{"type":"thinking_level_change","id":"ce6154e9","parentId":null,"timestamp":"2026-04-01T08:45:06.027Z","thinkingLevel":"medium"}
{"type":"model_change","id":"89adb4a1","parentId":"ce6154e9","timestamp":"2026-04-01T08:45:06.030Z","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\n## UNIT: Complete Slice S01 (\"General FYN-LLM Fitness Suite\") — Milestone M013\n\n## Your Role in the Pipeline\n\nExecutor agents built each task and wrote task summaries. You are the closer — verify the assembled work actually delivers the slice goal, then compress everything into a slice summary. After you finish, a **reassess-roadmap agent** reads your slice summary to decide if the remaining roadmap still makes sense. The slice summary is also the primary record of what this slice achieved — future slice researchers and planners read it as a dependency summary when their work builds on yours.\n\nWrite the summary for those downstream readers. What did this slice actually deliver? What patterns did it establish? What should the next slice know?\n\nAll relevant context has been preloaded below — the slice plan, all task summaries, and the milestone roadmap are inlined. Start working immediately without re-reading these files.\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 Plan\nSource: `.gsd/milestones/M013/slices/S01/S01-PLAN.md`\n\n# S01: General FYN-LLM Fitness Suite\n\n**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.\n**Demo:** After this: 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\n## Tasks\n- [x] **T01: Built pipeline.quality package with FitnessRunner (9 tests, 4 categories) and argparse CLI that prints formatted pass/fail report and exits 0/1** — 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\n  - Estimate: 1h\n  - Files: backend/pipeline/quality/__init__.py, backend/pipeline/quality/__main__.py, backend/pipeline/quality/fitness.py\n  - Verify: cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")' && python -m pipeline.quality fitness 2>&1; echo \"exit: $?\"\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 20 sections]\n\n**Match effort to complexity.** A simple slice with 1-2 tasks needs a brief summary and lightweight verification. A complex slice with 5 tasks across multiple subsystems needs thorough verification and a detailed summary. Scale the work below accordingly.\n\nThen:\n1. Use the **Slice Summary** and **UAT** output templates from the inlined context above\n2. If a `GSD Skill Preferences` block is present in system context, use it and the `<available_skills>` catalog in your system prompt to decide which skills to load and follow for this unit, without relaxing required verification or artifact rules.\n3. Run all slice-level verification checks defined in the slice plan. All must pass before marking the slice done. If any fail, fix them first.\n4. If the slice plan includes observability/diagnostic surfaces, confirm they work. Skip this for simple slices that don't have observability sections.\n5. If the slice involved runtime behavior, fill the **Operational Readiness** section (Q8) in the slice summary: health signal, failure signal, recovery procedure, and monitoring gaps. Omit entirely for simple slices with no runtime concerns.\n6. If this slice produced evidence that a requirement changed status (Active → Validated, Active → Deferred, etc.), call `gsd_save_decision` with scope=\"requirement\", decision=\"{requirement-id}\", choice=\"{new-status}\", rationale=\"{evidence}\". Do NOT write `.gsd/REQUIREMENTS.md` directly — the engine renders it from the database.\n7. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-SUMMARY.md` (compress all task summaries).\n8. