{"type":"thinking_level_change","id":"bea7014a","parentId":null,"timestamp":"2026-04-01T09:02:09.770Z","thinkingLevel":"medium"}
{"type":"model_change","id":"e71e4494","parentId":"bea7014a","timestamp":"2026-04-01T09:02:09.772Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S03 (\"Prompt Variant Generator & Automated A/B Loop\") — 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#### S02 Summary\nSource: `.gsd/milestones/M013/slices/S02/S02-SUMMARY.md`\n\n---\nid: S02\nparent: M013\nmilestone: M013\nprovides:\n  - ScoreRunner with score_page() and synthesize_and_score() methods\n  - VoiceDial class for 3-band prompt modification\n  - CLI score subcommand with --file, --slug, --voice-level\n  - 6-moment fixture file for offline testing\nrequires:\n  []\naffects:\n  - S03\nkey_files:\n  - backend/pipeline/quality/scorer.py\n  - backend/pipeline/quality/voice_dial.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/__init__.py\n  - backend/pipeline/quality/fixtures/sample_moments.json\n  - pipeline\nkey_decisions:\n  - Hardcoded scoring rubric in scorer.py rather than external prompt file — faster iteration during quality toolkit development\n  - Three discrete voice bands (low/mid/high) at 0.33/0.67 boundaries instead of continuous interpolation — simpler to reason about and test\n  - Mid band returns base prompt unmodified since existing stage5 prompt already targets ~0.6 voice preservation\n  - Project-root symlink + __init__.py sys.path bootstrap to support running from any CWD\npatterns_established:\n  - sys.path bootstrap in package __init__.py using os.path.realpath() to resolve through symlinks\n  - VoiceDial band-based prompt modification pattern reusable for other prompt dimension dials\nobservability_surfaces:\n  - Score report output with per-dimension scores and justification excerpts\n  - Band identification in voice-level output (band=low/mid/high)\n  - Clean connectivity error with endpoint URL on LLM failure\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S02/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M013/slices/S02/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T09:01:48.179Z\nblocker_discovered: false\n---\n\n# S02: Stage 5 Quality Scorer & Voice Preservation Dial\n\n**Built 5-dimension LLM-as-judge scorer with CLI `score` subcommand and 3-band voice preservation dial that modifies Stage 5 synthesis prompts, runnable from both project root and backend/ directory.**\n\n## What Happened\n\nT01 created the scoring infrastructure: `ScoreRunner` with a `ScoreResult` dataclass covering 5 quality dimensions (structural, content_specificity, voice_preservation, readability, factual_fidelity), each scored 0.0–1.0 by an LLM judge via a hardcoded rubric prompt. The `score` subcommand was added to the existing `pipeline.quality` CLI with mutually exclusive `--file`/`--slug` inputs and an optional `--voice-level` parameter. A 6-moment fixture file (`sample_moments.json`) with realistic music production content provides offline testing data.\n\nT02 added the `VoiceDial` class implementing three discrete bands (low ≤0.33, mid 0.34–0.66, high ≥0.67) that modify the Stage 5 synthesis system prompt. Low band appends voice suppression instructions (neutral third-person, no direct quotes). Mid band passes the base prompt through unmodified (it already targets ~0.6 voice preservation). High band appends amplification instructions (maximize direct quotes, preserve personality). `ScoreRunner.synthesize_and_score()` chains re-synthesis through the modified prompt followed by scoring.\n\nPost-task fix: The verification gate ran `python -m pipeline.quality score --help` from the project root, but the `pipeline` package lives under `backend/`. Added a `pipeline` symlink at project root pointing to `backend/pipeline`, and added a `sys.path` bootstrap in `backend/pipeline/quality/__init__.py` that resolves through the symlink to add `backend/` to `sys.path`. This ensures `config` and `pipeline.llm_client` imports resolve regardless of CWD.\n\n## Verification\n\nAll slice verification checks pass from both project root and backend/ directory:\n\n1. `python -m pipeline.quality score --help` — shows --file, --slug, --voice-level args (exit 0)\n2. `python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('ok')\"` — import succeeds (exit 0)\n3. `python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9)\"` — three distinct bands confirmed (exit 0)\n4. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json` — clean connectivity error, exit 1, no traceback\n5. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — re-synthesis path, clean connectivity error, exit 1, no traceback\n6. Fixture validation: 6 moments with required fields present\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nAdded project-root `pipeline` symlink and `sys.path` bootstrap in `__init__.py` to support running `python -m pipeline.quality` from the project root (not just from `backend/`). This was not in the original plan but required by the verification gate.