{"type":"thinking_level_change","id":"f18a3c8a","parentId":null,"timestamp":"2026-04-01T09:14:06.920Z","thinkingLevel":"medium"}
{"type":"model_change","id":"fd3a12fc","parentId":"f18a3c8a","timestamp":"2026-04-01T09:14:06.921Z","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/S04/S04-PLAN.md` and `.gsd/milestones/M013/slices/S04/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 S04 planned.\"\n## Requirements Advanced\n\n- R013 — Prompt optimization loop provides automated mechanism to improve prompt templates — generates variants, scores them, and identifies winners\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S04 (\"Expand to Pipeline Stages 2-4\") — 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/S04/S04-RESEARCH.md`\n\n# S04 Research: Expand to Pipeline Stages 2-4\n\n## Depth: Targeted\n\nKnown patterns (optimization loop, variant generation, scoring) applied to new stages. Main complexity is defining stage-appropriate scoring rubrics and fixture formats.\n\n## Summary\n\nExtending the optimizer from stage-5-only to stages 2-4 requires four changes per stage: (1) a scoring rubric tailored to what that stage produces, (2) fixture data matching the stage's input shape, (3) format markers for variant validation, and (4) a score-variant pathway that calls the right LLM prompt and parses the right schema.\n\nThe existing architecture in `optimizer.py` and `variant_generator.py` is well-factored for this — the main hardcoding is in the scorer's `SCORING_RUBRIC` (stage-5-specific dimensions) and the optimizer's `_score_variant` method (calls synthesis → scores technique page).\n\n## Requirement Coverage\n\n**R013** (Prompt Template System) — already validated but this slice extends it with automated optimization for stages 2-4, strengthening the \"re-run extraction on specific videos for calibration\" aspect.\n\n## Implementation Landscape\n\n### What exists\n\n| File | Role | Stage-5 Coupling |\n|---|---|---|\n| `backend/pipeline/quality/scorer.py` | `SCORING_RUBRIC`, `DIMENSIONS`, `ScoreResult`, `ScoreRunner` | SCORING_RUBRIC and DIMENSIONS are hardcoded to 5 stage-5 dimensions. ScoreResult dataclass has those 5 as named fields. `score_page()` expects page JSON shape. `synthesize_and_score()` loads stage5_synthesis.txt. |\n| `backend/pipeline/quality/optimizer.py` | `OptimizationLoop`, `OptimizationResult` | `run()` loads `stage{N}_synthesis.txt` (already parameterized). `_score_variant()` calls synthesis and expects `SynthesisResult` schema. Fixture loader expects `{creator_name, moments}`. |\n| `backend/pipeline/quality/variant_generator.py` | `PromptVariantGenerator`, `VARIANT_META_PROMPT` | `VARIANT_META_PROMPT` references \"synthesis prompt\" and synthesis-specific language. `_FORMAT_MARKERS` are `[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]` — stage-5-specific. |\n| `backend/pipeline/quality/__main__.py` | CLI | `_run_optimize()` rejects `args.stage != 5` with an error message. |\n| `backend/pipeline/quality/fixtures/sample_moments.json` | Test fixture | Stage 5 format: `{creator_name, topic_category, moments: [...]}` |\n\n### Prompt files and schemas per stage\n\n| Stage | Prompt File | Input Shape | Output Schema | What to Score |\n|---|---|---|---|---|\n| 2 (segmentation) | `stage2_segmentation.txt` | Transcript segments: `[idx] (start-end) text` | `SegmentationResult{segments: [{start_index, end_index, topic_label, summary}]}` | Coverage completeness (no gaps/overlaps), topic label specificity, segment boundary accuracy, summary quality |\n| 3 (extraction) | `stage3_extraction.txt` | Topic group segments: `(start-end) text` with topic label | `ExtractionResult{moments: [{title, summary, start_time, end_time, content_type, plugins, raw_transcript}]}` | Moment richness (detail density), timestamp accuracy, content_type correctness, summary actionability, plugin name normalization |\n| 4 (classification) | `stage4_classification.txt` | Moments list + taxonomy text | `ClassificationResult{classifications: [{moment_index, topic_category, topic_tags, content_type_override}]}` | Category accuracy, tag completeness, tag specificity, coverage (all moments classified), no-overlap (one category per moment) |\n\n### Format markers per stage\n\nStage 2: `\"segments\"`, `start_index`, `end_index`, `topic_label`\nStage 3: `\"moments\"`, `content_type`, `raw_transcript`, `plugins`\nStage 4: `\"classifications\"`, `moment_index`, `topic_category`, `topic_tags`\n\n### Architecture approach\n\nTwo viable approaches:\n\n**A. Stage-specific scorer classes** — Create `Stage2Scorer`, `Stage3Scorer`, `Stage4Scorer` alongside the existing `ScoreRunner` (which becomes `Stage5Scorer`). Each has its own rubric, dimensions, and `score_output()` method. `OptimizationLoop` dispatches to the right scorer based on `self.stage`.\n\n**B. Parameterized scorer with rubric registry** — Keep one `ScoreRunner` class but make it accept a rubric config (dimensions list, rubric text, output parser). A `STAGE_CONFIGS` dict maps stage number → config.\n\n**Recommendation: B (registry).