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-UAT.md` — a concrete UAT script with real test cases derived from the slice plan and task summaries. Include preconditions, numbered steps with expected outcomes, and edge cases. This must NOT be a placeholder or generic template — tailor every test case to what this slice actually built.\n9. Review task summaries for `key_decisions`. Append any significant decisions to `.gsd/DECISIONS.md` if missing.\n10. Review task summaries for patterns, gotchas, or non-obvious lessons learned. If any would save future agents from repeating investigation or hitting the same issues, append them to `.gsd/KNOWLEDGE.md`. Only add entries that are genuinely useful — don't pad with obvious observations.\n11. Call `gsd_complete_slice` with milestone_id, slice_id, the slice summary, and the UAT result. Do NOT manually mark the roadmap checkbox — the tool writes to the DB and renders the ROADMAP.md projection automatically.\n12. Do not run git commands — the system commits your changes and handles any merge after this unit succeeds.\n13. Update `.gsd/PROJECT.md` if it exists — refresh current state if needed.\n\n**You MUST call `gsd_complete_slice` with the slice summary and UAT content before finishing. The tool persists to both DB and disk and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-SUMMARY.md` and `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-UAT.md` automatically.**\n\nWhen done, say: \"Slice S01 complete.\"","display":false,"id":"53ed97e6","parentId":"89adb4a1","timestamp":"2026-04-01T08:45:06.035Z"}
{"type":"message","id":"e62d530b","parentId":"53ed97e6","timestamp":"2026-04-01T08:45:11.076Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by understanding what this slice delivered. I need to:\n\n1. Read the task summary for T01\n2. Verify the slice-level checks pass\n3. Write the slice summary and UAT\n4. Call gsd_complete_slice\n\nLet me read the task summary first.","thinkingSignature":"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"},{"type":"toolCall","id":"toolu_01Vqx4BFQrRBhJTWL1QbhgC1","name":"read","arguments":{"path":".gsd/milestones/M013/slices/S01/tasks/T01-SUMMARY.md"}},{"type":"toolCall","id":"toolu_017prRVrVKaN7sstmM5q6Tpg","name":"bash","arguments":{"command":"ls backend/pipeline/quality/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":197,"cacheRead":59647,"cacheWrite":5943,"totalTokens":65790,"cost":{"input":0.000015000000000000002,"output":0.0049250000000000006,"cacheRead":0.0298235,"cacheWrite":0.03714375,"total":0.07190725}},"stopReason":"toolUse","timestamp":1775033106031}}
{"type":"message","id":"05b0089f","parentId":"e62d530b","timestamp":"2026-04-01T08:45:11.088Z","message":{"role":"toolResult","toolCallId":"toolu_01Vqx4BFQrRBhJTWL1QbhgC1","toolName":"read","content":[{"type":"text","text":"---\nid: T01\nparent: S01\nmilestone: M013\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/pipeline/quality/__init__.py\", \"backend/pipeline/quality/__main__.py\", \"backend/pipeline/quality/fitness.py\"]\nkey_decisions: [\"Connectivity probe as first action in run_all() for fast failure before running tests\", \"Mandelbrot test uses thinking modality to exercise both LLM modes\", \"Generous validation thresholds to avoid flaky failures from LLM variance\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"Import check passed: python -c 'from pipeline.quality.fitness import FitnessRunner' succeeds. CLI with no live LLM prints clear connectivity error and exits 1 (no traceback). Help and no-subcommand cases work correctly.\"\ncompleted_at: 2026-04-01T08:45:02.672Z\nblocker_discovered: false\n---\n\n# T01: Built pipeline.quality package with FitnessRunner (9 tests, 4 categories) and argparse CLI that prints formatted pass/fail report and exits 0/1\n\n> Built pipeline.quality package with FitnessRunner (9 tests, 4 categories) and argparse CLI that prints formatted pass/fail report and exits 0/1\n\n## What Happened\n---\nid: T01\nparent: S01\nmilestone: M013\nkey_files:\n  - backend/pipeline/quality/__init__.