\n\n## Known Limitations\n\n`--slug` path prints \"DB loading not yet implemented\" and exits 1 (deferred by design). Voice-level path requires `prompts/stage5_synthesis.txt` to be reachable from CWD — works in Docker (WORKDIR=/app) and from `backend/`, but from project root the prompt file path resolves differently.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/scorer.py` — New: ScoreResult dataclass + ScoreRunner with score_page() and synthesize_and_score() methods\n- `backend/pipeline/quality/voice_dial.py` — New: VoiceDial class with 3-band prompt modification (low/mid/high)\n- `backend/pipeline/quality/__main__.py` — Added score subcommand with --file, --slug, --voice-level args\n- `backend/pipeline/quality/__init__.py` — Added sys.path bootstrap for project-root execution\n- `backend/pipeline/quality/fixtures/sample_moments.json` — New: 6-moment fixture with realistic music production content\n- `backend/pipeline/quality/fixtures/__init__.py` — New: empty package marker\n- `pipeline` — New: symlink to backend/pipeline for project-root execution\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: S03`, `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/S03/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S03 researched.\"","display":false,"id":"a649615c","parentId":"e71e4494","timestamp":"2026-04-01T09:02:09.774Z"}
{"type":"message","id":"dec19c4f","parentId":"a649615c","timestamp":"2026-04-01T09:02:18.934Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research what's needed for S03: \"Prompt Variant Generator & Automated A/B Loop\". The goal is to build a CLI command `python -m pipeline.quality optimize --stage 5 --iterations 10` that generates prompt variants, scores each against reference articles, and outputs a leaderboard with score trajectory chart.\n\nLet me explore the existing code to understand the foundation.","thinkingSignature":"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"},{"type":"toolCall","id":"toolu_014UDpD2eoRw2DDt4kHENNAC","name":"bash","arguments":{"command":"find backend/pipeline/quality -type f | head -30 && echo \"---\" && cat backend/pipeline/quality/__main__.py"}},{"type":"toolCall","id":"toolu_01KaW2AsCpR75L71yvebBXiq","name":"bash","arguments":{"command":"cat backend/pipeline/quality/scorer.py"}},{"type":"toolCall","id":"toolu_01Em62jy1rrp4QqPAbxRFLRE","name":"bash","arguments":{"command":"cat backend/pipeline/quality/voice_dial.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":271,"cacheRead":59647,"cacheWrite":9771,"totalTokens":69692,"cost":{"input":0.000015000000000000002,"output":0.006775000000000001,"cacheRead":0.0298235,"cacheWrite":0.061068750000000005,"total":0.09768225}},"stopReason":"toolUse","timestamp":1775034129773}}
{"type":"message","id":"740fc3b9","parentId":"dec19c4f","timestamp":"2026-04-01T09:02:18.955Z","message":{"role":"toolResult","toolCallId":"toolu_014UDpD2eoRw2DDt4kHENNAC","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/quality/__main__.py\nbackend/pipeline/quality/scorer.py\nbackend/pipeline/quality/fitness.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/__pycache__/fitness.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/__init__.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/scorer.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/voice_dial.cpython-312.pyc\nbackend/pipeline/quality/__pycache__/__main__.cpython-312.pyc\nbackend/pipeline/quality/voice_dial.py\nbackend/pipeline/quality/fixtures/sample_moments.json\nbackend/pipeline/quality/fixtures/__init__.py\n---\n\"\"\"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\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .scorer import ScoreRunner\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    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    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\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775034138955}}
{"type":"message","id":"7b793fc6","parentId":"740fc3b9","timestamp":"2026-04-01T09:02:18.956Z","message":{"role":"toolResult","toolCallId":"toolu_01KaW2AsCpR75L71yvebBXiq","toolName":"bash","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":1775034138955}}
