{"type":"thinking_level_change","id":"7f3c3865","parentId":null,"timestamp":"2026-04-01T08:57:07.601Z","thinkingLevel":"medium"}
{"type":"model_change","id":"1c19f3a8","parentId":"7f3c3865","timestamp":"2026-04-01T08:57:07.603Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"**VERIFICATION FAILED — AUTO-FIX ATTEMPT 1**\n\nThe verification gate ran after your previous attempt and found failures. Fix these issues before completing the task.\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## Verification Failures\n\n### ❌ `python -m pipeline.quality score --help` (exit code 1)\n```stderr\n/usr/local/bin/python: Error while finding module specification for 'pipeline.quality' (ModuleNotFoundError: No module named 'pipeline')\n\n```\n\n---\n\nYou are executing GSD auto-mode.\n\n## UNIT: Complete Slice S02 (\"Stage 5 Quality Scorer & Voice Preservation Dial\") — 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/S02/S02-PLAN.md`\n\n# S02: Stage 5 Quality Scorer & Voice Preservation Dial\n\n**Goal:** A `score` CLI subcommand that evaluates Stage 5 synthesis output across 5 quality dimensions using LLM-as-judge, plus a `--voice-level` parameter that modifies the synthesis prompt to dial voice preservation intensity — with the scorer proving the dial works by producing measurably different voice preservation scores at different levels.\n**Demo:** After this: 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\n## Tasks\n- [x] **T01: Built ScoreRunner with 5-dimension LLM-as-judge scoring rubric, CLI score subcommand, and realistic 6-moment sample fixture** — ## Description\n\nCreate the scorer module that evaluates a Stage 5 technique page across 5 quality dimensions using an LLM judge call. Wire it into the existing CLI as the `score` subcommand. Build a realistic fixture file for offline testing.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM endpoint | Print clear connectivity error with URL, exit 1 (same as fitness) | Same — openai timeout maps to APITimeoutError | Parse judge JSON response; if malformed, log raw excerpt and return zero scores with detail message |\n\n## Steps\n\n1. Read `backend/pipeline/quality/fitness.py` to understand the TestResult/FitnessRunner pattern and report formatting.\n2. Create `backend/pipeline/quality/scorer.py` with:\n   - `ScoreResult` dataclass: `structural`, `content_specificity`, `voice_preservation`, `readability`, `factual_fidelity` (each float 0.0–1.0), `composite` (weighted average, default equal weights), `justifications` (dict of dimension → string), `elapsed_seconds` (float).\n   - `ScoreRunner` class taking an `LLMClient` in __init__.\n   - `ScoreRunner.score_page(page_json: dict, moments: list[dict]) -> ScoreResult` method:\n     - Builds a scoring rubric prompt (hardcoded string in scorer.py — not a separate prompt file) that asks the LLM to evaluate the page against the source moments across the 5 dimensions.\n     - The rubric should specify what each dimension measures (see S02-RESEARCH.md for definitions) and instruct the LLM to return JSON: `{\"structural\": 0.8, \"content_specificity\": 0.7, ..., \"justifications\": {\"structural\": \"...\", ...}}`.\n     - Calls `self.client.complete()` with modality=\"chat\", response_model=BaseModel (JSON mode).\n     - Parses JSON response, validates all 5 dimension keys present and values in [0.0, 1.0].\n     - Computes composite as mean of 5 dimensions.\n     - Returns `ScoreResult`.\n   - `ScoreRunner.print_report(result: ScoreResult)` — formatted report matching fitness report style: header bar, per-dimension score with justification excerpt, composite score, timing.\n3. Create `backend/pipeline/quality/fixtures/sample_moments.json` with:\n   - `{\"creator_name\": \"ExampleCreator\", \"topic_category\": \"Sound design\", \"moments\": [...]}` — 5-6 realistic moments with `summary`, `transcript_excerpt`, `topic_tags`, `topic_category`, `start_time`, `end_time` fields. Content about a concrete music production technique (e.g., snare layering or bass resampling). Include direct quotes and specific plugin/setting mentions so voice preservation scoring has signal.\n4. Create `backend/pipeline/quality/fixtures/__init__.py` (empty).\n5. Update `backend/pipeline/quality/__main__.py`:\n   - Add `score` subcommand to argparse with args: `--file` (path to moments JSON), `--slug` (technique slug — placeholder, just store the arg, actual DB loading deferred), `--voice-level` (float, optional, default None — wired in T02).\n   - When `args.command == \"score\"`: validate that exactly one of --file or --slug is provided. If --file, load JSON, extract `moments` and `creator_name`. Create `ScoreRunner(client)`. If `--voice-level` is None, call `score_page()` directly with the moments. Print the report.\n   - For now, the --slug path prints \"DB loading not yet implemented\" and exits 1.\n6. Verify: `cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\"` exits 0.\n7. Verify: `cd backend && python -m pipeline.quality score --help` shows --file, --slug, --voice-level.\n8. Verify: `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` — hits connectivity error, prints endpoint URL, exits 1 with no traceback.\n\n## Must-Haves\n\n- [ ] ScoreResult dataclass with 5 float dimensions + composite + justifications + elapsed_seconds\n- [ ] ScoreRunner.score_page() sends rubric + page + moments to LLM, parses JSON response\n- [ ] Formatted report output with per-dimension scores and justifications\n- [ ] Fixture JSON with 5+ realistic moments including transcript excerpts and plugin mentions\n- [ ] `score` subcommand wired in __main__.py with --file, --slug, --voice-level args\n- [ ] Connectivity error handled cleanly (same pattern as fitness)\n\n## Verification\n\n- `cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\"` — exits 0\n- `cd backend && python -m pipeline.quality score --help` — shows all three args\n- `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` — connectivity error with URL, exit 1, no traceback\n- `cd backend && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5\"` — fixture valid\n  - Estimate: 1.5h\n  - Files: backend/pipeline/quality/scorer.py, backend/pipeline/quality/__main__.py, backend/pipeline/quality/fixtures/sample_moments.json, backend/pipeline/quality/fixtures/__init__.py\n  - Verify: cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\" && python -m pipeline.quality score --help && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5\"\n- [x] **T02: Added VoiceDial class with 3-band prompt modification and ScoreRunner.synthesize_and_score() that re-synthesizes from source moments at a given voice_level before scoring** — ## Description\n\nBuild the voice dial module that modifies the stage 5 synthesis prompt based on a voice_level parameter (0.0–1.0), and wire it into the scorer so `--voice-level` triggers re-synthesis from source moments before scoring. This completes the slice by enabling the key demo: running the scorer at voice_level 0.2 vs 0.8 produces measurably different voice preservation scores.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM endpoint (re-synthesis) | Same connectivity error pattern — print URL, exit 1 | Same | If synthesis returns unparseable JSON, log raw excerpt and exit 1 with message |\n| LLM endpoint (scoring judge) | Same as T01 scorer | Same | Same as T01 — zero scores with detail |\n| prompts/stage5_synthesis.txt | FileNotFoundError caught, print \"Prompt file not found: {path}\", exit 1 | N/A | N/A |\n\n## Steps\n\n1. Read the existing `prompts/stage5_synthesis.txt` to understand what voice language is already present (the research says it's roughly voice_level 0.6-0.7 baseline).\n2. Read `backend/pipeline/stages.py` functions `_load_prompt()`, `_get_stage_config()`, and `_synthesize_chunk()` to understand the exact synthesis call pattern — system_prompt + `<creator>...</creator>\\n<moments>...</moments>` user prompt format.\n3. Create `backend/pipeline/quality/voice_dial.py` with:\n   - `VoiceDial` class:\n     - `__init__(self, base_prompt: str)` — stores the base stage 5 system prompt.\n     - `modify(self, voice_level: float) -> str` — returns the modified system prompt.\n       - 3 bands: low (0.0–0.33), mid (0.34–0.66), high (0.67–1.0).\n       - Low band: append instruction to suppress direct quotes, write in neutral third-person encyclopedia style, avoid attributing opinions, minimize personality markers.\n       - Mid band: return base prompt unmodified (the existing prompt already has moderate voice preservation).\n       - High band: append instruction to maximize direct quotes from transcript, preserve every memorable phrase, prioritize creator's exact words over paraphrase, include personality and strong opinions.\n     - Band boundaries at 0.33 and 0.67. Within each band, no continuous interpolation — just the band's modifier.\n4. Add `ScoreRunner.synthesize_and_score(moments: list[dict], creator_name: str, voice_level: float) -> ScoreResult` method to `backend/pipeline/quality/scorer.py`:\n   - Loads stage5_synthesis.txt via `_load_prompt('stage5_synthesis.txt')` (import from pipeline.stages).\n   - Creates `VoiceDial(base_prompt)` and calls `modify(voice_level)` to get modified prompt.\n   - Gets stage config via `_get_stage_config(5)` for model_override and modality.\n   - Builds user prompt in the same format as `_synthesize_chunk`: `<creator>{name}</creator>\\n<moments>\\n{moments_json}\\n</moments>`.\n   - Calls `self.client.complete()` with the modified prompt, parses response as SynthesisResult using `self.client.parse_response(raw, SynthesisResult)`.\n   - If synthesis returns valid pages, takes the first page and calls `self.score_page()` on it.\n   - Returns the ScoreResult.\n5. Update `backend/pipeline/quality/__main__.py` — in the `score` command handler:\n   - If `--voice-level` is provided and `--file` is used: load moments from file, call `runner.synthesize_and_score(moments, creator_name, voice_level)` instead of `score_page()`.\n   - If `--voice-level` is provided without `--file`: error — voice-level requires moments input (--file or --slug with DB).\n6. Verify: `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; print('import ok')\"` exits 0.\n7. Verify: `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base prompt'); low = vd.modify(0.1); mid = vd.modify(0.5); high = vd.modify(0.9); assert low != mid; assert high != mid; assert 'suppress' in low.lower() or 'neutral' in low.lower(); assert 'quote' in high.lower() or 'direct' in high.lower(); print('dial ok')\"` — voice dial produces distinct prompts per band.\n8. Verify: `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — connectivity error, exit 1, no traceback.\n\n## Must-Haves\n\n- [ ] VoiceDial class with 3 bands (low/mid/high) producing distinct prompt modifications\n- [ ] Low band suppresses voice, high band amplifies it, mid band passes through unmodified\n- [ ] ScoreRunner.synthesize_and_score() re-synthesizes from moments using modified prompt, then scores\n- [ ] --voice-level wired into CLI and triggers re-synthesis flow\n- [ ] Stage 5 prompt loaded from prompts/stage5_synthesis.txt (not hardcoded)\n- [ ] Synthesis output parsed as SynthesisResult (reuses existing schema)\n\n## Verification\n\n- `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; print('import ok')\"` — exits 0\n- `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5) != vd.modify(0.9); print('bands ok')\"` — three distinct outputs\n- `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — connectivity error, exit 1, no traceback\n  - Estimate: 1.5h\n  - Files: backend/pipeline/quality/voice_dial.py, backend/pipeline/quality/scorer.py, backend/pipeline/quality/__main__.py\n  - Verify: cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9); print('bands ok')\" && python -m pipeline.quality score --help\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 26 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/S02/S02-SUMMARY.md` (compress all task summaries).\n8. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-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/S02/S02-SUMMARY.md` and `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-UAT.md` automatically.**\n\nWhen done, say: \"Slice S02 complete.\"","display":false,"id":"d74cb5bb","parentId":"1c19f3a8","timestamp":"2026-04-01T08:57:07.605Z"}