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/fitness.py\nkey_decisions:\n  - Connectivity probe as first action in run_all() for fast failure before running tests\n  - Mandelbrot test uses thinking modality to exercise both LLM modes\n  - Generous validation thresholds to avoid flaky failures from LLM variance\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T08:45:02.673Z\nblocker_discovered: false\n---\n\n# T01: Built pipeline.quality package with FitnessRunner (9 tests, 4 categories) and argparse CLI that prints formatted pass/fail report and exits 0/1\n\n**Built pipeline.quality package with FitnessRunner (9 tests, 4 categories) and argparse CLI that prints formatted pass/fail report and exits 0/1**\n\n## What Happened\n\nCreated backend/pipeline/quality/ with __init__.py, __main__.py (argparse CLI with fitness subcommand), and fitness.py (FitnessRunner class). The runner implements 9 tests across 4 categories: Mandelbrot reasoning (thinking modality), JSON compliance (simple + nested with Pydantic validation), instruction following (bullet count, keyword, lowercase), and diverse battery (summarization, classification, extraction). Connectivity pre-check fails fast with clear endpoint URLs. Every test returns a TestResult dataclass — no crashes on malformed/empty responses.\n\n## Verification\n\nImport check passed: python -c 'from pipeline.quality.fitness import FitnessRunner' succeeds. CLI with no live LLM prints clear connectivity error and exits 1 (no traceback). Help and no-subcommand cases work correctly.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")'` | 0 | ✅ pass | 1000ms |\n| 2 | `cd backend && python -m pipeline.quality fitness` | 1 | ✅ pass (expected connectivity error, no traceback) | 2000ms |\n\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/__init__.py`\n- `backend/pipeline/quality/__main__.py`\n- `backend/pipeline/quality/fitness.py`\n\n\n## Deviations\nNone.\n\n## Known Issues\nNone.\n"}],"isError":false,"timestamp":1775033111088}}
{"type":"message","id":"f704332c","parentId":"05b0089f","timestamp":"2026-04-01T08:45:11.096Z","message":{"role":"toolResult","toolCallId":"toolu_017prRVrVKaN7sstmM5q6Tpg","toolName":"bash","content":[{"type":"text","text":"fitness.py\n__init__.py\n__main__.py\n__pycache__\n"}],"isError":false,"timestamp":1775033111094}}
{"type":"message","id":"181a5cf2","parentId":"f704332c","timestamp":"2026-04-01T08:45:15.548Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me run the slice-level verification checks from the plan.","thinkingSignature":"EvgBClkIDBgCKkBamP+cp5lEQmkZMeCRLHIeycQcOTDV7hm5KvjDFVTuD8tbnvcaiiuqMnNxdTFwT3A49N2GuWDgyMkLHh0xYI2lMg9jbGF1ZGUtb3B1cy00LTY4ABIMwmQNlh6iTpJiqHChGgx1oQQ3qsfwD0gNNNgiMK89XDDyPnHNakq1wac7UcI1p+EMNQus37ijfjlJ8adte4FgG9SNawOfQ1N6bhqr4CpNWXjOKKbgHL43WV3cRvDnst7qcR8NL5SCzs/bqNAW5nT7/3KMf0KmXdQyk4MNKNL+/7BpTjWnWctIdqbXKJ2q6LxPYiMzGHKMrFiwTc4YAQ=="},{"type":"text","text":"Good — single task, single summary. Let me run the slice-level verification now."},{"type":"toolCall","id":"toolu_0158MNUKGenbvWMXCSm3j76S","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")'"}},{"type":"toolCall","id":"toolu_01AW6gxcc7fUeAbeX3sDbmqX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -m pipeline.quality fitness 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":212,"cacheRead":65590,"cacheWrite":1173,"totalTokens":66976,"cost":{"input":0.000005,"output":0.0053,"cacheRead":0.032795,"cacheWrite":0.00733125,"total":0.04543124999999999}},"stopReason":"toolUse","timestamp":1775033111094}}
{"type":"message","id":"dd612ad5","parentId":"181a5cf2","timestamp":"2026-04-01T08:45:16.087Z","message":{"role":"toolResult","toolCallId":"toolu_0158MNUKGenbvWMXCSm3j76S","toolName":"bash","content":[{"type":"text","text":"import ok\n"}],"isError":false,"timestamp":1775033116087}}
