{"type":"thinking_level_change","id":"52a21729","parentId":null,"timestamp":"2026-04-01T08:41:13.989Z","thinkingLevel":"medium"}
{"type":"model_change","id":"6c8ff558","parentId":"52a21729","timestamp":"2026-04-01T08:41:13.990Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-PLAN.md` and `.gsd/milestones/M013/slices/S01/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S01 planned.\"\n## UNIT: Plan 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### Slice Research\nSource: `.gsd/milestones/M013/slices/S01/S01-RESEARCH.md`\n\n# 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\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[...truncated 5 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Project**: `.gsd/PROJECT.md`\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M013/M013-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M013/slices/S01/S01-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"0c6d3c67","parentId":"6c8ff558","timestamp":"2026-04-01T08:41:13.992Z"}
{"type":"message","id":"9940b573","parentId":"0c6d3c67","timestamp":"2026-04-01T08:41:21.881Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the codebase to ground the plan in reality before decomposing.","thinkingSignature":"EogCClkIDBgCKkC7Y4YWOGrBVHKbpo/xSLILUWkWzOX0mpGGsf/c5mUNdf9Iv79mzW1fqCgfDGeuw8N2VrZMEvb0Ml+pPdvtCu8CMg9jbGF1ZGUtb3B1cy00LTY4ABIMPiBjnlcuSA1rqoYLGgy5Ja2iR2XKmaDdW5siMOc5xRU3g3XXEh2tjJ8j3FflYO8lUOFEdJmI7Xq5Pngz6ZWARJMpDGd7exphHhZjLCpdXHci2/13MmtjZyyRqdKuGqZ3Ktdesipg+3pFhUdEfmZGD5JoRB94+zPLCaXUtmNvE7hhWudvPNmuLtEjYVLiEhC9uRDZOYoySyMkzvmnXbvV9rM1FPmuENTbXhbSGAE="},{"type":"text","text":"Let me explore the relevant code to ground the plan."},{"type":"toolCall","id":"toolu_01CALLWnssU7Ae2jZ1hfkS9g","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend/pipeline -type f -name \"*.py\" | head -30"}},{"type":"toolCall","id":"toolu_01Lm3Kzij277Mua3SzH48NQL","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/ 2>/dev/null || echo \"quality/ does not exist yet\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":203,"cacheRead":59647,"cacheWrite":7756,"totalTokens":67608,"cost":{"input":0.00001,"output":0.0050750000000000005,"cacheRead":0.0298235,"cacheWrite":0.048475000000000004,"total":0.0833835}},"stopReason":"toolUse","timestamp":1775032873991}}
{"type":"message","id":"c4829aed","parentId":"9940b573","timestamp":"2026-04-01T08:41:21.892Z","message":{"role":"toolResult","toolCallId":"toolu_01CALLWnssU7Ae2jZ1hfkS9g","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"}],"isError":false,"timestamp":1775032881892}}
{"type":"message","id":"fb27cba1","parentId":"c4829aed","timestamp":"2026-04-01T08:41:21.895Z","message":{"role":"toolResult","toolCallId":"toolu_01Lm3Kzij277Mua3SzH48NQL","toolName":"bash","content":[{"type":"text","text":"quality/ does not exist yet\n"}],"isError":false,"timestamp":1775032881894}}
{"type":"message","id":"ab2aeae8","parentId":"fb27cba1","timestamp":"2026-04-01T08:41:25.215Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011tqRDrgaG8UmvH1dJyX91X","name":"read","arguments":{"path":"backend/pipeline/llm_client.py"}},{"type":"toolCall","id":"toolu_01Fzzed2mEy4dzWbqtTru893","name":"read","arguments":{"limit":60,"path":"backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":67403,"cacheWrite":390,"totalTokens":67909,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.033701499999999995,"cacheWrite":0.0024375,"total":0.039019}},"stopReason":"toolUse","timestamp":1775032881894}}
{"type":"message","id":"c5b28e62","parentId":"ab2aeae8","timestamp":"2026-04-01T08:41:25.218Z","message":{"role":"toolResult","toolCallId":"toolu_011tqRDrgaG8UmvH1dJyX91X","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":1775032885217}}
{"type":"message","id":"fcfc49a7","parentId":"c5b28e62","timestamp":"2026-04-01T08:41:25.222Z","message":{"role":"toolResult","toolCallId":"toolu_01Fzzed2mEy4dzWbqtTru893","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\n[27 more lines in file. Use offset=61 to continue.]"}],"isError":false,"timestamp":1775032885221}}