{"type":"message","id":"d32006e8","parentId":"d74cb5bb","timestamp":"2026-04-01T08:57:14.140Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The verification failed because `python -m pipeline.quality score --help` was run from the project root, but the module lives under `backend/`. Let me check the current state of the files to understand the issue and fix it.","thinkingSignature":"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"},{"type":"text","text":"The verification failure is clear: `python -m pipeline.quality` was run from the project root, but the module lives under `backend/`. Let me check the current state."},{"type":"toolCall","id":"toolu_012WkCahe7wasPcCLrDwaujU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/quality/ 2>/dev/null && echo \"---\" && head -50 backend/pipeline/quality/__main__.py 2>/dev/null"}},{"type":"toolCall","id":"toolu_01Do8t7revDoiDL59ECHJJab","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M013/slices/S02/tasks/T01-SUMMARY.md 2>/dev/null | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":296,"cacheRead":59647,"cacheWrite":8281,"totalTokens":68227,"cost":{"input":0.000015000000000000002,"output":0.0074,"cacheRead":0.0298235,"cacheWrite":0.051756250000000004,"total":0.08899475000000001}},"stopReason":"toolUse","timestamp":1775033827603}}
{"type":"message","id":"dd01e6d7","parentId":"d32006e8","timestamp":"2026-04-01T08:57:14.161Z","message":{"role":"toolResult","toolCallId":"toolu_012WkCahe7wasPcCLrDwaujU","toolName":"bash","content":[{"type":"text","text":"fitness.py\nfixtures\n__init__.py\n__main__.py\n__pycache__\nscorer.py\nvoice_dial.py\n---\n\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .scorer import ScoreRunner\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n"}],"isError":false,"timestamp":1775033834161}}
{"type":"message","id":"c326b3b7","parentId":"dd01e6d7","timestamp":"2026-04-01T08:57:14.165Z","message":{"role":"toolResult","toolCallId":"toolu_01Do8t7revDoiDL59ECHJJab","toolName":"bash","content":[{"type":"text","text":"---\nid: T01\nparent: S02\nmilestone: M013\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/pipeline/quality/scorer.py\", \"backend/pipeline/quality/__main__.py\", \"backend/pipeline/quality/fixtures/sample_moments.json\", \"backend/pipeline/quality/fixtures/__init__.py\"]\nkey_decisions: [\"Hardcoded scoring rubric in scorer.py for iteration speed\", \"Used mutually_exclusive_group for --file/--slug\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"All 4 verification commands pass: import check exits 0, --help shows all 3 args, connectivity error exits 1 with URL and no traceback, fixture validates with 6 moments.\"\ncompleted_at: 2026-04-01T08:53:38.205Z\nblocker_discovered: false\n---\n\n# T01: Built ScoreRunner with 5-dimension LLM-as-judge scoring rubric, CLI score subcommand, and realistic 6-moment sample fixture\n\n> Built ScoreRunner with 5-dimension LLM-as-judge scoring rubric, CLI score subcommand, and realistic 6-moment sample fixture\n\n## What Happened\n---\nid: T01\nparent: S02\nmilestone: M013\nkey_files:\n  - backend/pipeline/quality/scorer.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/fixtures/sample_moments.json\n  - backend/pipeline/quality/fixtures/__init__.py\nkey_decisions:\n  - Hardcoded scoring rubric in scorer.py for iteration speed\n  - Used mutually_exclusive_group for --file/--slug\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T08:53:38.205Z\nblocker_discovered: false\n---\n\n# T01: Built ScoreRunner with 5-dimension LLM-as-judge scoring rubric, CLI score subcommand, and realistic 6-moment sample fixture\n\n**Built ScoreRunner with 5-dimension LLM-as-judge scoring rubric, CLI score subcommand, and realistic 6-moment sample fixture**\n\n## What Happened\n\nCreated scorer.py with ScoreResult dataclass (5 float dimensions + composite + justifications + elapsed_seconds + error) and ScoreRunner class that sends technique page JSON and source moments to an LLM judge with a detailed scoring rubric. Updated __main__.py with score subcommand (--file, --slug mutually exclusive; --voice-level optional for T02). Created fixtures/sample_moments.json with 6 realistic music production moments including transcript excerpts with specific plugins/settings.\n\n## Verification\n\nAll 4 verification commands pass: import check exits 0, --help shows all 3 args, connectivity error exits 1 with URL and no traceback, fixture validates with 6 moments.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\"` | 0 | ✅ pass | 1000ms |\n| 2 | `cd backend && python -m pipeline.quality score --help` | 0 | ✅ pass | 1000ms |\n| 3 | `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` | 1 | ✅ pass | 3000ms |\n| 4 | `cd backend && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5\"` | 0 | ✅ pass | 1000ms |\n\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/scorer.py`\n- `backend/pipeline/quality/__main__.py`\n- `backend/pipeline/quality/fixtures/sample_moments.json`\n- `backend/pipeline/quality/fixtures/__init__.py`\n\n\n## Deviations\n"}],"isError":false,"timestamp":1775033834163}}
{"type":"message","id":"adf80a34","parentId":"c326b3b7","timestamp":"2026-04-01T08:57:27.861Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The verification failure is `python -m pipeline.quality score --help` run from the project root, not from `backend/`. The task verification commands all use `cd backend && python -m pipeline.quality score --help`. The failing command in the verification gate is `python -m pipeline.quality score --help` without the `cd backend` prefix.\n\nThe slice plan's demo says: \"Run `python -m pipeline.quality score`...\" but the actual verification commands in the task plans all use `cd backend &&`. The failing gate command is running from the project root.\n \nSo the core issue is that the verification gate is trying to run the module from the project root where the `pipeline` package doesn't exist. I can either restructure the module to be importable from the project root, or I need to figure out why the gate isn't using the `cd backend` prefix like the task plans do.\n\nLet me check if there's a setup.py or pyproject.toml that could make the module accessible from the root directory, or if I need to adjust how the gate is configured to run the command.","thinkingSignature":"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"},{"type":"text","text":"The verification gate ran `python -m pipeline.quality score --help` from the project root, but the module lives in `backend/`. Need to make it work from root. Let me check what exists."},{"type":"toolCall","id":"toolu_01Tg15pu2WYymPtqPLJZthGJ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la setup.py pyproject.toml backend/setup.py backend/pyproject.toml 2>/dev/null; echo \"---\"; ls pipeline 2>/dev/null || echo \"no root pipeline dir\""}},{"type":"toolCall","id":"toolu_01APMnBUsxuVxmM9NwUfQGhc","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M013/slices/S02/tasks/T02-SUMMARY.md 2>/dev/null | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":647,"cacheRead":67928,"cacheWrite":1739,"totalTokens":70315,"cost":{"input":0.000005,"output":0.016175000000000002,"cacheRead":0.033964,"cacheWrite":0.01086875,"total":0.061012750000000004}},"stopReason":"toolUse","timestamp":1775033834164}}
{"type":"message","id":"2fe697a7","parentId":"adf80a34","timestamp":"2026-04-01T08:57:27.882Z","message":{"role":"toolResult","toolCallId":"toolu_01Tg15pu2WYymPtqPLJZthGJ","toolName":"bash","content":[{"type":"text","text":"---\nno root pipeline dir\n"}],"isError":false,"timestamp":1775033847882}}
{"type":"message","id":"59da45c1","parentId":"2fe697a7","timestamp":"2026-04-01T08:57:27.888Z","message":{"role":"toolResult","toolCallId":"toolu_01APMnBUsxuVxmM9NwUfQGhc","toolName":"bash","content":[{"type":"text","text":"---\nid: T02\nparent: S02\nmilestone: M013\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/pipeline/quality/voice_dial.py\", \"backend/pipeline/quality/scorer.py\", \"backend/pipeline/quality/__main__.py\"]\nkey_decisions: [\"Three discrete bands (low/mid/high) at boundaries 0.33/0.67 instead of continuous interpolation\", \"Mid band returns base prompt unmodified since it already targets ~0.6 voice preservation\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"All 7 verification checks pass: imports for scorer and voice_dial, --help shows all args, standard score gives connectivity error at exit 1, fixture validates, voice dial produces three distinct bands, voice-level CLI exits cleanly at exit 1 with no traceback.\"\ncompleted_at: 2026-04-01T08:57:04.411Z\nblocker_discovered: false\n---\n\n# T02: Added VoiceDial class with 3-band prompt modification and ScoreRunner.synthesize_and_score() that re-synthesizes from source moments at a given voice_level before scoring\n\n> Added VoiceDial class with 3-band prompt modification and ScoreRunner.synthesize_and_score() that re-synthesizes from source moments at a given voice_level before scoring\n\n## What Happened\n---\nid: T02\nparent: S02\nmilestone: M013\nkey_files:\n  - backend/pipeline/quality/voice_dial.py\n  - backend/pipeline/quality/scorer.py\n  - backend/pipeline/quality/__main__.py\nkey_decisions:\n  - Three discrete bands (low/mid/high) at boundaries 0.33/0.67 instead of continuous interpolation\n  - Mid band returns base prompt unmodified since it already targets ~0.6 voice preservation\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T08:57:04.412Z\nblocker_discovered: false\n---\n\n# T02: Added VoiceDial class with 3-band prompt modification and ScoreRunner.synthesize_and_score() that re-synthesizes from source moments at a given voice_level before scoring\n\n**Added VoiceDial class with 3-band prompt modification and ScoreRunner.synthesize_and_score() that re-synthesizes from source moments at a given voice_level before scoring**\n\n## What Happened\n\nCreated voice_dial.py with VoiceDial class implementing three discrete bands (low/mid/high) that modify the Stage 5 synthesis prompt. Low band appends voice suppression instructions, mid band passes through unmodified, high band appends voice amplification instructions. Added synthesize_and_score() to ScoreRunner that loads the stage5 prompt, applies VoiceDial, calls LLM for re-synthesis, then scores the result. Updated CLI to route --voice-level through the re-synthesis path.\n\n## Verification\n\nAll 7 verification checks pass: imports for scorer and voice_dial, --help shows all args, standard score gives connectivity error at exit 1, fixture validates, voice dial produces three distinct bands, voice-level CLI exits cleanly at exit 1 with no traceback.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\"` | 0 | ✅ pass | 500ms |\n| 2 | `cd backend && python -m pipeline.quality score --help` | 0 | ✅ pass | 500ms |\n| 3 | `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` | 1 | ✅ pass | 2000ms |\n| 4 | `cd backend && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5\"` | 0 | ✅ pass | 200ms |\n| 5 | `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; print('import ok')\"` | 0 | ✅ pass | 200ms |\n| 6 | `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9); print('bands ok')\"` | 0 | ✅ pass | 200ms |\n| 7 | `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` | 1 | ✅ pass | 500ms |\n\n\n## Deviations\n\nVoice-level path exits with prompt-not-found instead of connectivity error because prompts/ resolves relative to CWD and isn't under backend/. This is correct runtime behavior.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/voice_dial.py`\n- `backend/pipeline/quality/scorer.py`\n- `backend/pipeline/quality/__main__.py`\n\n\n"}],"isError":false,"timestamp":1775033847886}}
