{"type":"thinking_level_change","id":"f393f3de","parentId":null,"timestamp":"2026-04-01T08:39:07.571Z","thinkingLevel":"medium"}
{"type":"model_change","id":"23f12d06","parentId":"f393f3de","timestamp":"2026-04-01T08:39:07.574Z","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## UNIT: Research Slice S01 (\"General FYN-LLM Fitness Suite\") — 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- (no dependencies)\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: S01`, `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/S01/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S01 researched.\"","display":false,"id":"0ef48f22","parentId":"23f12d06","timestamp":"2026-04-01T08:39:07.578Z"}
{"type":"message","id":"af9b33c7","parentId":"0ef48f22","timestamp":"2026-04-01T08:39:18.037Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand what this slice is about. S01 is \"General FYN-LLM Fitness Suite\" — a CLI tool that tests whether the FYN-LLM (the project's LLM backend) is functioning correctly. It should output pass/fail for a Mandelbrot question, JSON compliance, instruction following, and a diverse prompt battery against the live FYN-LLM.\n\nLet me explore the codebase to understand the current pipeline structure, how LLM calls are made, and where this fitness suite would fit.","thinkingSignature":"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"},{"type":"text","text":"Starting research for S01 — the FYN-LLM fitness suite. This is a new CLI tool, so I need to understand how the pipeline currently talks to the LLM, what endpoints/models are configured, and where a `pipeline.quality` module would live."},{"type":"toolCall","id":"toolu_01QLxrMySnUQmtTjwSU7sooC","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend/pipeline -type f -name '*.py' | head -30 && echo \"---\" && find /home/aux/projects/content-to-kb-automator/backend -type f -name 'config.py' -o -name 'settings.py' | head -5"}},{"type":"toolCall","id":"toolu_019keEFtJb473P8skjVi3Zw8","name":"bash","arguments":{"command":"rg -l \"openai|OpenAI|LLM_BASE_URL|llm\" /home/aux/projects/content-to-kb-automator/backend/ --type py -i | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":416,"cacheRead":59647,"cacheWrite":8074,"totalTokens":68140,"cost":{"input":0.000015000000000000002,"output":0.010400000000000001,"cacheRead":0.0298235,"cacheWrite":0.0504625,"total":0.090701}},"stopReason":"toolUse","timestamp":1775032747576}}
{"type":"message","id":"2e645ad7","parentId":"af9b33c7","timestamp":"2026-04-01T08:39:18.051Z","message":{"role":"toolResult","toolCallId":"toolu_01QLxrMySnUQmtTjwSU7sooC","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/embedding_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n---\n/home/aux/projects/content-to-kb-automator/backend/config.py\n"}],"isError":false,"timestamp":1775032758051}}
{"type":"message","id":"8c5a42cc","parentId":"2e645ad7","timestamp":"2026-04-01T08:39:18.060Z","message":{"role":"toolResult","toolCallId":"toolu_019keEFtJb473P8skjVi3Zw8","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/search_service.py\n/home/aux/projects/content-to-kb-automator/backend/config.py\n/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/embedding_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/routers/pipeline.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py\n/home/aux/projects/content-to-kb-automator/backend/models.py\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/mock_llm_responses.py\n/home/aux/projects/content-to-kb-automator/backend/tests/test_pipeline.py\n"}],"isError":false,"timestamp":1775032758058}}
{"type":"message","id":"5ad42015","parentId":"8c5a42cc","timestamp":"2026-04-01T08:39:23.716Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016C5qTeZs1rLKUfDWVfJvQ2","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py"}},{"type":"toolCall","id":"toolu_01LQxRicoDP5N21huxw65a4D","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":67721,"cacheWrite":770,"totalTokens":68622,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.0338605,"cacheWrite":0.0048125,"total":0.041928}},"stopReason":"toolUse","timestamp":1775032758059}}
{"type":"message","id":"470ebc2d","parentId":"5ad42015","timestamp":"2026-04-01T08:39:23.720Z","message":{"role":"toolResult","toolCallId":"toolu_016C5qTeZs1rLKUfDWVfJvQ2","toolName":"read","content":[{"type":"text","text":"\"\"\"Synchronous LLM client with primary/fallback endpoint logic.\n\nUses the OpenAI-compatible API (works with Ollama, vLLM, OpenWebUI, etc.).\nCelery tasks run synchronously, so this uses ``openai.OpenAI`` (not Async).\n\nSupports two modalities:\n- **chat**: Standard JSON mode with ``response_format: {\"type\": \"json_object\"}``\n- **thinking**: For reasoning models that emit ``<think>...</think>`` blocks\n  before their answer. Skips ``response_format``, appends JSON instructions to\n  the system prompt, and strips think tags from the response.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport re\nfrom typing import TYPE_CHECKING, TypeVar\n\nif TYPE_CHECKING:\n    from collections.abc import Callable\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\nT = TypeVar(\"T\", bound=BaseModel)\n\n\n# ── LLM Response wrapper ─────────────────────────────────────────────────────\n\nclass LLMResponse(str):\n    \"\"\"String subclass that carries LLM response metadata.