{"type":"thinking_level_change","id":"dd9d6b50","parentId":null,"timestamp":"2026-04-01T08:48:46.657Z","thinkingLevel":"medium"}
{"type":"model_change","id":"f1e771df","parentId":"dd9d6b50","timestamp":"2026-04-01T08:48:46.658Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-PLAN.md` and `.gsd/milestones/M013/slices/S02/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S02 planned.\"\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S02 (\"Stage 5 Quality Scorer & Voice Preservation Dial\") — Milestone M013\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M013/M013-ROADMAP.md`\n\n# M013: \n\n## Vision\nA fully automated CLI tool that tests FYN-LLM fitness, scores pipeline output across structural, content, voice preservation, readability, and preference dimensions, generates prompt variants, and runs unattended A/B optimization loops — producing reports with winning prompts and score trajectories. Starting with stage 5 synthesis (highest user-visible impact), expanding to all pipeline stages.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | General FYN-LLM Fitness Suite | medium | — | ✅ | Run `python -m pipeline.quality fitness` — outputs pass/fail for Mandelbrot question, JSON compliance, instruction following, and diverse prompt battery against live FYN-LLM |\n| S02 | Stage 5 Quality Scorer & Voice Preservation Dial | high | S01 | ⬜ | Run scorer on a reference article — outputs composite score across 5 dimensions. Run same article at voice_level 0.2 vs 0.8 — voice preservation score differs meaningfully |\n| S03 | Prompt Variant Generator & Automated A/B Loop | high | S02 | ⬜ | Run `python -m pipeline.quality optimize --stage 5 --iterations 10` — generates prompt variants, scores each against reference articles, outputs leaderboard and score trajectory chart |\n| S04 | Expand to Pipeline Stages 2-4 | medium | S03 | ⬜ | Run `python -m pipeline.quality optimize --stage 3 --iterations 5` — optimizes extraction prompts with stage-appropriate scoring |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M013/slices/S02/S02-RESEARCH.md`\n\n# S02 Research: Stage 5 Quality Scorer & Voice Preservation Dial\n\n## Summary\n\nThis slice adds a `score` subcommand to the existing `pipeline.quality` CLI that evaluates Stage 5 synthesis output (technique pages) across 5 dimensions, plus a `voice_level` parameter that modifies the stage 5 prompt to dial voice preservation up or down — with the scorer proving the dial works by producing measurably different voice preservation scores.\n\nThe work is **targeted research** — known patterns (LLM-as-judge scoring, prompt interpolation), applied to well-understood codebase structures.\n\n## Recommendation\n\nBuild a `scorer.py` module in `backend/pipeline/quality/` alongside the existing `fitness.py`. The scorer:\n\n1. Takes a reference technique page (either from DB by slug, or from a JSON file) and scores it across 5 dimensions using LLM-as-judge evaluation.\n2. Optionally re-synthesizes the page with a modified prompt (voice_level dial injected into the stage 5 system prompt) before scoring.\n3. Outputs a structured report with per-dimension scores and a composite.\n\nThe voice_level dial modifies the stage 5 synthesis prompt by interpolating a voice-emphasis instruction. At 0.0 (\"clinical\"), the prompt suppresses direct quotes and creator voice. At 1.0 (\"maximum voice\"), it amplifies quote preservation, creator opinions, and personality. The existing prompt (stage5_synthesis.txt) already has strong voice preservation language — that's roughly voice_level 0.6-0.7 baseline.\n\n## Implementation Landscape\n\n### Existing code to build on\n\n| File | What it provides | How S02 uses it |\n|------|-----------------|-----------------|\n| `backend/pipeline/quality/__main__.py` | argparse CLI with subcommand pattern | Add `score` subcommand with `--slug`, `--file`, `--voice-level` args |\n| `backend/pipeline/quality/fitness.py` | `TestResult` dataclass, `FitnessRunner` pattern | Follow same pattern: `ScoreRunner` class, `ScoreResult` dataclass |\n| `backend/pipeline/stages.py` → `_synthesize_chunk()` | Calls LLM with stage5 prompt + moments | Scorer can reuse `_load_prompt()`, `_get_llm_client()`, `_get_stage_config()` |\n| `backend/pipeline/schemas.py` → `SynthesizedPage`, `SynthesisResult` | Pydantic models for stage 5 output | Scorer validates re-synthesized output against these |\n| `backend/config.py` → `Settings` | All LLM endpoint config | Scorer uses same LLM client config |\n| `prompts/stage5_synthesis.txt` | The full synthesis prompt (~4KB) | Base prompt for voice_level interpolation |\n| `backend/pipeline/llm_client.py` → `LLMClient.complete()` | LLM call with fallback, modality support | Used for both re-synthesis and scoring judge calls |\n\n### The 5 scoring dimensions\n\nBased on the stage 5 prompt's quality guidelines, these are the natural scoring dimensions:\n\n1. **Structural quality** — Does the page have well-named sections (not generic \"Overview\"/\"Tips\"), appropriate section count, 2-5 paragraphs per section, proper body_sections structure?\n2. **Content specificity** — Does the prose contain concrete details (frequencies, ratios, ms values, plugin settings) vs vague generalities? Ratio of specific claims to filler sentences.\n3. **Voice preservation** — Does the page preserve the creator's actual words, opinions, warnings? Presence of direct quotes, attributed opinions, personality.\n4. **Readability / flow** — Does the page read as synthesis (not concatenation)? Logical section ordering, merged related info, no redundancy, contradiction handling.\n5. **Factual fidelity** — Does the page avoid inventing information not in the source moments? No phantom specifics, no hallucinated plugin names or settings.\n\nEach dimension scored 0.0–1.0 by the LLM judge. Composite = weighted average (weights configurable, default equal).\n\n### Voice level dial implementation\n\nThe dial works by interpolating a voice-emphasis modifier into the stage 5 system prompt before the LLM call. This is NOT a new prompt file — it's a runtime prefix/suffix injected into the existing prompt.\n\n**voice_level 0.0 (\"clinical\"):** Append instruction to suppress direct quotes, write in neutral third-person encyclopedia style, avoid attributing opinions.\n\n**voice_level 0.5 (\"balanced\"):** No modification — use the base prompt as-is. The existing prompt already asks for moderate voice preservation.\n\n**voice_level 1.0 (\"maximum voice\"):** Append instruction to maximize direct quotes from transcript, preserve every memorable phrase, prioritize creator's exact words over paraphrase, include personality and strong opinions even if tangential.\n\nLinear interpolation between these anchor points selects which modifier text to inject and how strongly. In practice, 3 bands (low/mid/high) with blended instructions is simpler and more reliable than continuous interpolation with an LLM.\n\n### Scoring approach: LLM-as-judge\n\nThe scorer sends the technique page JSON + the source moments to the LLM with a scoring rubric prompt. The rubric prompt is a new file (`prompts/scoring_rubric.txt` or hardcoded in scorer.py — hardcoded is simpler for iteration, can extract later).\n\nThe judge LLM call returns a JSON object with per-dimension scores and brief justifications. Using the same LLM that generated the content is acceptable for relative scoring (comparing voice_level 0.2 vs 0.8 on the same content) even though it's not ideal for absolute quality assessment.\n\n### Reference article sourcing\n\nThe scorer needs a technique page to evaluate. Two modes:\n- `--slug <slug>`: Load from DB (requires DB connection). Loads the TechniquePage + its KeyMoments + classification data.\n- `--file <path>`: Load from a JSON file (no DB needed). The file contains the synthesized page JSON + the source moments array. This mode is useful for testing without a running stack.\n\nFor the voice_level comparison demo, the scorer needs to re-synthesize from source moments, not just score an existing page. So the flow is:\n1. Load reference moments (from DB or file)\n2. Synthesize at voice_level X using modified prompt\n3. Score the result\n4. Repeat at voice_level Y\n5. Compare scores\n\n### Data needed for reference test\n\nThe scorer needs at least one set of real key moments to re-synthesize. Options:\n- Connect to the production DB on ub01 and pull a technique page + its moments\n- Create a fixture JSON file with sample moments for offline testing\n\nA fixture file is better for the CLI demo — no DB dependency. The fixture should contain a realistic set of 5-8 moments with actual transcript excerpts, plugin mentions, and classification data.\n\n### File structure after S02\n\n```\nbackend/pipeline/quality/\n  __init__.py          (existing)\n  __main__.py          (modified — add 'score' subcommand)\n  fitness.py           (existing, untouched)\n  scorer.py            (NEW — ScoreRunner, ScoreResult, scoring logic)\n  voice_dial.py        (NEW — voice level prompt modifier)\n  fixtures/            (NEW — sample moments JSON for offline testing)\n    sample_moments.json\n```\n\n### Natural task seams\n\n1. **T01: ScoreResult dataclass + scoring rubric + ScoreRunner skeleton** — Define the 5-dimension scoring schema, write the LLM judge rubric prompt, implement `ScoreRunner.score_page()` that sends a page to the judge and returns structured scores. Wire into CLI as `score` subcommand with `--slug` and `--file` args.\n\n2. **T02: Voice dial implementation + re-synthesis flow** — Implement `voice_dial.py` with prompt modifier logic (3 bands). Add `--voice-level` arg to the `score` subcommand. When voice_level is provided, re-synthesize the page from source moments using the modified prompt before scoring. Build the sample fixture file.