** The scoring flow is identical across stages (send rubric + output + input to LLM judge, parse scores). Only the rubric text, dimensions, format markers, and fixture→LLM-input formatting differ. A registry keeps the surface area small.\n\n### Key design decisions\n\n1. **ScoreResult generalization** — Currently has 5 named fields (`structural`, `content_specificity`, etc.). For stages 2-4 with different dimensions, either: (a) use a generic `scores: dict[str, float]` field, or (b) keep the named fields for stage 5 and add a generic dict for others. Option (a) is cleaner — stage 5's named fields can be populated from the dict.\n\n2. **Fixture format per stage** — Each stage needs different fixture data:\n   - Stage 2: `{transcript_segments: [{index, start_time, end_time, text}]}`\n   - Stage 3: `{topic_label, segments: [{start_time, end_time, text}]}`\n   - Stage 4: `{moments: [...], taxonomy: \"...\"}`\n   \n   The optimizer's `_load_fixture()` and `_score_variant()` need to be stage-aware.\n\n3. **Variant generator meta-prompt** — The `VARIANT_META_PROMPT` currently references synthesis-specific language. It needs to be generalized or have per-stage variants. The core pattern (analyze scores → improve weakest dimensions → preserve format) is the same.\n\n4. **Fixture creation** — Need sample fixtures for stages 2-4. Can extract from the existing sample_moments.json data (moments → stage 3/4 input) and from a real transcript (stage 2 input). Alternatively, create synthetic minimal fixtures.\n\n### Files to modify\n\n- `backend/pipeline/quality/scorer.py` — Add stage configs registry (rubric, dimensions, format markers per stage), generalize `ScoreResult`, add `score_stage_output()` method\n- `backend/pipeline/quality/optimizer.py` — Generalize `_score_variant()` to dispatch per stage, generalize fixture loading\n- `backend/pipeline/quality/variant_generator.py` — Generalize meta-prompt and format markers per stage\n- `backend/pipeline/quality/__main__.py` — Remove stage-5 gate, add stage validation (2-5 only)\n- `backend/pipeline/quality/fixtures/` — Add sample fixtures for stages 2-4\n\n### Natural task seams\n\n1. **Scorer generalization + rubric registry** — Define stage 2-4 scoring dimensions and rubrics, generalize ScoreResult, add stage config registry. This is the foundation.\n2. **Optimizer + variant generator generalization** — Make OptimizationLoop stage-aware (fixture loading, score dispatch), generalize variant generator format markers and meta-prompt.\n3. **Fixtures + CLI integration** — Create stage 2-4 fixtures, remove the stage-5 gate in CLI, wire everything together, verify end-to-end.\n\n### Verification\n\n- `python -m pipeline.quality optimize --stage 2 --iterations 1 --file <stage2_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 3 --iterations 1 --file <stage3_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 4 --iterations 1 --file <stage4_fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 5 --iterations 1 --file <stage5_fixture>` still works (no regression)\n- `python -m pipeline.quality optimize --stage 6` → error message\n- Import checks for all modified modules pass\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#### S03 Summary\nSource: `.gsd/milestones/M013/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M013\nmilestone: M013\nprovides:\n  - optimize CLI subcommand for automated prompt A/B testing\n  - PromptVariantGenerator for LLM-powered prompt mutation\n  - OptimizationLoop + OptimizationResult for iterative optimization with full history\n  - Leaderboard table, ASCII trajectory chart, and timestamped JSON result output\nrequires:\n  - slice: S02\n    provides: ScoreRunner, ScoreResult, LLMClient, 5-dimension scoring rubric, VoiceDial pattern\naffects:\n  - S04\nkey_files:\n  - backend/pipeline/quality/variant_generator.py\n  - backend/pipeline/quality/optimizer.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/results/.gitkeep\nkey_decisions:\n  - OptimizationLoop bypasses VoiceDial — owns the full prompt text directly to avoid double-application\n  - Variant validation uses both length diff and line-level symmetric difference to catch trivial mutations\n  - Reporting functions live in __main__.py rather than a separate reporting.py — keeps surface area small\npatterns_established:\n  - Meta-prompt pattern: LLM acts as prompt engineer, receives current prompt + per-dimension scores + rubric summary, outputs a modified variant targeting weakest dimensions\n  - Variant validation gate: min-diff threshold + format marker check before scoring, invalid variants logged and skipped\n  - Optimization history capture: full iteration×variant matrix stored in OptimizationResult for downstream leaderboard/charting/JSON export\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M013/slices/S03/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T09:12:07.490Z\nblocker_discovered: false\n---\n\n# S03: Prompt Variant Generator & Automated A/B Loop\n\n**Automated prompt optimization loop: LLM-powered variant generation, iterative scoring, CLI with leaderboard/trajectory output, and JSON result persistence.