{"type":"message","id":"b2a3e022","parentId":"dd612ad5","timestamp":"2026-04-01T08:45:18.989Z","message":{"role":"toolResult","toolCallId":"toolu_01AW6gxcc7fUeAbeX3sDbmqX","toolName":"bash","content":[{"type":"text","text":"Primary LLM endpoint failed (APIConnectionError: Connection error.), trying fallback at http://localhost:11434/v1\nFallback LLM endpoint also failed (APIConnectionError: Connection error.). Giving up.\n\n✗ Cannot reach LLM endpoint at http://localhost:11434/v1 (fallback http://localhost:11434/v1)\n  Error: Connection error.\n\nexit: 1\n"}],"isError":false,"timestamp":1775033118988}}
{"type":"message","id":"ea6d73f2","parentId":"b2a3e022","timestamp":"2026-04-01T08:45:23.125Z","message":{"role":"assistant","content":[{"type":"text","text":"Both checks pass. Import works, CLI exits 1 with a clear connectivity message and no traceback. Let me also verify the help output and the no-subcommand case."},{"type":"toolCall","id":"toolu_01MF4hFRME1TS7kMc5ypbPpZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -m pipeline.quality --help 2>&1; echo \"---\"; python -m pipeline.quality 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":143,"cacheRead":66763,"cacheWrite":344,"totalTokens":67251,"cost":{"input":0.000005,"output":0.003575,"cacheRead":0.0333815,"cacheWrite":0.00215,"total":0.0391115}},"stopReason":"toolUse","timestamp":1775033118988}}
{"type":"message","id":"8ee0e15b","parentId":"ea6d73f2","timestamp":"2026-04-01T08:45:24.133Z","message":{"role":"toolResult","toolCallId":"toolu_01MF4hFRME1TS7kMc5ypbPpZ","toolName":"bash","content":[{"type":"text","text":"usage: pipeline.quality [-h] {fitness} ...\n\nFYN-LLM quality assurance toolkit\n\npositional arguments:\n  {fitness}\n    fitness   Run LLM fitness tests across four categories\n\noptions:\n  -h, --help  show this help message and exit\n---\nusage: pipeline.quality [-h] {fitness} ...\n\nFYN-LLM quality assurance toolkit\n\npositional arguments:\n  {fitness}\n    fitness   Run LLM fitness tests across four categories\n\noptions:\n  -h, --help  show this help message and exit\nexit: 1\n"}],"isError":false,"timestamp":1775033124132}}
{"type":"message","id":"7862e97f","parentId":"8ee0e15b","timestamp":"2026-04-01T08:45:30.180Z","message":{"role":"assistant","content":[{"type":"text","text":"All verification passes. Let me check decisions/knowledge files for anything to append."},{"type":"toolCall","id":"toolu_01XK84EYAF6CmQYHoTFt8umf","name":"bash","arguments":{"command":"tail -30 /home/aux/projects/content-to-kb-automator/.gsd/DECISIONS.md 2>/dev/null || echo \"no DECISIONS.md\""}},{"type":"toolCall","id":"toolu_015k3y8gPp5LnGJeqy78meJ8","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/.gsd/PROJECT.md 2>/dev/null | head -50 || echo \"no PROJECT.md\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":67107,"cacheWrite":292,"totalTokens":67586,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.0335535,"cacheWrite":0.001825,"total":0.0400335}},"stopReason":"toolUse","timestamp":1775033124132}}
{"type":"message","id":"6e8398ab","parentId":"7862e97f","timestamp":"2026-04-01T08:45:30.193Z","message":{"role":"toolResult","toolCallId":"toolu_01XK84EYAF6CmQYHoTFt8umf","toolName":"bash","content":[{"type":"text","text":"\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"}],"isError":false,"timestamp":1775033130193}}