\n\n3. **T03: Integration verification + report formatting** — End-to-end test: score a reference article, run at voice_level 0.2 vs 0.8, verify scores differ meaningfully on the voice preservation dimension. Format the output report (similar to fitness report style). Verify CLI help text.\n\n### Risks\n\n| Risk | Likelihood | Mitigation |\n|------|-----------|------------|\n| LLM judge scores don't differentiate voice levels | Medium | The scoring rubric must be specific about what constitutes voice preservation (direct quotes present, attributed opinions, personality markers). If the judge is too coarse, sharpen the rubric with examples. |\n| No live LLM endpoint accessible from dev machine | High | Same as S01 — we can build and verify structure/imports locally, but actual scoring requires FYN-LLM. The fixture file allows verifying the data flow without a live endpoint. |\n| Re-synthesis is expensive (full stage 5 LLM call per voice level) | Low | Expected — this is a quality tool, not a hot path. Each run takes ~30-60s per synthesis. Acceptable for a CLI benchmarking tool. |\n\n### Skills check\n\nNo external skills needed. This is pure Python (dataclasses, Pydantic, argparse) using the existing LLM client. No new libraries required.\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n| D021 | M011 | scope | Which UI/UX assessment findings to implement in M011 | 12 of 16 findings approved; F01 (beginner paths), F02 (YouTube links), F03 (hide admin), F15 (CTA label) denied | User triaged each finding individually. Denied F01 because audience knows what they want. Denied F02 because no video URLs / don't want to link out. Denied F03 because admin dropdown is fine as-is. Denied F15 as low-value. | Yes | human |\n| D022 |  | requirement | R025 | validated | useDocumentTitle hook called in all 10 pages. Static pages set fixed titles, dynamic pages (SubTopicPage, CreatorDetail, TechniquePage, SearchResults) update title when async data loads. | Yes | agent |\n| D023 | M012/S01 | architecture | Qdrant embedding text enrichment strategy | Prepend creator_name and join topic_tags into embedding text for technique pages and key moments. Batch-resolve creator names at stage 6 start. | Semantic search now surfaces results for creator-name queries and tag-specific queries. Batch resolution avoids N+1 lookups during embedding. Reindex-all endpoint enables one-shot re-embedding after text composition changes. | Yes | agent |\n\n---\n\n[...truncated 5 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n#### S01 Summary\nSource: `.gsd/milestones/M013/slices/S01/S01-SUMMARY.md`\n\n---\nid: S01\nparent: M013\nmilestone: M013\nprovides:\n  - pipeline.quality package with FitnessRunner and argparse CLI entry point\n  - TestResult dataclass for structured test results\nrequires:\n  []\naffects:\n  - S02\n  - S03\nkey_files:\n  - backend/pipeline/quality/__init__.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/fitness.py\nkey_decisions:\n  - Connectivity probe as first action in run_all() for fast failure before running tests\n  - Mandelbrot test uses thinking modality to exercise both LLM modes\n  - Generous validation thresholds to avoid flaky failures from LLM variance\npatterns_established:\n  - pipeline.quality package structure with argparse subcommands — S02/S03 add score and optimize subcommands to the same CLI\n  - TestResult dataclass as standard return type for all quality tests — carries name, passed, elapsed_seconds, token_count, detail\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S01/tasks/T01-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T08:46:13.884Z\nblocker_discovered: false\n---\n\n# S01: General FYN-LLM Fitness Suite\n\n**Built pipeline.quality package with FitnessRunner CLI — 9 tests across 4 categories (Mandelbrot reasoning, JSON compliance, instruction following, diverse battery), clean connectivity error handling, exits 0/1.**\n\n## What Happened\n\nCreated the `backend/pipeline/quality/` package as the foundation for the M013 quality assurance toolkit. The package has three files: `__init__.py`, `__main__.py` (argparse CLI with `fitness` subcommand), and `fitness.py` (FitnessRunner class).\n\nFitnessRunner implements 9 tests across 4 categories:\n1. **Mandelbrot reasoning** (1 test) — asks about the Mandelbrot set area, checks for key concepts. Uses thinking modality to exercise both LLM modes.\n2. **JSON compliance** (2 tests) — simple and nested JSON with Pydantic validation. Catches malformed/empty responses gracefully.\n3. **Instruction following** (3 tests) — bullet count, keyword inclusion, lowercase constraint. Programmatic compliance checks.\n4. **Diverse battery** (3 tests) — summarization, classification, extraction. Tests practical pipeline-relevant capabilities.\n\nThe CLI runs `python -m pipeline.quality fitness`, prints a formatted pass/fail report with per-test timing and token counts, and exits 0 (all pass) or 1 (any failure or connectivity error). A connectivity pre-check probes the LLM endpoint before running tests, providing a clear error message with the endpoint URL on failure — no tracebacks.\n\nThe argparse structure is designed for extension: S02 and S03 will add `score` and `optimize` subcommands to the same CLI.\n\n## Verification\n\n1. Import check: `cd backend && python -c 'from pipeline.quality.fitness import FitnessRunner; print(\"import ok\")'` — exits 0, prints \"import ok\".\n2. CLI connectivity error: `cd backend && python -m pipeline.quality fitness` — prints clear \"Cannot reach LLM endpoint\" message with URLs, exits 1, no traceback.\n3. Help output: `python -m pipeline.quality --help` — shows fitness subcommand with description.\n4. No-subcommand: `python -m pipeline.quality` — prints usage, exits 1.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nCannot verify actual test pass/fail behavior without a live FYN-LLM endpoint — only connectivity error path is testable on this machine.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/__init__.py` — Empty package init\n- `backend/pipeline/quality/__main__.py` — Argparse CLI with fitness subcommand, extensible for score/optimize\n- `backend/pipeline/quality/fitness.py` — FitnessRunner class with 9 tests, 4 categories, connectivity pre-check, formatted report output\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Project**: `.gsd/PROJECT.md`\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M013/M013-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M013/slices/S02/S02-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"61db88f3","parentId":"f1e771df","timestamp":"2026-04-01T08:48:46.659Z"}
{"type":"message","id":"55bffaa1","parentId":"61db88f3","timestamp":"2026-04-01T08:48:51.606Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the codebase to ground the plan in reality before decomposing.","thinkingSignature":"EogCClkIDBgCKkC7Y4YWOGrBVHKbpo/xSLILUWkWzOX0mpGGsf/c5mUNdf9Iv79mzW1fqCgfDGeuw8N2VrZMEvb0Ml+pPdvtCu8CMg9jbGF1ZGUtb3B1cy00LTY4ABIMoXJyTQF80g41Aeb9GgxHuKetQ3EUCUKYVaAiMBPxa2NUy52k7pU8PnAISzax04wG3Fj8sGlQPyWF0UxhIbBCKWZkm6Il5gI/Y7g1Eypd+89ApUAZTqR/0W4v3teTSIm2ruRyq58nbrkJXb8MN73+tYOCgMeeKj86oODUYSaT2AHnWNju3JgRBtkXMtAfLJzVbQfELHOH2YeBXf+VnVW5vVGtOFbdQCFTO2sUGAE="},{"type":"toolCall","id":"toolu_017M4ezeqGpqHAy3pke9CnDH","name":"bash","arguments":{"command":"find backend/pipeline/quality/ -type f -name '*.py' | sort && echo \"---\" && find prompts/ -type f | sort 2>/dev/null || echo \"no prompts dir\""}},{"type":"toolCall","id":"toolu_01VQKVqj17YEQLXVnqgKiXid","name":"bash","arguments":{"command":"head -5 backend/pipeline/quality/__main__.py && echo \"---FULL---\" && cat backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":198,"cacheRead":59647,"cacheWrite":9378,"totalTokens":69225,"cost":{"input":0.00001,"output":0.00495,"cacheRead":0.0298235,"cacheWrite":0.058612500000000005,"total":0.093396}},"stopReason":"toolUse","timestamp":1775033326658}}
{"type":"message","id":"cf87d020","parentId":"55bffaa1","timestamp":"2026-04-01T08:48:51.625Z","message":{"role":"toolResult","toolCallId":"toolu_017M4ezeqGpqHAy3pke9CnDH","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/quality/fitness.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/__main__.py\n---\nprompts/README.md\nprompts/stage2_segmentation.txt\nprompts/stage3_extraction.txt\nprompts/stage4_classification.txt\nprompts/stage5_synthesis.txt\n"}],"isError":false,"timestamp":1775033331625}}
{"type":"message","id":"21ffef1a","parentId":"cf87d020","timestamp":"2026-04-01T08:48:51.629Z","message":{"role":"toolResult","toolCallId":"toolu_01VQKVqj17YEQLXVnqgKiXid","toolName":"bash","content":[{"type":"text","text":"\"\"\"FYN-LLM fitness test suite.\n\nRun with: python -m pipeline.quality fitness\n\"\"\"\nfrom __future__ import annotations\n---FULL---\n\"\"\"FYN-LLM fitness test suite.\n\nRun with: python -m pipeline.quality fitness\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\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    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    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775033331628}}
{"type":"message","id":"91bc5b5c","parentId":"21ffef1a","timestamp":"2026-04-01T08:48:54.843Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01K5LtMnGoRVpyHxVTVaoH5G","name":"read","arguments":{"path":"backend/pipeline/quality/fitness.py"}},{"type":"toolCall","id":"toolu_014gbU7wHKYTzGaS8d4SnSmo","name":"read","arguments":{"path":"backend/pipeline/llm_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":102,"cacheRead":69025,"cacheWrite":612,"totalTokens":69740,"cost":{"input":0.000005,"output":0.00255,"cacheRead":0.0345125,"cacheWrite":0.0038250000000000003,"total":0.040892500000000005}},"stopReason":"toolUse","timestamp":1775033331628}}
