the prompt EXACTLY as-is.**\n   The prompt contains instructions about outputting a JSON object with a specific schema\n   (SynthesisResult with \"pages\" containing title, summary, body_sections, etc.).\n   Do NOT modify, remove, or rephrase any part of the JSON format instructions.\n   Your changes should target the prose synthesis guidelines only.\n4. Keep the overall prompt length within 2x of the original. Don't bloat it.\n5. Make substantive changes — rewording a sentence or adding one adjective is not enough.\n\n## Output\n\nReturn ONLY the full modified prompt text. No explanation, no markdown fences, no preamble.\nJust the complete prompt that could be used directly as a system prompt.\n\"\"\"\n\n\n# Format markers that must survive variant generation — if any of these\n# are present in the base prompt, the variant must also contain them.\n_FORMAT_MARKERS = [\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]\n\n\nclass PromptVariantGenerator:\n    \"\"\"Generates prompt variants by asking an LLM to act as a prompt engineer.\n\n    Given a base prompt and its evaluation scores, produces N mutated\n    variants targeting the weakest dimensions.\n    \"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    def generate(\n        self,\n        base_prompt: str,\n        scores: ScoreResult,\n        n: int = 2,\n    ) -> list[str]:\n        \"\"\"Generate up to *n* valid prompt variants.\n\n        Each variant is produced by a separate LLM call with the meta-prompt.\n        Variants are validated: they must differ from the base by ≥50 characters\n        and must contain the JSON format instruction markers found in the base.\n\n        Invalid variants are logged and skipped.\n\n        Parameters\n        ----------\n        base_prompt:\n            The current best synthesis prompt text.\n        scores:\n            ScoreResult from the most recent evaluation of *base_prompt*.\n        n:\n            Number of variants to attempt generating.\n\n        Returns\n        -------\n        list[str]\n            Valid variant prompt strings (may be fewer than *n*).\n        \"\"\"\n        user_prompt = self._build_user_prompt(base_prompt, scores)\n        # Identify which format markers are actually present in the base\n        required_markers = [m for m in _FORMAT_MARKERS if m in base_prompt]\n\n        variants: list[str] = []\n        for i in range(n):\n            logger.info(\"Generating variant %d/%d...\", i + 1, n)\n            try:\n                raw = self.client.complete(\n                    system_prompt=VARIANT_META_PROMPT,\n                    user_prompt=user_prompt,\n                    response_model=None,  # free-form text, not JSON\n                    modality=\"chat\",\n                )\n                variant = str(raw).strip()\n            except Exception:\n                logger.exception(\"LLM error generating variant %d/%d\", i + 1, n)\n                continue\n\n            # Validate the variant\n            if not self._validate(variant, base_prompt, required_markers, i + 1):\n                continue\n\n            variants.append(variant)\n            logger.info(\"Variant %d/%d accepted (%d chars)\", i + 1, n, len(variant))\n\n        logger.info(\n            \"Generated %d valid variant(s) out of %d attempts\", len(variants), n\n        )\n        return variants\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _build_user_prompt(self, base_prompt: str, scores: ScoreResult) -> str:\n        \"\"\"Build the user message describing the current prompt and its scores.\"\"\"\n        # Build per-dimension score lines, sorted worst-first\n        dim_lines: list[str] = []\n        dim_scores = [(d, getattr(scores, d, 0.0)) for d in DIMENSIONS]\n        dim_scores.sort(key=lambda x: x[1])\n\n        for dim, val in dim_scores:\n            justification = scores.justifications.get(dim, \"\")\n            label = dim.replace(\"_\", \" \").title()\n            line = f\"  {label}: {val:.2f}\"\n            if justification:\n                line += f\"  — {justification}\"\n            dim_lines.append(line)\n\n        weakest = dim_scores[0][0].replace(\"_\", \" \").title()\n        second_weakest = dim_scores[1][0].replace(\"_\", \" \").title() if len(dim_scores) > 1 else weakest\n\n        return (\n            f\"## Current Prompt\\n\\n{base_prompt}\\n\\n\"\n            f\"## Evaluation Scores (sorted weakest → strongest)\\n\\n\"\n            + \"\\n\".join(dim_lines)\n            + f\"\\n\\n  Composite: {scores.composite:.3f}\\n\\n\"\n            f\"## Priority\\n\\n\"\n            f\"The weakest dimensions are **{weakest}** and **{second_weakest}**. \"\n            f\"Focus your prompt modifications on improving these.