{"type":"message","id":"8ae2ad6a","parentId":"7b793fc6","timestamp":"2026-04-01T09:02:18.966Z","message":{"role":"toolResult","toolCallId":"toolu_01Em62jy1rrp4QqPAbxRFLRE","toolName":"bash","content":[{"type":"text","text":"\"\"\"Voice preservation dial — modifies Stage 5 synthesis prompt by intensity band.\n\nThree bands control how much of the creator's original voice is preserved:\n  - Low  (0.0–0.33): Clinical, encyclopedic tone — suppress direct quotes\n  - Mid  (0.34–0.66): Base prompt unchanged (already ~0.6 voice preservation)\n  - High (0.67–1.0): Maximum voice — prioritize exact words, strong opinions\n\"\"\"\nfrom __future__ import annotations\n\n\n# ── Band modifier text ────────────────────────────────────────────────────────\n\n_LOW_BAND_MODIFIER = \"\"\"\n\n## Voice Suppression Override\n\nIMPORTANT — override the voice/tone guidelines above. For this synthesis:\n\n- Do NOT include any direct quotes from the creator. Rephrase all insights in neutral third-person encyclopedic style.\n- Do NOT attribute opinions or preferences to the creator by name (avoid \"he recommends\", \"she prefers\").\n- Remove all personality markers, humor, strong opinions, and conversational tone.\n- Write as a reference manual: factual, impersonal, technically precise.\n- Replace phrases like \"he warns against\" with neutral statements like \"this approach is generally avoided because.\"\n- Suppress colloquialisms and informal language entirely.\n\"\"\"\n\n_HIGH_BAND_MODIFIER = \"\"\"\n\n## Maximum Voice Preservation Override\n\nIMPORTANT — amplify the voice/tone guidelines above. For this synthesis:\n\n- Maximize the use of direct quotes from the transcript. Every memorable phrase, vivid metaphor, or strong opinion should be quoted verbatim with quotation marks.\n- Attribute all insights, preferences, and techniques to the creator by name — use their name frequently.\n- Preserve personality, humor, strong opinions, and conversational tone. If the creator is emphatic, the prose should feel emphatic.\n- Prioritize the creator's exact words over paraphrase. When a transcript excerpt contains a usable phrase, quote it rather than summarizing it.\n- Include warnings, caveats, and opinionated asides in the creator's own voice.\n- The resulting page should feel like the creator is speaking directly to the reader through the text.\n\"\"\"\n\n\n# ── VoiceDial class ───────────────────────────────────────────────────────────\n\nclass VoiceDial:\n    \"\"\"Modifies a Stage 5 synthesis prompt based on a voice_level parameter.\n\n    Parameters\n    ----------\n    base_prompt:\n        The original stage5_synthesis.txt system prompt content.\n    \"\"\"\n\n    # Band boundaries\n    LOW_UPPER = 0.33\n    HIGH_LOWER = 0.67\n\n    def __init__(self, base_prompt: str) -> None:\n        self.base_prompt = base_prompt\n\n    def modify(self, voice_level: float) -> str:\n        \"\"\"Return the system prompt modified for the given voice_level.\n\n        Parameters\n        ----------\n        voice_level:\n            Float 0.0–1.0. Values outside this range are clamped.\n\n        Returns\n        -------\n        str\n            Modified system prompt with band-appropriate instructions appended.\n        \"\"\"\n        voice_level = max(0.0, min(1.0, voice_level))\n\n        if voice_level <= self.LOW_UPPER:\n            return self.base_prompt + _LOW_BAND_MODIFIER\n        elif voice_level >= self.HIGH_LOWER:\n            return self.base_prompt + _HIGH_BAND_MODIFIER\n        else:\n            # Mid band — base prompt is already moderate voice preservation\n            return self.base_prompt\n\n    @staticmethod\n    def band_name(voice_level: float) -> str:\n        \"\"\"Return the human-readable band name for a voice_level value.\"\"\"\n        voice_level = max(0.0, min(1.0, voice_level))\n        if voice_level <= VoiceDial.LOW_UPPER:\n            return \"low\"\n        elif voice_level >= VoiceDial.HIGH_LOWER:\n            return \"high\"\n        return \"mid\"\n"}],"isError":false,"timestamp":1775034138964}}
{"type":"message","id":"3b78df77","parentId":"8ae2ad6a","timestamp":"2026-04-01T09:02:24.068Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the pipeline stages for understanding how stage 5 synthesis works, the LLM client, and the prompt template system."},{"type":"toolCall","id":"toolu_012JjDjGqSCc9LkhKUbLpcAB","name":"bash","arguments":{"command":"rg -n \"def _load_prompt|def _get_stage_config|def complete|def parse_response|class LLMClient\" backend/pipeline/llm_client.py backend/pipeline/stages.py | head -30"}},{"type":"toolCall","id":"toolu_016KQWcdPRZTQMGrpqEZndxk","name":"bash","arguments":{"command":"ls backend/prompts/"}},{"type":"toolCall","id":"toolu_01CymEGkB1QYdzqgVR6XkJpb","name":"bash","arguments":{"command":"cat backend/pipeline/schemas.py | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":222,"cacheRead":69418,"cacheWrite":6460,"totalTokens":76101,"cost":{"input":0.000005,"output":0.00555,"cacheRead":0.034709,"cacheWrite":0.040375,"total":0.08063899999999999}},"stopReason":"toolUse","timestamp":1775034138965}}