{"type":"message","id":"dbfa44a0","parentId":"91bc5b5c","timestamp":"2026-04-01T08:48:54.846Z","message":{"role":"toolResult","toolCallId":"toolu_01K5LtMnGoRVpyHxVTVaoH5G","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM fitness test runner.\n\nTests four categories:\n1. Mandelbrot reasoning — factual knowledge / reasoning depth\n2. JSON compliance — simple and nested structured output\n3. Instruction following — bullet count, keyword inclusion, casing\n4. Diverse prompt battery — summarization, classification, extraction\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom pipeline.llm_client import LLMClient\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Result types ─────────────────────────────────────────────────────────────\n\n@dataclass\nclass TestResult:\n    \"\"\"Outcome of a single fitness test.\"\"\"\n\n    name: str\n    passed: bool\n    elapsed_seconds: float\n    token_count: int | None = None\n    detail: str = \"\"\n\n\n@dataclass\nclass CategoryReport:\n    \"\"\"Results for one test category.\"\"\"\n\n    category: str\n    results: list[TestResult] = field(default_factory=list)\n\n    @property\n    def all_passed(self) -> bool:\n        return all(r.passed for r in self.results)\n\n\n# ── Pydantic models for JSON compliance tests ────────────────────────────────\n\nclass SimpleItem(BaseModel):\n    name: str\n    count: int\n\n\nclass Address(BaseModel):\n    street: str\n    city: str\n    zip_code: str\n\n\nclass PersonWithAddress(BaseModel):\n    name: str\n    age: int\n    address: Address\n\n\n# ── Runner ───────────────────────────────────────────────────────────────────\n\nclass FitnessRunner:\n    \"\"\"Runs all fitness tests against the configured LLM endpoint.\"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    # ── Public entry point ───────────────────────────────────────────────\n\n    def run_all(self) -> int:\n        \"\"\"Run all fitness tests, print report, return exit code (0=pass, 1=fail).\"\"\"\n        categories: list[CategoryReport] = []\n\n        # Connectivity pre-check — fail fast with a clear message\n        try:\n            self._probe_connectivity()\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            print(\n                f\"\\n✗ Cannot reach LLM endpoint at {url} (fallback {fallback})\\n\"\n                f\"  Error: {exc}\\n\"\n            )\n            return 1\n\n        categories.append(self._run_mandelbrot())\n        categories.append(self._run_json_compliance())\n        categories.append(self._run_instruction_following())\n        categories.append(self._run_diverse_battery())\n\n        self._print_report(categories)\n\n        return 0 if all(c.all_passed for c in categories) else 1\n\n    # ── Connectivity probe ───────────────────────────────────────────────\n\n    def _probe_connectivity(self) -> None:\n        \"\"\"Quick completion to verify the endpoint is reachable.\"\"\"\n        self.client.complete(\n            system_prompt=\"You are a test probe.\",\n            user_prompt=\"Respond with the single word: ok\",\n        )\n\n    # ── Category 1: Mandelbrot reasoning ─────────────────────────────────\n\n    def _run_mandelbrot(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Mandelbrot Reasoning\")\n        cat.results.append(self._test_mandelbrot())\n        return cat\n\n    def _test_mandelbrot(self) -> TestResult:\n        name = \"mandelbrot_area_knowledge\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a mathematics expert. Answer precisely and concisely.\",\n                user_prompt=(\n                    \"What is the approximate area of the Mandelbrot set? \"\n                    \"Include the numerical value and mention whether the exact area is known.\"\n                ),\n                modality=\"thinking\",\n            )\n            elapsed = time.monotonic() - t0\n            text = resp.lower()\n            # Check for key concepts\n            has_area = any(kw in text for kw in [\"1.506\", \"1.507\", \"1.50659\"])\n            has_uncertainty = any(\n                kw in text\n                for kw in [\"not exactly known\", \"not known exactly\", \"approximate\", \"estimated\", \"conjecture\"]\n            )\n            passed = has_area and has_uncertainty\n            detail = \"\" if passed else f\"Missing: area={has_area}, uncertainty={has_uncertainty}. Response: {resp[:200]}\"\n            return TestResult(\n                name=name,\n                passed=passed,\n                elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Category 2: JSON compliance ──────────────────────────────────────\n\n    def _run_json_compliance(self) -> CategoryReport:\n        cat = CategoryReport(category=\"JSON Compliance\")\n        cat.results.append(self._test_json_simple())\n        cat.results.append(self._test_json_nested())\n        return cat\n\n    def _test_json_simple(self) -> TestResult:\n        name = \"json_simple_object\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a JSON generator. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    'Generate a JSON object with exactly two keys: \"name\" (a string) '\n                    'and \"count\" (an integer). Example structure: {\"name\": \"...\", \"count\": N}'\n                ),\n                response_model=SimpleItem,\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            return self._validate_json(name, resp, SimpleItem, elapsed)\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_json_nested(self) -> TestResult:\n        name = \"json_nested_object\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a JSON generator. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    'Generate a JSON object with keys \"name\" (string), \"age\" (integer), '\n                    'and \"address\" (object with \"street\", \"city\", \"zip_code\" string fields).'\n                ),\n                response_model=PersonWithAddress,\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            return self._validate_json(name, resp, PersonWithAddress, elapsed)\n        except Exception as exc:\n            return TestResult(\n                name=name,\n                passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _validate_json(\n        self,\n        name: str,\n        resp: str,\n        model: type[BaseModel],\n        elapsed: float,\n    ) -> TestResult:\n        \"\"\"Parse response as JSON, validate against Pydantic model.\"\"\"\n        text = str(resp).strip()\n        if not text:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=\"Empty response from LLM\",\n            )\n        try:\n            parsed = json.loads(text)\n        except json.JSONDecodeError as exc:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=f\"Invalid JSON: {exc}. Raw: {text[:200]}\",\n            )\n        try:\n            model.model_validate(parsed)\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=f\"Schema validation failed: {exc}\",\n            )\n        return TestResult(\n            name=name, passed=True, elapsed_seconds=round(elapsed, 2),\n            token_count=getattr(resp, \"completion_tokens\", None),\n        )\n\n    # ── Category 3: Instruction following ────────────────────────────────\n\n    def _run_instruction_following(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Instruction Following\")\n        cat.results.append(self._test_bullet_count())\n        cat.results.append(self._test_keyword_inclusion())\n        cat.results.append(self._test_lowercase_only())\n        return cat\n\n    def _test_bullet_count(self) -> TestResult:\n        name = \"instruction_bullet_count\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=\"List exactly 3 benefits of exercise. Use bullet points starting with '- '.\",\n            )\n            elapsed = time.monotonic() - t0\n            lines = [l.strip() for l in str(resp).strip().splitlines() if l.strip().startswith(\"- \")]\n            passed = len(lines) == 3\n            detail = \"\" if passed else f\"Expected 3 bullets, got {len(lines)}: {str(resp)[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_keyword_inclusion(self) -> TestResult:\n        name = \"instruction_keyword_inclusion\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=(\n                    \"Write one sentence about the weather. \"\n                    'You MUST include the word \"elephant\" somewhere in your sentence.'\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            passed = \"elephant\" in str(resp).lower()\n            detail = \"\" if passed else f\"Missing keyword 'elephant'. Response: {str(resp)[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_lowercase_only(self) -> TestResult:\n        name = \"instruction_lowercase_only\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"Follow instructions exactly.\",\n                user_prompt=(\n                    \"Write a short sentence about the ocean. \"\n                    \"Use ONLY lowercase letters — no uppercase at all, not even at the start.