{"type":"message","id":"d6b20832","parentId":"6e8398ab","timestamp":"2026-04-01T08:45:30.201Z","message":{"role":"toolResult","toolCallId":"toolu_015k3y8gPp5LnGJeqy78meJ8","toolName":"bash","content":[{"type":"text","text":"# Chrysopedia\n\n**Knowledge base for music production techniques**, extracted from video content via an LLM-powered pipeline. Producers can search for specific techniques and find timestamped key moments, signal chains, and synthesized study guides — all derived from creator videos.\n\n## Current State\n\nTwelve milestones complete. The system is deployed and running on ub01 at `http://ub01:8096`.\n\n### What's Built\n\n- **Transcription pipeline** — Desktop script: video → ffmpeg audio extraction → Whisper large-v3 → timestamped transcript JSON\n- **6-stage LLM extraction pipeline** — Transcript segmentation → key moment extraction → classification/tagging → technique page synthesis → embedding/indexing. Per-stage model routing (chat vs thinking models). Resumable per-video per-stage.\n- **Article versioning** — Pre-overwrite snapshots with pipeline metadata (prompt SHA-256 hashes, model config) for benchmarking extraction quality across prompt iterations.\n- **Review queue UI** — Admin interface for approving/editing/rejecting extracted key moments.\n- **Search-first web UI** — Dark theme with cyan accents. Semantic search (Qdrant), topic/creator browse pages, technique detail pages with signal chain blocks and video source metadata. Search autocomplete with popular suggestions on focus and typeahead on 2+ characters.\n- **Docker Compose deployment** — API, worker, web, watcher, PostgreSQL, Redis, Qdrant, Ollama on ub01 with bind mounts at `/vmPool/r/services/chrysopedia_*`.\n- **Transcript folder watcher** — Standalone service monitors `/vmPool/r/services/chrysopedia_watch/` for new transcript JSON files, validates structure, POSTs to ingest API. Processed files move to `processed/`, failures to `failed/` with `.error` sidecar. Uses watchdog PollingObserver for ZFS reliability.\n- **Prompt template system** — Editable per-stage prompt files with SHA-256 tracking for reproducibility.\n- **Pipeline admin dashboard** — Admin page at `/admin/pipeline` with video list, status badges, retrigger/revoke controls, expandable event log with token usage, collapsible JSON response viewer, and live worker status indicator. Pipeline events instrumented via `PipelineEvent` model (stage, event_type, token counts, JSONB payload).\n- **Technique page 2-column layout** — Prose content (summary + study guide) on left, sidebar (key moments, signal chains, plugins, related techniques) on right at desktop widths. Sticky sidebar. Collapses to single column at ≤768px.\n- **Key moment card redesign** — Cards in sidebar show title prominently on its own line (h3), with source file, timestamp, and content type badge on a clean metadata row below.\n- **Admin navigation dropdown** — Header consolidated: Home, Topics, Creators visible; Admin dropdown reveals Review, Reports, Pipeline. ModeToggle removed from header.\n- **Pipeline head/tail log view** — Pipeline event log shows Head/Tail toggle with configurable event count. Token counts visible per event and per video.\n- **Pipeline debug mode** — Redis-backed toggle captures full LLM system prompt, user prompt, and response text in pipeline_events. Per-stage token summary endpoint for cost/usage analysis.\n- **Debug payload viewer** — Admin UI component for viewing, copying, and exporting full LLM I/O from debug-mode pipeline runs.\n- **Admin UX improvements** — Debug mode toggle, status filter pills, cleaner labels, pruned dead UI elements, review queue cross-links.\n- **Git commit SHA tracking** — Pipeline captures current git commit SHA. Technique page versions display commit hash in metadata.\n- **Technique page tag polish** — Sidebar reordered (Plugins Referenced at top), creator name prominent, tags use coherent color system.\n- **Topics page redesign** — 7 categories (including Music Theory) with card layout, descriptions, sub-topic counts, colored left borders.\n- **App footer** — Every page shows version, build date, commit SHA (linked to GitHub), and repo link. Build-time constants via Vite define with Docker ARG/ENV passthrough.