**\n\n## What Happened\n\nBuilt two core modules and wired them into the existing quality CLI. `variant_generator.py` provides `PromptVariantGenerator` — given a base prompt and its per-dimension scores, it calls the LLM with a meta-prompt to generate N variants targeting the weakest scoring dimensions. Each variant is validated: must differ from the base by ≥50 chars (via line-level symmetric difference), must preserve JSON format markers (`SynthesisResult`, `\"pages\"`). Invalid variants are logged and skipped.\n\n`optimizer.py` provides `OptimizationLoop` — loads the base stage 5 prompt and fixture data, runs a baseline score, then iterates: generate variants → score each via synthesis + the existing 5-dimension scorer → keep the best → repeat. The loop handles LLM errors gracefully (errored variants are skipped, not fatal). `OptimizationResult` captures the full history (iteration, variant index, prompt text, all scores) for downstream reporting.\n\nThe `optimize` CLI subcommand accepts `--stage`, `--iterations`, `--variants-per-iter`, `--file`, and `--output-dir`. Stage validation restricts to stage 5 (others print a clear message and exit 1). After the loop completes, three reporting functions fire: `print_leaderboard()` shows top 5 variants ranked by composite score with per-dimension breakdown; `print_trajectory()` renders a 15-row ASCII chart of best composite per iteration; `write_results_json()` persists the full result with config metadata to a timestamped JSON file.\n\nOne deviation from the plan: `OptimizationLoop._score_variant()` does its own synthesis call rather than delegating to `ScoreRunner.synthesize_and_score()`, because the loop owns the full prompt text directly and bypassing VoiceDial avoids double-application of voice modifiers.\n\n## Verification\n\nAll slice-level verification checks passed:\n\n1. `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')\"` → exit 0\n2. `python -c \"from pipeline.quality.optimizer import OptimizationLoop, OptimizationResult; print('optimizer ok')\"` → exit 0\n3. `python -m pipeline.quality optimize --help` → shows all 5 args (--stage, --iterations, --variants-per-iter, --file, --output-dir)\n4. `python -m pipeline.quality optimize --stage 3 --iterations 1 --file ...` → prints \"only stage 5 is supported\" and exits 1\n5. `backend/pipeline/quality/results/.gitkeep` exists\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nOptimizationLoop._score_variant() performs its own synthesis call instead of delegating to ScoreRunner.synthesize_and_score(). This avoids double-application of VoiceDial modifiers since the optimization loop owns the full prompt text directly.\n\n## Known Limitations\n\nOnly stage 5 optimization is supported — other stages print an error and exit 1. This is by design; S04 will extend to stages 2-4.\n\n## Follow-ups\n\nS04 extends optimization to pipeline stages 2-4 with stage-appropriate scoring dimensions.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/variant_generator.py` — New module: PromptVariantGenerator with meta-prompt, LLM-powered variant generation, and validation (min-diff + format markers)\n- `backend/pipeline/quality/optimizer.py` — New module: OptimizationLoop (generate→score→select cycles) and OptimizationResult dataclass with full history\n- `backend/pipeline/quality/__main__.py` — Added optimize subparser, print_leaderboard(), print_trajectory(), write_results_json() reporting functions\n- `backend/pipeline/quality/results/.gitkeep` — Created results output directory\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/S04/S04-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":"c4292148","parentId":"fd3a12fc","timestamp":"2026-04-01T09:14:06.923Z"}
{"type":"message","id":"dfebcfb1","parentId":"c4292148","timestamp":"2026-04-01T09:14:12.893Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the current codebase to ground the plan in reality before decomposing.","thinkingSignature":"EpACClkIDBgCKkAauW5fwvB1b6DDVquuzgcqzHS6W4I7Yu37oNKzCtniwqo2O1F8vnn+Tiq2PHWDrv+uuVfKdMVrJDE0O8v33nB9Mg9jbGF1ZGUtb3B1cy00LTY4ABIMx6yDfh4rB31pKdFBGgyXUMuKsd6htfWQtsciMJUfk5smkGGSXdnX1hh5IgCKPhYrIzCOfuHQrsHX6l1m6kg1+W2TmEfqUQ1PC8YCViplZdPxk1TsxPUsUHizRyRJNEyuOTkcRRTME223WbosLEfQNIT7nFenKGsSQ5ms1X5CDoWbBp2JJpDlCBZhplHiVA0Zok7/pU2i7+X9e9m9qfYMSlHPYnsdBKsb0S4QKwv6MWdDdokYAQ=="},{"type":"text","text":"Let me explore the current state of the files I'll be modifying."},{"type":"toolCall","id":"toolu_01Bi4DfRt6Nt8TYPFjzoLzaZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/scorer.py"}},{"type":"toolCall","id":"toolu_01GvKnxRwmxNiTuzPfakTj5s","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/optimizer.py"}},{"type":"toolCall","id":"toolu_01JNrVzaLbGQvsZrs9Tbe5BE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/variant_generator.py"}},{"type":"toolCall","id":"toolu_01MnAznUDpXiSed6MSBHfs5B","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":314,"cacheRead":59647,"cacheWrite":9626,"totalTokens":69589,"cost":{"input":0.00001,"output":0.007850000000000001,"cacheRead":0.0298235,"cacheWrite":0.0601625,"total":0.097846}},"stopReason":"toolUse","timestamp":1775034846922}}