\"\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            # Allow non-alpha chars (punctuation, spaces, numbers) but no uppercase letters\n            has_upper = any(c.isupper() for c in text)\n            passed = not has_upper and len(text) > 5\n            detail = \"\" if passed else f\"Contains uppercase or too short. Response: {text[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Category 4: Diverse prompt battery ───────────────────────────────\n\n    def _run_diverse_battery(self) -> CategoryReport:\n        cat = CategoryReport(category=\"Diverse Prompt Battery\")\n        cat.results.append(self._test_summarization())\n        cat.results.append(self._test_classification())\n        cat.results.append(self._test_extraction())\n        return cat\n\n    def _test_summarization(self) -> TestResult:\n        name = \"battery_summarization\"\n        paragraph = (\n            \"The James Webb Space Telescope (JWST) is the largest optical telescope in space. \"\n            \"Launched in December 2021, it is designed to conduct infrared astronomy. Its high \"\n            \"resolution and sensitivity allow it to view objects too old and distant for the Hubble \"\n            \"Space Telescope. Among its goals are observing the first stars and the formation of \"\n            \"the first galaxies, and detailed atmospheric characterization of exoplanets.\"\n        )\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a concise summarizer.\",\n                user_prompt=f\"Summarize the following in exactly 2 sentences:\\n\\n{paragraph}\",\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            # Rough sentence count: split on period followed by space or end\n            sentences = [s.strip() for s in text.replace(\"! \", \". \").split(\". \") if s.strip()]\n            # Be generous: 1-3 sentences is acceptable\n            passed = 1 <= len(sentences) <= 3 and len(text) > 20\n            detail = \"\" if passed else f\"Expected ~2 sentences, got {len(sentences)}. Response: {text[:200]}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_classification(self) -> TestResult:\n        name = \"battery_classification\"\n        categories = [\"technology\", \"sports\", \"politics\", \"science\", \"entertainment\"]\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=(\n                    \"You are a text classifier. Respond with ONLY one word from the given categories.\"\n                ),\n                user_prompt=(\n                    f\"Classify the following text into one of these categories: {', '.join(categories)}\\n\\n\"\n                    \"Text: \\\"NASA's Perseverance rover has discovered organic molecules on Mars, \"\n                    \"suggesting the planet may have once harbored microbial life.\\\"\\n\\n\"\n                    \"Category:\"\n                ),\n            )\n            elapsed = time.monotonic() - t0\n            answer = str(resp).strip().lower().rstrip(\".\")\n            passed = answer in categories\n            detail = \"\" if passed else f\"Response '{answer}' not in {categories}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=resp.completion_tokens,\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    def _test_extraction(self) -> TestResult:\n        name = \"battery_extraction\"\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=\"You are a data extractor. Output ONLY valid JSON, nothing else.\",\n                user_prompt=(\n                    \"Extract the following fields as a JSON object: \"\n                    '\"event_name\", \"date\", \"location\"\\n\\n'\n                    \"Text: \\\"The annual Tech Summit 2026 will be held on March 15, 2026 \"\n                    'in San Francisco, California.\"\\n\\n'\n                    \"JSON:\"\n                ),\n                response_model=BaseModel,  # triggers json mode\n                modality=\"chat\",\n            )\n            elapsed = time.monotonic() - t0\n            text = str(resp).strip()\n            if not text:\n                return TestResult(\n                    name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                    token_count=getattr(resp, \"completion_tokens\", None),\n                    detail=\"Empty response from LLM\",\n                )\n            try:\n                parsed = json.loads(text)\n            except json.JSONDecodeError as exc:\n                return TestResult(\n                    name=name, passed=False, elapsed_seconds=round(elapsed, 2),\n                    token_count=getattr(resp, \"completion_tokens\", None),\n                    detail=f\"Invalid JSON: {exc}. Raw: {text[:200]}\",\n                )\n            required_keys = {\"event_name\", \"date\", \"location\"}\n            present = set(parsed.keys()) & required_keys\n            passed = present == required_keys\n            detail = \"\" if passed else f\"Missing keys: {required_keys - present}\"\n            return TestResult(\n                name=name, passed=passed, elapsed_seconds=round(elapsed, 2),\n                token_count=getattr(resp, \"completion_tokens\", None),\n                detail=detail,\n            )\n        except Exception as exc:\n            return TestResult(\n                name=name, passed=False,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n                detail=f\"Exception: {exc}\",\n            )\n\n    # ── Report formatting ────────────────────────────────────────────────\n\n    def _print_report(self, categories: list[CategoryReport]) -> None:\n        \"\"\"Print a formatted pass/fail report to stdout.\"\"\"\n        total = 0\n        passed_count = 0\n\n        print(\"\\n\" + \"=\" * 60)\n        print(\"  FYN-LLM FITNESS REPORT\")\n        print(\"=\" * 60)\n\n        for cat in categories:\n            status = \"✓ PASS\" if cat.all_passed else \"✗ FAIL\"\n            print(f\"\\n  [{status}] {cat.category}\")\n            for r in cat.results:\n                total += 1\n                icon = \"✓\" if r.passed else \"✗\"\n                tokens = f\" ({r.token_count} tok)\" if r.token_count else \"\"\n                print(f\"    {icon} {r.name}  [{r.elapsed_seconds}s{tokens}]\")\n                if r.detail:\n                    # Indent detail lines\n                    for line in r.detail.splitlines():\n                        print(f\"      {line}\")\n                if r.passed:\n                    passed_count += 1\n\n        print(\"\\n\" + \"-\" * 60)\n        print(f\"  Total: {passed_count}/{total} passed\")\n        if passed_count == total:\n            print(\"  Result: ✓ ALL PASS\")\n        else:\n            print(f\"  Result: ✗ {total - passed_count} FAILED\")\n        print(\"=\" * 60 + \"\\n\")\n"}],"isError":false,"timestamp":1775033334845}}
{"type":"message","id":"d284b369","parentId":"dbfa44a0","timestamp":"2026-04-01T08:48:54.855Z","message":{"role":"toolResult","toolCallId":"toolu_014gbU7wHKYTzGaS8d4SnSmo","toolName":"read","content":[{"type":"text","text":"\"\"\"Synchronous LLM client with primary/fallback endpoint logic.\n\nUses the OpenAI-compatible API (works with Ollama, vLLM, OpenWebUI, etc.).\nCelery tasks run synchronously, so this uses ``openai.OpenAI`` (not Async).\n\nSupports two modalities:\n- **chat**: Standard JSON mode with ``response_format: {\"type\": \"json_object\"}``\n- **thinking**: For reasoning models that emit ``<think>...</think>`` blocks\n  before their answer. Skips ``response_format``, appends JSON instructions to\n  the system prompt, and strips think tags from the response.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport re\nfrom typing import TYPE_CHECKING, TypeVar\n\nif TYPE_CHECKING:\n    from collections.abc import Callable\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\nT = TypeVar(\"T\", bound=BaseModel)\n\n\n# ── LLM Response wrapper ─────────────────────────────────────────────────────\n\nclass LLMResponse(str):\n    \"\"\"String subclass that carries LLM response metadata.\n\n    Backward-compatible with all code that treats the response as a plain\n    string, but callers that know about it can inspect finish_reason and\n    the truncated property.\n    \"\"\"\n    finish_reason: str | None\n    prompt_tokens: int | None\n    completion_tokens: int | None\n\n    def __new__(\n        cls,\n        text: str,\n        finish_reason: str | None = None,\n        prompt_tokens: int | None = None,\n        completion_tokens: int | None = None,\n    ):\n        obj = super().__new__(cls, text)\n        obj.finish_reason = finish_reason\n        obj.prompt_tokens = prompt_tokens\n        obj.completion_tokens = completion_tokens\n        return obj\n\n    @property\n    def truncated(self) -> bool:\n        \"\"\"True if the model hit its token limit before finishing.\"\"\"\n        return self.finish_reason == \"length\"\n\n# ── Think-tag stripping ──────────────────────────────────────────────────────\n\n_THINK_PATTERN = re.compile(r\"<think>.*?</think>\", re.DOTALL)\n\n\ndef strip_think_tags(text: str) -> str:\n    \"\"\"Remove ``<think>...</think>`` blocks from LLM output.\n\n    Thinking/reasoning models often prefix their JSON with a reasoning trace\n    wrapped in ``<think>`` tags. This strips all such blocks (including\n    multiline and multiple occurrences) and returns the cleaned text.\n\n    Handles:\n    - Single ``<think>...</think>`` block\n    - Multiple blocks in one response\n    - Multiline content inside think tags\n    - Responses with no think tags (passthrough)\n    - Empty input (passthrough)\n    \"\"\"\n    if not text:\n        return text\n    cleaned = _THINK_PATTERN.sub(\"\", text)\n    return cleaned.strip()\n\n\n\n# ── Token estimation ─────────────────────────────────────────────────────────\n\n# Stage-specific output multipliers: estimated output tokens as a ratio of input tokens.