\n- **Key moment search links fixed** — Search results for key moments link to parent technique page with anchor scroll to the specific moment.\n- **Test data and jargon cleanup** — Removed TestCreator from production data. Removed yellow \"livestream sourced\" jargon banner from search results. Clean footer version info.\n- **Homepage card enrichment** — Technique cards on homepage show topic tag pills and key moment count. Creator detail pages show technique-count-by-topic instead of meaningless '0 views'.\n- **Homepage first impression** — Hero tagline (\"Production Knowledge, Distilled\"), value proposition, 3-step how-it-works grid, \"Start Exploring\" CTA, popular topic quick-links, featured technique spotlight (random), and enriched 2-column recently-added grid with deduplication.\n- **About page** — /about with what/how/who sections (purpose, 5-step extraction pipeline, maintainer links). Footer link added.\n- **Dedicated sub-topic pages** — Clicking a sub-topic on Topics page loads `/topics/:category/:subtopic` with techniques grouped by creator, breadcrumb navigation, and loading/error/empty states.\n- **Related techniques cross-linking** — Every technique page shows up to 4 related techniques scored by creator overlap, topic match, and shared tags. Curated links take priority; dynamic results fill remaining slots.\n- **Topic color coding & page transitions** — Per-category accent colors on sub-topic pages and search results. CSS-only fade-in page enter animation on all 7 public pages.\n- **Search autocomplete** — Shared SearchAutocomplete component with popular suggestions on empty focus and debounced typeahead on 2+ characters. Used on Home and SearchResults pages.\n- **Interaction delight** — Card hover animations (scale + shadow), staggered entrance animations on card grids, featured technique glow treatment, Random Technique discovery button.\n- **Topics collapse/expand** — Topics page loads with all categories collapsed. Smooth CSS grid-template-rows animation on expand/collapse.\n- **Creator stats pills** — Creator detail page topic stats rendered as colored pill badges matching category colors.\n- **Tag overflow** — Shared TagList component caps visible tags at 4 with \"+N more\" overflow pill. Applied across all 5 tag-rendering sites.\n- **Empty subtopic handling** — Subtopics with 0 techniques show \"Coming soon\" badge instead of dead-end links.\n- **Accessibility & SEO fixes** — Single h1 per page, skip-to-content keyboard link, AA-compliant muted text contrast (#828291), descriptive per-route browser tab titles via useDocumentTitle hook.\n- **Multi-field composite search** — Search tokenizes multi-word queries, AND-matches each token across creator/title/tags/category/body fields. Partial matches fallback when no exact cross-field match exists. Qdrant embeddings enriched with creator names and topic tags. Admin reindex-all endpoint for re-embedding after changes.\n- **Sort controls on all list views** — Reusable SortDropdown component on SearchResults, SubTopicPage, and CreatorDetail. Sort options: relevance/newest/oldest/alpha/creator (context-appropriate per page). Preference persists in sessionStorage across navigation.\n\n### Stack\n\n"}],"isError":false,"timestamp":1775033130199}}