{"type":"message","id":"6aee3217","parentId":"3b78df77","timestamp":"2026-04-01T09:02:24.088Z","message":{"role":"toolResult","toolCallId":"toolu_012JjDjGqSCc9LkhKUbLpcAB","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/llm_client.py:163:class LLMClient:\nbackend/pipeline/llm_client.py:179:    def complete(\nbackend/pipeline/llm_client.py:350:    def parse_response(self, text: str, model: type[T]) -> T:\nbackend/pipeline/stages.py:244:def _load_prompt(template_name: str) -> str:\nbackend/pipeline/stages.py:262:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n"}],"isError":false,"timestamp":1775034144088}}
{"type":"message","id":"1096be02","parentId":"6aee3217","timestamp":"2026-04-01T09:02:24.089Z","message":{"role":"toolResult","toolCallId":"toolu_016KQWcdPRZTQMGrpqEZndxk","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":1775034144088}}
{"type":"message","id":"d76afa0f","parentId":"1096be02","timestamp":"2026-04-01T09:02:24.100Z","message":{"role":"toolResult","toolCallId":"toolu_01CymEGkB1QYdzqgVR6XkJpb","toolName":"bash","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"}],"isError":false,"timestamp":1775034144098}}
{"type":"message","id":"00413d0f","parentId":"d76afa0f","timestamp":"2026-04-01T09:02:28.740Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EUxVckbwpyL5DEVLmuwCvS","name":"bash","arguments":{"command":"rg -n \"prompts\" backend/config.py | head -10 && echo \"---\" && rg -rn \"prompts_path|prompt.*dir|prompt.*folder\" backend/ --max-count=5"}},{"type":"toolCall","id":"toolu_01SJzSnkZ5mGhwTTuchJgQb7","name":"bash","arguments":{"command":"sed -n '240,290p' backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01AARoDVFDo7TdWkUmNEcA8j","name":"bash","arguments":{"command":"find . -name \"stage5_synthesis.txt\" -o -name \"*.txt\" -path \"*/prompts/*\" 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":224,"cacheRead":75878,"cacheWrite":1270,"totalTokens":77373,"cost":{"input":0.000005,"output":0.0056,"cacheRead":0.037939,"cacheWrite":0.0079375,"total":0.0514815}},"stopReason":"toolUse","timestamp":1775034144098}}
{"type":"message","id":"420bf438","parentId":"00413d0f","timestamp":"2026-04-01T09:02:28.765Z","message":{"role":"toolResult","toolCallId":"toolu_01EUxVckbwpyL5DEVLmuwCvS","toolName":"bash","content":[{"type":"text","text":"64:    prompts_path: str = \"./prompts\"\n66:    # Debug mode — when True, pipeline captures full LLM prompts and responses\n---\nbackend/config.py:    n: str = \"./prompts\"\nbackend/tests/test_pipeline.py:    # Create nectory\nbackend/tests/test_pipeline.py:    n = tmp_path / \"prompts\"\nbackend/tests/test_pipeline.py:    n()\nbackend/tests/test_pipeline.py:    (n / \"stage2_segmentation.txt\").write_text(\"You are a segmentation assistant.\")\nbackend/tests/test_pipeline.py:        s.n = str(n)\nbackend/pipeline/stages.py:    \"\"\"Read a nectory.\nbackend/pipeline/stages.py:    path = Path(settings.n) / template_name\nbackend/pipeline/stages.py:    n = Path(settings.n)\nbackend/pipeline/stages.py:        filepath = n / filename\n"}],"isError":false,"timestamp":1775034148765}}
{"type":"message","id":"e251aa23","parentId":"420bf438","timestamp":"2026-04-01T09:02:28.765Z","message":{"role":"toolResult","toolCallId":"toolu_01SJzSnkZ5mGhwTTuchJgQb7","toolName":"bash","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"}],"isError":false,"timestamp":1775034148765}}
{"type":"message","id":"cf7be998","parentId":"e251aa23","timestamp":"2026-04-01T09:02:28.778Z","message":{"role":"toolResult","toolCallId":"toolu_01AARoDVFDo7TdWkUmNEcA8j","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":1775034148777}}
{"type":"message","id":"3b975391","parentId":"cf7be998","timestamp":"2026-04-01T09:02:33.906Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I understand the existing infrastructure. Let me look at what the S03 slice needs to deliver — a prompt variant generator and automated A/B optimization loop."},{"type":"toolCall","id":"toolu_01AK9KTXRoKPG79gnMkTACen","name":"bash","arguments":{"command":"wc -l prompts/stage5_synthesis.txt && head -30 prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01PYPHNAdho3xipHoGeLWbAU","name":"bash","arguments":{"command":"sed -n '160,230p' backend/pipeline/llm_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":167,"cacheRead":77148,"cacheWrite":1108,"totalTokens":78424,"cost":{"input":0.000005,"output":0.004175,"cacheRead":0.038574,"cacheWrite":0.006925000000000001,"total":0.049679}},"stopReason":"toolUse","timestamp":1775034148777}}