{"type":"message","id":"c5d39f91","parentId":"59da45c1","timestamp":"2026-04-01T08:57:46.938Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The verification gate ran `python -m pipeline.quality score --help` from the project root, but the `pipeline` package lives inside `backend/`. The task summaries show all verification was done with `cd backend && python -m pipeline.quality ...`.\n\nThe failing check is `python -m pipeline.quality score --help` from the project root. I need to either:\n1. Make the module importable from the project root\n2. Or understand what the expected invocation is\n\nThe slice plan's verification says `cd backend && python -m pipeline.quality score --help`. But the verification gate ran it without the `cd backend`. \n\n\nI'm realizing the project doesn't have a `setup.py` or `pyproject.toml`, so the simplest fix would be to either add `backend/` to the PYTHONPATH or create a symlink at the root that points to the backend package. The verification gate expects to run from the project root, but the slice plan explicitly uses `cd backend`, so I need to bridge that gap. the imports are resolved. So I need to modify `__main__.py` to insert `backend/` into `sys.path` before any relative imports happen—that way `config` and `pipeline` modules become accessible regardless of where the command is run from.","thinkingSignature":"[encoded data, 4248 characters]"},{"type":"toolCall","id":"toolu_01RgiBQ3QANCaSGz1Y3BasrH","name":"read","arguments":{"path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":810,"cacheRead":69667,"cacheWrite":1633,"totalTokens":72111,"cost":{"input":0.000005,"output":0.02025,"cacheRead":0.034833499999999996,"cacheWrite":0.01020625,"total":0.06529475}},"stopReason":"toolUse","timestamp":1775033847887}}
{"type":"message","id":"361af3c9","parentId":"c5d39f91","timestamp":"2026-04-01T08:57:46.943Z","message":{"role":"toolResult","toolCallId":"toolu_01RgiBQ3QANCaSGz1Y3BasrH","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .scorer import ScoreRunner\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n        default=None,\n        help=\"Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.\",\n    )\n\n    args = parser.parse_args()\n\n    if args.command is None:\n        parser.print_help()\n        return 1\n\n    if args.command == \"fitness\":\n        settings = get_settings()\n        client = LLMClient(settings)\n        runner = FitnessRunner(client)\n        return runner.run_all()\n\n    if args.command == \"score\":\n        return _run_score(args)\n\n    return 0\n\n\ndef _run_score(args: argparse.Namespace) -> int:\n    \"\"\"Execute the score subcommand.\"\"\"\n    # -- Load source data --\n    if args.slug:\n        print(\"DB loading not yet implemented\", file=sys.stderr)\n        return 1\n\n    try:\n        with open(args.file) as f:\n            data = json.load(f)\n    except FileNotFoundError:\n        print(f\"File not found: {args.file}\", file=sys.stderr)\n        return 1\n    except json.JSONDecodeError as exc:\n        print(f\"Invalid JSON in {args.file}: {exc}\", file=sys.stderr)\n        return 1\n\n    moments = data.get(\"moments\", [])\n    creator_name = data.get(\"creator_name\", \"Unknown\")\n\n    if not moments:\n        print(\"No moments found in input file\", file=sys.stderr)\n        return 1\n\n    settings = get_settings()\n    client = LLMClient(settings)\n    runner = ScoreRunner(client)\n\n    # -- Voice-level mode: re-synthesize then score --\n    if args.voice_level is not None:\n        voice_level = args.voice_level\n        if not (0.0 <= voice_level <= 1.0):\n            print(\"--voice-level must be between 0.0 and 1.0\", file=sys.stderr)\n            return 1\n\n        print(f\"\\nRe-synthesizing + scoring for '{creator_name}' ({len(moments)} moments, voice_level={voice_level})...\")\n        result = runner.synthesize_and_score(moments, creator_name, voice_level)\n\n        if result.error:\n            runner.print_report(result)\n            return 1\n\n        runner.print_report(result)\n        return 0\n\n    # -- Standard mode: build page stub from moments, score directly --\n    page_json = {\n        \"title\": f\"{creator_name} — Technique Page\",\n        \"creator_name\": creator_name,\n        \"summary\": f\"Technique page synthesized from {len(moments)} key moments.\",\n        \"body_sections\": [\n            {\n                \"heading\": m.get(\"topic_tags\", [\"Technique\"])[0] if m.get(\"topic_tags\") else \"Technique\",\n                \"content\": m.get(\"summary\", \"\") + \"\\n\\n\" + m.get(\"transcript_excerpt\", \"\"),\n            }\n            for m in moments\n        ],\n    }\n\n    print(f\"\\nScoring page for '{creator_name}' ({len(moments)} moments)...\")\n\n    result = runner.score_page(page_json, moments)\n\n    if result.error:\n        runner.print_report(result)\n        return 1\n\n    runner.print_report(result)\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775033866941}}
{"type":"message","id":"9d7a2f8d","parentId":"361af3c9","timestamp":"2026-04-01T08:57:52.629Z","message":{"role":"assistant","content":[{"type":"text","text":"The verification failure is that `python -m pipeline.quality score --help` is being run from the project root, not from `backend/`. The task executors ran everything with `cd backend &&` prefix which worked, but the verification gate ran without it. The fix: the module needs to work from the project root too. I'll check how the `backend/` directory is structured."},{"type":"toolCall","id":"toolu_01MmKJFN8a1GVqB4aopWsHVP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/__init__.py 2>/dev/null && echo \"exists\" || echo \"missing\""}},{"type":"toolCall","id":"toolu_01Uv5RYh7bqPztwbx29Av5Sp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -c \"import sys; sys.path.insert(0, 'backend'); from pipeline.quality.scorer import ScoreRunner; print('ok')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":260,"cacheRead":71300,"cacheWrite":1299,"totalTokens":72860,"cost":{"input":0.000005,"output":0.006500000000000001,"cacheRead":0.03565,"cacheWrite":0.008118750000000001,"total":0.05027375}},"stopReason":"toolUse","timestamp":1775033866942}}
{"type":"message","id":"337131db","parentId":"9d7a2f8d","timestamp":"2026-04-01T08:57:52.648Z","message":{"role":"toolResult","toolCallId":"toolu_01MmKJFN8a1GVqB4aopWsHVP","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/__init__.py\nexists\n"}],"isError":false,"timestamp":1775033872648}}
{"type":"message","id":"c8580b10","parentId":"337131db","timestamp":"2026-04-01T08:57:53.147Z","message":{"role":"toolResult","toolCallId":"toolu_01Uv5RYh7bqPztwbx29Av5Sp","toolName":"bash","content":[{"type":"text","text":"ok\n"}],"isError":false,"timestamp":1775033873146}}
{"type":"message","id":"83ce94a1","parentId":"c8580b10","timestamp":"2026-04-01T08:57:59.206Z","message":{"role":"assistant","content":[{"type":"text","text":"The issue is purely about `sys.path`. Running `python -m pipeline.quality` from the project root fails because `backend/` isn't on the path. The cleanest fix: add a `__main__.py` path hook or a thin wrapper. But the verification command is specifically `python -m pipeline.quality score --help` — that requires `pipeline` to be findable as a top-level package.\n\nLet me check if there's an existing pattern (e.g., PYTHONPATH in docker or a setup.py):"},{"type":"toolCall","id":"toolu_01VVfYJNGmAFLWQoDU69oNA5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -r \"PYTHONPATH\" docker-compose.yml Dockerfile* 2>/dev/null | head -10; echo \"---\"; grep -r \"sys.path\" backend/pipeline/quality/__main__.py 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":240,"cacheRead":72599,"cacheWrite":333,"totalTokens":73173,"cost":{"input":0.000005,"output":0.006,"cacheRead":0.0362995,"cacheWrite":0.00208125,"total":0.044385749999999995}},"stopReason":"toolUse","timestamp":1775033873146}}
{"type":"message","id":"91701ff1","parentId":"83ce94a1","timestamp":"2026-04-01T08:57:59.227Z","message":{"role":"toolResult","toolCallId":"toolu_01VVfYJNGmAFLWQoDU69oNA5","toolName":"bash","content":[{"type":"text","text":"---\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775033879225}}
{"type":"message","id":"903b17d8","parentId":"91701ff1","timestamp":"2026-04-01T08:58:02.212Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Y1RUjU1xAyZxLcNpA3LFHw","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn \"PYTHONPATH\\|sys\\.path\" docker-compose.yml Dockerfile.api Dockerfile.worker backend/pipeline/quality/__init__.py 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":72932,"cacheWrite":269,"totalTokens":73317,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.036466,"cacheWrite":0.00168125,"total":0.04102725}},"stopReason":"toolUse","timestamp":1775033879226}}
{"type":"message","id":"6e94957e","parentId":"903b17d8","timestamp":"2026-04-01T08:58:02.228Z","message":{"role":"toolResult","toolCallId":"toolu_01Y1RUjU1xAyZxLcNpA3LFHw","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775033882227}}
{"type":"message","id":"7b4c1cbe","parentId":"6e94957e","timestamp":"2026-04-01T08:58:05.757Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LQPueAtzpLYAYbbjx3yCMF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -5 Dockerfile.api 2>/dev/null; echo \"===\"; grep WORKDIR Dockerfile.api 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":101,"cacheRead":73201,"cacheWrite":129,"totalTokens":73432,"cost":{"input":0.000005,"output":0.0025250000000000003,"cacheRead":0.0366005,"cacheWrite":0.00080625,"total":0.03993675}},"stopReason":"toolUse","timestamp":1775033882227}}
{"type":"message","id":"cd9c4235","parentId":"7b4c1cbe","timestamp":"2026-04-01T08:58:05.776Z","message":{"role":"toolResult","toolCallId":"toolu_01LQPueAtzpLYAYbbjx3yCMF","toolName":"bash","content":[{"type":"text","text":"===\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775033885773}}
{"type":"message","id":"87cd888a","parentId":"cd9c4235","timestamp":"2026-04-01T08:58:08.853Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0194JjPwjvwNagLCej2tfNmz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls Dockerfile* 2>/dev/null; ls docker/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":73330,"cacheWrite":130,"totalTokens":73549,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.036664999999999996,"cacheWrite":0.0008125000000000001,"total":0.039682499999999996}},"stopReason":"toolUse","timestamp":1775033885774}}
{"type":"message","id":"a1ac8eb7","parentId":"87cd888a","timestamp":"2026-04-01T08:58:08.873Z","message":{"role":"toolResult","toolCallId":"toolu_0194JjPwjvwNagLCej2tfNmz","toolName":"bash","content":[{"type":"text","text":"Dockerfile.api\nDockerfile.web\nnginx.conf\n"}],"isError":false,"timestamp":1775033888870}}
{"type":"message","id":"393ac554","parentId":"a1ac8eb7","timestamp":"2026-04-01T08:58:12.010Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Vn9bZKMRLgKpeMkd2meoLs","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"WORKDIR\\|PYTHONPATH\\|sys.path\\|COPY.*backend\" Dockerfile.api"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":73330,"cacheWrite":244,"totalTokens":73672,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.036664999999999996,"cacheWrite":0.001525,"total":0.040619999999999996}},"stopReason":"toolUse","timestamp":1775033888871}}
{"type":"message","id":"72fc4a66","parentId":"393ac554","timestamp":"2026-04-01T08:58:12.026Z","message":{"role":"toolResult","toolCallId":"toolu_01Vn9bZKMRLgKpeMkd2meoLs","toolName":"bash","content":[{"type":"text","text":"grep: Dockerfile.api: No such file or directory\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775033892024}}
{"type":"message","id":"d3e495a5","parentId":"72fc4a66","timestamp":"2026-04-01T08:58:14.849Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_018FCwryQWYXo8DszZ7AmoSc","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/Dockerfile.api"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":72,"cacheRead":73574,"cacheWrite":137,"totalTokens":73784,"cost":{"input":0.000005,"output":0.0018000000000000002,"cacheRead":0.036787,"cacheWrite":0.00085625,"total":0.039448250000000004}},"stopReason":"toolUse","timestamp":1775033892025}}
{"type":"message","id":"b1425d14","parentId":"d3e495a5","timestamp":"2026-04-01T08:58:14.859Z","message":{"role":"toolResult","toolCallId":"toolu_018FCwryQWYXo8DszZ7AmoSc","toolName":"bash","content":[{"type":"text","text":"ls: cannot access '/home/aux/projects/content-to-kb-automator/Dockerfile.api': No such file or directory\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775033894857}}
{"type":"message","id":"300f3f05","parentId":"b1425d14","timestamp":"2026-04-01T08:58:17.833Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01PJp3jvWEm1pQvyWtrD9Rdw","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -maxdepth 2 -name \"Dockerfile*\" -o -name \"docker-compose*\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":73574,"cacheWrite":267,"totalTokens":73940,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.036787,"cacheWrite":0.00166875,"total":0.040910749999999996}},"stopReason":"toolUse","timestamp":1775033894858}}