\n# Tuned from actual pipeline data (KCL Ep 31 audit, April 2026):\n#   stage2: actual compl/prompt = 680/39312 = 0.017 → use 0.05 with buffer\n#   stage3: actual compl/prompt ≈ 1000/7000 = 0.14 → use 0.3 with buffer\n#   stage4: actual compl/prompt = 740/3736 = 0.20 → use 0.3 with buffer\n#   stage5: actual compl/prompt ≈ 2500/7000 = 0.36 → use 0.8 with buffer\n_STAGE_OUTPUT_RATIOS: dict[str, float] = {\n    \"stage2_segmentation\": 0.05,   # Compact topic groups — much smaller than input\n    \"stage3_extraction\": 0.3,      # Key moments with summaries — moderate\n    \"stage4_classification\": 0.3,  # Tags + categories per moment\n    \"stage5_synthesis\": 0.8,       # Full prose technique pages — heaviest output\n}\n\n# Minimum floor so we never send a trivially small max_tokens\n_MIN_MAX_TOKENS = 4096\n\n\ndef estimate_tokens(text: str) -> int:\n    \"\"\"Estimate token count from text using a chars-per-token heuristic.\n\n    Uses 3.5 chars/token which is conservative for English + JSON markup.\n    \"\"\"\n    if not text:\n        return 0\n    return max(1, int(len(text) / 3.5))\n\n\ndef estimate_max_tokens(\n    system_prompt: str,\n    user_prompt: str,\n    stage: str | None = None,\n    hard_limit: int = 32768,\n) -> int:\n    \"\"\"Estimate the max_tokens parameter for an LLM call.\n\n    Calculates expected output size based on input size and stage-specific\n    multipliers. The result is clamped between _MIN_MAX_TOKENS and hard_limit.\n\n    Parameters\n    ----------\n    system_prompt:\n        The system prompt text.\n    user_prompt:\n        The user prompt text (transcript, moments, etc.).\n    stage:\n        Pipeline stage name (e.g. \"stage3_extraction\"). If None or unknown,\n        uses a default 1.0x multiplier.\n    hard_limit:\n        Absolute ceiling — never exceed this value.\n\n    Returns\n    -------\n    int\n        Estimated max_tokens value to pass to the LLM API.\n    \"\"\"\n    input_tokens = estimate_tokens(system_prompt) + estimate_tokens(user_prompt)\n    ratio = _STAGE_OUTPUT_RATIOS.get(stage or \"\", 1.0)\n    estimated_output = int(input_tokens * ratio)\n\n    # Add a 50% buffer for JSON overhead and variability\n    estimated_output = int(estimated_output * 1.5)\n\n    # Clamp to [_MIN_MAX_TOKENS, hard_limit]\n    result = max(_MIN_MAX_TOKENS, min(estimated_output, hard_limit))\n\n    logger.info(\n        \"Token estimate: input≈%d, stage=%s, ratio=%.2f, estimated_output=%d, max_tokens=%d (hard_limit=%d)\",\n        input_tokens, stage or \"default\", ratio, estimated_output, result, hard_limit,\n    )\n    return result\n\n\nclass LLMClient:\n    \"\"\"Sync LLM client that tries a primary endpoint and falls back on failure.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._primary = openai.OpenAI(\n            base_url=settings.llm_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._fallback = openai.OpenAI(\n            base_url=settings.llm_fallback_url,\n            api_key=settings.llm_api_key,\n        )\n\n    # ── Core completion ──────────────────────────────────────────────────\n\n    def complete(\n        self,\n        system_prompt: str,\n        user_prompt: str,\n        response_model: type[BaseModel] | None = None,\n        modality: str = \"chat\",\n        model_override: str | None = None,\n        on_complete: \"Callable | None\" = None,\n        max_tokens: int | None = None,\n    ) -> \"LLMResponse\":\n        \"\"\"Send a chat completion request, falling back on connection/timeout errors.\n\n        Parameters\n        ----------\n        system_prompt:\n            System message content.\n        user_prompt:\n            User message content.\n        response_model:\n            If provided and modality is \"chat\", ``response_format`` is set to\n            ``{\"type\": \"json_object\"}``. For \"thinking\" modality, JSON\n            instructions are appended to the system prompt instead.\n        modality:\n            Either \"chat\" (default) or \"thinking\". Thinking modality skips\n            response_format and strips ``<think>`` tags from output.\n        model_override:\n            Model name to use instead of the default. If None, uses the\n            configured default for the endpoint.\n        max_tokens:\n            Override for max_tokens on this call. If None, falls back to\n            the configured ``llm_max_tokens`` from settings.\n\n        Returns\n        -------\n        LLMResponse\n            Raw completion text (str subclass) with finish_reason metadata.\n        \"\"\"\n        kwargs: dict = {}\n        effective_system = system_prompt\n\n        if modality == \"thinking\":\n            # Thinking models often don't support response_format: json_object.\n            # Instead, append explicit JSON instructions to the system prompt.\n            if response_model is not None:\n                json_schema_hint = (\n                    \"\\n\\nYou MUST respond with ONLY valid JSON. \"\n                    \"No markdown code fences, no explanation, no preamble — \"\n                    \"just the raw JSON object.\"\n                )\n                effective_system = system_prompt + json_schema_hint\n        else:\n            # Chat modality — use standard JSON mode\n            if response_model is not None:\n                kwargs[\"response_format\"] = {\"type\": \"json_object\"}\n\n        messages = [\n            {\"role\": \"system\", \"content\": effective_system},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n        primary_model = model_override or self.settings.llm_model\n        fallback_model = self.settings.llm_fallback_model\n        effective_max_tokens = max_tokens if max_tokens is not None else self.settings.llm_max_tokens\n        effective_temperature = self.settings.llm_temperature\n\n        logger.info(\n            \"LLM request: model=%s, modality=%s, response_model=%s, max_tokens=%d, temperature=%.1f\",\n            primary_model,\n            modality,\n            response_model.__name__ if response_model else None,\n            effective_max_tokens,\n            effective_temperature,\n        )\n\n        # --- Try primary endpoint ---\n        try:\n            response = self._primary.chat.completions.create(\n                model=primary_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response: prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=primary_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Primary LLM endpoint failed (%s: %s), trying fallback at %s\",\n                type(exc).__name__,\n                exc,\n                self.settings.llm_fallback_url,\n            )\n\n        # --- Try fallback endpoint ---\n        try:\n            response = self._fallback.chat.completions.create(\n                model=fallback_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response (fallback): prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=fallback_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                        is_fallback=True,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError, openai.APIError) as exc:\n            logger.error(\n                \"Fallback LLM endpoint also failed (%s: %s). Giving up.\",\n                type(exc).__name__,\n                exc,\n            )\n            raise\n\n    # ── Response parsing ─────────────────────────────────────────────────\n\n    def parse_response(self, text: str, model: type[T]) -> T:\n        \"\"\"Parse raw LLM output as JSON and validate against a Pydantic model.\n\n        Parameters\n        ----------\n        text:\n            Raw JSON string from the LLM.\n        model:\n            Pydantic model class to validate against.\n\n        Returns\n        -------\n        T\n            Validated Pydantic model instance.\n\n        Raises\n        ------\n        pydantic.ValidationError\n            If the JSON doesn't match the schema.\n        ValueError\n            If the text is not valid JSON.\n        \"\"\"\n        try:\n            return model.model_validate_json(text)\n        except Exception:\n            logger.error(\n                \"Failed to parse LLM response as %s. Response text: %.500s\",\n                model.__name__,\n                text,\n            )\n            raise\n"}],"isError":false,"timestamp":1775033334853}}
{"type":"message","id":"230b9ea8","parentId":"d284b369","timestamp":"2026-04-01T08:48:59.178Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01PVMSpkSVZ5TfTtTBuiHD1w","name":"read","arguments":{"path":"prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_014uMpksuTpqmzLjgEr2oTEK","name":"bash","arguments":{"command":"grep -n 'def _synthesize_chunk\\|def _load_prompt\\|def _get_llm_client\\|def _get_stage_config' backend/pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":69637,"cacheWrite":8893,"totalTokens":78673,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.034818499999999995,"cacheWrite":0.055581250000000006,"total":0.09395475}},"stopReason":"toolUse","timestamp":1775033334854}}