\n\n    Backward-compatible with all code that treats the response as a plain\n    string, but callers that know about it can inspect finish_reason and\n    the truncated property.\n    \"\"\"\n    finish_reason: str | None\n    prompt_tokens: int | None\n    completion_tokens: int | None\n\n    def __new__(\n        cls,\n        text: str,\n        finish_reason: str | None = None,\n        prompt_tokens: int | None = None,\n        completion_tokens: int | None = None,\n    ):\n        obj = super().__new__(cls, text)\n        obj.finish_reason = finish_reason\n        obj.prompt_tokens = prompt_tokens\n        obj.completion_tokens = completion_tokens\n        return obj\n\n    @property\n    def truncated(self) -> bool:\n        \"\"\"True if the model hit its token limit before finishing.\"\"\"\n        return self.finish_reason == \"length\"\n\n# ── Think-tag stripping ──────────────────────────────────────────────────────\n\n_THINK_PATTERN = re.compile(r\"<think>.*?</think>\", re.DOTALL)\n\n\ndef strip_think_tags(text: str) -> str:\n    \"\"\"Remove ``<think>...</think>`` blocks from LLM output.\n\n    Thinking/reasoning models often prefix their JSON with a reasoning trace\n    wrapped in ``<think>`` tags. This strips all such blocks (including\n    multiline and multiple occurrences) and returns the cleaned text.\n\n    Handles:\n    - Single ``<think>...</think>`` block\n    - Multiple blocks in one response\n    - Multiline content inside think tags\n    - Responses with no think tags (passthrough)\n    - Empty input (passthrough)\n    \"\"\"\n    if not text:\n        return text\n    cleaned = _THINK_PATTERN.sub(\"\", text)\n    return cleaned.strip()\n\n\n\n# ── Token estimation ─────────────────────────────────────────────────────────\n\n# Stage-specific output multipliers: estimated output tokens as a ratio of input tokens.\n# Tuned from actual pipeline data (KCL Ep 31 audit, April 2026):\n#   stage2: actual compl/prompt = 680/39312 = 0.017 → use 0.05 with buffer\n#   stage3: actual compl/prompt ≈ 1000/7000 = 0.14 → use 0.3 with buffer\n#   stage4: actual compl/prompt = 740/3736 = 0.20 → use 0.3 with buffer\n#   stage5: actual compl/prompt ≈ 2500/7000 = 0.36 → use 0.8 with buffer\n_STAGE_OUTPUT_RATIOS: dict[str, float] = {\n    \"stage2_segmentation\": 0.05,   # Compact topic groups — much smaller than input\n    \"stage3_extraction\": 0.3,      # Key moments with summaries — moderate\n    \"stage4_classification\": 0.3,  # Tags + categories per moment\n    \"stage5_synthesis\": 0.8,       # Full prose technique pages — heaviest output\n}\n\n# Minimum floor so we never send a trivially small max_tokens\n_MIN_MAX_TOKENS = 4096\n\n\ndef estimate_tokens(text: str) -> int:\n    \"\"\"Estimate token count from text using a chars-per-token heuristic.\n\n    Uses 3.5 chars/token which is conservative for English + JSON markup.\n    \"\"\"\n    if not text:\n        return 0\n    return max(1, int(len(text) / 3.5))\n\n\ndef estimate_max_tokens(\n    system_prompt: str,\n    user_prompt: str,\n    stage: str | None = None,\n    hard_limit: int = 32768,\n) -> int:\n    \"\"\"Estimate the max_tokens parameter for an LLM call.\n\n    Calculates expected output size based on input size and stage-specific\n    multipliers. The result is clamped between _MIN_MAX_TOKENS and hard_limit.\n\n    Parameters\n    ----------\n    system_prompt:\n        The system prompt text.\n    user_prompt:\n        The user prompt text (transcript, moments, etc.).\n    stage:\n        Pipeline stage name (e.g. \"stage3_extraction\"). If None or unknown,\n        uses a default 1.0x multiplier.\n    hard_limit:\n        Absolute ceiling — never exceed this value.\n\n    Returns\n    -------\n    int\n        Estimated max_tokens value to pass to the LLM API.\n    \"\"\"\n    input_tokens = estimate_tokens(system_prompt) + estimate_tokens(user_prompt)\n    ratio = _STAGE_OUTPUT_RATIOS.get(stage or \"\", 1.0)\n    estimated_output = int(input_tokens * ratio)\n\n    # Add a 50% buffer for JSON overhead and variability\n    estimated_output = int(estimated_output * 1.5)\n\n    # Clamp to [_MIN_MAX_TOKENS, hard_limit]\n    result = max(_MIN_MAX_TOKENS, min(estimated_output, hard_limit))\n\n    logger.info(\n        \"Token estimate: input≈%d, stage=%s, ratio=%.2f, estimated_output=%d, max_tokens=%d (hard_limit=%d)\",\n        input_tokens, stage or \"default\", ratio, estimated_output, result, hard_limit,\n    )\n    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            if response_model is not None:\n                kwargs[\"response_format\"] = {\"type\": \"json_object\"}\n\n        messages = [\n            {\"role\": \"system\", \"content\": effective_system},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n        primary_model = model_override or self.settings.llm_model\n        fallback_model = self.settings.llm_fallback_model\n        effective_max_tokens = max_tokens if max_tokens is not None else self.settings.llm_max_tokens\n        effective_temperature = self.settings.llm_temperature\n\n        logger.info(\n            \"LLM request: model=%s, modality=%s, response_model=%s, max_tokens=%d, temperature=%.1f\",\n            primary_model,\n            modality,\n            response_model.