{"type":"message","id":"482e3349","parentId":"3b975391","timestamp":"2026-04-01T09:02:33.926Z","message":{"role":"toolResult","toolCallId":"toolu_01AK9KTXRoKPG79gnMkTACen","toolName":"bash","content":[{"type":"text","text":"129 prompts/stage5_synthesis.txt\nYou are an expert technical writer specializing in music production education. Your task is to synthesize a set of related key moments from the same creator into a single, high-quality technique page that serves as a definitive reference on the topic.\n\n## What you are creating\n\nA Chrysopedia technique page is NOT a generic article or wiki entry. It is a focused reference document that a music producer will consult mid-session when they need to understand and apply a specific technique. The reader is Alt+Tabbing from their DAW, looking for actionable knowledge, and wants to absorb the key insight and get back to work in under 2 minutes.\n\nThe page has two complementary sections:\n\n1. **Study guide prose** — rich, detailed paragraphs organized by sub-aspect of the technique. This is for learning and deep understanding. It reads like notes from an expert mentor, not a textbook.\n2. **Key moments index** — a compact list of the individual source moments that contributed to this page, each with a descriptive title that enables quick scanning.\n\nBoth sections are essential. The prose synthesizes and explains; the moment index lets readers quickly locate the specific insight they need.\n\n## Voice and tone\n\nWrite as if you are a knowledgeable colleague explaining what you learned from watching this creator's content. The tone should be:\n\n- **Direct and confident** — state what the creator does, not \"the creator appears to\" or \"it seems like they\"\n- **Technical but accessible** — use production terminology naturally, but explain non-obvious concepts when the creator's explanation adds value\n- **Preserving the creator's voice** — when the creator uses a memorable phrase, vivid metaphor, or strong opinion, quote them directly with quotation marks. These are often the most valuable parts. Examples: 'He warns against using OTT on snares — says it \"smears the snap into mush.\"' or 'Her reasoning: \"every bus you add is another place you'll be tempted to put a compressor that doesn't need to be there.\"'\n- **Specific over general** — always prefer concrete details (frequencies, ratios, ms values, plugin names, specific settings) over vague descriptions. \"Uses compression\" is never acceptable if the source moments contain specifics.\n\n## Body sections structure\n\nDo NOT use generic section names like \"Overview,\" \"Step-by-Step Process,\" \"Key Settings,\" or \"Tips and Variations.\" These produce lifeless, formulaic output.\n\nInstead, derive section names from the actual content. Each section should cover one sub-aspect of the technique. Use descriptive names that tell the reader exactly what they'll learn:\n\nGood section names (examples):\n- \"Layer construction\" / \"Saturation and the crunch character\" / \"Mix context and bus processing\"\n"}],"isError":false,"timestamp":1775034153925}}
{"type":"message","id":"7eec2dcd","parentId":"482e3349","timestamp":"2026-04-01T09:02:33.943Z","message":{"role":"toolResult","toolCallId":"toolu_01PYPHNAdho3xipHoGeLWbAU","toolName":"bash","content":[{"type":"text","text":"    return result\n\n\nclass LLMClient:\n    \"\"\"Sync LLM client that tries a primary endpoint and falls back on failure.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._primary = openai.OpenAI(\n            base_url=settings.llm_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._fallback = openai.OpenAI(\n            base_url=settings.llm_fallback_url,\n            api_key=settings.llm_api_key,\n        )\n\n    # ── Core completion ──────────────────────────────────────────────────\n\n    def complete(\n        self,\n        system_prompt: str,\n        user_prompt: str,\n        response_model: type[BaseModel] | None = None,\n        modality: str = \"chat\",\n        model_override: str | None = None,\n        on_complete: \"Callable | None\" = None,\n        max_tokens: int | None = None,\n    ) -> \"LLMResponse\":\n        \"\"\"Send a chat completion request, falling back on connection/timeout errors.\n\n        Parameters\n        ----------\n        system_prompt:\n            System message content.\n        user_prompt:\n            User message content.\n        response_model:\n            If provided and modality is \"chat\", ``response_format`` is set to\n            ``{\"type\": \"json_object\"}``. For \"thinking\" modality, JSON\n            instructions are appended to the system prompt instead.\n        modality:\n            Either \"chat\" (default) or \"thinking\". Thinking modality skips\n            response_format and strips ``<think>`` tags from output.\n        model_override:\n            Model name to use instead of the default. If None, uses the\n            configured default for the endpoint.\n        max_tokens:\n            Override for max_tokens on this call. If None, falls back to\n            the configured ``llm_max_tokens`` from settings.\n\n        Returns\n        -------\n        LLMResponse\n            Raw completion text (str subclass) with finish_reason metadata.\n        \"\"\"\n        kwargs: dict = {}\n        effective_system = system_prompt\n\n        if modality == \"thinking\":\n            # Thinking models often don't support response_format: json_object.\n            # Instead, append explicit JSON instructions to the system prompt.\n            if response_model is not None:\n                json_schema_hint = (\n                    \"\\n\\nYou MUST respond with ONLY valid JSON. \"\n                    \"No markdown code fences, no explanation, no preamble — \"\n                    \"just the raw JSON object.\"\n                )\n                effective_system = system_prompt + json_schema_hint\n        else:\n            # Chat modality — use standard JSON mode\n"}],"isError":false,"timestamp":1775034153940}}