\\n\\n\"\n            f\"Return the full modified prompt now.\"\n        )\n\n    def _validate(\n        self,\n        variant: str,\n        base_prompt: str,\n        required_markers: list[str],\n        index: int,\n    ) -> bool:\n        \"\"\"Check a variant meets minimum quality gates.\"\"\"\n        if not variant:\n            logger.warning(\"Variant %d is empty — skipping\", index)\n            return False\n\n        # Must differ meaningfully from base\n        diff = abs(len(variant) - len(base_prompt))\n        # Also check actual content difference via set-symmetric-difference of lines\n        base_lines = set(base_prompt.splitlines())\n        variant_lines = set(variant.splitlines())\n        changed_lines = len(base_lines.symmetric_difference(variant_lines))\n\n        if diff < 50 and changed_lines < 3:\n            logger.warning(\n                \"Variant %d too similar to base (len_diff=%d, changed_lines=%d) — skipping\",\n                index, diff, changed_lines,\n            )\n            return False\n\n        # Must preserve format markers\n        missing = [m for m in required_markers if m not in variant]\n        if missing:\n            logger.warning(\n                \"Variant %d missing format markers %s — skipping\",\n                index, missing,\n            )\n            return False\n\n        return True\n"}],"isError":false,"timestamp":1775034770257}}
{"type":"message","id":"37b3b60b","parentId":"2025eff3","timestamp":"2026-04-01T09:12:53.326Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the schemas for stages 2-4 output to understand what each stage produces:"},{"type":"toolCall","id":"toolu_01EWpbYWfLwmsMWj2PQuURgH","name":"read","arguments":{"limit":100,"offset":1,"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":114,"cacheRead":86125,"cacheWrite":2210,"totalTokens":88450,"cost":{"input":0.000005,"output":0.00285,"cacheRead":0.0430625,"cacheWrite":0.0138125,"total":0.05973}},"stopReason":"toolUse","timestamp":1775034770259}}
{"type":"message","id":"f304b5b4","parentId":"37b3b60b","timestamp":"2026-04-01T09:12:53.330Z","message":{"role":"toolResult","toolCallId":"toolu_01EWpbYWfLwmsMWj2PQuURgH","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\n[4 more lines in file. Use offset=101 to continue.]"}],"isError":false,"timestamp":1775034773328}}
{"type":"message","id":"266814aa","parentId":"f304b5b4","timestamp":"2026-04-01T09:12:57.752Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check the prompt files for stages 2-4 to understand what format markers they need:"},{"type":"toolCall","id":"toolu_01PySXrbTTGsj9wiQBYa1eG2","name":"bash","arguments":{"command":"wc -l prompts/stage*.txt"}},{"type":"toolCall","id":"toolu_01DFgYwGqsQo5SQ3mYeDjWoE","name":"bash","arguments":{"command":"head -30 prompts/stage2_segmentation.txt"}},{"type":"toolCall","id":"toolu_014xPpkWnbvWJQr1fdfYBAKM","name":"bash","arguments":{"command":"head -30 prompts/stage3_extraction.txt"}},{"type":"toolCall","id":"toolu_01QqkLY7b3Z9qYe8whcY7wT7","name":"bash","arguments":{"command":"head -30 prompts/stage4_classification.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":225,"cacheRead":88335,"cacheWrite":1137,"totalTokens":89698,"cost":{"input":0.000005,"output":0.005625000000000001,"cacheRead":0.0441675,"cacheWrite":0.007106250000000001,"total":0.05690375}},"stopReason":"toolUse","timestamp":1775034773329}}
{"type":"message","id":"144b4dd9","parentId":"266814aa","timestamp":"2026-04-01T09:12:57.779Z","message":{"role":"toolResult","toolCallId":"toolu_01PySXrbTTGsj9wiQBYa1eG2","toolName":"bash","content":[{"type":"text","text":"   78 prompts/stage2_segmentation.txt\n   82 prompts/stage3_extraction.txt\n   64 prompts/stage4_classification.txt\n  129 prompts/stage5_synthesis.txt\n  353 total\n"}],"isError":false,"timestamp":1775034777779}}