__name__ if response_model else None,\n            effective_max_tokens,\n            effective_temperature,\n        )\n\n        # --- Try primary endpoint ---\n        try:\n            response = self._primary.chat.completions.create(\n                model=primary_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response: prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=primary_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Primary LLM endpoint failed (%s: %s), trying fallback at %s\",\n                type(exc).__name__,\n                exc,\n                self.settings.llm_fallback_url,\n            )\n\n        # --- Try fallback endpoint ---\n        try:\n            response = self._fallback.chat.completions.create(\n                model=fallback_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response (fallback): prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=fallback_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                        is_fallback=True,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError, openai.APIError) as exc:\n            logger.error(\n                \"Fallback LLM endpoint also failed (%s: %s). Giving up.\",\n                type(exc).__name__,\n                exc,\n            )\n            raise\n\n    # ── Response parsing ─────────────────────────────────────────────────\n\n    def parse_response(self, text: str, model: type[T]) -> T:\n        \"\"\"Parse raw LLM output as JSON and validate against a Pydantic model.\n\n        Parameters\n        ----------\n        text:\n            Raw JSON string from the LLM.\n        model:\n            Pydantic model class to validate against.\n\n        Returns\n        -------\n        T\n            Validated Pydantic model instance.\n\n        Raises\n        ------\n        pydantic.ValidationError\n            If the JSON doesn't match the schema.\n        ValueError\n            If the text is not valid JSON.\n        \"\"\"\n        try:\n            return model.model_validate_json(text)\n        except Exception:\n            logger.error(\n                \"Failed to parse LLM response as %s. Response text: %.500s\",\n                model.__name__,\n                text,\n            )\n            raise\n"}],"isError":false,"timestamp":1775032763718}}
{"type":"message","id":"ed1c6e71","parentId":"470ebc2d","timestamp":"2026-04-01T08:39:23.727Z","message":{"role":"toolResult","toolCallId":"toolu_01LQxRicoDP5N21huxw65a4D","toolName":"read","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"fyn-llm-agent-chat\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"fyn-llm-agent-chat\"\n\n    # Per-stage model overrides (optional — falls back to llm_model / \"chat\")\n    llm_stage2_model: str | None = \"fyn-llm-agent-chat\"   # segmentation — mechanical, fast chat\n    llm_stage2_modality: str = \"chat\"\n    llm_stage3_model: str | None = \"fyn-llm-agent-think\"  # extraction — reasoning\n    llm_stage3_modality: str = \"thinking\"\n    llm_stage4_model: str | None = \"fyn-llm-agent-chat\"   # classification — mechanical, fast chat\n    llm_stage4_modality: str = \"chat\"\n    llm_stage5_model: str | None = \"fyn-llm-agent-think\"  # synthesis — reasoning\n    llm_stage5_modality: str = \"thinking\"\n\n    # Dynamic token estimation — each stage calculates max_tokens from input size\n    llm_max_tokens_hard_limit: int = 32768   # Hard ceiling for dynamic estimator\n    llm_max_tokens: int = 32768              # Fallback when no estimate is provided (must not exceed hard_limit)\n    llm_temperature: float = 0.0             # Deterministic output for structured JSON extraction\n\n    # Stage 5 synthesis chunking — max moments per LLM call before splitting\n    synthesis_chunk_size: int = 30\n\n    # Embedding endpoint\n    embedding_api_url: str = \"http://localhost:11434/v1\"\n    embedding_model: str = \"nomic-embed-text\"\n    embedding_dimensions: int = 768\n\n    # Qdrant\n    qdrant_url: str = \"http://localhost:6333\"\n    qdrant_collection: str = \"chrysopedia\"\n\n    # Prompt templates\n    prompts_path: str = \"./prompts\"\n\n    # Debug mode — when True, pipeline captures full LLM prompts and responses\n    debug_mode: bool = False\n\n    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\n\n    # Git commit SHA (set at Docker build time or via env var)\n    git_commit_sha: str = \"unknown\"\n\n    model_config = {\n        \"env_file\": \".env\",\n        \"env_file_encoding\": \"utf-8\",\n        \"case_sensitive\": False,\n    }\n\n\n@lru_cache\ndef get_settings() -> Settings:\n    \"\"\"Return cached application settings (singleton).\"\"\"\n    return Settings()\n"}],"isError":false,"timestamp":1775032763726}}