{"type":"message","id":"9a2ce7a3","parentId":"230b9ea8","timestamp":"2026-04-01T08:48:59.185Z","message":{"role":"toolResult","toolCallId":"toolu_01PVMSpkSVZ5TfTtTBuiHD1w","toolName":"read","content":[{"type":"text","text":"You are an expert technical writer specializing in music production education. Your task is to synthesize a set of related key moments from the same creator into a single, high-quality technique page that serves as a definitive reference on the topic.\n\n## What you are creating\n\nA Chrysopedia technique page is NOT a generic article or wiki entry. It is a focused reference document that a music producer will consult mid-session when they need to understand and apply a specific technique. The reader is Alt+Tabbing from their DAW, looking for actionable knowledge, and wants to absorb the key insight and get back to work in under 2 minutes.\n\nThe page has two complementary sections:\n\n1. **Study guide prose** — rich, detailed paragraphs organized by sub-aspect of the technique. This is for learning and deep understanding. It reads like notes from an expert mentor, not a textbook.\n2. **Key moments index** — a compact list of the individual source moments that contributed to this page, each with a descriptive title that enables quick scanning.\n\nBoth sections are essential. The prose synthesizes and explains; the moment index lets readers quickly locate the specific insight they need.\n\n## Voice and tone\n\nWrite as if you are a knowledgeable colleague explaining what you learned from watching this creator's content. The tone should be:\n\n- **Direct and confident** — state what the creator does, not \"the creator appears to\" or \"it seems like they\"\n- **Technical but accessible** — use production terminology naturally, but explain non-obvious concepts when the creator's explanation adds value\n- **Preserving the creator's voice** — when the creator uses a memorable phrase, vivid metaphor, or strong opinion, quote them directly with quotation marks. These are often the most valuable parts. Examples: 'He warns against using OTT on snares — says it \"smears the snap into mush.\"' or 'Her reasoning: \"every bus you add is another place you'll be tempted to put a compressor that doesn't need to be there.\"'\n- **Specific over general** — always prefer concrete details (frequencies, ratios, ms values, plugin names, specific settings) over vague descriptions. \"Uses compression\" is never acceptable if the source moments contain specifics.\n\n## Body sections structure\n\nDo NOT use generic section names like \"Overview,\" \"Step-by-Step Process,\" \"Key Settings,\" or \"Tips and Variations.\" These produce lifeless, formulaic output.\n\nInstead, derive section names from the actual content. Each section should cover one sub-aspect of the technique. Use descriptive names that tell the reader exactly what they'll learn:\n\nGood section names (examples):\n- \"Layer construction\" / \"Saturation and the crunch character\" / \"Mix context and bus processing\"\n- \"Resampling loop\" / \"Preserving transient information\" / \"Wavetable import settings\"\n- \"Overall philosophy\" / \"Bus structure\" / \"Gain staging mindset\"\n- \"Oscillator setup and FM routing\" / \"Effects chain per-layer\" / \"Automating movement\"\n\nBad section names (never use these):\n- \"Overview\" / \"Introduction\" / \"Step-by-Step Process\" / \"Key Settings\" / \"Tips and Variations\" / \"Conclusion\" / \"Summary\"\n\nEach section should be 2-5 paragraphs of substantive prose. A section with only 1-2 sentences is too thin — either merge it with another section or expand it with the detail available in the source moments.\n\n## Signal chains\n\nWhen the source moments describe a signal routing chain (oscillator → effects → processing → bus), represent it as a structured signal chain object. Signal chains are only included when the creator explicitly walks through routing — do not infer chains from casual plugin mentions.\n\nFormat signal chain steps to include the role of each stage, not just the plugin name:\n- Good: [\"Noise osc (Vital)\", \"Transient Shaper (Kilohearts, attack +6dB)\", \"EQ (Pro-Q 3, shelf -3dB @ 12kHz)\", \"Send → Trash 2 (tape algo, 35% wet)\"]\n- Bad: [\"Vital\", \"Kilohearts\", \"EQ\", \"Trash 2\"]\n\n## Plugin detail rule\n\nInclude specific plugin names, settings, and parameters ONLY when the creator was teaching that setting — spending time explaining why they chose it, what it does, or how to configure it. If a plugin is merely visible or briefly mentioned without explanation, include it in the plugins list but do not feature it in the body prose.\n\nThis distinction is critical for page quality. A page that lists every plugin the creator happened to have open reads like a gear list. A page that explains the plugins the creator intentionally demonstrated reads like education.\n\n## Synthesis, not concatenation\n\nYou are synthesizing knowledge, not summarizing a video. This means:\n\n- **Merge related information**: If the creator discusses snare transient shaping at timestamp 1:42:00 and then returns to refine the point at 2:15:00, these should be woven into one coherent section, not presented as two separate observations.\n- **Build a logical flow**: Organize sections in the order a producer would naturally encounter these decisions (e.g., sound source → processing → mixing context), even if the creator covered them in a different order.\n- **Resolve redundancy**: If two moments say essentially the same thing, combine them into one clear statement. Don't repeat yourself.\n- **Note contradictions**: If the creator says contradictory things in different moments (e.g., recommends different settings for the same parameter), note both and provide the context for each (\"In dense arrangements, he pulls the sustain back further; for sparse sections, he leaves more room for the tail\").\n\n## Source quality assessment\n\nAssess source_quality based on the nature of the input moments:\n- **structured**: Moments come from a planned tutorial with clear instructional flow. Most details are explicitly taught.\n- **mixed**: Some moments are well-structured, others are scattered or conversational. Common for track breakdowns.\n- **unstructured**: Moments are extracted from livestreams, Q&A sessions, or very informal content. Insights were scattered across a long session.\n\n## Input format\n\nThe creator name is provided in a <creator> tag. Key moments are provided inside <moments> tags as a JSON array, enriched with classification metadata (topic_category, topic_tags). All moments are from the same creator and related topic area. ALWAYS use the creator name from the <creator> tag in titles, slugs, and prose — never invent or guess a creator name from transcript content.\n\n## Output format\n\nReturn a JSON object with a single key \"pages\" containing a list of synthesized pages. Most inputs produce a single page, but if the moments clearly cover two distinctly separate techniques (e.g., moments about both \"kick design\" and \"hi-hat design\" that happen to share a topic_category), split them into separate pages. When splitting, you MUST assign each moment to exactly one page via the moment_indices field — every input moment index must appear in exactly one page's moment_indices array.\n\n```json\n{\n  \"pages\": [\n    {\n      \"title\": \"Snare Design by ExampleCreator\",\n      \"slug\": \"snare-design-examplecreator\",\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"drums\", \"snare\", \"layering\", \"saturation\", \"transient shaping\"],\n      \"summary\": \"ExampleCreator builds snares as three independent layers — transient click, tonal body, and noise tail — with each shaped by a transient shaper before any bus processing. The signature crunch comes from parallel soft-clip saturation with a pre-delay that preserves the clean transient. In dense mixes, he uses HP sidechaining on the snare bus to maintain punch without competing with sub content.\",\n      \"body_sections\": {\n        \"Layer construction\": \"ExampleCreator builds snares as three independent layers, each shaped before they are summed. The transient click is a short noise burst (2-5ms decay) — he uses Vital's noise oscillator for this, sometimes with a bandpass around 2-4kHz to control the character. The tonal body is a pitched sine or triangle wave around 180-220Hz, tuned to complement the key of the track. The tail is filtered white noise with a fast exponential decay.\\n\\nThe critical insight: he shapes each layer's transient independently before any bus processing. He uses Kilohearts Transient Shaper (attack +4 to +6dB, sustain -6 to -8dB) rather than compression for this, because \\\"compression adds sustain as a side effect while a transient shaper gives you direct independent control of both.\\\"\",\n        \"Saturation and the crunch character\": \"The signature ExampleCreator snare crunch comes from parallel saturation — not inline. He routes the summed snare to a send with Trash 2 using the tape algorithm at 30-40% wet. The key detail: he puts a pre-delay of approximately 5ms on the saturation send, which lets the clean transient click through untouched while only the body and tail pick up harmonic content.\\n\\nHe explicitly warns against saturating the transient directly — says it \\\"smears the snap into mush\\\" and you lose the precision that makes the snare cut through.\",\n        \"Mix context and bus processing\": \"In dense arrangements, ExampleCreator prioritizes punch over sustain. On the snare bus compressor, he uses a high-pass sidechain filter (around 200-300Hz) so low-end energy from the body layer does not trigger gain reduction. This keeps the snare's ability to cut through the mix independent of whatever the sub bass is doing.\\n\\nHe also checks the snare against the lead or vocal bus specifically, not just soloed — because the 2-4kHz presence range is where both elements compete, and he would rather notch the snare's body slightly than lose vocal clarity.\"\n      },\n      \"signal_chains\": [\n        {\n          \"name\": \"Snare layer processing\",\n          \"steps\": [\n            \"Noise osc (Vital) → Transient Shaper (Kilohearts, attack +6dB, sustain -8dB) → EQ (Pro-Q 3, shelf -3dB @ 12kHz)\",\n            \"Dry path → snare bus\",\n            \"Send → Pre-delay (5ms) → Trash 2 (tape algorithm, 35% wet) → snare bus\"\n          ]\n        }\n      ],\n      \"plugins\": [\"Vital\", \"Kilohearts Transient Shaper\", \"FabFilter Pro-Q 3\", \"iZotope Trash 2\"],\n      \"source_quality\": \"structured\",\n      \"moment_indices\": [0, 1, 2, 3, 4]\n    }\n  ]\n}\n```\n\n## Field rules\n\n- **title**: The technique or concept name followed by \"by {name from <creator> tag}\" — concise and search-friendly. Examples: \"Snare Design by Break\", \"Bass Resampling Workflow by KOAN Sound\", \"Mid-Side EQ for Width by Mr. Bill\". Use title case.