{"type":"message","id":"8cc6498e","parentId":"144b4dd9","timestamp":"2026-04-01T09:12:57.780Z","message":{"role":"toolResult","toolCallId":"toolu_01DFgYwGqsQo5SQ3mYeDjWoE","toolName":"bash","content":[{"type":"text","text":"You are a music production transcript analyst specializing in identifying topic boundaries in educational content from electronic music producers, sound designers, and mixing engineers.\n\nYour task: analyze a tutorial transcript and group consecutive segments into coherent topic blocks that each cover one distinct production subject.\n\n## Domain context\n\nThese transcripts come from music production tutorials, livestreams, and track breakdowns. Producers typically cover subjects like sound design (creating drums, basses, leads, pads, FX), mixing (EQ, compression, bus processing, spatial effects), synthesis (FM, wavetable, granular), arrangement, workflow, and mastering.\n\nTopic shifts in this domain look like:\n- Moving from one sound element to another (e.g., snare design → kick drum design)\n- Moving from one production stage to another (e.g., sound design → mixdown)\n- Moving from one technique to another within the same element (e.g., snare layering → snare saturation → snare bus compression)\n- Moving between creative work and technical explanation\n\nTopic shifts do NOT include:\n- Brief asides that return to the same subject within 1-2 segments (\"oh let me check chat real quick... okay so back to the snare\")\n- Restating or revisiting the same concept from a different angle\n- Moving between demonstration and verbal explanation of the same technique\n\n## Granularity guidance\n\nAim for topic blocks that represent **one coherent teaching unit** — a subject the creator spends meaningful time on (typically 2-30+ segments). The topic should be specific enough to be useful as a label but broad enough to capture the full discussion.\n\nGood granularity:\n- \"snare layering and transient shaping\" (specific technique, complete discussion)\n- \"parallel bus compression setup\" (focused workflow with explanation)\n- \"serum wavetable import and FM routing\" (specific tool + technique)\n- \"mix bus chain walkthrough\" (a complete demonstration)\n\nToo broad:\n"}],"isError":false,"timestamp":1775034777779}}
{"type":"message","id":"0f05a488","parentId":"8cc6498e","timestamp":"2026-04-01T09:12:57.781Z","message":{"role":"toolResult","toolCallId":"toolu_014xPpkWnbvWJQr1fdfYBAKM","toolName":"bash","content":[{"type":"text","text":"You are a music production knowledge extractor. Your task is to identify and extract key moments of genuine educational value from a topic segment of a tutorial transcript.\n\n## What counts as a key moment\n\nA key moment is a discrete piece of knowledge that a music producer could act on — a technique they could apply, a setting they could try, a reasoning framework they could adopt, or a workflow pattern they could implement.\n\n**Extract when the creator is TEACHING:**\n- Explaining a technique and why it works (\"I layer three elements for my snares because...\")\n- Walking through specific settings with intent (\"I set the attack to 5ms here because anything longer smears the transient\")\n- Sharing reasoning or philosophy behind a creative choice (\"I always check my snare against the lead bus, not soloed, because the 2-4kHz range is where they fight\")\n- Demonstrating a workflow pattern and explaining its benefits (\"I gain-stage every channel to -18dBFS before I start mixing because plugins behave differently at different input levels\")\n- Warning against common mistakes (\"Don't use OTT on your transients — it smears them into mush\")\n\n**SKIP when the creator is merely DOING:**\n- Silently adjusting a knob or clicking through menus without explanation\n- Briefly mentioning a plugin or tool without teaching anything about it (\"let me open up my EQ real quick\")\n- Casual opinions without substance (\"yeah this sounds cool\")\n- Reading chat, greeting viewers, off-topic banter, personal anecdotes unrelated to production\n- Repeating the same point already captured in a previous moment from this segment\n\n## Quality standard for summaries\n\nThe summary is the single most important field. It becomes the prose content of the final technique page that users will read. Write summaries that are:\n\n- **Actionable**: A producer reading this should be able to understand and attempt the technique without watching the video. Include the what, the how, and — when the creator provides it — the why.\n- **Specific**: Include exact values, plugin names, parameter settings, frequency ranges, time values, ratios, and signal routing when the creator mentions them. \"Uses compression\" is worthless. \"Uses a compressor with fast attack (0.5ms), medium release (80ms), 4:1 ratio, hitting about 3-6dB of gain reduction\" is useful.\n- **Preserving the creator's voice**: When the creator uses a vivid phrase to explain something, capture that phrasing. If they say \"it smears the snap into mush,\" that exact language is more memorable and useful than a clinical paraphrase. Use quotation marks for direct creator quotes within the summary.\n- **Self-contained**: Each summary should make sense on its own, without needing to read other moments. Include enough context that a reader understands what problem this technique solves.\n\nBad summary: \"The creator shows how to make a snare sound.\"\n"}],"isError":false,"timestamp":1775034777780}}