{"type":"message","id":"23032eaa","parentId":"ed1c6e71","timestamp":"2026-04-01T08:39:31.702Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. The LLM client uses `openai.OpenAI` with primary/fallback endpoints. The model names are `fyn-llm-agent-chat` and `fyn-llm-agent-think`. Now let me check what test infrastructure exists and how the project's CLI entry points work."},{"type":"toolCall","id":"toolu_01VeY89pMazaQHu6WSBdLUmU","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py && cat /home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py"}},{"type":"toolCall","id":"toolu_01JtuszcUHPNfuKaaXNNirHJ","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend -name '__main__.py' -o -name 'cli.py' 2>/dev/null | head -5 && echo \"---\" && ls /home/aux/projects/content-to-kb-automator/backend/tests/ 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_01ADD3fY7bsRS2LZpt4xr5u8","name":"bash","arguments":{"command":"rg -l \"quality|fitness\" /home/aux/projects/content-to-kb-automator/backend/ --type py 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":360,"cacheRead":68491,"cacheWrite":4853,"totalTokens":73705,"cost":{"input":0.000005,"output":0.009000000000000001,"cacheRead":0.0342455,"cacheWrite":0.03033125,"total":0.07358175}},"stopReason":"toolUse","timestamp":1775032763727}}
{"type":"message","id":"fa878a59","parentId":"23032eaa","timestamp":"2026-04-01T08:39:31.726Z","message":{"role":"toolResult","toolCallId":"toolu_01VeY89pMazaQHu6WSBdLUmU","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py\n"}],"isError":false,"timestamp":1775032771726}}
{"type":"message","id":"92a31319","parentId":"fa878a59","timestamp":"2026-04-01T08:39:31.737Z","message":{"role":"toolResult","toolCallId":"toolu_01JtuszcUHPNfuKaaXNNirHJ","toolName":"bash","content":[{"type":"text","text":"---\nconftest.py\nfixtures\n__init__.py\n__pycache__\ntest_ingest.py\ntest_pipeline.py\ntest_public_api.py\ntest_search.py\n"}],"isError":false,"timestamp":1775032771737}}
{"type":"message","id":"dc726abe","parentId":"92a31319","timestamp":"2026-04-01T08:39:31.741Z","message":{"role":"toolResult","toolCallId":"toolu_01ADD3fY7bsRS2LZpt4xr5u8","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/backend/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/mock_llm_responses.py\n/home/aux/projects/content-to-kb-automator/backend/models.py\n"}],"isError":false,"timestamp":1775032771737}}
{"type":"message","id":"9fe70ba0","parentId":"dc726abe","timestamp":"2026-04-01T08:39:36.563Z","message":{"role":"assistant","content":[{"type":"text","text":"No existing `quality` module or `__main__.py`. Let me check the pipeline schemas to understand the Pydantic models used for LLM responses, and look at how stages.py calls the LLM."},{"type":"toolCall","id":"toolu_01AtMCapLXvoSQC32BPsJuvf","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01NYGv95jraQHgY4VoFQudoi","name":"bash","arguments":{"command":"rg \"def (stage|run_pipeline)\" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":189,"cacheRead":73344,"cacheWrite":637,"totalTokens":74171,"cost":{"input":0.000005,"output":0.004725,"cacheRead":0.036671999999999996,"cacheWrite":0.00398125,"total":0.04538324999999999}},"stopReason":"toolUse","timestamp":1775032771738}}
{"type":"message","id":"bcfa9ce4","parentId":"9fe70ba0","timestamp":"2026-04-01T08:39:36.571Z","message":{"role":"toolResult","toolCallId":"toolu_01AtMCapLXvoSQC32BPsJuvf","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775032776571}}
{"type":"message","id":"b382a088","parentId":"bcfa9ce4","timestamp":"2026-04-01T08:39:36.577Z","message":{"role":"toolResult","toolCallId":"toolu_01NYGv95jraQHgY4VoFQudoi","toolName":"bash","content":[{"type":"text","text":"def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\ndef stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\ndef stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\ndef stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\ndef stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\ndef run_pipeline(video_id: str, trigger: str = \"manual\") -> str:\n"}],"isError":false,"timestamp":1775032776575}}
{"type":"message","id":"123470bf","parentId":"b382a088","timestamp":"2026-04-01T08:39:42.188Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the LLM is invoked from stages.py — specifically the complete() call pattern, and the prompt template loading."},{"type":"toolCall","id":"toolu_01TMzttKt5UXG6Q6UjRcMsJG","name":"bash","arguments":{"command":"rg \"complete\\(|_load_prompt|load_prompt|prompts_path\" /home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_01WPFonZQsssNfeD5AoyCbeK","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/backend/prompts/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":192,"cacheRead":73981,"cacheWrite":1437,"totalTokens":75611,"cost":{"input":0.000005,"output":0.0048000000000000004,"cacheRead":0.036990499999999996,"cacheWrite":0.00898125,"total":0.050776749999999995}},"stopReason":"toolUse","timestamp":1775032776576}}
{"type":"message","id":"661cf588","parentId":"123470bf","timestamp":"2026-04-01T08:39:42.206Z","message":{"role":"toolResult","toolCallId":"toolu_01TMzttKt5UXG6Q6UjRcMsJG","toolName":"bash","content":[{"type":"text","text":"def _load_prompt(template_name: str) -> str:\n    path = Path(settings.prompts_path) / template_name\n        retry_raw = llm.complete(\n        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt, run_id=run_id, request_params=_s2_request_params),\n        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\", system_prompt=system_prompt, user_prompt=user_prompt, run_id=run_id, context_label=topic_label, request_params=_s3_request_params),\n    raw = llm.complete(\n        system_prompt = _load_prompt(\"stage4_classification.txt\")\n    prompts_path = Path(settings.prompts_path)\n        filepath = prompts_path / filename\n    raw = llm.complete(\n        merge_system_prompt = _load_prompt(\"stage5_merge.txt\")\n        raw = llm.complete(\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n"}],"isError":false,"timestamp":1775032782205}}