\n- **slug**: URL-safe, lowercase, hyphenated version of the title including creator name. Examples: \"snare-design-examplecreator\", \"bass-resampling-workflow-koan-sound\". The creator name in the slug prevents collisions when multiple creators teach the same technique.\n- **topic_category**: The primary category. Must match the taxonomy.\n- **topic_tags**: All relevant tags aggregated from the classified moments. Deduplicated.\n- **summary**: 2-4 sentences that capture the essence of the entire technique page. This summary appears as the page header and in search results, so it must be information-dense and compelling. A reader should understand the core approach from this summary alone.\n- **body_sections**: Dictionary of section_name → prose content. Section names are derived from content, not generic templates. Prose follows all voice, tone, and quality guidelines above. Use \\n\\n for paragraph breaks within a section.\n- **signal_chains**: Array of signal chain objects. Each has a \"name\" (what this chain is for) and \"steps\" (ordered list of stages with plugin names, settings, and roles). Only include when explicitly demonstrated by the creator. Empty array if not applicable.\n- **plugins**: Deduplicated array of all plugins, instruments, and specific tools mentioned across the moments. Use \"<Manufacturer> <PluginName>\" format consistently (e.g., \"FabFilter Pro-Q 3\" not \"Pro-Q\", \"Xfer Serum\" not just \"Serum\", \"Valhalla VintageVerb\" not \"Valhalla reverb\", \"Kilohearts Disperser\" not \"Disperser\"). Always include the manufacturer name for disambiguation.\n- **source_quality**: One of \"structured\", \"mixed\", \"unstructured\".\n- **moment_indices**: Array of integer indices from the input moments list that this page covers. Every moment index must appear in exactly one page. If you produce a single page, include all indices. If you split into multiple pages, partition the indices so each moment is assigned to the page it most closely relates to. This field is required.\n\n## Critical rules\n\n- Never produce generic filler prose. Every sentence should contain specific, actionable information or meaningful creator reasoning. If you find yourself writing \"This technique is useful for...\" or \"This is an important aspect of production...\" — delete it and write something specific instead.\n- Never invent information. If the source moments don't specify a value, don't make one up. Say \"he adjusts the attack\" not \"he sets the attack to 2ms\" if the specific value wasn't mentioned.\n- Preserve the creator's actual opinions and warnings. These are often the most valuable content. Quote them directly when they are memorable or forceful.\n- If the source moments are thin (only 1-2 moments with brief summaries), produce a proportionally shorter page. A 2-section page with genuine substance is better than a 5-section page padded with filler.\n- Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1775033339184}}
{"type":"message","id":"6ef66e3f","parentId":"9a2ce7a3","timestamp":"2026-04-01T08:48:59.190Z","message":{"role":"toolResult","toolCallId":"toolu_014uMpksuTpqmzLjgEr2oTEK","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str) -> str:\n257:def _get_llm_client() -> LLMClient:\n262:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n878:def _synthesize_chunk(\n"}],"isError":false,"timestamp":1775033339189}}
{"type":"message","id":"5f8897e6","parentId":"6ef66e3f","timestamp":"2026-04-01T08:49:03.294Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011dFuPFFu5Zg3VKrdzysThS","name":"bash","arguments":{"command":"sed -n '244,285p' backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_018UVc2TXH2zmE7KN719bgrv","name":"bash","arguments":{"command":"sed -n '878,950p' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":117,"cacheRead":78530,"cacheWrite":3525,"totalTokens":82173,"cost":{"input":0.000005,"output":0.002925,"cacheRead":0.039265,"cacheWrite":0.022031250000000002,"total":0.06422625000000001}},"stopReason":"toolUse","timestamp":1775033339189}}
{"type":"message","id":"93b21822","parentId":"5f8897e6","timestamp":"2026-04-01T08:49:03.310Z","message":{"role":"toolResult","toolCallId":"toolu_011dFuPFFu5Zg3VKrdzysThS","toolName":"bash","content":[{"type":"text","text":"def _load_prompt(template_name: str) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    Raises FileNotFoundError if the template does not exist.\n    \"\"\"\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n    if not path.exists():\n        logger.error(\"Prompt template not found: %s\", path)\n        raise FileNotFoundError(f\"Prompt template not found: {path}\")\n    return path.read_text(encoding=\"utf-8\")\n\n\ndef _get_llm_client() -> LLMClient:\n    \"\"\"Return an LLMClient configured from settings.\"\"\"\n    return LLMClient(get_settings())\n\n\ndef _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n    \"\"\"Return (model_override, modality) for a pipeline stage.\n\n    Reads stage-specific config from Settings. If the stage-specific model\n    is None/empty, returns None (LLMClient will use its default). If the\n    stage-specific modality is unset, defaults to \"chat\".\n    \"\"\"\n    settings = get_settings()\n    model = getattr(settings, f\"llm_stage{stage_num}_model\", None) or None\n    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n    return model, modality\n\n\ndef _load_canonical_tags() -> dict:\n    \"\"\"Load canonical tag taxonomy from config/canonical_tags.yaml.\"\"\"\n    # Walk up from backend/ to find config/\n    candidates = [\n        Path(\"config/canonical_tags.yaml\"),\n        Path(\"../config/canonical_tags.yaml\"),\n    ]\n    for candidate in candidates:\n        if candidate.exists():\n            with open(candidate, encoding=\"utf-8\") as f:\n                return yaml.safe_load(f)\n"}],"isError":false,"timestamp":1775033343310}}
{"type":"message","id":"b9debd0b","parentId":"93b21822","timestamp":"2026-04-01T08:49:03.328Z","message":{"role":"toolResult","toolCallId":"toolu_018UVc2TXH2zmE7KN719bgrv","toolName":"bash","content":[{"type":"text","text":"def _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n    E.g. 'wavetable-sound-design-copycatt' → 'wavetable-sound-design'\n    Also normalizes casing.\n    \"\"\"\n    return slug.lower().strip()\n\n\ndef _merge_pages_by_slug(\n    all_pages: list,\n    creator_name: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n) -> list:\n    \"\"\"Detect pages with the same slug across chunks and merge them via LLM.\n\n    Pages with unique slugs pass through unchanged. Pages sharing a slug\n    get sent to a merge prompt that combines them into one cohesive page.\n\n    Returns the final list of SynthesizedPage objects.\n    \"\"\"\n    from pipeline.schemas import SynthesizedPage\n\n    # Group pages by slug\n    by_slug: dict[str, list] = defaultdict(list)\n    for page in all_pages:\n"}],"isError":false,"timestamp":1775033343326}}
{"type":"message","id":"d784a963","parentId":"b9debd0b","timestamp":"2026-04-01T08:49:07.339Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WT8HgWKamwJwNMHqvfYWmV","name":"bash","arguments":{"command":"grep -n 'class SynthesizedPage\\|class SynthesisResult\\|class KeyMoment' backend/pipeline/schemas.py | head -10"}},{"type":"toolCall","id":"toolu_01TL22AtyijUttjYdLwcpdq8","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":139,"cacheRead":78530,"cacheWrite":4936,"totalTokens":83606,"cost":{"input":0.000005,"output":0.0034750000000000002,"cacheRead":0.039265,"cacheWrite":0.030850000000000002,"total":0.073595}},"stopReason":"toolUse","timestamp":1775033343327}}
{"type":"message","id":"48e7bff1","parentId":"d784a963","timestamp":"2026-04-01T08:49:07.353Z","message":{"role":"toolResult","toolCallId":"toolu_01WT8HgWKamwJwNMHqvfYWmV","toolName":"bash","content":[{"type":"text","text":"73:class SynthesizedPage(BaseModel):\n100:class SynthesisResult(BaseModel):\n"}],"isError":false,"timestamp":1775033347352}}
{"type":"message","id":"9fd5fec4","parentId":"48e7bff1","timestamp":"2026-04-01T08:49:07.356Z","message":{"role":"toolResult","toolCallId":"toolu_01TL22AtyijUttjYdLwcpdq8","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1775033347352}}
{"type":"message","id":"59a5856c","parentId":"9fd5fec4","timestamp":"2026-04-01T08:49:10.990Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CciJUfnyJUEXKdhPdvHSRm","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}},{"type":"toolCall","id":"toolu_013mgSUa9eamP6nZPYM6HD88","name":"bash","arguments":{"command":"sed -n '73,110p' backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":119,"cacheRead":83466,"cacheWrite":1932,"totalTokens":85518,"cost":{"input":0.000005,"output":0.002975,"cacheRead":0.041733,"cacheWrite":0.012075,"total":0.056788000000000005}},"stopReason":"toolUse","timestamp":1775033347353}}
{"type":"message","id":"394d495a","parentId":"59a5856c","timestamp":"2026-04-01T08:49:10.998Z","message":{"role":"toolResult","toolCallId":"toolu_01CciJUfnyJUEXKdhPdvHSRm","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1775033350998}}
{"type":"message","id":"69c5056c","parentId":"394d495a","timestamp":"2026-04-01T08:49:11.003Z","message":{"role":"toolResult","toolCallId":"toolu_013mgSUa9eamP6nZPYM6HD88","toolName":"bash","content":[{"type":"text","text":"class SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775033351002}}