{"type":"message","id":"213da8df","parentId":"dfebcfb1","timestamp":"2026-04-01T09:14:12.947Z","message":{"role":"toolResult","toolCallId":"toolu_01Bi4DfRt6Nt8TYPFjzoLzaZ","toolName":"bash","content":[{"type":"text","text":"\"\"\"Stage 5 quality scorer — LLM-as-judge evaluation across 5 dimensions.\n\nEvaluates a synthesized technique page against source moments on:\n1. Structural quality — section naming, count, paragraph depth\n2. Content specificity — concrete details vs vague generalities\n3. Voice preservation — direct quotes, attributed opinions, personality\n4. Readability / flow — synthesis quality, logical ordering, no redundancy\n5. Factual fidelity — no hallucinated specifics, grounded in source moments\n\nRun via: python -m pipeline.quality score --file <path>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport sys\nimport time\nfrom dataclasses import dataclass, field\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.voice_dial import VoiceDial\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Scoring rubric (hardcoded for iteration speed) ───────────────────────────\n\nSCORING_RUBRIC = \"\"\"\\\nYou are an expert evaluator of synthesized technique articles for music production education.\n\nYou will be given:\n1. A synthesized technique page (JSON with title, summary, body_sections)\n2. The source key moments (transcript excerpts, summaries, tags) used to create it\n\nEvaluate the page across these 5 dimensions, scoring each 0.0 to 1.0:\n\n**structural** — Section naming and organization\n- 0.9-1.0: Well-named specific sections (not generic \"Overview\"/\"Tips\"), appropriate count (3-6), 2-5 paragraphs per section\n- 0.5-0.7: Acceptable structure but some generic section names or uneven depth\n- 0.0-0.3: Poor structure — too few/many sections, generic names, single-paragraph sections\n\n**content_specificity** — Concrete technical details\n- 0.9-1.0: Rich in frequencies (Hz), time values (ms), ratios, plugin names, specific settings, dB values\n- 0.5-0.7: Some specific details but padded with vague statements (\"adjust to taste\", \"experiment with settings\")\n- 0.0-0.3: Mostly vague generalities with few concrete values from the source material\n\n**voice_preservation** — Creator's authentic voice\n- 0.9-1.0: Direct quotes preserved, opinions attributed to creator by name, personality and strong views retained\n- 0.5-0.7: Some paraphrased references to creator's views but few direct quotes\n- 0.0-0.3: Encyclopedia style — creator's voice completely smoothed out, no attribution\n\n**readability** — Synthesis quality and flow\n- 0.9-1.0: Reads as a cohesive article, related info merged, logical flow, no redundancy or contradiction\n- 0.5-0.7: Generally readable but some awkward transitions or minor repetition\n- 0.0-0.3: Feels like concatenated bullet points, disjointed, redundant passages\n\n**factual_fidelity** — Grounded in source material\n- 0.9-1.0: Every claim traceable to source moments, no invented plugin names/settings/techniques\n- 0.5-0.7: Mostly grounded but 1-2 details seem embellished or not directly from sources\n- 0.0-0.3: Contains hallucinated specifics — plugin names, settings, or techniques not in sources\n\nReturn ONLY a JSON object with this exact structure:\n{\n  \"structural\": <float 0.0-1.0>,\n  \"content_specificity\": <float 0.0-1.0>,\n  \"voice_preservation\": <float 0.0-1.0>,\n  \"readability\": <float 0.0-1.0>,\n  \"factual_fidelity\": <float 0.0-1.0>,\n  \"justifications\": {\n    \"structural\": \"<1-2 sentence justification>\",\n    \"content_specificity\": \"<1-2 sentence justification>\",\n    \"voice_preservation\": \"<1-2 sentence justification>\",\n    \"readability\": \"<1-2 sentence justification>\",\n    \"factual_fidelity\": \"<1-2 sentence justification>\"\n  }\n}\n\"\"\"\n\nDIMENSIONS = [\n    \"structural\",\n    \"content_specificity\",\n    \"voice_preservation\",\n    \"readability\",\n    \"factual_fidelity\",\n]\n\n\n# ── Result type ──────────────────────────────────────────────────────────────\n\n@dataclass\nclass ScoreResult:\n    \"\"\"Outcome of scoring a technique page across 5 quality dimensions.\"\"\"\n\n    structural: float = 0.0\n    content_specificity: float = 0.0\n    voice_preservation: float = 0.0\n    readability: float = 0.0\n    factual_fidelity: float = 0.0\n    composite: float = 0.0\n    justifications: dict[str, str] = field(default_factory=dict)\n    elapsed_seconds: float = 0.0\n    error: str | None = None\n\n\n# ── Runner ───────────────────────────────────────────────────────────────────\n\nclass ScoreRunner:\n    \"\"\"Scores a Stage 5 technique page using LLM-as-judge evaluation.\"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    def score_page(\n        self,\n        page_json: dict,\n        moments: list[dict],\n    ) -> ScoreResult:\n        \"\"\"Evaluate a technique page against source moments.\n\n        Parameters\n        ----------\n        page_json:\n            Synthesized page dict (title, summary, body_sections).\n        moments:\n            Source key moments with transcript_excerpt, summary, etc.\n\n        Returns\n        -------\n        ScoreResult with per-dimension scores and justifications.