{"type":"message","id":"3e6bf716","parentId":"661cf588","timestamp":"2026-04-01T08:39:42.217Z","message":{"role":"toolResult","toolCallId":"toolu_01WPFonZQsssNfeD5AoyCbeK","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775032782215}}
{"type":"message","id":"39ed580a","parentId":"3e6bf716","timestamp":"2026-04-01T08:39:46.905Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016Dk8wvx6fz98R4sLJu9nEE","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name 'prompts' -type d 2>/dev/null"}},{"type":"toolCall","id":"toolu_01PGoiFE6UXs1dgPkxaG6MpY","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":141,"cacheRead":75418,"cacheWrite":632,"totalTokens":76192,"cost":{"input":0.000005,"output":0.0035250000000000004,"cacheRead":0.037709,"cacheWrite":0.00395,"total":0.045189}},"stopReason":"toolUse","timestamp":1775032782216}}
{"type":"message","id":"39b1480a","parentId":"39ed580a","timestamp":"2026-04-01T08:39:46.944Z","message":{"role":"toolResult","toolCallId":"toolu_016Dk8wvx6fz98R4sLJu9nEE","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/prompts\n"}],"isError":false,"timestamp":1775032786944}}
{"type":"message","id":"b5f68469","parentId":"39b1480a","timestamp":"2026-04-01T08:39:46.948Z","message":{"role":"toolResult","toolCallId":"toolu_01PGoiFE6UXs1dgPkxaG6MpY","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032786944}}
{"type":"message","id":"0b232047","parentId":"b5f68469","timestamp":"2026-04-01T08:39:50.106Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YQTjmiqpfAZoz8crcUuY1V","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/prompts/"}},{"type":"toolCall","id":"toolu_019Lyyz6KxUQrJo3wfgKpDc5","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/requirements.txt 2>/dev/null || cat /home/aux/projects/content-to-kb-automator/requirements.txt 2>/dev/null || echo \"No requirements.txt found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":169,"cacheRead":76050,"cacheWrite":222,"totalTokens":76442,"cost":{"input":0.000005,"output":0.0042250000000000005,"cacheRead":0.038024999999999996,"cacheWrite":0.0013875,"total":0.043642499999999994}},"stopReason":"toolUse","timestamp":1775032786945}}
{"type":"message","id":"56ce4cd4","parentId":"0b232047","timestamp":"2026-04-01T08:39:50.123Z","message":{"role":"toolResult","toolCallId":"toolu_01YQTjmiqpfAZoz8crcUuY1V","toolName":"bash","content":[{"type":"text","text":"README.md\nstage2_segmentation.txt\nstage3_extraction.txt\nstage4_classification.txt\nstage5_synthesis.txt\n"}],"isError":false,"timestamp":1775032790122}}
{"type":"message","id":"003e2af4","parentId":"56ce4cd4","timestamp":"2026-04-01T08:39:50.128Z","message":{"role":"toolResult","toolCallId":"toolu_019Lyyz6KxUQrJo3wfgKpDc5","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":1775032790126}}
{"type":"message","id":"33351208","parentId":"003e2af4","timestamp":"2026-04-01T08:39:54.524Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me check the roadmap demo description more carefully and look at what the Mandelbrot question test implies."},{"type":"toolCall","id":"toolu_016JZmdXXDy5tsNd2wDQzZVW","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/M013-ROADMAP.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":76272,"cacheWrite":540,"totalTokens":76928,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.038135999999999996,"cacheWrite":0.003375,"total":0.044391}},"stopReason":"toolUse","timestamp":1775032790127}}
{"type":"message","id":"191c41fe","parentId":"33351208","timestamp":"2026-04-01T08:39:54.530Z","message":{"role":"toolResult","toolCallId":"toolu_016JZmdXXDy5tsNd2wDQzZVW","toolName":"read","content":[{"type":"text","text":"# 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"}],"isError":false,"timestamp":1775032794527}}
{"type":"message","id":"cd1e4efd","parentId":"191c41fe","timestamp":"2026-04-01T08:39:57.494Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check if there's a milestone context doc with more specifics."},{"type":"toolCall","id":"toolu_016ajroET4QQWtPC2QS9GJDQ","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":101,"cacheRead":76812,"cacheWrite":526,"totalTokens":77440,"cost":{"input":0.000005,"output":0.0025250000000000003,"cacheRead":0.038405999999999996,"cacheWrite":0.0032875,"total":0.04422349999999999}},"stopReason":"toolUse","timestamp":1775032794528}}
{"type":"message","id":"36791b93","parentId":"cd1e4efd","timestamp":"2026-04-01T08:39:57.504Z","message":{"role":"toolResult","toolCallId":"toolu_016ajroET4QQWtPC2QS9GJDQ","toolName":"bash","content":[{"type":"text","text":"M013-ROADMAP.md\nslices\n"}],"isError":false,"timestamp":1775032797502}}
{"type":"message","id":"d9b8804b","parentId":"36791b93","timestamp":"2026-04-01T08:40:02.853Z","message":{"role":"assistant","content":[{"type":"text","text":"No context or research doc yet. Let me check the LLM endpoint details — what model names and URLs are configured in the deployment."},{"type":"toolCall","id":"toolu_01AWBzk38jzj38Y8aSqeuKVK","name":"bash","arguments":{"command":"rg \"llm_api_url|llm_model|llm_fallback\" /home/aux/projects/content-to-kb-automator/.env 2>/dev/null || rg \"LLM_API_URL|LLM_MODEL|LLM_FALLBACK\" /home/aux/projects/content-to-kb-automator/.env 2>/dev/null || echo \"No .env\""}},{"type":"toolCall","id":"toolu_01GaSzkvzVs8s8QvF4F7dfNV","name":"bash","arguments":{"command":"rg \"llm_api_url|llm_model|LLM_API_URL|LLM_MODEL\" /home/aux/projects/content-to-kb-automator/docker-compose.yml 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":274,"cacheRead":77338,"cacheWrite":125,"totalTokens":77738,"cost":{"input":0.000005,"output":0.00685,"cacheRead":0.038668999999999995,"cacheWrite":0.00078125,"total":0.04630524999999999}},"stopReason":"toolUse","timestamp":1775032797503}}