{"type":"message","id":"79bb3dc5","parentId":"fcfc49a7","timestamp":"2026-04-01T08:41:30.433Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RwRjpKkLna4v1HxZ9e4Zfh","name":"read","arguments":{"offset":61,"path":"backend/config.py"}},{"type":"toolCall","id":"toolu_01Jw93ivbqH76kUuDp2Sckni","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01J691q7Byn7u5j2gf7soYci","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":174,"cacheRead":67793,"cacheWrite":4650,"totalTokens":72618,"cost":{"input":0.000005,"output":0.004350000000000001,"cacheRead":0.033896499999999996,"cacheWrite":0.0290625,"total":0.067314}},"stopReason":"toolUse","timestamp":1775032885221}}
{"type":"message","id":"866f627e","parentId":"79bb3dc5","timestamp":"2026-04-01T08:41:30.435Z","message":{"role":"toolResult","toolCallId":"toolu_01RwRjpKkLna4v1HxZ9e4Zfh","toolName":"read","content":[{"type":"text","text":"    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":1775032890435}}
{"type":"message","id":"dcca0ba2","parentId":"866f627e","timestamp":"2026-04-01T08:41:30.437Z","message":{"role":"toolResult","toolCallId":"toolu_01Jw93ivbqH76kUuDp2Sckni","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1775032890436}}
{"type":"message","id":"0fad635e","parentId":"dcca0ba2","timestamp":"2026-04-01T08:41:30.439Z","message":{"role":"toolResult","toolCallId":"toolu_01J691q7Byn7u5j2gf7soYci","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1775032890436}}
{"type":"message","id":"f935b51f","parentId":"0fad635e","timestamp":"2026-04-01T08:41:36.134Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good. I have the templates and understand the codebase. Let me now check requirements.","thinkingSignature":"EpECClkIDBgCKkCV74TGAqxt2GfQwSSuBBhydHWiEZVBNnaDZxT6ckctBdhUsA6yCwk/JdDvQIic9bBuvnm9B5i6dXwApsiQxEiJMg9jbGF1ZGUtb3B1cy00LTY4ABIMqEwgg1PVjX+IrfHEGgwUTMv+SlhtwIvxEPUiMC4UHVZU7F7aCFcgfFrWvuw0R/KQyxX0FXtF/iOuHBQGLnYvdryw3h80x8K/y65gnSpmN+3LQ8zL+S2OXIswXQTXc8DIHnMl1pADLI7hYSQwGUkw8fORP9eRara/N6weaqK9BbtGWZcUPxESjhAvWx1el2Vj+VQARLcT1xQx+ca+InvzQ0lCS0yh7Q3RZrHsrmUhi1jYddw8GAE="},{"type":"text","text":"Now let me check requirements quickly."},{"type":"toolCall","id":"toolu_01WVFcdUTmNXJBno3Njys2tn","name":"bash","arguments":{"command":"grep -A2 \"M013\\|S01.*quality\\|fitness\" /home/aux/projects/content-to-kb-automator/.gsd/REQUIREMENTS.md 2>/dev/null | head -30 || echo \"No M013 requirements found\""}},{"type":"toolCall","id":"toolu_01LmZQZhyEh7uPRqMbPbF77m","name":"bash","arguments":{"command":"grep \"Active\" /home/aux/projects/content-to-kb-automator/.gsd/REQUIREMENTS.md 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":226,"cacheRead":72443,"cacheWrite":3009,"totalTokens":75679,"cost":{"input":0.000005,"output":0.0056500000000000005,"cacheRead":0.0362215,"cacheWrite":0.01880625,"total":0.060682749999999994}},"stopReason":"toolUse","timestamp":1775032890437}}
{"type":"message","id":"4035502a","parentId":"f935b51f","timestamp":"2026-04-01T08:41:36.150Z","message":{"role":"toolResult","toolCallId":"toolu_01WVFcdUTmNXJBno3Njys2tn","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032896150}}
{"type":"message","id":"80205771","parentId":"4035502a","timestamp":"2026-04-01T08:41:36.155Z","message":{"role":"toolResult","toolCallId":"toolu_01LmZQZhyEh7uPRqMbPbF77m","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775032896152}}
{"type":"message","id":"060e382e","parentId":"80205771","timestamp":"2026-04-01T08:41:39.197Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QNmHB6ovyVYJa672NxLT1h","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/.gsd/REQUIREMENTS.md 2>/dev/null | head -50 || echo \"No REQUIREMENTS.md\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":75452,"cacheWrite":257,"totalTokens":75805,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.037725999999999996,"cacheWrite":0.00160625,"total":0.04171224999999999}},"stopReason":"toolUse","timestamp":1775032896153}}