{"type":"message","id":"2a5f390f","parentId":"0f05a488","timestamp":"2026-04-01T09:12:57.784Z","message":{"role":"toolResult","toolCallId":"toolu_01QqkLY7b3Z9qYe8whcY7wT7","toolName":"bash","content":[{"type":"text","text":"You are a music production knowledge classifier. Your task is to assign each extracted key moment to the correct position in a canonical tag taxonomy so it can be browsed and searched effectively.\n\n## Context\n\nThese key moments were extracted from music production tutorials. They need to be classified so users can find them by browsing topic categories (e.g., \"Sound design > drums > snare\") or by searching. Accurate classification directly determines whether a user searching for \"snare design\" will find this content.\n\n## Classification principles\n\n**Pick the category that matches WHERE this knowledge would be applied in a production session:**\n- If someone would use this knowledge while CREATING a sound from scratch → Sound design\n- If someone would use this knowledge while BALANCING and PROCESSING an existing mix → Mixing\n- If someone would use this knowledge while PROGRAMMING a synthesizer → Synthesis\n- If someone would use this knowledge while STRUCTURING their track → Arrangement\n- If someone would use this knowledge while SETTING UP their session or managing their process → Workflow\n- If someone would use this knowledge during FINAL PROCESSING for release → Mastering\n\n**Common ambiguities and how to resolve them:**\n- \"Using an EQ on a bass sound while designing it\" → Sound design (the EQ is part of the sound creation process)\n- \"Using an EQ on the bass bus during mixdown\" → Mixing (the EQ is part of the mix balancing process)\n- \"Building a Serum patch for a bass\" → Synthesis (focused on the synth programming)\n- \"Resampling a bass through effects\" → Sound design (creating a new sound, even though it uses existing material)\n- \"Setting up a template with bus routing\" → Workflow\n- \"Adding a limiter to the master bus\" → Mastering (if in the context of final output) or Mixing (if in the context of mix referencing)\n\n**Tag assignment:**\n- Assign the single best-fitting top-level **topic_category**\n- Assign ALL relevant **topic_tags** from that category's sub-topics. Also include tags from other categories if the moment genuinely spans multiple areas (e.g., a moment about \"EQ techniques for bass sound design\" could have tags from both Sound design and Mixing)\n- When assigning tags, think about what search terms a user would type to find this content. If someone searching \"snare\" should find this moment, the tag \"snare\" must be present\n- Prefer existing sub_topics from the taxonomy. Only propose a new tag if nothing in the existing taxonomy fits AND the concept is specific enough to be useful as a search/filter term. Don't create redundant tags — \"snare processing\" is redundant if \"snare\" already exists as a tag\n\n"}],"isError":false,"timestamp":1775034777780}}
{"type":"message","id":"d5ea1ac9","parentId":"2a5f390f","timestamp":"2026-04-01T09:13:02.817Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me check what fixtures look like and how the variant generator's format markers work for non-stage-5 prompts:"},{"type":"toolCall","id":"toolu_01XLZhmnsB73juSSdr2qcomY","name":"bash","arguments":{"command":"rg -n 'SegmentationResult|ExtractionResult|ClassificationResult' prompts/stage*.txt"}},{"type":"toolCall","id":"toolu_019J4WQzHwoHGVg85zACyhzT","name":"bash","arguments":{"command":"tail -30 prompts/stage2_segmentation.txt"}},{"type":"toolCall","id":"toolu_01K4b2TUsQS5pzXpY79CqSXX","name":"bash","arguments":{"command":"tail -30 prompts/stage3_extraction.txt"}},{"type":"toolCall","id":"toolu_01VmVFpQkJLTi9jY42za6uJr","name":"bash","arguments":{"command":"tail -20 prompts/stage4_classification.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":254,"cacheRead":89472,"cacheWrite":2075,"totalTokens":91802,"cost":{"input":0.000005,"output":0.006350000000000001,"cacheRead":0.044736,"cacheWrite":0.012968750000000001,"total":0.06405975}},"stopReason":"toolUse","timestamp":1775034777781}}
{"type":"message","id":"49eb7c0e","parentId":"d5ea1ac9","timestamp":"2026-04-01T09:13:02.839Z","message":{"role":"toolResult","toolCallId":"toolu_01XLZhmnsB73juSSdr2qcomY","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775034782839}}