{"type":"message","id":"e8be121a","parentId":"300f3f05","timestamp":"2026-04-01T08:58:17.843Z","message":{"role":"toolResult","toolCallId":"toolu_01PJp3jvWEm1pQvyWtrD9Rdw","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/docker/Dockerfile.api\n/home/aux/projects/content-to-kb-automator/docker/Dockerfile.web\n/home/aux/projects/content-to-kb-automator/docker-compose.yml\n"}],"isError":false,"timestamp":1775033897842}}
{"type":"message","id":"a4795a88","parentId":"e8be121a","timestamp":"2026-04-01T08:58:20.564Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DHqPjz3gyoUzjmGP9KVoEk","name":"bash","arguments":{"command":"grep -n \"WORKDIR\\|PYTHONPATH\" /home/aux/projects/content-to-kb-automator/docker/Dockerfile.api"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":87,"cacheRead":73841,"cacheWrite":177,"totalTokens":74106,"cost":{"input":0.000005,"output":0.0021750000000000003,"cacheRead":0.036920499999999995,"cacheWrite":0.00110625,"total":0.04020675}},"stopReason":"toolUse","timestamp":1775033897843}}
{"type":"message","id":"86f44101","parentId":"a4795a88","timestamp":"2026-04-01T08:58:20.580Z","message":{"role":"toolResult","toolCallId":"toolu_01DHqPjz3gyoUzjmGP9KVoEk","toolName":"bash","content":[{"type":"text","text":"3:WORKDIR /app\n"}],"isError":false,"timestamp":1775033900577}}
{"type":"message","id":"18beba1b","parentId":"86f44101","timestamp":"2026-04-01T08:58:29.884Z","message":{"role":"assistant","content":[{"type":"text","text":"In Docker, WORKDIR is `/app` and `backend/` contents are likely copied there. So inside Docker, `python -m pipeline.quality` works. The verification gate is running from the project root outside Docker. The simplest fix: add a `sys.path` insert at the top of `__main__.py` so `backend/` is on the path when run from the project root.\n\nActually, looking at this more carefully — the verification command is `python -m pipeline.quality score --help`. For Python to even find the `pipeline` package, `backend/` must be on `sys.path`. A `sys.path` hack inside `__main__.py` can't help because Python can't find `__main__.py` in the first place.\n\nThe right fix: add a thin wrapper script at the project root, OR ensure verification runs from `backend/`. But the verification gate explicitly runs `python -m pipeline.quality score --help` from the project root. So I need to make `pipeline` importable from root. The cleanest approach: a `PYTHONPATH=backend` prefix, or a symlink pipeline -> backend/pipeline.\n\nBut the verification command is fixed. Let me check if there's a `.env` or config that sets PYTHONPATH:"},{"type":"toolCall","id":"toolu_01XYpwNEnos2dG9EMeuJA4CV","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .env 2>/dev/null | grep -i python; echo \"---\"; cat backend/.env 2>/dev/null | grep -i python"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":381,"cacheRead":74018,"cacheWrite":107,"totalTokens":74507,"cost":{"input":0.000005,"output":0.009525,"cacheRead":0.037009,"cacheWrite":0.0006687500000000001,"total":0.04720775}},"stopReason":"toolUse","timestamp":1775033900578}}
{"type":"message","id":"ebcf42ed","parentId":"18beba1b","timestamp":"2026-04-01T08:58:29.902Z","message":{"role":"toolResult","toolCallId":"toolu_01XYpwNEnos2dG9EMeuJA4CV","toolName":"bash","content":[{"type":"text","text":"---\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775033909900}}
{"type":"message","id":"87a67efd","parentId":"ebcf42ed","timestamp":"2026-04-01T08:58:39.617Z","message":{"role":"assistant","content":[{"type":"text","text":"The simplest reliable fix: create a symlink from `pipeline` at project root pointing to `backend/pipeline`. This makes `python -m pipeline.quality` work from project root.\n\nWait — but `backend/pipeline/quality/__main__.py` imports `from config import get_settings` and `from pipeline.llm_client import LLMClient`. If I symlink, those imports would fail from project root because `config` isn't at root either.\n\nBetter approach: make `__main__.py` add `backend/` to `sys.path` early, and create a project-root `pipeline` package stub that bootstraps. Actually, the real problem is simpler — Python needs to find the package first.\n\nLet me think about this differently. The S02 plan verification commands all use `cd backend &&`. The verification gate command `python -m pipeline.quality score --help` doesn't. I need to either:\n\n1. Make the module findable from root (hard due to `config` and other imports)\n2. Add a wrapper script that the gate can call instead\n\nLet me check the S01 fitness slice — how did that pass verification?"},{"type":"toolCall","id":"toolu_01P938t5HxRX6RhYDVvBqpZH","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M013/slices/S01/S01-PLAN.md 2>/dev/null | grep -A5 \"Verify\\|verify\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":365,"cacheRead":74125,"cacheWrite":410,"totalTokens":74901,"cost":{"input":0.000005,"output":0.009125000000000001,"cacheRead":0.0370625,"cacheWrite":0.0025625,"total":0.048755}},"stopReason":"toolUse","timestamp":1775033909901}}
{"type":"message","id":"f2d044a6","parentId":"87a67efd","timestamp":"2026-04-01T08:58:39.635Z","message":{"role":"toolResult","toolCallId":"toolu_01P938t5HxRX6RhYDVvBqpZH","toolName":"bash","content":[{"type":"text","text":"   - `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--\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"}],"isError":false,"timestamp":1775033919633}}
{"type":"message","id":"e391e7fe","parentId":"f2d044a6","timestamp":"2026-04-01T08:58:48.804Z","message":{"role":"assistant","content":[{"type":"text","text":"S01 also used `cd backend &&`. The verification gate is applying a different command pattern. The most pragmatic fix: make the module runnable from project root by ensuring `backend/` is on `sys.path`. I'll add a `__init__` bootstrap in the package, but the real issue is Python can't even find the package.\n\nThe cleanest solution: add a thin `pipeline` symlink at root won't work due to sibling imports. Instead, I'll modify `__main__.py` to manipulate `sys.path` if needed, but Python needs to find the package first.\n\nActually — the real solution is to add `backend` to the Python path via a `.pth` file or make the `__main__.py` discoverable. Let me try another approach: a top-level wrapper script.\n\nWait, the verification command is `python -m pipeline.quality score --help`. The only way this works from project root is if `pipeline` is a package findable on `sys.path`. Let me create a minimal solution:"},{"type":"toolCall","id":"toolu_01F15SUyzRUNADVJ85jdLrQv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -c \"import site; print(site.getusersitepackages()); print(site.getsitepackages())\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":326,"cacheRead":74535,"cacheWrite":801,"totalTokens":75663,"cost":{"input":0.000005,"output":0.008150000000000001,"cacheRead":0.037267499999999995,"cacheWrite":0.00500625,"total":0.050428749999999994}},"stopReason":"toolUse","timestamp":1775033919634}}
{"type":"message","id":"d6f6a609","parentId":"e391e7fe","timestamp":"2026-04-01T08:58:48.844Z","message":{"role":"toolResult","toolCallId":"toolu_01F15SUyzRUNADVJ85jdLrQv","toolName":"bash","content":[{"type":"text","text":"/home/aux/.local/lib/python3.12/site-packages\n['/usr/local/lib/python3.12/dist-packages', '/usr/lib/python3/dist-packages', '/usr/lib/python3.12/dist-packages']\n"}],"isError":false,"timestamp":1775033928841}}
{"type":"message","id":"0eb293d1","parentId":"d6f6a609","timestamp":"2026-04-01T08:58:53.257Z","message":{"role":"assistant","content":[{"type":"text","text":"The simplest approach that doesn't pollute system packages: create a `sitecustomize.py` or just add a symlink. But the cleanest project-level fix is to ensure `backend` is on the path. Let me check if there's a `conftest.py` or any existing path manipulation:"},{"type":"toolCall","id":"toolu_01WR2uNXNHCpCSbqhhppygqY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"conftest.py\" -o -name \"sitecustomize.py\" -o -name \"*.pth\" 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":177,"cacheRead":75336,"cacheWrite":399,"totalTokens":75913,"cost":{"input":0.000005,"output":0.004425,"cacheRead":0.037668,"cacheWrite":0.0024937500000000003,"total":0.04459175}},"stopReason":"toolUse","timestamp":1775033928842}}
{"type":"message","id":"24f63c68","parentId":"0eb293d1","timestamp":"2026-04-01T08:58:53.314Z","message":{"role":"toolResult","toolCallId":"toolu_01WR2uNXNHCpCSbqhhppygqY","toolName":"bash","content":[{"type":"text","text":"./backend/tests/conftest.py\n"}],"isError":false,"timestamp":1775033933312}}
{"type":"message","id":"c27b39c5","parentId":"24f63c68","timestamp":"2026-04-01T08:58:56.247Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01PraTeaYpowUP9wU6jtg2wF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -20 backend/tests/conftest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":80,"cacheRead":75735,"cacheWrite":199,"totalTokens":76015,"cost":{"input":0.000005,"output":0.002,"cacheRead":0.0378675,"cacheWrite":0.00124375,"total":0.04111625}},"stopReason":"toolUse","timestamp":1775033933313}}
{"type":"message","id":"53fb5cb8","parentId":"c27b39c5","timestamp":"2026-04-01T08:58:56.264Z","message":{"role":"toolResult","toolCallId":"toolu_01PraTeaYpowUP9wU6jtg2wF","toolName":"bash","content":[{"type":"text","text":"\"\"\"Shared fixtures for Chrysopedia integration tests.\n\nProvides:\n- Async SQLAlchemy engine/session against a real PostgreSQL test database\n- Sync SQLAlchemy engine/session for pipeline stage tests (Celery stages are sync)\n- httpx.AsyncClient wired to the FastAPI app with dependency overrides\n- Pre-ingest fixture for pipeline tests\n- Sample transcript fixture path and temporary storage directory\n\nKey design choice: function-scoped engine with NullPool avoids asyncpg\n\"another operation in progress\" errors caused by session-scoped connection\nreuse between the ASGI test client and verification queries.\n\"\"\"\n\nimport json\nimport os\nimport pathlib\nimport uuid\n\nimport pytest\n"}],"isError":false,"timestamp":1775033936261}}
{"type":"message","id":"80c06622","parentId":"53fb5cb8","timestamp":"2026-04-01T08:59:06.044Z","message":{"role":"assistant","content":[{"type":"text","text":"OK. The most practical fix: create a thin `pipeline/__init__.py` at the project root that bootstraps the real package. Actually no — that creates a real package that shadows the backend one.\n\nThe correct fix is simple: the `__main__.py` should handle being run from the project root. But Python's `-m` flag requires the package on `sys.path` before it can even load `__main__.py`.\n\nMost practical: create a `run_quality.py` wrapper at project root. But the verification command is hardcoded as `python -m pipeline.quality score --help`.\n\nThe only clean way to make `python -m pipeline.quality` work from root: make `pipeline` discoverable. I'll use a `.pth` file in the user site-packages, or... I'll just make `PYTHONPATH` include `backend/`. But I can't control what the verification gate sets.\n\nActually, there's a standard Python approach: create a `setup.py` or `pyproject.toml` in the project root that installs `backend/` as an editable package. But that seems heavy.\n\nSimplest fix that works: create a symlink `pipeline -> backend/pipeline` at the project root, and ensure the `config` module is also accessible. Let me check what `config` is:"},{"type":"toolCall","id":"toolu_019neHVL5yNt8CPzeqiZ8Vgs","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -5 backend/config.py 2>/dev/null || echo \"no config.py\"; ls backend/config/ 2>/dev/null || echo \"no config dir\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":401,"cacheRead":75934,"cacheWrite":254,"totalTokens":76590,"cost":{"input":0.000005,"output":0.010025000000000001,"cacheRead":0.037967,"cacheWrite":0.0015875000000000002,"total":0.0495845}},"stopReason":"toolUse","timestamp":1775033936262}}
{"type":"message","id":"efb18880","parentId":"80c06622","timestamp":"2026-04-01T08:59:06.059Z","message":{"role":"toolResult","toolCallId":"toolu_019neHVL5yNt8CPzeqiZ8Vgs","toolName":"bash","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\nno config dir\n"}],"isError":false,"timestamp":1775033946057}}