\n        \"\"\"\n        # Build the user prompt with the page and source moments\n        user_prompt = (\n            \"## Synthesized Technique Page\\n\\n\"\n            f\"```json\\n{json.dumps(page_json, indent=2)}\\n```\\n\\n\"\n            \"## Source Key Moments\\n\\n\"\n            f\"```json\\n{json.dumps(moments, indent=2)}\\n```\\n\\n\"\n            \"Score this page across all 5 dimensions.\"\n        )\n\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=SCORING_RUBRIC,\n                user_prompt=user_prompt,\n                response_model=BaseModel,  # triggers JSON mode\n                modality=\"chat\",\n            )\n            elapsed = round(time.monotonic() - t0, 2)\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            elapsed = round(time.monotonic() - t0, 2)\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            return ScoreResult(\n                elapsed_seconds=elapsed,\n                error=(\n                    f\"Cannot reach LLM endpoint at {url} (fallback {fallback}). \"\n                    f\"Error: {exc}\"\n                ),\n            )\n\n        # Parse the LLM judge response\n        raw_text = str(resp).strip()\n        try:\n            parsed = json.loads(raw_text)\n        except json.JSONDecodeError:\n            logger.error(\"Malformed judge response (not JSON): %.300s\", raw_text)\n            return ScoreResult(\n                elapsed_seconds=elapsed,\n                error=f\"Malformed judge response (not valid JSON). Raw excerpt: {raw_text[:200]}\",\n            )\n\n        return self._parse_scores(parsed, elapsed)\n\n    def _parse_scores(self, parsed: dict, elapsed: float) -> ScoreResult:\n        \"\"\"Extract and validate scores from parsed JSON response.\"\"\"\n        scores: dict[str, float] = {}\n        justifications: dict[str, str] = {}\n\n        raw_justifications = parsed.get(\"justifications\", {})\n        if not isinstance(raw_justifications, dict):\n            raw_justifications = {}\n\n        for dim in DIMENSIONS:\n            raw = parsed.get(dim)\n            if raw is None:\n                logger.warning(\"Missing dimension '%s' in judge response\", dim)\n                scores[dim] = 0.0\n                justifications[dim] = \"(missing from judge response)\"\n                continue\n\n            try:\n                val = float(raw)\n                scores[dim] = max(0.0, min(1.0, val))  # clamp\n            except (TypeError, ValueError):\n                logger.warning(\"Invalid value for '%s': %r\", dim, raw)\n                scores[dim] = 0.0\n                justifications[dim] = f\"(invalid value: {raw!r})\"\n                continue\n\n            justifications[dim] = str(raw_justifications.get(dim, \"\"))\n\n        composite = sum(scores.values()) / len(DIMENSIONS)\n\n        return ScoreResult(\n            structural=scores[\"structural\"],\n            content_specificity=scores[\"content_specificity\"],\n            voice_preservation=scores[\"voice_preservation\"],\n            readability=scores[\"readability\"],\n            factual_fidelity=scores[\"factual_fidelity\"],\n            composite=round(composite, 3),\n            justifications=justifications,\n            elapsed_seconds=elapsed,\n        )\n\n    def synthesize_and_score(\n        self,\n        moments: list[dict],\n        creator_name: str,\n        voice_level: float,\n    ) -> ScoreResult:\n        \"\"\"Re-synthesize from source moments with a voice-dialed prompt, then score.\n\n        Loads the stage 5 synthesis prompt from disk, applies the VoiceDial\n        modifier at the given voice_level, calls the LLM to produce a\n        SynthesisResult, then scores the first page.\n\n        Parameters\n        ----------\n        moments:\n            Source key moments (dicts with summary, transcript_excerpt, etc.)\n        creator_name:\n            Creator name to inject into the synthesis prompt.\n        voice_level:\n            Float 0.0–1.0 controlling voice preservation intensity.\n\n        Returns\n        -------\n        ScoreResult with per-dimension scores after voice-dialed re-synthesis.\n        \"\"\"\n        from pipeline.schemas import SynthesisResult\n        from pipeline.stages import _get_stage_config, _load_prompt\n\n        # Load and modify the stage 5 system prompt\n        try:\n            base_prompt = _load_prompt(\"stage5_synthesis.txt\")\n        except FileNotFoundError as exc:\n            return ScoreResult(error=f\"Prompt file not found: {exc}\")\n\n        dial = VoiceDial(base_prompt)\n        modified_prompt = dial.modify(voice_level)\n        band = dial.band_name(voice_level)\n\n        # Build user prompt in the same format as _synthesize_chunk\n        moments_json = json.dumps(moments, indent=2)\n        user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        model_override, modality = _get_stage_config(5)\n\n        print(f\"  Re-synthesizing at voice_level={voice_level} (band={band})...