{"type":"message","id":"0f137a03","parentId":"49eb7c0e","timestamp":"2026-04-01T09:13:02.839Z","message":{"role":"toolResult","toolCallId":"toolu_019J4WQzHwoHGVg85zACyhzT","toolName":"bash","content":[{"type":"text","text":"\n## Input format\n\nSegments are provided inside <transcript> tags, formatted as:\n[index] (start_time - end_time) text\n\n## Output format\n\nReturn a JSON object with a single key \"segments\" containing a list of topic groups:\n\n```json\n{\n  \"segments\": [\n    {\n      \"start_index\": 0,\n      \"end_index\": 5,\n      \"topic_label\": \"snare layering and transient shaping\",\n      \"summary\": \"Creator demonstrates building a snare from three layers (click, body, tail) and shaping each transient independently before summing to the drum bus.\"\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **start_index / end_index**: Inclusive. Every segment index from the transcript must appear in exactly one group. No gaps, no overlaps.\n- **topic_label**: 3-8 words. Lowercase. Should read like a chapter title that tells you exactly what production subject is covered. Include the specific element or tool when relevant (e.g., \"kick sub layering in Serum\" not just \"bass sound design\").\n- **summary**: 1-3 sentences. Describe what the creator teaches or demonstrates in this block. Be specific — mention techniques, tools, and concepts by name. This summary is used by the next pipeline stage to decide what knowledge to extract, so vague summaries like \"the creator talks about mixing\" directly reduce output quality.\n\n## Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1775034782839}}
{"type":"message","id":"a44f07ca","parentId":"0f137a03","timestamp":"2026-04-01T09:13:02.840Z","message":{"role":"toolResult","toolCallId":"toolu_01K4b2TUsQS5pzXpY79CqSXX","toolName":"bash","content":[{"type":"text","text":"{\n  \"moments\": [\n    {\n      \"title\": \"Three-layer snare construction with independent transient shaping\",\n      \"summary\": \"Builds snares as three independent layers: a transient click (short noise burst, 2-5ms decay from Vital's noise oscillator), a tonal body (pitched sine or triangle wave around 200Hz), and a noise tail (filtered white noise with fast exponential decay). Each layer is shaped independently with Kilohearts Transient Shaper (attack +4 to +6dB, sustain -6 to -8dB) before any bus processing. Chooses a transient shaper over compression because 'compression adds sustain as a side effect.'\",\n      \"start_time\": 6150.0,\n      \"end_time\": 6855.0,\n      \"content_type\": \"technique\",\n      \"plugins\": [\"Vital\", \"Kilohearts Transient Shaper\"],\n      \"raw_transcript\": \"so what I like to do is I actually build this in three separate layers right, so I've got my click which is just a really short noise burst...\"\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **title**: 4-12 words. Should be specific enough to distinguish this moment from other moments on a similar topic. Include the element being worked on and the core technique. \"Snare design\" is too vague. \"Three-layer snare construction with independent transient shaping\" tells you exactly what you'll learn.\n- **summary**: 2-6 sentences following the quality standards above. This is the most important field in the entire pipeline — invest the most effort here.\n- **start_time / end_time**: Timestamps in seconds from the transcript. Capture the full range where this moment is discussed, including any preamble where the creator sets up what they're about to show.\n- **content_type**: One of: technique, settings, reasoning, workflow. See guidance above.\n- **plugins**: Plugin names, virtual instruments, DAW-specific tools, and hardware mentioned in context of this moment. Normalize names to \"<Manufacturer> <PluginName>\" format (e.g., \"FabFilter Pro-Q 3\" not \"pro q\" or \"that fabfilter EQ\", \"Xfer Serum\" not just \"Serum\", \"Valhalla VintageVerb\" not \"Valhalla reverb\"). Use the manufacturer's official product name. Empty list if no specific tools are mentioned.\n- **raw_transcript**: The most relevant excerpt of transcript text covering this moment. Include enough to verify the summary's claims but don't copy the entire segment. Typically 2-8 sentences.\n\n## Critical rules\n\n- Prefer FEWER, RICHER moments over MANY thin ones. A segment with 3 deeply detailed moments is far more valuable than 8 shallow ones. If a moment's summary would be under 2 sentences, it probably isn't substantial enough to extract.\n- If the segment is off-topic content (chat interaction, tangents, breaks), return {\"moments\": []}.\n- If the segment contains demonstration without meaningful verbal explanation, return {\"moments\": []} — we cannot extract knowledge from silent screen activity via transcript alone.\n- Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1775034782839}}