{"type":"message","id":"fe1c3edf","parentId":"d9b8804b","timestamp":"2026-04-01T08:40:02.877Z","message":{"role":"toolResult","toolCallId":"toolu_01AWBzk38jzj38Y8aSqeuKVK","toolName":"bash","content":[{"type":"text","text":"No .env\n"}],"isError":false,"timestamp":1775032802876}}
{"type":"message","id":"dbc109a9","parentId":"fe1c3edf","timestamp":"2026-04-01T08:40:02.882Z","message":{"role":"toolResult","toolCallId":"toolu_01GaSzkvzVs8s8QvF4F7dfNV","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032802876}}
{"type":"message","id":"3bef5770","parentId":"dbc109a9","timestamp":"2026-04-01T08:40:06.354Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GwYBTtuEXNcv4ZGJPVLU2g","name":"bash","arguments":{"command":"rg -i \"LLM|fyn-llm\" /home/aux/projects/content-to-kb-automator/docker-compose.yml 2>/dev/null | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":77463,"cacheWrite":341,"totalTokens":77902,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.038731499999999995,"cacheWrite":0.00213125,"total":0.04329275}},"stopReason":"toolUse","timestamp":1775032802878}}
{"type":"message","id":"31786c79","parentId":"3bef5770","timestamp":"2026-04-01T08:40:06.368Z","message":{"role":"toolResult","toolCallId":"toolu_01GwYBTtuEXNcv4ZGJPVLU2g","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032806367}}
{"type":"message","id":"d6c6be3d","parentId":"31786c79","timestamp":"2026-04-01T08:40:08.958Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SaRgcE9XKzfbGsLm1hkhs3","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name 'docker-compose*' -type f 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":85,"cacheRead":77804,"cacheWrite":111,"totalTokens":78001,"cost":{"input":0.000005,"output":0.002125,"cacheRead":0.038902,"cacheWrite":0.00069375,"total":0.04172575}},"stopReason":"toolUse","timestamp":1775032806367}}
{"type":"message","id":"b374ad9c","parentId":"d6c6be3d","timestamp":"2026-04-01T08:40:09.010Z","message":{"role":"toolResult","toolCallId":"toolu_01SaRgcE9XKzfbGsLm1hkhs3","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/docker-compose.yml\n"}],"isError":false,"timestamp":1775032809007}}
{"type":"message","id":"b1d8b6d6","parentId":"b374ad9c","timestamp":"2026-04-01T08:40:12.175Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YYPKeexQdRpXwgecKNqp7N","name":"bash","arguments":{"command":"rg \"environment:\" -A 20 /home/aux/projects/content-to-kb-automator/docker-compose.yml | rg -i \"llm|model\" | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":77804,"cacheWrite":229,"totalTokens":78131,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.038902,"cacheWrite":0.00143125,"total":0.04276325}},"stopReason":"toolUse","timestamp":1775032809008}}
{"type":"message","id":"68076a06","parentId":"b1d8b6d6","timestamp":"2026-04-01T08:40:12.194Z","message":{"role":"toolResult","toolCallId":"toolu_01YYPKeexQdRpXwgecKNqp7N","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032812191}}
{"type":"message","id":"415ea79c","parentId":"68076a06","timestamp":"2026-04-01T08:40:15.269Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DWfCT7a8z2XCH4DbzQ5hHp","name":"bash","arguments":{"command":"rg \"env_file|\\.env\" /home/aux/projects/content-to-kb-automator/docker-compose.yml | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":86,"cacheRead":78033,"cacheWrite":111,"totalTokens":78231,"cost":{"input":0.000005,"output":0.00215,"cacheRead":0.039016499999999996,"cacheWrite":0.00069375,"total":0.04186524999999999}},"stopReason":"toolUse","timestamp":1775032812192}}
{"type":"message","id":"efb3301a","parentId":"415ea79c","timestamp":"2026-04-01T08:40:15.290Z","message":{"role":"toolResult","toolCallId":"toolu_01DWfCT7a8z2XCH4DbzQ5hHp","toolName":"bash","content":[{"type":"text","text":"    env_file:\n      - path: .env\n    env_file:\n      - path: .env\n"}],"isError":false,"timestamp":1775032815287}}
{"type":"message","id":"60a8b62d","parentId":"efb3301a","timestamp":"2026-04-01T08:40:23.857Z","message":{"role":"assistant","content":[{"type":"text","text":"The LLM config comes from `.env` which lives on ub01, not locally. The defaults in `config.py` point to `localhost:11434` with model `fyn-llm-agent-chat`. The fitness suite needs to hit the same LLM endpoint the pipeline uses.\n\nNow let me understand the scope. The demo says: \"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\nThis is a CLI module under `pipeline.quality` with a `fitness` subcommand. The tests are:\n1. **Mandelbrot question** — a known \"can it reason?\" sanity check\n2. **JSON compliance** — send a prompt requesting JSON, verify valid JSON comes back\n3. **Instruction following** — give specific formatting instructions, verify compliance\n4. **Diverse prompt battery** — multiple prompts testing various capabilities\n\nLet me check if there's a `__main__.py` pattern anywhere that would inform CLI structure."},{"type":"toolCall","id":"toolu_0183Deb9QhMiUKY8eaUKxsM1","name":"bash","arguments":{"command":"python3 -c \"import argparse; print('argparse available')\" 2>/dev/null && python3 -c \"import click; print('click available')\" 2>/dev/null || echo \"click not available\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":343,"cacheRead":78033,"cacheWrite":235,"totalTokens":78612,"cost":{"input":0.000005,"output":0.008575000000000001,"cacheRead":0.039016499999999996,"cacheWrite":0.00146875,"total":0.04906525}},"stopReason":"toolUse","timestamp":1775032815288}}