\")\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=modified_prompt,\n                user_prompt=user_prompt,\n                response_model=SynthesisResult,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            url = self.client.settings.llm_api_url\n            fallback = self.client.settings.llm_fallback_url\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=(\n                    f\"Cannot reach LLM endpoint at {url} (fallback {fallback}). \"\n                    f\"Error: {exc}\"\n                ),\n            )\n\n        # Parse synthesis response\n        raw_text = str(raw).strip()\n        try:\n            synthesis = self.client.parse_response(raw_text, SynthesisResult)\n        except (json.JSONDecodeError, ValueError, Exception) as exc:\n            logger.error(\"Malformed synthesis response: %.300s\", raw_text)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Malformed synthesis response: {exc}. Raw excerpt: {raw_text[:200]}\",\n            )\n\n        if not synthesis.pages:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=\"Synthesis returned no pages.\",\n            )\n\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        print(f\"  Synthesis complete ({elapsed_synth}s). Scoring...\")\n        result = self.score_page(page_json, moments)\n        # Include synthesis time in total\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def print_report(self, result: ScoreResult) -> None:\n        \"\"\"Print a formatted scoring report to stdout.\"\"\"\n        print(\"\\n\" + \"=\" * 60)\n        print(\"  STAGE 5 QUALITY SCORE REPORT\")\n        print(\"=\" * 60)\n\n        if result.error:\n            print(f\"\\n  ✗ Error: {result.error}\\n\")\n            print(\"=\" * 60 + \"\\n\")\n            return\n\n        for dim in DIMENSIONS:\n            score = getattr(result, dim)\n            bar = self._score_bar(score)\n            justification = result.justifications.get(dim, \"\")\n            print(f\"\\n  {dim.replace('_', ' ').title()}\")\n            print(f\"    Score: {score:.2f}  {bar}\")\n            if justification:\n                # Wrap justification at ~60 chars\n                for line in self._wrap(justification, 56):\n                    print(f\"    {line}\")\n\n        print(\"\\n\" + \"-\" * 60)\n        print(f\"  Composite: {result.composite:.3f}\")\n        print(f\"  Time: {result.elapsed_seconds}s\")\n        print(\"=\" * 60 + \"\\n\")\n\n    @staticmethod\n    def _score_bar(score: float, width: int = 20) -> str:\n        \"\"\"Render a visual bar for a 0-1 score.\"\"\"\n        filled = int(score * width)\n        return \"█\" * filled + \"░\" * (width - filled)\n\n    @staticmethod\n    def _wrap(text: str, width: int) -> list[str]:\n        \"\"\"Simple word wrap.\"\"\"\n        words = text.split()\n        lines: list[str] = []\n        current = \"\"\n        for word in words:\n            if current and len(current) + len(word) + 1 > width:\n                lines.append(current)\n                current = word\n            else:\n                current = f\"{current} {word}\" if current else word\n        if current:\n            lines.append(current)\n        return lines\n"}],"isError":false,"timestamp":1775034852946}}
{"type":"message","id":"6a0a70ea","parentId":"213da8df","timestamp":"2026-04-01T09:14:12.949Z","message":{"role":"toolResult","toolCallId":"toolu_01GvKnxRwmxNiTuzPfakTj5s","toolName":"bash","content":[{"type":"text","text":"\"\"\"Automated prompt optimization loop for Stage 5 synthesis.\n\nOrchestrates a generate→score→select cycle:\n1. Score the current best prompt against reference fixtures\n2. Generate N variants targeting weak dimensions\n3. Score each variant\n4. Keep the best scorer as the new baseline\n5. Repeat for K iterations\n\nUsage (via CLI):\n    python -m pipeline.quality optimize --stage 5 --iterations 10\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, ScoreResult, ScoreRunner\nfrom pipeline.quality.variant_generator import PromptVariantGenerator\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass OptimizationResult:\n    \"\"\"Full result of an optimization run.\"\"\"\n\n    best_prompt: str = \"\"\n    best_score: ScoreResult = field(default_factory=ScoreResult)\n    history: list[dict] = field(default_factory=list)\n    elapsed_seconds: float = 0.0\n\n\nclass OptimizationLoop:\n    \"\"\"Runs iterative prompt optimization for a pipeline stage.\n\n    Each iteration generates *variants_per_iter* prompt mutations,\n    scores each against reference fixture data, and keeps the\n    highest-composite-scoring variant as the new baseline.\n\n    Parameters\n    ----------\n    client:\n        LLMClient instance for LLM calls (synthesis + scoring + variant gen).\n    stage:\n        Pipeline stage number (currently only 5 is supported).\n    fixture_path:\n        Path to a JSON fixture file containing ``creator_name`` and ``moments``.\n    iterations:\n        Number of generate→score→select cycles.\n    variants_per_iter:\n        Number of variant prompts to generate per iteration.\n    \"\"\"\n\n    def __init__(\n        self,\n        client: LLMClient,\n        stage: int,\n        fixture_path: str,\n        iterations: int = 5,\n        variants_per_iter: int = 2,\n    ) -> None:\n        self.client = client\n        self.stage = stage\n        self.fixture_path = fixture_path\n        self.iterations = iterations\n        self.variants_per_iter = variants_per_iter\n\n        self.scorer = ScoreRunner(client)\n        self.generator = PromptVariantGenerator(client)\n\n    def run(self) -> OptimizationResult:\n        \"\"\"Execute the full optimization loop.