{"type":"message","id":"260b775d","parentId":"060e382e","timestamp":"2026-04-01T08:41:39.209Z","message":{"role":"toolResult","toolCallId":"toolu_01QNmHB6ovyVYJa672NxLT1h","toolName":"bash","content":[{"type":"text","text":"# 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"}],"isError":false,"timestamp":1775032899207}}
{"type":"message","id":"f4c713d6","parentId":"260b775d","timestamp":"2026-04-01T08:41:54.288Z","message":{"role":"assistant","content":[{"type":"text","text":"No active requirements are owned by this slice — the research doc confirms this is new infrastructure. Good.\n\nNow I have a clear picture. The research recommends this could be 2 tasks (scaffold + full battery, then polish). I agree — the total code is ~200-300 lines across 3 files. Two tasks is right: one to build the full working fitness suite, one to harden error handling and verify against the live endpoint.\n\nActually, looking more carefully at the scope, this is a single coherent piece of work. The \"CLI scaffold\" and \"test battery\" aren't independently valuable — there's no useful increment at \"CLI runs but only prints hello.\" The meaningful increment is \"fitness suite runs all tests and reports results.\" Error handling for unreachable endpoints is part of doing the job right, not a separate task.\n\nI'll plan 2 tasks: T01 builds the complete working fitness suite (package structure, CLI, all four test categories, pass/fail output, exit codes, error handling). T02 runs verification against the live FYN-LLM endpoint from ub01 and fixes any issues discovered.\n\nWait — the executor won't have access to ub01's live LLM endpoint from this machine. Let me check the network setup."},{"type":"toolCall","id":"toolu_01AozCXmrXBYHeezu79YtvP2","name":"bash","arguments":{"command":"grep -E \"llm_api_url|llm_fallback_url|10\\.0\\.0\" /home/aux/projects/content-to-kb-automator/backend/.env 2>/dev/null || echo \"No .env found\"; grep -E \"llm_api_url|llm_fallback_url\" /home/aux/projects/content-to-kb-automator/.env 2>/dev/null || echo \"No root .env\""}},{"type":"toolCall","id":"toolu_01DicEHsLwXgVJJwW2ScKnWz","name":"bash","arguments":{"command":"grep -rn \"llm_api_url\\|LLM_API_URL\" /home/aux/projects/content-to-kb-automator/docker-compose.yml 2>/dev/null | head -5; grep -rn \"llm_api_url\\|LLM_API_URL\" /home/aux/projects/content-to-kb-automator/.env.example 2>/dev/null | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":571,"cacheRead":75452,"cacheWrite":1247,"totalTokens":77271,"cost":{"input":0.000005,"output":0.014275000000000001,"cacheRead":0.037725999999999996,"cacheWrite":0.00779375,"total":0.05979975}},"stopReason":"toolUse","timestamp":1775032899208}}
{"type":"message","id":"e587f4d5","parentId":"f4c713d6","timestamp":"2026-04-01T08:41:54.305Z","message":{"role":"toolResult","toolCallId":"toolu_01AozCXmrXBYHeezu79YtvP2","toolName":"bash","content":[{"type":"text","text":"No .env found\nNo root .env\n"}],"isError":false,"timestamp":1775032914304}}
{"type":"message","id":"1f32ce00","parentId":"e587f4d5","timestamp":"2026-04-01T08:41:54.311Z","message":{"role":"toolResult","toolCallId":"toolu_01DicEHsLwXgVJJwW2ScKnWz","toolName":"bash","content":[{"type":"text","text":"13:LLM_API_URL=https://chat.forgetyour.name/api/v1\n"}],"isError":false,"timestamp":1775032914308}}