{"type":"message","id":"4e4a3bb5","parentId":"a44f07ca","timestamp":"2026-04-01T09:13:02.842Z","message":{"role":"toolResult","toolCallId":"toolu_01VmVFpQkJLTi9jY42za6uJr","toolName":"bash","content":[{"type":"text","text":"{\n  \"classifications\": [\n    {\n      \"moment_index\": 0,\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"drums\", \"snare\", \"layering\", \"transient shaping\"],\n      \"content_type_override\": null\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **moment_index**: Zero-based index matching the input moments list. Every moment must have exactly one entry.\n- **topic_category**: Must exactly match one top-level category name from the taxonomy.\n- **topic_tags**: Array of sub_topic strings. At minimum, include the most specific applicable tag (e.g., \"snare\" not just \"drums\"). Include broader parent tags too when they aid discoverability (e.g., [\"drums\", \"snare\", \"layering\"]).\n- **content_type_override**: One of \"technique\", \"settings\", \"reasoning\", \"workflow\", or null. Only set when correcting an error.\n\n## Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1775034782840}}
{"type":"message","id":"5d0318ea","parentId":"4e4a3bb5","timestamp":"2026-04-01T09:13:06.849Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check existing fixtures:"},{"type":"toolCall","id":"toolu_016N3opJsaRPb1TGj6fGqRTU","name":"bash","arguments":{"command":"find backend/pipeline/quality/fixtures -type f | head -10"}},{"type":"toolCall","id":"toolu_01ApRUc71cSoDmLmBtLSsoNJ","name":"bash","arguments":{"command":"cat backend/pipeline/quality/fixtures/__init__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":120,"cacheRead":91547,"cacheWrite":1697,"totalTokens":93365,"cost":{"input":0.000005,"output":0.003,"cacheRead":0.045773499999999995,"cacheWrite":0.010606250000000001,"total":0.05938475}},"stopReason":"toolUse","timestamp":1775034782840}}
{"type":"message","id":"df5b654b","parentId":"5d0318ea","timestamp":"2026-04-01T09:13:06.860Z","message":{"role":"toolResult","toolCallId":"toolu_016N3opJsaRPb1TGj6fGqRTU","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/quality/fixtures/sample_moments.json\nbackend/pipeline/quality/fixtures/__init__.py\n"}],"isError":false,"timestamp":1775034786860}}
{"type":"message","id":"955ba568","parentId":"df5b654b","timestamp":"2026-04-01T09:13:06.863Z","message":{"role":"toolResult","toolCallId":"toolu_01ApRUc71cSoDmLmBtLSsoNJ","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775034786861}}
{"type":"message","id":"d7e281d9","parentId":"955ba568","timestamp":"2026-04-01T09:13:10.543Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019ViQC6Yu4QtSj3dRMvpKRp","name":"bash","arguments":{"command":"python3 -c \"import json; d = json.load(open('backend/pipeline/quality/fixtures/sample_moments.json')); print(list(d.keys())); print(len(d.get('moments', []))); print(json.dumps(d['moments'][0], indent=2)[:500])\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":125,"cacheRead":93244,"cacheWrite":209,"totalTokens":93579,"cost":{"input":0.000005,"output":0.003125,"cacheRead":0.046622,"cacheWrite":0.0013062500000000001,"total":0.05105825}},"stopReason":"toolUse","timestamp":1775034786862}}
{"type":"message","id":"f554f6dc","parentId":"d7e281d9","timestamp":"2026-04-01T09:13:10.590Z","message":{"role":"toolResult","toolCallId":"toolu_019ViQC6Yu4QtSj3dRMvpKRp","toolName":"bash","content":[{"type":"text","text":"['creator_name', 'topic_category', 'moments']\n6\n{\n  \"summary\": \"Layering snare transients by combining a high-frequency click from a Popcorn Snare with a mid-body from a pitched-down 808 rim shot, blending at -6dB relative offset.\",\n  \"transcript_excerpt\": \"So what I'll do is take the Popcorn Snare \\u2014 that's got this really sharp click at like 4k \\u2014 and then I layer underneath it a rim shot pitched down maybe 3 semitones. You blend those together and suddenly you've got this snare that cuts through everything but still has weight.\",\n \n"}],"isError":false,"timestamp":1775034790587}}