\n\n        Returns\n        -------\n        OptimizationResult\n            Contains the best prompt, its scores, full iteration history,\n            and wall-clock elapsed time.\n        \"\"\"\n        from pipeline.stages import _load_prompt\n\n        t0 = time.monotonic()\n\n        # Load base prompt\n        prompt_file = f\"stage{self.stage}_synthesis.txt\"\n        try:\n            base_prompt = _load_prompt(prompt_file)\n        except FileNotFoundError:\n            logger.error(\"Prompt file not found: %s\", prompt_file)\n            return OptimizationResult(\n                best_prompt=\"\",\n                best_score=ScoreResult(error=f\"Prompt file not found: {prompt_file}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        # Load fixture data\n        try:\n            fixture = self._load_fixture()\n        except (FileNotFoundError, json.JSONDecodeError, KeyError) as exc:\n            logger.error(\"Failed to load fixture: %s\", exc)\n            return OptimizationResult(\n                best_prompt=base_prompt,\n                best_score=ScoreResult(error=f\"Fixture load error: {exc}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        moments = fixture[\"moments\"]\n        creator_name = fixture[\"creator_name\"]\n        history: list[dict] = []\n\n        # Score the baseline\n        print(f\"\\n{'='*60}\")\n        print(f\"  PROMPT OPTIMIZATION — Stage {self.stage}\")\n        print(f\"  Iterations: {self.iterations}, Variants/iter: {self.variants_per_iter}\")\n        print(f\"{'='*60}\\n\")\n\n        print(\"  Scoring baseline prompt...\")\n        best_score = self.scorer.synthesize_and_score(\n            moments=moments,\n            creator_name=creator_name,\n            voice_level=0.5,\n        )\n        best_prompt = base_prompt\n\n        history.append({\n            \"iteration\": 0,\n            \"variant_index\": 0,\n            \"prompt_text\": base_prompt[:200] + \"...\" if len(base_prompt) > 200 else base_prompt,\n            \"prompt_length\": len(base_prompt),\n            \"composite\": best_score.composite,\n            \"scores\": {d: getattr(best_score, d) for d in DIMENSIONS},\n            \"error\": best_score.error,\n            \"label\": \"baseline\",\n        })\n\n        if best_score.error:\n            print(f\"  ✗ Baseline scoring failed: {best_score.error}\")\n            print(\"  Aborting optimization — fix the baseline first.\\n\")\n            return OptimizationResult(\n                best_prompt=best_prompt,\n                best_score=best_score,\n                history=history,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        self._print_iteration_summary(0, best_score, is_baseline=True)\n\n        # Iterate\n        for iteration in range(1, self.iterations + 1):\n            print(f\"\\n  ── Iteration {iteration}/{self.iterations} ──\")\n\n            # Generate variants\n            variants = self.generator.generate(\n                base_prompt=best_prompt,\n                scores=best_score,\n                n=self.variants_per_iter,\n            )\n\n            if not variants:\n                print(\"  ⚠ No valid variants generated — skipping iteration\")\n                continue\n\n            # Score each variant\n            iteration_best_score = best_score\n            iteration_best_prompt = best_prompt\n\n            for vi, variant_prompt in enumerate(variants):\n                print(f\"  Scoring variant {vi + 1}/{len(variants)}...\")\n\n                # Temporarily replace the base prompt with the variant for synthesis\n                score = self._score_variant(\n                    variant_prompt, moments, creator_name,\n                )\n\n                history.append({\n                    \"iteration\": iteration,\n                    \"variant_index\": vi + 1,\n                    \"prompt_text\": variant_prompt[:200] + \"...\" if len(variant_prompt) > 200 else variant_prompt,\n                    \"prompt_length\": len(variant_prompt),\n                    \"composite\": score.composite,\n                    \"scores\": {d: getattr(score, d) for d in DIMENSIONS},\n                    \"error\": score.error,\n                    \"label\": f\"iter{iteration}_v{vi+1}\",\n                })\n\n                if score.error:\n                    print(f\"    ✗ Variant {vi + 1} errored: {score.error}\")\n                    continue\n\n                if score.composite > iteration_best_score.composite:\n                    iteration_best_score = score\n                    iteration_best_prompt = variant_prompt\n                    print(f\"    ✓ New best: {score.composite:.3f} (was {best_score.composite:.3f})\")\n                else:\n                    print(f\"    · Score {score.composite:.3f} ≤ current best {iteration_best_score.composite:.3f}\")\n\n            # Update global best if this iteration improved\n            