{"type":"message","id":"5df18016","parentId":"7eec2dcd","timestamp":"2026-04-01T09:02:39.240Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me understand the full shape of what's needed. The slice needs: (1) prompt variant generator, (2) automated A/B loop, (3) leaderboard + trajectory output. Let me check if there are any existing charting/reporting patterns."},{"type":"toolCall","id":"toolu_01SamPmKUsyYMmFxrN3WFkuF","name":"bash","arguments":{"command":"rg -l \"matplotlib|plotly|chart|ascii.*chart|tabulate|rich.*table\" backend/ 2>/dev/null | head -10 && echo \"---\" && pip list 2>/dev/null | grep -iE \"matplotlib|plotly|tabulate|rich\" | head -5"}},{"type":"toolCall","id":"toolu_017JhKRjqC4GrX9KzE7mCqTi","name":"bash","arguments":{"command":"cat backend/requirements.txt 2>/dev/null || cat requirements.txt 2>/dev/null | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":239,"cacheRead":78256,"cacheWrite":1517,"totalTokens":80013,"cost":{"input":0.000005,"output":0.005975,"cacheRead":0.039127999999999996,"cacheWrite":0.00948125,"total":0.05458924999999999}},"stopReason":"toolUse","timestamp":1775034153941}}
{"type":"message","id":"dfb53be0","parentId":"5df18016","timestamp":"2026-04-01T09:02:39.831Z","message":{"role":"toolResult","toolCallId":"toolu_01SamPmKUsyYMmFxrN3WFkuF","toolName":"bash","content":[{"type":"text","text":"---\nrich                  13.7.1\n"}],"isError":false,"timestamp":1775034159831}}
{"type":"message","id":"c458aa0c","parentId":"dfb53be0","timestamp":"2026-04-01T09:02:39.833Z","message":{"role":"toolResult","toolCallId":"toolu_017JhKRjqC4GrX9KzE7mCqTi","toolName":"bash","content":[{"type":"text","text":"fastapi>=0.115.0,<1.0\nuvicorn[standard]>=0.32.0,<1.0\nsqlalchemy[asyncio]>=2.0,<3.0\nasyncpg>=0.30.0,<1.0\nalembic>=1.14.0,<2.0\npydantic>=2.0,<3.0\npydantic-settings>=2.0,<3.0\ncelery[redis]>=5.4.0,<6.0\nredis>=5.0,<6.0\npython-dotenv>=1.0,<2.0\npython-multipart>=0.0.9,<1.0\nhttpx>=0.27.0,<1.0\nopenai>=1.0,<2.0\nqdrant-client>=1.9,<2.0\npyyaml>=6.0,<7.0\npsycopg2-binary>=2.9,<3.0\nwatchdog>=4.0,<5.0\n# Test dependencies\npytest>=8.0,<10.0\npytest-asyncio>=0.24,<1.0\n"}],"isError":false,"timestamp":1775034159831}}
{"type":"message","id":"78eecf61","parentId":"c458aa0c","timestamp":"2026-04-01T09:02:43.576Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015dccLxReRNEtcQnYCjWDrR","name":"bash","arguments":{"command":"cat backend/pipeline/quality/fixtures/sample_moments.json | python3 -c \"import json,sys; d=json.load(sys.stdin); print(f'Keys: {list(d.keys())}'); print(f'Moments: {len(d.get(\\\"moments\\\",[]))}')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":123,"cacheRead":79773,"cacheWrite":588,"totalTokens":80485,"cost":{"input":0.000005,"output":0.003075,"cacheRead":0.0398865,"cacheWrite":0.0036750000000000003,"total":0.046641499999999995}},"stopReason":"toolUse","timestamp":1775034159832}}
{"type":"message","id":"1d2816c8","parentId":"78eecf61","timestamp":"2026-04-01T09:02:43.627Z","message":{"role":"toolResult","toolCallId":"toolu_015dccLxReRNEtcQnYCjWDrR","toolName":"bash","content":[{"type":"text","text":"Keys: ['creator_name', 'topic_category', 'moments']\nMoments: 6\n"}],"isError":false,"timestamp":1775034163625}}
{"type":"message","id":"1a309d7f","parentId":"1d2816c8","timestamp":"2026-04-01T09:03:39.869Z","message":{"role":"assistant","content":[{"type":"text","text":"I have enough to write the research. The slice is moderately complex — it needs a prompt variant generator (LLM-powered mutation of prompt text), an A/B loop orchestrator, scoring integration with existing `ScoreRunner`, and a leaderboard/trajectory report. All building on established patterns."},{"type":"toolCall","id":"toolu_01VQKjn7S8FCuEXAWXAASWHY","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S03 Research: Prompt Variant Generator & Automated A/B Loop\n\n## Summary\n\nThis slice builds an automated optimization loop that generates prompt variants for Stage 5 synthesis, scores each against reference articles using the existing `ScoreRunner`, and outputs a leaderboard with score trajectories. The core deliverable is `python -m pipeline.quality optimize --stage 5 --iterations 10`.