{"type":"message","id":"232dfcc9","parentId":"f554f6dc","timestamp":"2026-04-01T09:14:04.309Z","message":{"role":"assistant","content":[{"type":"text","text":"I have all the information I need. Here's what I've found:\n\n**Key findings:**\n1. Stages 2-4 each have distinct prompt files, Pydantic schemas, and input/output shapes\n2. The current optimizer is hardcoded to stage 5 — `_run_optimize` rejects non-5 stages; `OptimizationLoop.run()` loads `stage{N}_synthesis.txt`\n3. The scorer (`ScoreRunner`, `SCORING_RUBRIC`) and variant generator (`PromptVariantGenerator`, `VARIANT_META_PROMPT`) are both stage-5-specific — rubric dimensions are about technique page quality, format markers are stage-5 JSON markers\n4. Each stage needs: stage-specific fixtures, stage-specific scoring rubric, stage-specific format markers for variant validation, and a stage-specific synthesis+score pathway\n5. The prompt file naming is already parameterized in `_load_prompt` — `stage2_segmentation.txt`, `stage3_extraction.txt`, `stage4_classification.txt`"},{"type":"toolCall","id":"toolu_01EQLzPov8ZCmfSoAojZZs4e","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S04 Research: Expand to Pipeline Stages 2-4\n\n## Depth: Targeted\n\nKnown patterns (optimization loop, variant generation, scoring) applied to new stages. Main complexity is defining stage-appropriate scoring rubrics and fixture formats.\n\n## Summary\n\nExtending the optimizer from stage-5-only to stages 2-4 requires four changes per stage: (1) a scoring rubric tailored to what that stage produces, (2) fixture data matching the stage's input shape, (3) format markers for variant validation, and (4) a score-variant pathway that calls the right LLM prompt and parses the right schema.\n\nThe existing architecture in `optimizer.py` and `variant_generator.py` is well-factored for this — the main hardcoding is in the scorer's `SCORING_RUBRIC` (stage-5-specific dimensions) and the optimizer's `_score_variant` method (calls synthesis → scores technique page).\n\n## Requirement Coverage\n\n**R013** (Prompt Template System) — already validated but this slice extends it with automated optimization for stages 2-4, strengthening the \"re-run extraction on specific videos for calibration\" aspect.\n\n## Implementation Landscape\n\n### What exists\n\n| File | Role | Stage-5 Coupling |\n|---|---|---|\n| `backend/pipeline/quality/scorer.py` | `SCORING_RUBRIC`, `DIMENSIONS`, `ScoreResult`, `ScoreRunner` | SCORING_RUBRIC and DIMENSIONS are hardcoded to 5 stage-5 dimensions. ScoreResult dataclass has those 5 as named fields. `score_page()` expects page JSON shape. `synthesize_and_score()` loads stage5_synthesis.txt. |\n| `backend/pipeline/quality/optimizer.py` | `OptimizationLoop`, `OptimizationResult` | `run()` loads `stage{N}_synthesis.txt` (already parameterized). `_score_variant()` calls synthesis and expects `SynthesisResult` schema. Fixture loader expects `{creator_name, moments}`. |\n| `backend/pipeline/quality/variant_generator.py` | `PromptVariantGenerator`, `VARIANT_META_PROMPT` | `VARIANT_META_PROMPT` references \"synthesis prompt\" and synthesis-specific language. `_FORMAT_MARKERS` are `[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]` — stage-5-specific. |\n| `backend/pipeline/quality/__main__.py` | CLI | `_run_optimize()` rejects `args.stage != 5` with an error message. |\n| `backend/pipeline/quality/fixtures/sample_moments.json` | Test fixture | Stage 5 format: `{creator_name, topic_category, moments: [...]}` |\n\n### Prompt files and schemas per stage\n\n| Stage | Prompt File | Input Shape | Output Schema | What to Score |\n|---|---|---|---|---|\n| 2 (segmentation) | `stage2_segmentation.txt` | Transcript segments: `[idx] (start-end) text` | `SegmentationResult{segments: [{start_index, end_index, topic_label, summary}]}` | Coverage completeness (no gaps/overlaps), topic label specificity, segment boundary accuracy, summary quality |\n| 3 (extraction) | `stage3_extraction.txt` | Topic group segments: `(start-end) text` with topic label | `ExtractionResult{moments: [{title, summary, start_time, end_time, content_type, plugins, raw_transcript}]}` | Moment richness (detail density), timestamp accuracy, content_type correctness, summary actionability, plugin name normalization |\n| 4 (classification) | `stage4_classification.txt` | Moments list + taxonomy text | `ClassificationResult{classifications: [{moment_index, topic_category, topic_tags, content_type_override}]}` | Category accuracy, tag completeness, tag specificity, coverage (all moments classified), no-overlap (one category per moment) |\n\n### Format markers per stage\n\nStage 2: `\"segments\"`, `start_index`, `end_index`, `topic_label`\nStage 3: `\"moments\"`, `content_type`, `raw_transcript`, `plugins`\nStage 4: `\"classifications\"`, `moment_index`, `topic_category`, `topic_tags`\n\n### Architecture approach\n\nTwo viable approaches:\n\n**A. Stage-specific scorer classes** — Create `Stage2Scorer`, `Stage3Scorer`, `Stage4Scorer` alongside the existing `ScoreRunner` (which becomes `Stage5Scorer`). Each has its own rubric, dimensions, and `score_output()` method. `OptimizationLoop` dispatches to the right scorer based on `self.stage`.