{"type":"message","id":"f9c644a9","parentId":"d6b20832","timestamp":"2026-04-01T08:46:13.881Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"All verification passes. This is a simple 1-task slice. Let me complete it.","thinkingSignature":"EoYCClkIDBgCKkDse6ZenEKFPpNVa+BaS8UJ/2VR+XbVmZMcxr9tPG/TZrocKNa9jdw2kI/J7K7+KUU6WNlmPneuFB8OVaBvFFTxMg9jbGF1ZGUtb3B1cy00LTY4ABIMFUDtsHqZdZUuKvPKGgwxmAlaG+zNybIPFUMiMOSX5RSDseuI56mw6+Pl0RYNEciOM+nlvDsfZjVUra9CRIpKs1tR1TYTRMiVkU/X7ipbTniIzwZuRs+iOs70t+b5ryB0v/XVGr/himoZLWdf/QtgVXkYGVZ69LSqcKrqrEObCWY2WF+YdiI0zeAhxt7weX0s2XsR+PIFcVGl3rSCHH6qXfkNbVqggQYVwBgB"},{"type":"text","text":"All verification passes. Simple 1-task slice — no decisions to append (the task's key decisions are implementation-level, not architectural). No new knowledge entries needed (connectivity probe pattern is standard)."},{"type":"toolCall","id":"toolu_01FuXYPi1seyJ66MVU6mf7o6","name":"gsd_complete_slice","arguments":{"affects":["S02","S03"],"deviations":"None.","drillDownPaths":[".gsd/milestones/M013/slices/S01/tasks/T01-SUMMARY.md"],"filesModified":[{"description":"Empty package init","path":"backend/pipeline/quality/__init__.py"},{"description":"Argparse CLI with fitness subcommand, extensible for score/optimize","path":"backend/pipeline/quality/__main__.py"},{"description":"FitnessRunner class with 9 tests, 4 categories, connectivity pre-check, formatted report output","path":"backend/pipeline/quality/fitness.py"}],"followUps":"None.","keyDecisions":["Connectivity probe as first action in run_all() for fast failure before running tests","Mandelbrot test uses thinking modality to exercise both LLM modes","Generous validation thresholds to avoid flaky failures from LLM variance"],"keyFiles":["backend/pipeline/quality/__init__.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fitness.py"],"knownLimitations":"Cannot verify actual test pass/fail behavior without a live FYN-LLM endpoint — only connectivity error path is testable on this machine.","milestoneId":"M013","narrative":"Created the `backend/pipeline/quality/` package as the foundation for the M013 quality assurance toolkit. The package has three files: `__init__.py`, `__main__.py` (argparse CLI with `fitness` subcommand), and `fitness.py` (FitnessRunner class).\n\nFitnessRunner implements 9 tests across 4 categories:\n1. **Mandelbrot reasoning** (1 test) — asks about the Mandelbrot set area, checks for key concepts. Uses thinking modality to exercise both LLM modes.\n2. **JSON compliance** (2 tests) — simple and nested JSON with Pydantic validation. Catches malformed/empty responses gracefully.\n3. **Instruction following** (3 tests) — bullet count, keyword inclusion, lowercase constraint. Programmatic compliance checks.\n4. **Diverse battery** (3 tests) — summarization, classification, extraction. Tests practical pipeline-relevant capabilities.\n\nThe CLI runs `python -m pipeline.quality fitness`, prints a formatted pass/fail report with per-test timing and token counts, and exits 0 (all pass) or 1 (any failure or connectivity error). A connectivity pre-check probes the LLM endpoint before running tests, providing a clear error message with the endpoint URL on failure — no tracebacks.\n\nThe argparse structure is designed for extension: S02 and S03 will add `score` and `optimize` subcommands to the same CLI.","observabilitySurfaces":[],"oneLiner":"Built pipeline.quality package with FitnessRunner CLI — 9 tests across 4 categories (Mandelbrot reasoning, JSON compliance, instruction following, diverse battery), clean connectivity error handling, exits 0/1.","patternsEstablished":["pipeline.quality package structure with argparse subcommands — S02/S03 add score and optimize subcommands to the same CLI","TestResult dataclass as standard return type for all quality tests — carries name, passed, elapsed_seconds, token_count, detail"],"provides":["pipeline.quality package with FitnessRunner and argparse CLI entry point","TestResult dataclass for structured test results"],"requirementsAdvanced":[],"requirementsInvalidated":[],"requirementsSurfaced":[],"requirementsValidated":[],"requires":[],"sliceId":"S01","sliceTitle":"General FYN-LLM Fitness Suite","uatContent":"## UAT: S01 — General FYN-LLM Fitness Suite\n\n### Preconditions\n- Backend source at `backend/` with `pipeline/quality/` package