\n\nThe existing infrastructure is solid: `ScoreRunner` handles scoring, `VoiceDial` demonstrates prompt modification patterns, `LLMClient` handles LLM calls with primary/fallback. The new work is: (1) an LLM-powered prompt variant generator, (2) an optimization loop orchestrator, (3) leaderboard + trajectory reporting.\n\n## Recommendation\n\nBuild in three tasks:\n\n1. **Prompt Variant Generator** — `PromptVariantGenerator` class that uses the LLM to mutate a base prompt, producing N variants per iteration. This is the riskiest piece (LLM must produce valid, meaningfully different prompts).\n2. **Optimization Loop** — `OptimizationLoop` class that orchestrates: load base prompt → generate variants → score each → select winners → repeat. Stores results in a simple JSON log file.\n3. **CLI + Reporting** — Wire the `optimize` subcommand into `__main__.py`, add leaderboard table and score trajectory ASCII chart output.\n\n## Implementation Landscape\n\n### Existing Code to Build On\n\n| File | What it provides | How S03 uses it |\n|------|-----------------|-----------------|\n| `backend/pipeline/quality/scorer.py` | `ScoreRunner.score_page()`, `ScoreRunner.synthesize_and_score()`, `ScoreResult` dataclass | Core scoring for each variant. `synthesize_and_score()` already chains synthesis → scoring. |\n| `backend/pipeline/quality/voice_dial.py` | `VoiceDial` class with band-based prompt modification | Pattern reference for prompt modification. S03's variant generator is more flexible — LLM-driven mutations vs fixed bands. |\n| `backend/pipeline/quality/__main__.py` | CLI with `fitness` and `score` subcommands | Add `optimize` subcommand here. |\n| `backend/pipeline/quality/__init__.py` | `sys.path` bootstrap for project-root execution | Already handles CWD resolution. |\n| `backend/pipeline/stages.py` | `_load_prompt()`, `_get_stage_config()` | Load the base prompt template, get model/modality config. Already used by `synthesize_and_score()`. |\n| `backend/pipeline/schemas.py` | `SynthesisResult`, `SynthesizedPage` | Parse synthesis output. Already used by `synthesize_and_score()`. |\n| `backend/pipeline/llm_client.py` | `LLMClient.complete()`, `LLMClient.parse_response()` | Drive both variant generation and synthesis calls. |\n| `backend/pipeline/quality/fixtures/sample_moments.json` | 6-moment fixture file | Default test data for optimization runs. |\n| `prompts/stage5_synthesis.txt` | 129-line Stage 5 synthesis prompt | Base prompt to optimize. Path: `./prompts/` from backend CWD or project root via symlink. |\n\n### Prompt Variant Generation Strategy\n\nThe generator should use the LLM itself to produce prompt variants. Approach:\n\n1. **Meta-prompt**: A system prompt instructing the LLM to act as a prompt engineer. Given the current best prompt and its scores, produce a variant that targets the weakest dimension(s).\n2. **Mutation types**: The meta-prompt should guide the LLM toward specific mutation strategies:\n   - **Targeted improvement**: Focus on the lowest-scoring dimension (e.g., if `structural` is 0.4, modify section naming instructions)\n   - **Balanced tweak**: Small modifications across multiple dimensions\n   - **Creative restructure**: Reorganize prompt sections or add new constraints\n3. **Constraint**: Variants must preserve the core JSON output format instructions (the synthesis must still produce valid `SynthesisResult` JSON). The meta-prompt must instruct the LLM not to modify the output format section.\n4. **Output**: The variant generator returns the full modified prompt text as a string.\n\nKey risk: The LLM might produce variants that are too similar to the base or that break the output format. Mitigation: validate that the variant differs by at least N characters from the base, and that it still contains the JSON format instructions.\n\n### Optimization Loop Design\n\n```\nOptimizationLoop:\n  __init__(client, stage, fixture_path, iterations, variants_per_iter)\n  \n  run() → OptimizationResult:\n    base_prompt = _load_prompt(f\"stage{stage}_synthesis.txt\")\n    baseline_score = synthesize_and_score(base_prompt, moments)\n    \n    best = (base_prompt, baseline_score)\n    history = [baseline_score]\n    \n    for i in range(iterations):\n      variants = generator.generate(best.prompt, best.scores, n=variants_per_iter)\n      for variant in variants:\n        score = synthesize_and_score(variant, moments)\n        history.append(score)\n        if score.composite > best.composite:\n          best = (variant, score)\n    \n    return OptimizationResult(best, history, leaderboard)\n```\n\nDesign decisions to note:\n- **Variants per iteration**: Default 2-3 (each requires a synthesis + scoring LLM call, so cost is ~2 calls per variant per iteration).\n- **Selection strategy**: Simple greedy — keep the best-scoring variant as the seed for next iteration. No population/crossover complexity.