{"type":"message","id":"2c5c3249","parentId":"60a8b62d","timestamp":"2026-04-01T08:40:23.936Z","message":{"role":"toolResult","toolCallId":"toolu_0183Deb9QhMiUKY8eaUKxsM1","toolName":"bash","content":[{"type":"text","text":"argparse available\nclick available\n"}],"isError":false,"timestamp":1775032823933}}
{"type":"message","id":"63202f2e","parentId":"2c5c3249","timestamp":"2026-04-01T08:40:28.327Z","message":{"role":"assistant","content":[{"type":"text","text":"Both available. Given the project uses no CLI framework currently (no `click` in requirements.txt), argparse is the right call — zero new dependencies.\n\nLet me also check what the existing test infrastructure looks like for patterns to follow."},{"type":"toolCall","id":"toolu_01KX4DBPMrvc2Vfu6NrS86F7","name":"read","arguments":{"limit":40,"path":"/home/aux/projects/content-to-kb-automator/backend/tests/test_pipeline.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":78268,"cacheWrite":362,"totalTokens":78773,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.039133999999999995,"cacheWrite":0.0022625,"total":0.0449515}},"stopReason":"toolUse","timestamp":1775032823934}}
{"type":"message","id":"b29c412a","parentId":"63202f2e","timestamp":"2026-04-01T08:40:28.332Z","message":{"role":"toolResult","toolCallId":"toolu_01KX4DBPMrvc2Vfu6NrS86F7","toolName":"read","content":[{"type":"text","text":"\"\"\"Integration tests for the LLM extraction pipeline.\n\nTests run against a real PostgreSQL test database with mocked LLM and Qdrant\nclients. Pipeline stages are sync (Celery tasks), so tests call stage\nfunctions directly with sync SQLAlchemy sessions.\n\nTests (a)–(f) call pipeline stages directly. Tests (g)–(i) use the async\nHTTP client. Test (j) verifies LLM fallback logic.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport os\nimport pathlib\nimport uuid\nfrom unittest.mock import MagicMock, patch, PropertyMock\n\nimport openai\nimport pytest\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom sqlalchemy.pool import NullPool\n\nfrom models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TranscriptSegment,\n)\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\n\n\n[723 more lines in file. Use offset=41 to continue.]"}],"isError":false,"timestamp":1775032828330}}
{"type":"message","id":"8e2af55d","parentId":"b29c412a","timestamp":"2026-04-01T08:41:10.733Z","message":{"role":"assistant","content":[{"type":"text","text":"Good enough. I now have a clear picture of the codebase. Let me compile the research."},{"type":"toolCall","id":"toolu_01AYiBJ4X5BsJedYL4eUPDKS","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S01 Research — General FYN-LLM Fitness Suite\n\n## Summary\n\nThis slice creates a new CLI module at `pipeline.quality` that runs a battery of tests against the live FYN-LLM endpoint and reports pass/fail for each. No active requirements are owned — this is new infrastructure for M013. The work is straightforward: make HTTP calls to an OpenAI-compatible API, validate responses against expected patterns, report results.\n\n## Recommendation\n\nBuild a `pipeline/quality/` package with `__main__.py` (argparse CLI) and `fitness.py` (test runner). Reuse the existing `LLMClient` from `pipeline/llm_client.py` — it already handles primary/fallback endpoints and JSON mode. Use argparse (stdlib), not click, to avoid adding dependencies.\n\n## Implementation Landscape\n\n### Entry Point\n\nThe demo says `python -m pipeline.quality fitness`. This requires:\n- `backend/pipeline/quality/__init__.py`\n- `backend/pipeline/quality/__main__.py` — argparse dispatcher\n- `backend/pipeline/quality/fitness.py` — test battery logic\n\nMust be runnable from `backend/` directory (where `pipeline` is a package). The `config.py` Settings class loads from env vars / `.env`, so the fitness tool gets LLM config for free via `get_settings()`.\n\n### LLM Client Reuse\n\n`pipeline/llm_client.py` provides `LLMClient` with:\n- `complete(system_prompt, user_prompt, response_model=None, modality=\"chat\", model_override=None)` → `LLMResponse` (str subclass with `finish_reason`, `truncated`)\n- `parse_response(text, model)` → validated Pydantic model\n- Primary/fallback endpoint logic\n- JSON mode via `response_format: {\"type\": \"json_object\"}`\n- Think-tag stripping for reasoning models\n\nThe fitness suite should instantiate `LLMClient(get_settings())` and call `complete()` directly. No need to go through Celery or the pipeline stages.\n\n### Model Configuration\n\nFrom `config.py`:\n- **Primary:** `llm_api_url` (default `http://localhost:11434/v1`), model `fyn-llm-agent-chat`\n- **Fallback:** `llm_fallback_url`, model `fyn-llm-agent-chat`\n- **Thinking model:** `fyn-llm-agent-think` (used by stages 3, 5)\n- **Temperature:** 0.0 (deterministic)\n\nThe fitness suite should test both `chat` and `thinking` modalities since the pipeline uses both.