present\n- Python 3.x with project dependencies installed\n- No live LLM endpoint required for error-path tests (tests 1-4)\n- Live FYN-LLM endpoint required for functional tests (tests 5-7)\n\n### Test Cases\n\n#### TC1: Import Check\n1. Run `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"ok\")'`\n2. **Expected:** Prints \"ok\", exits 0\n\n#### TC2: CLI Help\n1. Run `cd backend && python -m pipeline.quality --help`\n2. **Expected:** Shows usage with `fitness` subcommand listed and described\n\n#### TC3: No Subcommand\n1. Run `cd backend && python -m pipeline.quality`\n2. **Expected:** Prints usage message, exits non-zero\n\n#### TC4: Connectivity Error (no LLM running)\n1. Ensure no LLM is running on localhost\n2. Run `cd backend && python -m pipeline.quality fitness`\n3. **Expected:** Prints \"Cannot reach LLM endpoint at {url}\" with both primary and fallback URLs. Exits 1. No Python traceback visible in output.\n\n#### TC5: Full Fitness Run (requires live FYN-LLM)\n1. Ensure FYN-LLM is reachable at configured endpoint\n2. Run `cd backend && python -m pipeline.quality fitness`\n3. **Expected:** Prints formatted report with 9 test results across 4 categories. Each test shows name, PASS/FAIL, elapsed time. Summary line at end shows pass count / total. Exits 0 if all pass, 1 if any fail.\n\n#### TC6: Mandelbrot Thinking Mode (requires live FYN-LLM)\n1. Run fitness suite with live LLM\n2. **Expected:** Mandelbrot test uses thinking modality (visible in verbose output or code inspection). Tests LLM's reasoning capability, not just chat completion.\n\n#### TC7: JSON Compliance with Malformed Response (requires live FYN-LLM)\n1. If LLM returns non-JSON for a JSON test\n2. **Expected:** Test marks as FAIL with clear detail message (\"Failed to parse JSON\" or similar). No crash or traceback.\n\n### Edge Cases\n\n#### EC1: Empty LLM Response\n- If LLM returns empty string, affected tests should FAIL with descriptive detail, not crash with IndexError or similar.\n\n#### EC2: Timeout\n- If LLM endpoint is reachable but extremely slow, the connectivity probe should eventually time out and report the failure clearly.","verification":"1. Import check: `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")'` — exits 0, prints \"import ok\".\n2. CLI connectivity error: `cd backend && python -m pipeline.quality fitness` — prints clear \"Cannot reach LLM endpoint\" message with URLs, exits 1, no traceback.\n3. Help output: `python -m pipeline.quality --help` — shows fitness subcommand with description.\n4. No-subcommand: `python -m pipeline.quality` — prints usage, exits 1."}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2078,"cacheRead":67399,"cacheWrite":5036,"totalTokens":74514,"cost":{"input":0.000005,"output":0.05195,"cacheRead":0.0336995,"cacheWrite":0.031475,"total":0.1171295}},"stopReason":"toolUse","timestamp":1775033130200}}
{"type":"message","id":"77f6ed7b","parentId":"f9c644a9","timestamp":"2026-04-01T08:46:13.930Z","message":{"role":"toolResult","toolCallId":"toolu_01FuXYPi1seyJ66MVU6mf7o6","toolName":"gsd_complete_slice","content":[{"type":"text","text":"Completed slice S01 (M013)"}],"details":{"operation":"complete_slice","sliceId":"S01","milestoneId":"M013","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-SUMMARY.md","uatPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S01/S01-UAT.md"},"isError":false,"timestamp":1775033173928}}
{"type":"message","id":"c48ad38a","parentId":"77f6ed7b","timestamp":"2026-04-01T08:46:16.181Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S01 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10,"cacheRead":72435,"cacheWrite":2073,"totalTokens":74519,"cost":{"input":0.000005,"output":0.00025,"cacheRead":0.0362175,"cacheWrite":0.01295625,"total":0.04942875}},"stopReason":"stop","timestamp":1775033173929}}