{"type":"message","id":"cceaedd0","parentId":"efb18880","timestamp":"2026-04-01T08:59:10.382Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Cq8HGy5mJfnVzjXXodDDyH","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"\nfrom pipeline.quality.__main__ import main\nimport inspect, ast, sys\n# Get all imports in __main__.py\nwith open('pipeline/quality/__main__.py') as f:\n    tree = ast.parse(f.read())\nfor node in ast.walk(tree):\n    if isinstance(node, (ast.Import, ast.ImportFrom)):\n        if isinstance(node, ast.ImportFrom):\n            print(f'from {node.module} import ...')\n        else:\n            for alias in node.names:\n                print(f'import {alias.name}')\n\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":210,"cacheRead":76188,"cacheWrite":445,"totalTokens":76844,"cost":{"input":0.000005,"output":0.00525,"cacheRead":0.038093999999999996,"cacheWrite":0.0027812500000000003,"total":0.04613025}},"stopReason":"toolUse","timestamp":1775033946058}}
{"type":"message","id":"8def5d0d","parentId":"cceaedd0","timestamp":"2026-04-01T08:59:10.902Z","message":{"role":"toolResult","toolCallId":"toolu_01Cq8HGy5mJfnVzjXXodDDyH","toolName":"bash","content":[{"type":"text","text":"from __future__ import ...\nimport argparse\nimport json\nimport sys\nfrom config import ...\nfrom pipeline.llm_client import ...\nfrom fitness import ...\nfrom scorer import ...\n"}],"isError":false,"timestamp":1775033950900}}
{"type":"message","id":"bab69761","parentId":"8def5d0d","timestamp":"2026-04-01T08:59:22.745Z","message":{"role":"assistant","content":[{"type":"text","text":"So `__main__.py` imports `config` and `pipeline.llm_client` which are both under `backend/`. Making this work from root requires `backend/` on `sys.path`.\n\nThe right fix: modify `__main__.py` to add `backend/` to `sys.path` before any other imports. But `__main__.py` is loaded as part of `python -m pipeline.quality`, which means Python already found the `pipeline` package. So `pipeline` must be importable first.\n\nI'll use the approach that works: add a `pipeline` package at root level that acts as a namespace redirect. Actually, that's fragile. \n\nBest approach: add `backend` to PYTHONPATH via a `.env` that the verification harness might source, or better — make the `__main__.py` self-bootstrapping with a `sys.path` fixup at the very top, AND create a tiny `pipeline` stub at root to let Python find it.\n\nWait — actually the cleanest: create a `__main__.py` at the project root in a new `pipeline/` package that just does the path fixup and delegates. But that means TWO `pipeline` packages.\n\nLet me just do the simplest thing that works: a `conftest`-style path fixup. I'll add a `sitecustomize.py` at the project root... no, that needs to be on `sys.path` too.\n\nOK. The real practical solution: modify the `backend/pipeline/quality/__main__.py` to insert `backend/` onto `sys.path` at the very top (before other imports), and create a minimal project-root symlink for the `pipeline` package."},{"type":"toolCall","id":"toolu_013YEMgFPNwbACxjeS7inW95","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ln -sf backend/pipeline pipeline && python -m pipeline.quality score --help 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":475,"cacheRead":76633,"cacheWrite":264,"totalTokens":77373,"cost":{"input":0.000005,"output":0.011875,"cacheRead":0.038316499999999996,"cacheWrite":0.00165,"total":0.0518465}},"stopReason":"toolUse","timestamp":1775033950901}}
{"type":"message","id":"cea813df","parentId":"bab69761","timestamp":"2026-04-01T08:59:22.872Z","message":{"role":"toolResult","toolCallId":"toolu_013YEMgFPNwbACxjeS7inW95","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<frozen runpy>\", line 198, in _run_module_as_main\n  File \"<frozen runpy>\", line 88, in _run_code\n  File \"/home/aux/projects/content-to-kb-automator/pipeline/quality/__main__.py\", line 15, in <module>\n    from config import get_settings\nImportError: cannot import name 'get_settings' from 'config' (unknown location)\nexit: 1\n"}],"isError":false,"timestamp":1775033962870}}
{"type":"message","id":"78fb3652","parentId":"cea813df","timestamp":"2026-04-01T08:59:32.819Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — symlink gets Python to find the package, but now `from config import get_settings` fails because `config.py` lives in `backend/`. Fix: add `sys.path` insert at top of `__main__.py`."},{"type":"toolCall","id":"toolu_01UYXGzfYaeMuXirnzzv74dj","name":"edit","arguments":{"newText":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport sys\n\n# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\nif _backend_abs not in sys.path:\n    sys.path.insert(0, _backend_abs)\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient","oldText":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":471,"cacheRead":76897,"cacheWrite":607,"totalTokens":77976,"cost":{"input":0.000005,"output":0.011775,"cacheRead":0.0384485,"cacheWrite":0.0037937500000000002,"total":0.054022249999999994}},"stopReason":"toolUse","timestamp":1775033962871}}
{"type":"message","id":"18a7a178","parentId":"78fb3652","timestamp":"2026-04-01T08:59:32.827Z","message":{"role":"toolResult","toolCallId":"toolu_01UYXGzfYaeMuXirnzzv74dj","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":"   9 from __future__ import annotations\n  10 \n  11 import argparse\n  12 import json\n+ 13 import os\n  14 import sys\n  15 \n+ 16 # Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n+ 17 # resolve when running from the project root via symlink.\n+ 18 _backend_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\")\n+ 19 _backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\n+ 20 if _backend_abs not in sys.path:\n+ 21     sys.path.insert(0, _backend_abs)\n+ 22 \n  23 from config import get_settings\n  24 from pipeline.llm_client import LLMClient\n  25 \n  26 from .fitness import FitnessRunner","firstChangedLine":13},"isError":false,"timestamp":1775033972824}}
{"type":"message","id":"5735bced","parentId":"18a7a178","timestamp":"2026-04-01T08:59:36.537Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CugywtfbUk14bY2m2FggSG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -m pipeline.quality score --help 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":77504,"cacheWrite":496,"totalTokens":78089,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.038751999999999995,"cacheWrite":0.0031000000000000003,"total":0.04405699999999999}},"stopReason":"toolUse","timestamp":1775033972825}}
{"type":"message","id":"61c80490","parentId":"5735bced","timestamp":"2026-04-01T08:59:36.645Z","message":{"role":"toolResult","toolCallId":"toolu_01CugywtfbUk14bY2m2FggSG","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<frozen runpy>\", line 198, in _run_module_as_main\n  File \"<frozen runpy>\", line 88, in _run_code\n  File \"/home/aux/projects/content-to-kb-automator/pipeline/quality/__main__.py\", line 23, in <module>\n    from config import get_settings\nImportError: cannot import name 'get_settings' from 'config' (unknown location)\nexit: 1\n"}],"isError":false,"timestamp":1775033976642}}
{"type":"message","id":"45560db8","parentId":"61c80490","timestamp":"2026-04-01T08:59:42.787Z","message":{"role":"assistant","content":[{"type":"text","text":"The symlink points to `backend/pipeline`, so `__file__` is `pipeline/quality/__main__.py` and `../..` resolves to the project root, not `backend/`. When Python follows the symlink, the `__file__` uses the symlink path, not the real path. Let me fix the path computation:"},{"type":"toolCall","id":"toolu_01L9MaaWH6onKX1RsfrZhGED","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -c \"\nimport os\nf = 'pipeline/quality/__main__.py'\nreal = os.path.realpath(f)\nprint('symlink path:', f)\nprint('real path:', real)\nprint('real ../../:', os.path.normpath(os.path.join(os.path.dirname(real), '..', '..')))\nprint('sym ../../:', os.path.normpath(os.path.join(os.path.dirname(f), '..', '..')))\n\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":262,"cacheRead":78000,"cacheWrite":220,"totalTokens":78483,"cost":{"input":0.000005,"output":0.00655,"cacheRead":0.039,"cacheWrite":0.0013750000000000001,"total":0.04693}},"stopReason":"toolUse","timestamp":1775033976643}}
{"type":"message","id":"229d9ec4","parentId":"45560db8","timestamp":"2026-04-01T08:59:42.812Z","message":{"role":"toolResult","toolCallId":"toolu_01L9MaaWH6onKX1RsfrZhGED","toolName":"bash","content":[{"type":"text","text":"symlink path: pipeline/quality/__main__.py\nreal path: /home/aux/projects/content-to-kb-automator/backend/pipeline/quality/__main__.py\nreal ../../: /home/aux/projects/content-to-kb-automator/backend\nsym ../../: .\n"}],"isError":false,"timestamp":1775033982811}}
{"type":"message","id":"6dac7b34","parentId":"229d9ec4","timestamp":"2026-04-01T08:59:47.453Z","message":{"role":"assistant","content":[{"type":"text","text":"Need to use `os.path.realpath(__file__)` to resolve through the symlink:"},{"type":"toolCall","id":"toolu_01L8KFxY352FeaZnpDBb57mu","name":"edit","arguments":{"newText":"# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\", \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))","oldText":"# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":294,"cacheRead":78220,"cacheWrite":343,"totalTokens":78858,"cost":{"input":0.000005,"output":0.007350000000000001,"cacheRead":0.03911,"cacheWrite":0.0021437500000000003,"total":0.04860875}},"stopReason":"toolUse","timestamp":1775033982811}}
{"type":"message","id":"adbb00fb","parentId":"6dac7b34","timestamp":"2026-04-01T08:59:47.462Z","message":{"role":"toolResult","toolCallId":"toolu_01L8KFxY352FeaZnpDBb57mu","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":"  14 import sys\n  15 \n  16 # Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n  17 # resolve when running from the project root via symlink.\n- 18 _backend_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\")\n+ 18 _backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\", \"..\")\n  19 _backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\n  20 if _backend_abs not in sys.path:\n  21     sys.path.insert(0, _backend_abs)\n  22 ","firstChangedLine":18},"isError":false,"timestamp":1775033987460}}
{"type":"message","id":"3986bd94","parentId":"adbb00fb","timestamp":"2026-04-01T08:59:50.252Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Kumjb2bdJZUkGcb6Nc83wM","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -m pipeline.quality score --help 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":78563,"cacheWrite":319,"totalTokens":78971,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.0392815,"cacheWrite":0.0019937500000000003,"total":0.04348025}},"stopReason":"toolUse","timestamp":1775033987461}}
{"type":"message","id":"8826f2c8","parentId":"3986bd94","timestamp":"2026-04-01T08:59:50.767Z","message":{"role":"toolResult","toolCallId":"toolu_01Kumjb2bdJZUkGcb6Nc83wM","toolName":"bash","content":[{"type":"text","text":"usage: pipeline.quality score [-h] (--file FILE | --slug SLUG)\n                              [--voice-level VOICE_LEVEL]\n\noptions:\n  -h, --help            show this help message and exit\n  --file FILE           Path to a moments JSON file (creator_name, moments\n                        array)\n  --slug SLUG           Technique slug to load from the database\n  --voice-level VOICE_LEVEL\n                        Voice preservation dial (0.0=clinical, 1.0=maximum\n                        voice). Triggers re-synthesis before scoring.\nexit: 0\n"}],"isError":false,"timestamp":1775033990765}}
{"type":"message","id":"eef31d5c","parentId":"8826f2c8","timestamp":"2026-04-01T08:59:57.652Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me run all the verification checks from both root and backend/:"},{"type":"toolCall","id":"toolu_01Vu2Xte34h3b6AcPFW27r3q","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && echo \"=== From project root ===\" && python -m pipeline.quality score --help 2>&1 && echo \"--- import check ---\" && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\" 2>&1 && echo \"--- voice dial ---\" && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9); print('bands ok')\" 2>&1 && echo \"--- fixture check ---\" && python -c \"import json; d=json.load(open('backend/pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5; print('fixture ok')\" 2>&1 && echo \"ALL PASS\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":311,"cacheRead":78882,"cacheWrite":237,"totalTokens":79431,"cost":{"input":0.000005,"output":0.007775000000000001,"cacheRead":0.039441,"cacheWrite":0.0014812500000000001,"total":0.04870225}},"stopReason":"toolUse","timestamp":1775033990765}}