{"type":"message","id":"027dcca0","parentId":"1f32ce00","timestamp":"2026-04-01T08:42:36.564Z","message":{"role":"assistant","content":[{"type":"text","text":"The executor won't have a live LLM to test against. The verification strategy should be: code correctness verifiable by import/syntax checks, and the demo command runs against the live endpoint when deployed on ub01. For the plan, verification will be import-level checks and a dry-run with a mock/unreachable endpoint to verify graceful error handling.\n\nThis is straightforward enough for a single task. The total deliverable is ~3 files, ~250 lines. But the research identified a natural 2-task split that makes sense for context isolation: T01 builds everything, T02 does integration verification via Docker on ub01. Since T02 depends on deploying to ub01 and this is the initial dev machine, I'll keep it as a single task with robust offline verification.\n\nOne task it is."},{"type":"toolCall","id":"toolu_01JbTNUYvRDrEwUfwDxfFREA","name":"gsd_plan_slice","arguments":{"goal":"A CLI command `python -m pipeline.quality fitness` that tests FYN-LLM fitness across four categories (Mandelbrot reasoning, JSON compliance, instruction following, diverse prompt battery), prints a pass/fail report with timing, and exits 0/1.","integrationClosure":"- Upstream consumed: `backend/pipeline/llm_client.py` (LLMClient), `backend/config.py` (Settings, get_settings)\n- New wiring: `backend/pipeline/quality/` package with `__main__.py` entry point\n- Remaining for milestone: S02 (scorer), S03 (optimizer), S04 (multi-stage) build on this CLI scaffold","milestoneId":"M013","observabilityImpact":"- Runtime signals: pass/fail per test with elapsed time and token counts printed to stdout\n- Inspection surfaces: CLI exit code (0=all pass, 1=any fail), stdout report\n- Failure visibility: connection errors caught and reported with endpoint URL; individual test failures show expected vs actual","proofLevel":"Contract — code compiles, imports succeed, unreachable-endpoint error path verified locally. Full integration (live LLM) requires deployment to ub01.","sliceId":"S01","successCriteria":"- `python -m pipeline.quality fitness` produces pass/fail output for all test categories\n- Exit code 0 on all-pass, 1 on any failure\n- Unreachable endpoint produces a clear connectivity error, not a raw traceback\n- Reuses existing `LLMClient` and `get_settings()` — no new dependencies","tasks":[{"description":"Create `backend/pipeline/quality/` package with `__init__.py`, `__main__.py` (argparse CLI with `fitness` subcommand), and `fitness.py` (FitnessRunner class implementing all four test categories). Reuse LLMClient from `pipeline/llm_client.py` and Settings from `config.py`.\n\n## Steps\n\n1. Create `backend/pipeline/quality/__init__.py` (empty)\n2. Create `backend/pipeline/quality/__main__.py` with argparse dispatcher — `fitness` subcommand calls `FitnessRunner.run_all()`. Future subcommands (score, optimize) will be added in S02/S03.\n3. Create `backend/pipeline/quality/fitness.py` with `FitnessRunner` class:\n   - Constructor takes `LLMClient` instance\n   - `run_all()` method runs all tests, prints formatted report, returns exit code\n   - `test_mandelbrot()` — asks about the Mandelbrot set area, checks response contains key concepts (area approximately 1.5065, not exactly known). Use generous keyword matching.\n   - `test_json_compliance_simple()` — requests JSON with a simple Pydantic model (e.g., {\"name\": str, \"count\": int}), verifies valid JSON parse and model validation\n   - `test_json_compliance_nested()` — requests JSON with nested model, same validation\n   - `test_instruction_following()` — 2-3 tests: respond in exactly N bullet points, include a specific word, use only lowercase. Check compliance programmatically.\n   - `test_summarization()` — give a paragraph, ask for 2-sentence summary, verify response length is reasonable\n   - `test_classification()` — give text, ask to classify from fixed list, verify response is one of the options\n   - `test_extraction()` — give structured text, ask to extract specific fields as JSON, verify fields present\n   - Each test method returns a `TestResult` dataclass with name, passed, elapsed_seconds, token_count, detail (failure reason)\n4. Wire the entry point: `__main__.py` instantiates `LLMClient(get_settings())`, creates `FitnessRunner`, calls `run_all()`, exits with returned code\n5. Handle connectivity errors: wrap the initial LLM call in a try/except for `openai.APIConnectionError` and `openai.APITimeoutError` — print a clear \"Cannot reach LLM endpoint at {url}\" message and exit 1\n6. Test both modalities: include at least one test using `modality='thinking'` (for the thinking model) since the pipeline uses both chat and thinking modes\n7. Verify: `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner'` succeeds (import check). Run `cd backend && python -m pipeline.quality fitness 2>&1; echo \"exit: $?