\n- **Persistence**: Write a JSON log file after each iteration with prompt text, scores, and metadata. This allows resuming and provides the data for trajectory charts.\n- **Stage parameter**: Initially only `--stage 5` works. The `--stage` flag is present for S04 expansion.\n\n### Reporting\n\n- **Leaderboard**: Top N variants by composite score, showing per-dimension breakdown. Use formatted terminal output (existing `print_report` pattern or `rich` library which is installed).\n- **Score trajectory**: ASCII line chart showing composite score across iterations. Can use a simple text-based chart (20-30 lines of code) — no need for matplotlib. The iteration count is small (10-20) so ASCII is sufficient.\n- **Output file**: Save full results to a JSON file at `backend/pipeline/quality/results/` for later analysis.\n\n### CLI Interface\n\n```\npython -m pipeline.quality optimize \\\n  --stage 5 \\\n  --iterations 10 \\\n  --variants-per-iter 2 \\\n  --file backend/pipeline/quality/fixtures/sample_moments.json \\\n  [--output-dir backend/pipeline/quality/results/]\n```\n\nAdd to `__main__.py` alongside existing `fitness` and `score` subcommands.\n\n### Dependencies\n\nNo new pip packages needed. `rich` is already installed for table output if desired, but plain formatted print (matching existing `print_report` style) is simpler and consistent.\n\n### File Plan\n\n| File | Action | Description |\n|------|--------|-------------|\n| `backend/pipeline/quality/variant_generator.py` | Create | `PromptVariantGenerator` class with LLM-powered prompt mutation |\n| `backend/pipeline/quality/optimizer.py` | Create | `OptimizationLoop` class orchestrating generate→score→select cycle |\n| `backend/pipeline/quality/__main__.py` | Modify | Add `optimize` subcommand |\n| `backend/pipeline/quality/results/` | Create dir | Output directory for optimization run results |\n\n### Verification Strategy\n\n1. `python -m pipeline.quality optimize --help` — shows --stage, --iterations, --variants-per-iter, --file args\n2. Import check: `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('ok')\"`\n3. Import check: `python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('ok')\"`\n4. Dry-run with fixture data (will fail at LLM call but should show clean error, not traceback): `python -m pipeline.quality optimize --stage 5 --iterations 1 --file backend/pipeline/quality/fixtures/sample_moments.json`\n5. Unit-level: `PromptVariantGenerator` meta-prompt construction can be tested without LLM by verifying prompt assembly\n\n### Risks\n\n1. **LLM variant quality**: The meta-prompt for generating variants is the trickiest part. If the LLM produces near-identical variants or breaks output format, the loop produces no improvement. Mitigation: validate variant diff and format preservation.\n2. **Cost per iteration**: Each variant needs synthesis + scoring = 2 LLM calls. 10 iterations × 2 variants = 40 LLM calls total. With a slow/rate-limited endpoint, this could take 10-20 minutes. Add progress output per iteration.\n3. **Prompt file path resolution**: Known issue from S02 — `_load_prompt` uses `settings.prompts_path` which defaults to `./prompts`. Works from backend/ but from project root needs the symlink. Already handled by existing symlink setup.\n\n### Natural Task Seams\n\n1. **T01: PromptVariantGenerator** — Self-contained class in new file. Testable via import + prompt assembly check. No dependency on optimizer.\n2. **T02: OptimizationLoop + CLI** — Depends on T01. Wires generator + scorer into loop, adds CLI subcommand, adds reporting/output.\n\nTwo tasks is the natural decomposition. T01 is the riskiest (meta-prompt design), T02 is integration + reporting.\n","milestone_id":"M013","slice_id":"S03"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2507,"cacheRead":80361,"cacheWrite":159,"totalTokens":83028,"cost":{"input":0.000005,"output":0.06267500000000001,"cacheRead":0.0401805,"cacheWrite":0.00099375,"total":0.10385425000000002}},"stopReason":"toolUse","timestamp":1775034163626}}
{"type":"message","id":"64d2d8f7","parentId":"1a309d7f","timestamp":"2026-04-01T09:03:39.875Z","message":{"role":"toolResult","toolCallId":"toolu_01VQKjn7S8FCuEXAWXAASWHY","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M013/slices/S03/S03-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M013/slices/S03/S03-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775034219873}}
{"type":"message","id":"19b6ecff","parentId":"64d2d8f7","timestamp":"2026-04-01T09:03:41.993Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S03 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":80520,"cacheWrite":2545,"totalTokens":83077,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.04026,"cacheWrite":0.01590625,"total":0.05644625}},"stopReason":"stop","timestamp":1775034219874}}