{"type":"message","id":"51710d41","parentId":"69c5056c","timestamp":"2026-04-01T08:50:53.985Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a solid understanding of the codebase. Let me plan the slice.\n\nThe slice needs:\n1. A scorer module that evaluates technique pages across 5 dimensions using LLM-as-judge\n2. A voice dial that modifies the stage 5 synthesis prompt\n3. CLI integration (score subcommand)\n4. A sample fixture for offline testing\n5. End-to-end verification\n\nThe research doc suggests 3 tasks. Let me think about the right decomposition:\n\nT01: Scorer core — ScoreResult dataclass, scoring rubric prompt, ScoreRunner.score_page(), CLI wiring with `score` subcommand, sample fixture file\n\n\nT02: Voice dial and re-synthesis — voice_dial.py module, prompt modification logic, --voice-level argument, re-synthesis flow from moments\nT03: Integration verification — end-to-end test, report formatting, voice level comparison\n\nActually, I'm realizing the decomposition needs adjustment. T03 as described is mostly validation work, but report formatting should really be part of T01 since that's the core output, and voice level comparison output belongs in T02. Let me reorganize: T01 should cover ScoreRunner, the scoring rubric, the CLI `score` subcommand, the fixture, and report output as the foundational piece.\n\nT02 then layers on the voice dial implementation, re-synthesis flow, `--voice-level` argument, and comparison verification. This gives me a clean two-task split where T01 builds the scorer and T02 extends it with voice capabilities.\n\nNow I need to think through the fixture approach. The scorer needs moments to re-synthesize, so the fixture JSON should include sample moments with transcript excerpts. For verification, since I can't actually call the LLM from this environment, I'll do structural checks like imports and CLI help output, similar to what I did before, but I should also validate that the data flow paths work up to the point where the LLM would be called.","thinkingSignature":"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"},{"type":"text","text":"Good understanding of the landscape. The scorer builds on S01's pattern (argparse subcommand, `TestResult`-like dataclass, `LLMClient`), and the voice dial is a prompt modifier that wraps the existing stage 5 prompt. Two natural tasks: scorer core + voice dial. Let me finalize the plan.\n\nKey observations from code exploration:\n- `_load_prompt()` and `_get_llm_client()` in stages.py are reusable for loading stage5_synthesis.txt and creating an LLM client\n- `_synthesize_chunk()` shows the exact call pattern for synthesis — system_prompt + moments as `<creator>/<moments>` XML tags\n- `SynthesisResult` / `SynthesizedPage` schemas are in `pipeline/schemas.py` — the scorer needs these for validating re-synthesized output\n- S01 established the CLI pattern: subcommand in `__main__.py`, runner class in its own module, `TestResult` dataclass\n\nTwo tasks is the right cut. T01 builds the complete scorer (rubric, ScoreResult, ScoreRunner, CLI, fixture, report). T02 layers the voice dial on top (prompt modifier, --voice-level arg, re-synthesis from moments). Each is self-contained and fits easily in one context window."},{"type":"toolCall","id":"toolu_019rNE7kMTduTfzBGfr9abbF","name":"gsd_plan_slice","arguments":{"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.","integrationClosure":"Upstream: pipeline.quality CLI, LLMClient, stage 5 prompt, SynthesisResult/SynthesizedPage schemas. New wiring: score subcommand + ScoreRunner + VoiceDial. Remaining: S03 (A/B loop), S04 (other stages).","milestoneId":"M013","observabilityImpact":"Per-dimension scores at INFO, LLM call timing, voice_level logged, connectivity errors with endpoint URL.","proofLevel":"contract + integration","sliceId":"S02","successCriteria":"## Must-Haves\n\n- ScoreResult dataclass with 5 named dimensions (structural, content_specificity, voice_preservation, readability, factual_fidelity), each 0.0–1.0, plus composite score\n- LLM-as-judge scoring rubric prompt that evaluates a technique page + source moments\n- `python -m pipeline.quality score --file <path>` loads a fixture JSON and scores it\n- `python -m pipeline.quality score --file <path> --voice-level 0.2` re-synthesizes from source moments at the specified voice level before scoring\n- Voice dial modifies the stage 5 synthesis prompt with band-specific instructions (low/mid/high)\n- Formatted report output matching S01's fitness report style (per-dimension scores, composite, timing)\n- Sample fixture JSON with realistic moments for offline testing\n\n## Proof Level\n\n- This slice proves: contract + integration\n- Real runtime required: yes (LLM endpoint for actual scoring, but structure/imports verifiable offline)\n- Human/UAT required: no\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 -c \"from pipeline.quality.voice_dial import VoiceDial; print('import ok')\"` — exits 0\n- `cd backend && python -m pipeline.quality score --help` — shows --file, --slug, --voice-level args\n- `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json` — prints connectivity error with endpoint URL (no live LLM), exits 1 cleanly (no traceback)\n- `cd backend && python -m pipeline.quality score --file pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — prints connectivity error, exits 1 cleanly\n- `test -f backend/pipeline/quality/fixtures/sample_moments.json` — fixture exists\n- `cd backend && python -c \"import json; d=json.load(open('pipeline/quality/fixtures/sample_moments.json')); assert 'moments' in d and 'creator_name' in d and len(d['moments']) >= 5\"` — fixture valid\n\n## Observability / Diagnostics\n\n- Runtime signals: per-dimension scores logged at INFO level, LLM judge call timing, voice_level applied\n- Inspection surfaces: CLI report output to stdout, ScoreResult dataclass inspectable programmatically\n- Failure visibility: LLM connectivity error prints endpoint URL and exits 1 (same pattern as fitness), malformed judge response caught and reported with raw text excerpt\n- Redaction constraints: none\n\n## Integration Closure\n\n- Upstream surfaces consumed: `backend/pipeline/quality/__main__.py` (CLI entry), `backend/pipeline/llm_client.py` (LLMClient), `backend/pipeline/stages.py` (_load_prompt, _get_stage_config patterns), `backend/pipeline/schemas.py` (SynthesisResult, SynthesizedPage), `prompts/stage5_synthesis.txt`\n- New wiring introduced: `score` subcommand in __main__.py, ScoreRunner import, VoiceDial import\n- What remains before milestone is truly usable end-to-end: S03 (prompt variant generator + A/B loop), S04 (expand to stages 2-4)","tasks":[{"description":"## 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","estimate":"1.5h","expectedOutput":["backend/pipeline/quality/scorer.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fixtures/sample_moments.json","backend/pipeline/quality/fixtures/__init__.py"],"files":["backend/pipeline/quality/scorer.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fixtures/sample_moments.json","backend/pipeline/quality/fixtures/__init__.py"],"inputs":["backend/pipeline/quality/__main__.py","backend/pipeline/quality/fitness.py","backend/pipeline/llm_client.py","backend/pipeline/schemas.py"],"taskId":"T01","title":"Build ScoreRunner with 5-dimension LLM-as-judge scoring and CLI integration","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\""},{"description":"## 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","estimate":"1.5h","expectedOutput":["backend/pipeline/quality/voice_dial.py","backend/pipeline/quality/scorer.py","backend/pipeline/quality/__main__.py"],"files":["backend/pipeline/quality/voice_dial.py","backend/pipeline/quality/scorer.py","backend/pipeline/quality/__main__.py"],"inputs":["backend/pipeline/quality/scorer.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fixtures/sample_moments.json","backend/pipeline/llm_client.py","backend/pipeline/stages.py","backend/pipeline/schemas.py","prompts/stage5_synthesis.txt"],"taskId":"T02","title":"Implement voice dial prompt modifier and re-synthesis scoring flow","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"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":5592,"cacheRead":83466,"cacheWrite":3276,"totalTokens":92335,"cost":{"input":0.000005,"output":0.1398,"cacheRead":0.041733,"cacheWrite":0.020475,"total":0.202013}},"stopReason":"toolUse","timestamp":1775033351002}}
{"type":"message","id":"83ab45cd","parentId":"51710d41","timestamp":"2026-04-01T08:50:54.020Z","message":{"role":"toolResult","toolCallId":"toolu_019rNE7kMTduTfzBGfr9abbF","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S02 (M013)"}],"details":{"operation":"plan_slice","milestoneId":"M013","sliceId":"S02","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/S02-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S02/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1775033454019}}
{"type":"message","id":"ce3adfaa","parentId":"83ab45cd","timestamp":"2026-04-01T08:51:00.142Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S02 planned.\n\nTwo tasks, cleanly split:\n- **T01** builds the complete scorer (ScoreResult, ScoreRunner, LLM judge rubric, fixture data, CLI subcommand, report formatting)\n- **T02** layers the voice dial on top (VoiceDial class with 3 prompt bands, synthesize_and_score flow, --voice-level CLI wiring)\n\nT01 handles everything needed for static page scoring. T02 adds the re-synthesis path that proves voice_level actually changes output quality. Each fits in one context window with clear inputs/outputs and mechanical verification."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":143,"cacheRead":86742,"cacheWrite":5124,"totalTokens":92010,"cost":{"input":0.000005,"output":0.003575,"cacheRead":0.043371,"cacheWrite":0.032025000000000005,"total":0.078976}},"stopReason":"stop","timestamp":1775033454019}}