\n\n### Four Test Categories\n\n1. **Mandelbrot question** — Ask \"Is the area of the Mandelbrot set known?\" or similar. Expects a coherent, factually correct answer. Pass/fail based on response containing key facts (area ≈ 1.5065..., exactly known = no). This tests basic reasoning capability.\n\n2. **JSON compliance** — Send a prompt requesting structured JSON output with `response_model` set. Verify: (a) response is valid JSON, (b) parses against expected Pydantic schema. Test with both simple and nested schemas.\n\n3. **Instruction following** — Give specific formatting constraints: \"respond in exactly 3 bullet points\", \"use only lowercase\", \"include the word 'chrysopedia' in your response\". Check compliance programmatically.\n\n4. **Diverse prompt battery** — Multiple prompts exercising different capabilities relevant to the pipeline:\n   - Summarization (give text, ask for summary)\n   - Classification (give text, ask for category from a fixed list)\n   - Extraction (give text, ask for specific fields)\n   - Long-context handling (send a large prompt, verify coherent response)\n\n### Output Format\n\nCLI should print a clear pass/fail report to stdout. Something like:\n```\nFYN-LLM Fitness Suite\n======================\nEndpoint: http://10.0.0.10:3333/v1\nModel: fyn-llm-agent-chat\n\n[PASS] Mandelbrot reasoning     (1.2s, 340 tokens)\n[PASS] JSON compliance (simple) (0.8s, 120 tokens)\n[PASS] JSON compliance (nested) (0.9s, 280 tokens)\n[FAIL] Instruction following: expected 3 bullets, got 5\n[PASS] Summarization            (1.1s, 200 tokens)\n...\n\nResults: 8/10 passed\n```\n\nExit code 0 if all pass, 1 if any fail.\n\n### File Structure\n\n```\nbackend/pipeline/quality/\n  __init__.py          # Empty or minimal\n  __main__.py          # argparse: subcommands fitness, (future: score, optimize)\n  fitness.py           # FitnessRunner class with test methods\n```\n\n### Key Constraints\n\n- **No new dependencies** — `openai`, `pydantic`, `argparse` are all already available\n- **Runs outside Docker** — must work with `python -m pipeline.quality fitness` from `backend/` dir with appropriate env vars\n- **Runs inside Docker** — should also work via `docker exec chrysopedia-api python -m pipeline.quality fitness`\n- **Settings reuse** — use `config.get_settings()` for LLM endpoint config\n- **Non-destructive** — fitness tests don't write to DB, don't trigger pipeline stages, don't modify any state\n\n### Natural Task Seams\n\n1. **CLI scaffold + basic connectivity test** — Create `quality/` package, `__main__.py` with argparse, `fitness.py` with a simple \"can we reach the LLM\" test. Verify `python -m pipeline.quality fitness` runs and prints something.\n\n2. **Full test battery** — Implement all four test categories (Mandelbrot, JSON compliance, instruction following, diverse battery). Each test is a method on a `FitnessRunner` class. Print pass/fail report with timing.\n\n3. **Polish + verification** — Exit codes, error handling (endpoint unreachable = clear error, not traceback), summary line, possibly `--verbose` flag for detailed output.\n\nTasks 1 and 2 could merge into a single task since the total code is modest (~200-300 lines). Task 3 is small cleanup.\n\n### Verification\n\n```bash\ncd backend && python -m pipeline.quality fitness\n```\nMust produce pass/fail output for all test categories. Exit code 0 on all-pass, 1 on any failure. When run against an unreachable endpoint, should print a clear connectivity error rather than a raw traceback.\n\n### Risks\n\n- **LLM endpoint availability** — Tests hit a live LLM. If the endpoint is down, all tests fail. The CLI should catch connection errors and report \"endpoint unreachable\" clearly.\n- **Model behavior variability** — Even at temperature 0.0, model outputs aren't perfectly deterministic across restarts. Pass/fail criteria should be generous (check for key content, not exact strings).\n- **Mandelbrot validation** — Need to define what constitutes a \"correct\" answer without being too brittle. Check for presence of key concepts rather than exact phrasing.\n","milestone_id":"M013","slice_id":"S01"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1842,"cacheRead":78630,"cacheWrite":442,"totalTokens":80915,"cost":{"input":0.000005,"output":0.04605,"cacheRead":0.039314999999999996,"cacheWrite":0.0027625,"total":0.0881325}},"stopReason":"toolUse","timestamp":1775032828330}}
{"type":"message","id":"79d7bdb0","parentId":"8e2af55d","timestamp":"2026-04-01T08:41:10.741Z","message":{"role":"toolResult","toolCallId":"toolu_01AYiBJ4X5BsJedYL4eUPDKS","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M013/slices/S01/S01-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M013/slices/S01/S01-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775032870738}}
{"type":"message","id":"761ad0c9","parentId":"79d7bdb0","timestamp":"2026-04-01T08:41:13.583Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S01 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":79072,"cacheWrite":1880,"totalTokens":80964,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.039535999999999995,"cacheWrite":0.01175,"total":0.051566}},"stopReason":"stop","timestamp":1775032870739}}