\"` — should print connectivity error and exit 1 (no live LLM on this machine) rather than a traceback.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM endpoint | Catch `APIConnectionError`, print clear message, exit 1 | Catch `APITimeoutError`, same handling | Log raw response, mark test as FAIL with detail |\n\n## Negative Tests\n\n- **Unreachable endpoint**: Running with default localhost config (no LLM running) should print a connectivity error, not a traceback\n- **Malformed LLM response**: If the LLM returns non-JSON for a JSON test, the test should FAIL with a clear message, not crash\n- **Empty response**: If the LLM returns empty string, tests should FAIL gracefully","estimate":"1h","expectedOutput":["`backend/pipeline/quality/__init__.py` — empty package init","`backend/pipeline/quality/__main__.py` — argparse CLI with fitness subcommand","`backend/pipeline/quality/fitness.py` — FitnessRunner class with all test methods and formatted report output"],"files":["backend/pipeline/quality/__init__.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fitness.py"],"inputs":["`backend/pipeline/llm_client.py` — LLMClient class with complete() method, LLMResponse wrapper, primary/fallback endpoint logic","`backend/config.py` — Settings class with llm_api_url, llm_model, llm_api_key, get_settings() factory"],"taskId":"T01","title":"Build the FYN-LLM fitness test suite CLI","verify":"cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")' && python -m pipeline.quality fitness 2>&1; echo \"exit: $?\""}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1942,"cacheRead":76699,"cacheWrite":668,"totalTokens":79310,"cost":{"input":0.000005,"output":0.04855,"cacheRead":0.0383495,"cacheWrite":0.004175,"total":0.0910795}},"stopReason":"toolUse","timestamp":1775032914309}}
{"type":"message","id":"9cf63a46","parentId":"027dcca0","timestamp":"2026-04-01T08:42:36.617Z","message":{"role":"toolResult","toolCallId":"toolu_01JbTNUYvRDrEwUfwDxfFREA","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S01 (M013)"}],"details":{"operation":"plan_slice","milestoneId":"M013","sliceId":"S01","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/tasks/T01-PLAN.md"]},"isError":false,"timestamp":1775032956615}}
{"type":"message","id":"59513d84","parentId":"9cf63a46","timestamp":"2026-04-01T08:42:44.250Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n\n1. **Completion semantics**: T01 builds all three files, implements all four test categories, wires the CLI entry point, and handles error paths. Completing it makes the slice goal true.\n2. **Requirement coverage**: No active requirements owned. All must-haves (pass/fail output, exit codes, connectivity error handling, LLMClient reuse) map to T01.\n3. **Task completeness**: Steps, must-haves, verification, inputs (concrete file paths), and expected output (concrete file paths) all specified.\n4. **Dependency correctness**: Single task, no ordering issues.\n5. **Key links**: T01 explicitly references importing from `pipeline.llm_client` and `config`.\n6. **Scope sanity**: 7 steps, 3 files — well within limits.\n7. **Feature completeness**: T01 produces the full runnable CLI, not scaffolding.\n\nSlice S01 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":224,"cacheRead":77367,"cacheWrite":1971,"totalTokens":79563,"cost":{"input":0.000005,"output":0.0056,"cacheRead":0.038683499999999996,"cacheWrite":0.01231875,"total":0.05660725}},"stopReason":"stop","timestamp":1775032956616}}