{"type":"message","id":"3b3f15de","parentId":"eef31d5c","timestamp":"2026-04-01T08:59:58.637Z","message":{"role":"toolResult","toolCallId":"toolu_01Vu2Xte34h3b6AcPFW27r3q","toolName":"bash","content":[{"type":"text","text":"=== From project root ===\nusage: pipeline.quality score [-h] (--file FILE | --slug SLUG)\n                              [--voice-level VOICE_LEVEL]\n\noptions:\n  -h, --help            show this help message and exit\n  --file FILE           Path to a moments JSON file (creator_name, moments\n                        array)\n  --slug SLUG           Technique slug to load from the database\n  --voice-level VOICE_LEVEL\n                        Voice preservation dial (0.0=clinical, 1.0=maximum\n                        voice). Triggers re-synthesis before scoring.\n--- import check ---\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n  File \"/home/aux/projects/content-to-kb-automator/pipeline/quality/scorer.py\", line 23, in <module>\n    from pipeline.llm_client import LLMClient\n  File \"/home/aux/projects/content-to-kb-automator/pipeline/llm_client.py\", line 25, in <module>\n    from config import Settings\nImportError: cannot import name 'Settings' from 'config' (unknown location)\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775033998635}}
{"type":"message","id":"5c51f6de","parentId":"3b3f15de","timestamp":"2026-04-01T09:00:03.361Z","message":{"role":"assistant","content":[{"type":"text","text":"Direct import from root still fails because `scorer.py` also needs the path fix. The `__main__.py` path fix only runs when using `python -m`. For direct imports, the path isn't set yet.\n\nThe better approach: put the path fixup in `pipeline/quality/__init__.py` so it runs on any import of the package:"},{"type":"toolCall","id":"toolu_012m7JbJ8Uaz5jaDasnDcKJd","name":"read","arguments":{"path":"backend/pipeline/quality/__init__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":137,"cacheRead":79119,"cacheWrite":618,"totalTokens":79875,"cost":{"input":0.000005,"output":0.003425,"cacheRead":0.0395595,"cacheWrite":0.0038625,"total":0.046852}},"stopReason":"toolUse","timestamp":1775033998636}}
{"type":"message","id":"ef7f58df","parentId":"5c51f6de","timestamp":"2026-04-01T09:00:03.366Z","message":{"role":"toolResult","toolCallId":"toolu_012m7JbJ8Uaz5jaDasnDcKJd","toolName":"read","content":[{"type":"text","text":""}],"isError":false,"timestamp":1775034003364}}
{"type":"message","id":"555d9348","parentId":"ef7f58df","timestamp":"2026-04-01T09:00:07.504Z","message":{"role":"assistant","content":[{"type":"text","text":"Empty file. Let me add the path fixup there:"},{"type":"toolCall","id":"toolu_01WEsgdr5gJBkXMZCotRc8V6","name":"write","arguments":{"content":"\"\"\"FYN-LLM quality assurance toolkit.\"\"\"\n\nimport os\nimport sys\n\n# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\nif _backend_abs not in sys.path:\n    sys.path.insert(0, _backend_abs)\n","path":"backend/pipeline/quality/__init__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":227,"cacheRead":79737,"cacheWrite":163,"totalTokens":80128,"cost":{"input":0.000005,"output":0.005675,"cacheRead":0.0398685,"cacheWrite":0.0010187500000000001,"total":0.04656725}},"stopReason":"toolUse","timestamp":1775034003365}}
{"type":"message","id":"f1bfff61","parentId":"555d9348","timestamp":"2026-04-01T09:00:07.508Z","message":{"role":"toolResult","toolCallId":"toolu_01WEsgdr5gJBkXMZCotRc8V6","toolName":"write","content":[{"type":"text","text":"Successfully wrote 416 bytes to backend/pipeline/quality/__init__.py"}],"isError":false,"timestamp":1775034007506}}
{"type":"message","id":"f7a00b40","parentId":"f1bfff61","timestamp":"2026-04-01T09:00:12.153Z","message":{"role":"assistant","content":[{"type":"text","text":"Wait — the path needs to go up TWO levels from `quality/`: `quality/ -> pipeline/ -> backend/`. Let me fix:"},{"type":"toolCall","id":"toolu_01EiPqVnE4fvMufoxctXW1UE","name":"write","arguments":{"content":"\"\"\"FYN-LLM quality assurance toolkit.\"\"\"\n\nimport os\nimport sys\n\n# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\", \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\nif _backend_abs not in sys.path:\n    sys.path.insert(0, _backend_abs)\n","path":"backend/pipeline/quality/__init__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":247,"cacheRead":79900,"cacheWrite":255,"totalTokens":80403,"cost":{"input":0.000005,"output":0.006175,"cacheRead":0.03995,"cacheWrite":0.0015937500000000001,"total":0.047723749999999995}},"stopReason":"toolUse","timestamp":1775034007507}}
{"type":"message","id":"6d0debe8","parentId":"f7a00b40","timestamp":"2026-04-01T09:00:12.158Z","message":{"role":"toolResult","toolCallId":"toolu_01EiPqVnE4fvMufoxctXW1UE","toolName":"write","content":[{"type":"text","text":"Successfully wrote 422 bytes to backend/pipeline/quality/__init__.py"}],"isError":false,"timestamp":1775034012155}}
{"type":"message","id":"2b265a81","parentId":"6d0debe8","timestamp":"2026-04-01T09:00:17.573Z","message":{"role":"assistant","content":[{"type":"text","text":"Now remove the duplicate fixup from `__main__.py` since `__init__.py` runs first:"},{"type":"toolCall","id":"toolu_01UrhkaUZmoUenAaFgrSbGWF","name":"edit","arguments":{"newText":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient","oldText":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness  — Run LLM fitness tests across four categories\n  score    — Score a Stage 5 technique page across 5 quality dimensions\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport sys\n\n# Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n# resolve when running from the project root via symlink.\n_backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\", \"..\")\n_backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\nif _backend_abs not in sys.path:\n    sys.path.insert(0, _backend_abs)\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":449,"cacheRead":80155,"cacheWrite":275,"totalTokens":80880,"cost":{"input":0.000005,"output":0.011225,"cacheRead":0.040077499999999995,"cacheWrite":0.00171875,"total":0.05302624999999999}},"stopReason":"toolUse","timestamp":1775034012157}}
{"type":"message","id":"2df57122","parentId":"2b265a81","timestamp":"2026-04-01T09:00:17.578Z","message":{"role":"toolResult","toolCallId":"toolu_01UrhkaUZmoUenAaFgrSbGWF","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":"   9 from __future__ import annotations\n  10 \n  11 import argparse\n  12 import json\n- 13 import os\n  13 import sys\n  14 \n- 16 # Ensure backend/ is on sys.path so sibling modules (config, pipeline.llm_client)\n- 17 # resolve when running from the project root via symlink.\n- 18 _backend_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"..\", \"..\")\n- 19 _backend_abs = os.path.normpath(os.path.abspath(_backend_dir))\n- 20 if _backend_abs not in sys.path:\n- 21     sys.path.insert(0, _backend_abs)\n- 22 \n  15 from config import get_settings\n  16 from pipeline.llm_client import LLMClient\n  17 \n  18 from .fitness import FitnessRunner","firstChangedLine":13},"isError":false,"timestamp":1775034017577}}
{"type":"message","id":"72d61471","parentId":"2df57122","timestamp":"2026-04-01T09:00:22.023Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EfcgNS3zy9WyEj8qzSWUP1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && echo \"=== From project root ===\" && python -m pipeline.quality score --help 2>&1 && echo \"--- import scorer ---\" && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\" 2>&1 && echo \"--- import voice_dial ---\" && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9); print('bands ok')\" 2>&1 && echo \"--- fixture ---\" && python -c \"import json; d=json.load(open('backend/pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5; print('fixture ok')\" 2>&1 && echo \"ALL PASS\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":297,"cacheRead":80430,"cacheWrite":474,"totalTokens":81202,"cost":{"input":0.000005,"output":0.007425,"cacheRead":0.040215,"cacheWrite":0.0029625000000000003,"total":0.0506075}},"stopReason":"toolUse","timestamp":1775034017577}}
{"type":"message","id":"41c2ce3c","parentId":"72d61471","timestamp":"2026-04-01T09:00:23.043Z","message":{"role":"toolResult","toolCallId":"toolu_01EfcgNS3zy9WyEj8qzSWUP1","toolName":"bash","content":[{"type":"text","text":"=== From project root ===\nusage: pipeline.quality score [-h] (--file FILE | --slug SLUG)\n                              [--voice-level VOICE_LEVEL]\n\noptions:\n  -h, --help            show this help message and exit\n  --file FILE           Path to a moments JSON file (creator_name, moments\n                        array)\n  --slug SLUG           Technique slug to load from the database\n  --voice-level VOICE_LEVEL\n                        Voice preservation dial (0.0=clinical, 1.0=maximum\n                        voice). Triggers re-synthesis before scoring.\n--- import scorer ---\nimport ok\n--- import voice_dial ---\nbands ok\n--- fixture ---\nfixture ok\nALL PASS\n"}],"isError":false,"timestamp":1775034023041}}
{"type":"message","id":"1fa51f80","parentId":"41c2ce3c","timestamp":"2026-04-01T09:00:27.097Z","message":{"role":"assistant","content":[{"type":"text","text":"All checks pass from project root. Let me also verify it still works from `backend/`:"},{"type":"toolCall","id":"toolu_017PKkzZL2BeBn2xF7Wc3Y4V","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -m pipeline.quality score --help 2>&1 && echo \"--- import ---\" && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\" && echo \"BACKEND PASS\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":145,"cacheRead":80904,"cacheWrite":477,"totalTokens":81527,"cost":{"input":0.000005,"output":0.003625,"cacheRead":0.040451999999999995,"cacheWrite":0.00298125,"total":0.047063249999999994}},"stopReason":"toolUse","timestamp":1775034023042}}
{"type":"message","id":"fad06c43","parentId":"1fa51f80","timestamp":"2026-04-01T09:00:28.068Z","message":{"role":"toolResult","toolCallId":"toolu_017PKkzZL2BeBn2xF7Wc3Y4V","toolName":"bash","content":[{"type":"text","text":"usage: pipeline.quality score [-h] (--file FILE | --slug SLUG)\n                              [--voice-level VOICE_LEVEL]\n\noptions:\n  -h, --help            show this help message and exit\n  --file FILE           Path to a moments JSON file (creator_name, moments\n                        array)\n  --slug SLUG           Technique slug to load from the database\n  --voice-level VOICE_LEVEL\n                        Voice preservation dial (0.0=clinical, 1.0=maximum\n                        voice). Triggers re-synthesis before scoring.\n--- import ---\nimport ok\nBACKEND PASS\n"}],"isError":false,"timestamp":1775034028066}}
{"type":"message","id":"fc2a7969","parentId":"fad06c43","timestamp":"2026-04-01T09:00:31.959Z","message":{"role":"assistant","content":[{"type":"text","text":"Works from both locations. Now let me also confirm the connectivity error test (the score subcommand hitting LLM):"},{"type":"toolCall","id":"toolu_01Pbudc7XsJLxWgASffAfSZZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":127,"cacheRead":81381,"cacheWrite":301,"totalTokens":81810,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.0406905,"cacheWrite":0.0018812500000000001,"total":0.04575175}},"stopReason":"toolUse","timestamp":1775034028067}}
{"type":"message","id":"9069d8ce","parentId":"fc2a7969","timestamp":"2026-04-01T09:00:35.322Z","message":{"role":"toolResult","toolCallId":"toolu_01Pbudc7XsJLxWgASffAfSZZ","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\nScoring page for 'KOAN Sound' (6 moments)...\n\n============================================================\n  STAGE 5 QUALITY SCORE REPORT\n============================================================\n\n  ✗ Error: Cannot reach LLM endpoint at http://localhost:11434/v1 (fallback http://localhost:11434/v1). Error: Connection error.\n\n============================================================\n\nexit: 1\n"}],"isError":false,"timestamp":1775034035320}}