\n\n**B. Parameterized scorer with rubric registry** — Keep one `ScoreRunner` class but make it accept a rubric config (dimensions list, rubric text, output parser). A `STAGE_CONFIGS` dict maps stage number → config.\n\n**Recommendation: B (registry).** The scoring flow is identical across stages (send rubric + output + input to LLM judge, parse scores). Only the rubric text, dimensions, format markers, and fixture→LLM-input formatting differ. A registry keeps the surface area small.\n\n### Key design decisions\n\n1. **ScoreResult generalization** — Currently has 5 named fields (`structural`, `content_specificity`, etc.). For stages 2-4 with different dimensions, either: (a) use a generic `scores: dict[str, float]` field, or (b) keep the named fields for stage 5 and add a generic dict for others. Option (a) is cleaner — stage 5's named fields can be populated from the dict.\n\n2. **Fixture format per stage** — Each stage needs different fixture data:\n   - Stage 2: `{transcript_segments: [{index, start_time, end_time, text}]}`\n   - Stage 3: `{topic_label, segments: [{start_time, end_time, text}]}`\n   - Stage 4: `{moments: [...], taxonomy: \"...\"}`\n   \n   The optimizer's `_load_fixture()` and `_score_variant()` need to be stage-aware.\n\n3. **Variant generator meta-prompt** — The `VARIANT_META_PROMPT` currently references synthesis-specific language. It needs to be generalized or have per-stage variants. The core pattern (analyze scores → improve weakest dimensions → preserve format) is the same.\n\n4. **Fixture creation** — Need sample fixtures for stages 2-4. Can extract from the existing sample_moments.json data (moments → stage 3/4 input) and from a real transcript (stage 2 input). Alternatively, create synthetic minimal fixtures.\n\n### Files to modify\n\n- `backend/pipeline/quality/scorer.py` — Add stage configs registry (rubric, dimensions, format markers per stage), generalize `ScoreResult`, add `score_stage_output()` method\n- `backend/pipeline/quality/optimizer.py` — Generalize `_score_variant()` to dispatch per stage, generalize fixture loading\n- `backend/pipeline/quality/variant_generator.py` — Generalize meta-prompt and format markers per stage\n- `backend/pipeline/quality/__main__.py` — Remove stage-5 gate, add stage validation (2-5 only)\n- `backend/pipeline/quality/fixtures/` — Add sample fixtures for stages 2-4\n\n### Natural task seams\n\n1. **Scorer generalization + rubric registry** — Define stage 2-4 scoring dimensions and rubrics, generalize ScoreResult, add stage config registry. This is the foundation.\n2. **Optimizer + variant generator generalization** — Make OptimizationLoop stage-aware (fixture loading, score dispatch), generalize variant generator format markers and meta-prompt.\n3. **Fixtures + CLI integration** — Create stage 2-4 fixtures, remove the stage-5 gate in CLI, wire everything together, verify end-to-end.\n\n### Verification\n\n- `python -m pipeline.quality optimize --stage 2 --iterations 1 --file <stage2_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 3 --iterations 1 --file <stage3_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 4 --iterations 1 --file <stage4_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 5 --iterations 1 --file <stage5_fixture>` still works (no regression)\n- `python -m pipeline.quality optimize --stage 6` → error message\n- Import checks for all modified modules pass\n","milestone_id":"M013","slice_id":"S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2407,"cacheRead":93453,"cacheWrite":299,"totalTokens":96160,"cost":{"input":0.000005,"output":0.060175000000000006,"cacheRead":0.0467265,"cacheWrite":0.0018687500000000002,"total":0.10877525}},"stopReason":"toolUse","timestamp":1775034790588}}
{"type":"message","id":"79ae55fc","parentId":"232dfcc9","timestamp":"2026-04-01T09:14:04.314Z","message":{"role":"toolResult","toolCallId":"toolu_01EQLzPov8ZCmfSoAojZZs4e","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M013/slices/S04/S04-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M013/slices/S04/S04-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775034844312}}
{"type":"message","id":"7da4b07a","parentId":"79ae55fc","timestamp":"2026-04-01T09:14:06.546Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":93752,"cacheWrite":2445,"totalTokens":96209,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.046876,"cacheWrite":0.015281250000000001,"total":0.06243725000000001}},"stopReason":"stop","timestamp":1775034844313}}