{"type":"message","id":"3036fcd2","parentId":"9069d8ce","timestamp":"2026-04-01T09:00:39.415Z","message":{"role":"assistant","content":[{"type":"text","text":"Clean error, exit 1, no traceback. Now let me verify the voice-level path too:"},{"type":"toolCall","id":"toolu_01BkfUKVtNxHoYGwH9BfTjiZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 0.3 2>&1; echo \"exit: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":132,"cacheRead":81682,"cacheWrite":277,"totalTokens":82092,"cost":{"input":0.000005,"output":0.0033,"cacheRead":0.040840999999999995,"cacheWrite":0.0017312500000000001,"total":0.045877249999999994}},"stopReason":"toolUse","timestamp":1775034035321}}
{"type":"message","id":"e972d2a8","parentId":"3036fcd2","timestamp":"2026-04-01T09:00:43.747Z","message":{"role":"toolResult","toolCallId":"toolu_01BkfUKVtNxHoYGwH9BfTjiZ","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\nRe-synthesizing + scoring for 'KOAN Sound' (6 moments, voice_level=0.3)...\n  Re-synthesizing at voice_level=0.3 (band=low)...\n\n============================================================\n  STAGE 5 QUALITY SCORE REPORT\n============================================================\n\n  ✗ Error: Cannot reach LLM endpoint at http://localhost:11434/v1 (fallback http://localhost:11434/v1). Error: Connection error.\n\n============================================================\n\nexit: 1\n"}],"isError":false,"timestamp":1775034043744}}
{"type":"message","id":"a101d391","parentId":"e972d2a8","timestamp":"2026-04-01T09:00:48.019Z","message":{"role":"assistant","content":[{"type":"text","text":"All passing. Now let me check if the symlink is tracked in git:"},{"type":"toolCall","id":"toolu_01FwHr5s1SjWZsbcXuurHrYp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && git status --short pipeline 2>/dev/null; echo \"---\"; ls -la pipeline"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":81959,"cacheWrite":314,"totalTokens":82379,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.040979499999999995,"cacheWrite":0.0019625000000000003,"total":0.045571999999999994}},"stopReason":"toolUse","timestamp":1775034043745}}
{"type":"message","id":"11dc20e6","parentId":"a101d391","timestamp":"2026-04-01T09:00:48.041Z","message":{"role":"toolResult","toolCallId":"toolu_01FwHr5s1SjWZsbcXuurHrYp","toolName":"bash","content":[{"type":"text","text":"?? pipeline\n---\nlrwxrwxrwx 1 aux aux 16 Apr  1 08:59 pipeline -> backend/pipeline\n"}],"isError":false,"timestamp":1775034048038}}
{"type":"message","id":"50165091","parentId":"11dc20e6","timestamp":"2026-04-01T09:00:51.650Z","message":{"role":"assistant","content":[{"type":"text","text":"The symlink is untracked. It needs to be committed so the verification gate works. Let me check `.gitignore`:"},{"type":"toolCall","id":"toolu_019r6rTct6pM9nU2Y5RjnUqC","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gitignore 2>/dev/null | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":82273,"cacheWrite":155,"totalTokens":82540,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.0411365,"cacheWrite":0.00096875,"total":0.044885249999999995}},"stopReason":"toolUse","timestamp":1775034048039}}
{"type":"message","id":"219d59b7","parentId":"50165091","timestamp":"2026-04-01T09:00:51.659Z","message":{"role":"toolResult","toolCallId":"toolu_019r6rTct6pM9nU2Y5RjnUqC","toolName":"bash","content":[{"type":"text","text":".bg-shell/\n.gsd/gsd.db\n.gsd/gsd.db-shm\n.gsd/gsd.db-wal\n.gsd/event-log.jsonl\n.gsd/state-manifest.json\n\n# ── GSD baseline (auto-generated) ──\n.DS_Store\nThumbs.db\n*.swp\n*.swo\n*~\n.idea/\n.vscode/\n*.code-workspace\n.env\n.env.*\n!.env.example\nnode_modules/\n.next/\ndist/\nbuild/\n__pycache__/\n*.pyc\n.venv/\nvenv/\ntarget/\nvendor/\n*.log\n"}],"isError":false,"timestamp":1775034051658}}
{"type":"message","id":"d36b65ec","parentId":"219d59b7","timestamp":"2026-04-01T09:00:56.996Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — `pipeline` isn't gitignored. It'll be picked up by the system's automatic commit.\n\nNow let me complete the slice. Let me quickly read the task summaries to compile the slice summary:"},{"type":"toolCall","id":"toolu_01DgzDczwGdFVhWTnD4Dx5HP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M013/slices/S02/tasks/T02-SUMMARY.md 2>/dev/null | tail -40"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":151,"cacheRead":82428,"cacheWrite":289,"totalTokens":82869,"cost":{"input":0.000005,"output":0.003775,"cacheRead":0.041214,"cacheWrite":0.0018062500000000001,"total":0.04680025}},"stopReason":"toolUse","timestamp":1775034051659}}
{"type":"message","id":"66872b19","parentId":"d36b65ec","timestamp":"2026-04-01T09:00:57.016Z","message":{"role":"toolResult","toolCallId":"toolu_01DgzDczwGdFVhWTnD4Dx5HP","toolName":"bash","content":[{"type":"text","text":"\nCreated voice_dial.py with VoiceDial class implementing three discrete bands (low/mid/high) that modify the Stage 5 synthesis prompt. Low band appends voice suppression instructions, mid band passes through unmodified, high band appends voice amplification instructions. Added synthesize_and_score() to ScoreRunner that loads the stage5 prompt, applies VoiceDial, calls LLM for re-synthesis, then scores the result. Updated CLI to route --voice-level through the re-synthesis path.\n\n## Verification\n\nAll 7 verification checks pass: imports for scorer and voice_dial, --help shows all args, standard score gives connectivity error at exit 1, fixture validates, voice dial produces three distinct bands, voice-level CLI exits cleanly at exit 1 with no traceback.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('import ok')\"` | 0 | ✅ pass | 500ms |\n| 2 | `cd backend && python -m pipeline.quality score --help` | 0 | ✅ pass | 500ms |\n| 3 | `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` | 1 | ✅ pass | 2000ms |\n| 4 | `cd backend && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and len(d['moments']) >= 5\"` | 0 | ✅ pass | 200ms |\n| 5 | `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; print('import ok')\"` | 0 | ✅ pass | 200ms |\n| 6 | `cd backend && python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9); print('bands ok')\"` | 0 | ✅ pass | 200ms |\n| 7 | `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` | 1 | ✅ pass | 500ms |\n\n\n## Deviations\n\nVoice-level path exits with prompt-not-found instead of connectivity error because prompts/ resolves relative to CWD and isn't under backend/. This is correct runtime behavior.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/voice_dial.py`\n- `backend/pipeline/quality/scorer.py`\n- `backend/pipeline/quality/__main__.py`\n\n\n## Deviations\nVoice-level path exits with prompt-not-found instead of connectivity error because prompts/ resolves relative to CWD and isn't under backend/. This is correct runtime behavior.\n\n## Known Issues\nNone.\n"}],"isError":false,"timestamp":1775034057012}}
{"type":"message","id":"7a86d79d","parentId":"66872b19","timestamp":"2026-04-01T09:01:48.177Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have all the context. Let me complete the slice."},{"type":"toolCall","id":"toolu_01LS9Kw96H2x3VbWhFfpYPBn","name":"gsd_complete_slice","arguments":{"affects":["S03"],"deviations":"Added project-root `pipeline` symlink and `sys.path` bootstrap in `__init__.py` to support running `python -m pipeline.quality` from the project root (not just from `backend/`). This was not in the original plan but required by the verification gate.","drillDownPaths":[".gsd/milestones/M013/slices/S02/tasks/T01-SUMMARY.md",".gsd/milestones/M013/slices/S02/tasks/T02-SUMMARY.md"],"filesModified":[{"description":"New: ScoreResult dataclass + ScoreRunner with score_page() and synthesize_and_score() methods","path":"backend/pipeline/quality/scorer.py"},{"description":"New: VoiceDial class with 3-band prompt modification (low/mid/high)","path":"backend/pipeline/quality/voice_dial.py"},{"description":"Added score subcommand with --file, --slug, --voice-level args","path":"backend/pipeline/quality/__main__.py"},{"description":"Added sys.path bootstrap for project-root execution","path":"backend/pipeline/quality/__init__.py"},{"description":"New: 6-moment fixture with realistic music production content","path":"backend/pipeline/quality/fixtures/sample_moments.json"},{"description":"New: empty package marker","path":"backend/pipeline/quality/fixtures/__init__.py"},{"description":"New: symlink to backend/pipeline for project-root execution","path":"pipeline"}],"followUps":"None.","keyDecisions":["Hardcoded scoring rubric in scorer.py rather than external prompt file — faster iteration during quality toolkit development","Three discrete voice bands (low/mid/high) at 0.33/0.67 boundaries instead of continuous interpolation — simpler to reason about and test","Mid band returns base prompt unmodified since existing stage5 prompt already targets ~0.6 voice preservation","Project-root symlink + __init__.py sys.path bootstrap to support running from any CWD"],"keyFiles":["backend/pipeline/quality/scorer.py","backend/pipeline/quality/voice_dial.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/__init__.py","backend/pipeline/quality/fixtures/sample_moments.json","pipeline"],"knownLimitations":"`--slug` path prints \"DB loading not yet implemented\" and exits 1 (deferred by design). Voice-level path requires `prompts/stage5_synthesis.txt` to be reachable from CWD — works in Docker (WORKDIR=/app) and from `backend/`, but from project root the prompt file path resolves differently.","milestoneId":"M013","narrative":"T01 created the scoring infrastructure: `ScoreRunner` with a `ScoreResult` dataclass covering 5 quality dimensions (structural, content_specificity, voice_preservation, readability, factual_fidelity), each scored 0.0–1.0 by an LLM judge via a hardcoded rubric prompt. The `score` subcommand was added to the existing `pipeline.quality` CLI with mutually exclusive `--file`/`--slug` inputs and an optional `--voice-level` parameter. A 6-moment fixture file (`sample_moments.json`) with realistic music production content provides offline testing data.\n\nT02 added the `VoiceDial` class implementing three discrete bands (low ≤0.33, mid 0.34–0.66, high ≥0.67) that modify the Stage 5 synthesis system prompt. Low band appends voice suppression instructions (neutral third-person, no direct quotes). Mid band passes the base prompt through unmodified (it already targets ~0.6 voice preservation). High band appends amplification instructions (maximize direct quotes, preserve personality). `ScoreRunner.synthesize_and_score()` chains re-synthesis through the modified prompt followed by scoring.\n\nPost-task fix: The verification gate ran `python -m pipeline.quality score --help` from the project root, but the `pipeline` package lives under `backend/`. Added a `pipeline` symlink at project root pointing to `backend/pipeline`, and added a `sys.path` bootstrap in `backend/pipeline/quality/__init__.py` that resolves through the symlink to add `backend/` to `sys.path`. This ensures `config` and `pipeline.llm_client` imports resolve regardless of CWD.","observabilitySurfaces":["Score report output with per-dimension scores and justification excerpts","Band identification in voice-level output (band=low/mid/high)","Clean connectivity error with endpoint URL on LLM failure"],"oneLiner":"Built 5-dimension LLM-as-judge scorer with CLI `score` subcommand and 3-band voice preservation dial that modifies Stage 5 synthesis prompts, runnable from both project root and backend/ directory.","patternsEstablished":["sys.path bootstrap in package __init__.py using os.path.realpath() to resolve through symlinks","VoiceDial band-based prompt modification pattern reusable for other prompt dimension dials"],"provides":["ScoreRunner with score_page() and synthesize_and_score() methods","VoiceDial class for 3-band prompt modification","CLI score subcommand with --file, --slug, --voice-level","6-moment fixture file for offline testing"],"requirementsAdvanced":[],"requirementsInvalidated":[],"requirementsSurfaced":[],"requirementsValidated":[],"requires":[],"sliceId":"S02","sliceTitle":"Stage 5 Quality Scorer & Voice Preservation Dial","uatContent":"## UAT: Stage 5 Quality Scorer & Voice Preservation Dial\n\n### Preconditions\n- Python 3.12+ available\n- Working directory: project root (`/home/aux/projects/content-to-kb-automator`)\n- No live LLM endpoint required (tests verify error handling)\n\n### Test Case 1: CLI Help Output\n**Steps:**\n1. Run `python -m pipeline.quality score --help`\n\n**Expected:**\n- Exit code 0\n- Output shows three arguments: `--file`, `--slug`, `--voice-level`\n- `--file` and `--slug` shown as mutually exclusive (required group)\n- `--voice-level` shown as optional with float type\n\n### Test Case 2: Score with File Input (No LLM)\n**Steps:**\n1. Run `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json`\n\n**Expected:**\n- Exit code 1 (no LLM available)\n- Output includes \"Cannot reach LLM endpoint\" with the endpoint URL\n- No Python traceback in output\n- Shows \"Scoring page for 'KOAN Sound' (6 moments)...\"\n\n### Test Case 3: Score with Voice Level (No LLM)\n**Steps:**\n1. Run `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 0.3`\n\n**Expected:**\n- Exit code 1 (no LLM available)\n- Output includes \"Re-synthesizing + scoring\" and \"voice_level=0.3\"\n- Output includes \"band=low\" (0.3 falls in low band ≤0.33)\n- No Python traceback\n\n### Test Case 4: Voice Dial Band Boundaries\n**Steps:**\n1. Run:\n```python\nfrom pipeline.quality.voice_dial import VoiceDial\nvd = VoiceDial(\"base prompt text\")\nlow = vd.modify(0.1)\nmid = vd.modify(0.5)\nhigh = vd.modify(0.9)\n```\n\n**Expected:**\n- `low != mid` — low band appends voice suppression instructions\n- `mid != high` — high band appends voice amplification instructions\n- `mid == \"base prompt text\"` — mid band returns base prompt unmodified\n- `low` contains words like \"suppress\", \"neutral\", or \"third-person\"\n- `high` contains words like \"quote\", \"direct\", or \"personality\"\n\n### Test Case 5: Invalid Voice Level\n**Steps:**\n1. Run `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 1.5`\n\n**Expected:**\n- Exit code 1\n- Error message: \"--voice-level must be between 0.0 and 1.0\"\n\n### Test Case 6: Missing Required Input\n**Steps:**\n1. Run `python -m pipeline.quality score`\n\n**Expected:**\n- Exit code 2 (argparse error)\n- Error message about requiring --file or --slug\n\n### Test Case 7: Slug Path (Deferred)\n**Steps:**\n1. Run `python -m pipeline.quality score --slug test-technique`\n\n**Expected:**\n- Exit code 1\n- Output: \"DB loading not yet implemented\"\n\n### Test Case 8: Fixture File Validity\n**Steps:**\n1. Run:\n```python\nimport json\nwith open(\"backend/pipeline/quality/fixtures/sample_moments.json\") as f:\n    data = json.load(f)\n```\n\n**Expected:**\n- `data[\"creator_name\"]` is a non-empty string\n- `data[\"moments\"]` is a list with ≥5 entries\n- Each moment has: `summary`, `transcript_excerpt`, `topic_tags`, `topic_category`\n- At least one moment contains specific plugin/setting mentions in transcript_excerpt\n\n### Test Case 9: Import from Project Root\n**Steps:**\n1. From project root, run:\n```python\nfrom pipeline.quality.scorer import ScoreRunner, ScoreResult\nfrom pipeline.quality.voice_dial import VoiceDial\n```\n\n**Expected:**\n- Both imports succeed without error\n- Works from project root (not just backend/)","verification":"All slice verification checks pass from both project root and backend/ directory:\n\n1. `python -m pipeline.quality score --help` — shows --file, --slug, --voice-level args (exit 0)\n2. `python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('ok')\"` — import succeeds (exit 0)\n3. `python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9)\"` — three distinct bands confirmed (exit 0)\n4. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json` — clean connectivity error, exit 1, no traceback\n5. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — re-synthesis path, clean connectivity error, exit 1, no traceback\n6. Fixture validation: 6 moments with required fields present"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2993,"cacheRead":82717,"cacheWrite":940,"totalTokens":86651,"cost":{"input":0.000005,"output":0.074825,"cacheRead":0.0413585,"cacheWrite":0.005875,"total":0.1220635}},"stopReason":"toolUse","timestamp":1775034057014}}
{"type":"message","id":"a045a528","parentId":"7a86d79d","timestamp":"2026-04-01T09:01:48.206Z","message":{"role":"toolResult","toolCallId":"toolu_01LS9Kw96H2x3VbWhFfpYPBn","toolName":"gsd_complete_slice","content":[{"type":"text","text":"Completed slice S02 (M013)"}],"details":{"operation":"complete_slice","sliceId":"S02","milestoneId":"M013","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-SUMMARY.md","uatPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-UAT.md"},"isError":false,"timestamp":1775034108204}}
{"type":"message","id":"9a8b16b5","parentId":"a045a528","timestamp":"2026-04-01T09:01:51.954Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me add the knowledge entry about the symlink pattern:"},{"type":"toolCall","id":"toolu_01Fnshhg5VmytXAYyLadsqzH","name":"read","arguments":{"limit":5,"offset":1,"path":".gsd/KNOWLEDGE.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":106,"cacheRead":83657,"cacheWrite":3022,"totalTokens":86786,"cost":{"input":0.000005,"output":0.00265,"cacheRead":0.0418285,"cacheWrite":0.0188875,"total":0.063371}},"stopReason":"toolUse","timestamp":1775034108205}}
{"type":"message","id":"2cc020d9","parentId":"9a8b16b5","timestamp":"2026-04-01T09:01:51.960Z","message":{"role":"toolResult","toolCallId":"toolu_01Fnshhg5VmytXAYyLadsqzH","toolName":"read","content":[{"type":"text","text":"# KNOWLEDGE\n\n## Healthcheck in slim Python Docker images: use os.kill(1,0) not pgrep\n\n**Context:** The `python:3.x-slim` Docker image doesn't include `procps`, so `pgrep -f watcher.py` fails in healthcheck commands. This applies to any slim-based image where a healthcheck needs to verify the main process is alive.\n\n[220 more lines in file. Use offset=6 to continue.]"}],"isError":false,"timestamp":1775034111958}}
{"type":"message","id":"fdf61f13","parentId":"2cc020d9","timestamp":"2026-04-01T09:01:55.086Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013tvjA6Q9vXwBjadZmk2JMN","name":"bash","arguments":{"command":"tail -5 /home/aux/projects/content-to-kb-automator/.gsd/KNOWLEDGE.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":78,"cacheRead":86679,"cacheWrite":223,"totalTokens":86981,"cost":{"input":0.000005,"output":0.0019500000000000001,"cacheRead":0.043339499999999996,"cacheWrite":0.00139375,"total":0.046688249999999994}},"stopReason":"toolUse","timestamp":1775034111959}}
{"type":"message","id":"c98efdc4","parentId":"fdf61f13","timestamp":"2026-04-01T09:01:55.096Z","message":{"role":"toolResult","toolCallId":"toolu_013tvjA6Q9vXwBjadZmk2JMN","toolName":"bash","content":[{"type":"text","text":"## Multi-token AND search with partial_matches fallback\n\n**Context:** Single-pattern ILIKE search (e.g., `%keota snare%`) only matches when the exact phrase appears in one field. Users expect \"keota snare\" to find content where \"keota\" matches the creator and \"snare\" matches the title/tags.\n\n**Fix:** Tokenize by whitespace. For each token, generate OR conditions across all searchable fields (title, summary, tags, category, creator name). AND all per-token conditions. When a multi-token AND query returns zero results, fall back to partial_matches: query each token separately, score results by token coverage, return top 5 with a muted UI treatment. This gives exact matches priority while still providing useful results for imprecise queries.\n"}],"isError":false,"timestamp":1775034115095}}
{"type":"message","id":"cc5c1cf8","parentId":"c98efdc4","timestamp":"2026-04-01T09:02:03.747Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EwMb5XTtfMvatbooENaYFn","name":"edit","arguments":{"newText":"**Fix:** Tokenize by whitespace. For each token, generate OR conditions across all searchable fields (title, summary, tags, category, creator name). AND all per-token conditions. When a multi-token AND query returns zero results, fall back to partial_matches: query each token separately, score results by token coverage, return top 5 with a muted UI treatment. This gives exact matches priority while still providing useful results for imprecise queries.\n\n## Project-root symlink + sys.path bootstrap for nested Python packages\n\n**Context:** When a Python package lives under a subdirectory (e.g., `backend/pipeline/`), `python -m pipeline.quality` fails from the project root because `pipeline` isn't on `sys.path`. Task executors worked around this with `cd backend &&` prefix, but CI/verification gates may run from project root.\n\n**Fix:** Create a symlink at project root (`pipeline -> backend/pipeline`) so Python finds the package. Add a `sys.path` bootstrap in the package's `__init__.py` that uses `os.path.realpath(__file__)` to resolve through the symlink and insert the real parent directory (`backend/`) onto `sys.path`. This ensures sibling imports (e.g., `from config import ...`) resolve correctly. The `realpath()` call is critical — without it, the path resolves relative to the symlink location, not the real file location.","oldText":"**Fix:** Tokenize by whitespace. For each token, generate OR conditions across all searchable fields (title, summary, tags, category, creator name). AND all per-token conditions. When a multi-token AND query returns zero results, fall back to partial_matches: query each token separately, score results by token coverage, return top 5 with a muted UI treatment. This gives exact matches priority while still providing useful results for imprecise queries.","path":".gsd/KNOWLEDGE.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":504,"cacheRead":86902,"cacheWrite":267,"totalTokens":87674,"cost":{"input":0.000005,"output":0.0126,"cacheRead":0.043451,"cacheWrite":0.00166875,"total":0.05772474999999999}},"stopReason":"toolUse","timestamp":1775034115095}}
{"type":"message","id":"59b98ae3","parentId":"cc5c1cf8","timestamp":"2026-04-01T09:02:03.755Z","message":{"role":"toolResult","toolCallId":"toolu_01EwMb5XTtfMvatbooENaYFn","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in .gsd/KNOWLEDGE.md."}],"details":{"diff":" 221 \n 222 **Context:** Single-pattern ILIKE search (e.g., `%keota snare%`) only matches when the exact phrase appears in one field. Users expect \"keota snare\" to find content where \"keota\" matches the creator and \"snare\" matches the title/tags.\n 223 \n 224 **Fix:** Tokenize by whitespace. For each token, generate OR conditions across all searchable fields (title, summary, tags, category, creator name). AND all per-token conditions. When a multi-token AND query returns zero results, fall back to partial_matches: query each token separately, score results by token coverage, return top 5 with a muted UI treatment. This gives exact matches priority while still providing useful results for imprecise queries.\n+225 \n+226 ## Project-root symlink + sys.path bootstrap for nested Python packages\n+227 \n+228 **Context:** When a Python package lives under a subdirectory (e.g., `backend/pipeline/`), `python -m pipeline.quality` fails from the project root because `pipeline` isn't on `sys.path`. Task executors worked around this with `cd backend &&` prefix, but CI/verification gates may run from project root.\n+229 \n+230 **Fix:** Create a symlink at project root (`pipeline -> backend/pipeline`) so Python finds the package. Add a `sys.path` bootstrap in the package's `__init__.py` that uses `os.path.realpath(__file__)` to resolve through the symlink and insert the real parent directory (`backend/`) onto `sys.path`. This ensures sibling imports (e.g., `from config import ...`) resolve correctly. The `realpath()` call is critical — without it, the path resolves relative to the symlink location, not the real file location.","firstChangedLine":225},"isError":false,"timestamp":1775034123753}}
{"type":"message","id":"4e1fa70d","parentId":"59b98ae3","timestamp":"2026-04-01T09:02:09.345Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S02 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10,"cacheRead":87169,"cacheWrite":528,"totalTokens":87708,"cost":{"input":0.000005,"output":0.00025,"cacheRead":0.0435845,"cacheWrite":0.0033,"total":0.047139499999999994}},"stopReason":"stop","timestamp":1775034123754}}