if iteration_best_score.composite > best_score.composite:\n                best_score = iteration_best_score\n                best_prompt = iteration_best_prompt\n                print(f\"  ★ Iteration {iteration} improved: {best_score.composite:.3f}\")\n            else:\n                print(f\"  · No improvement in iteration {iteration}\")\n\n            self._print_iteration_summary(iteration, best_score)\n\n        # Final report\n        elapsed = round(time.monotonic() - t0, 2)\n        self._print_final_report(best_score, history, elapsed)\n\n        return OptimizationResult(\n            best_prompt=best_prompt,\n            best_score=best_score,\n            history=history,\n            elapsed_seconds=elapsed,\n        )\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _load_fixture(self) -> dict:\n        \"\"\"Load and validate the fixture JSON file.\"\"\"\n        path = Path(self.fixture_path)\n        if not path.exists():\n            raise FileNotFoundError(f\"Fixture not found: {path}\")\n        data = json.loads(path.read_text(encoding=\"utf-8\"))\n\n        if \"moments\" not in data:\n            raise KeyError(\"Fixture must contain 'moments' key\")\n        if \"creator_name\" not in data:\n            raise KeyError(\"Fixture must contain 'creator_name' key\")\n\n        return data\n\n    def _score_variant(\n        self,\n        variant_prompt: str,\n        moments: list[dict],\n        creator_name: str,\n    ) -> ScoreResult:\n        \"\"\"Score a variant prompt by running synthesis + scoring.\n\n        Uses the variant as a direct system prompt for synthesis, bypassing\n        VoiceDial (the optimization loop owns the full prompt text).\n        \"\"\"\n        from pipeline.schemas import SynthesisResult\n        from pipeline.stages import _get_stage_config\n\n        import json as _json\n        import openai as _openai\n\n        model_override, modality = _get_stage_config(self.stage)\n\n        moments_json = _json.dumps(moments, indent=2)\n        user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=variant_prompt,\n                user_prompt=user_prompt,\n                response_model=SynthesisResult,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (_openai.APIConnectionError, _openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Synthesis LLM error: {exc}\",\n            )\n        except Exception as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            logger.exception(\"Unexpected error during variant synthesis\")\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Unexpected synthesis error: {exc}\",\n            )\n\n        # Parse synthesis\n        raw_text = str(raw).strip()\n        try:\n            synthesis = self.client.parse_response(raw_text, SynthesisResult)\n        except Exception as exc:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Variant synthesis parse error: {exc}\",\n            )\n\n        if not synthesis.pages:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=\"Variant synthesis returned no pages\",\n            )\n\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        result = self.scorer.score_page(page_json, moments)\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def _print_iteration_summary(\n        self,\n        iteration: int,\n        score: ScoreResult,\n        is_baseline: bool = False,\n    ) -> None:\n        \"\"\"Print a compact one-line summary of the current best scores.\"\"\"\n        label = \"BASELINE\" if is_baseline else f\"ITER {iteration}\"\n        dims = \"  \".join(\n            f\"{d[:4]}={getattr(score, d):.2f}\" for d in DIMENSIONS\n        )\n        print(f\"  [{label}] composite={score.composite:.3f}  {dims}\")\n\n    def _print_final_report(\n        self,\n        best_score: ScoreResult,\n        history: list[dict],\n        elapsed: float,\n    ) -> None:\n        \"\"\"Print the final optimization summary.\"\"\"\n        print(f\"\\n{'='*60}\")\n        print(\"  OPTIMIZATION COMPLETE\")\n        print(f\"{'='*60}\")\n        print(f\"  Total time: {elapsed}s\")\n        print(f\"  Iterations: {self.iterations}\")\n        print(f\"  Variants scored: {len(history) - 1}\")  # minus baseline\n\n        baseline_composite = history[0][\"composite\"] if history else 0.0\n        improvement = best_score.composite - baseline_composite\n\n        print(f\"\\n  Baseline composite: {baseline_composite:.3f}\")\n        print(f\"  Best composite:     {best_score.composite:.3f}\")\n        if improvement > 0:\n            print(f\"  Improvement:        +{improvement:.3f}\")\n        else:\n            print(f\"  Improvement:        {improvement:.3f} (no gain)\")\n\n        print(f\"\\n  Per-dimension best scores:\")\n        for d in DIMENSIONS:\n            val = getattr(best_score, d)\n            bar = \"█\" * int(val * 20) + \"░\" * (20 - int(val * 20))\n            print(f\"    {d.replace('_', ' ').title():25s} {val:.2f}  {bar}\")\n\n        errored = sum(1 for h in history if h.get(\"error\"))\n        if errored:\n            print(f\"\\n  ⚠ {errored} variant(s) errored during scoring\")\n\n        print(f\"{'='*60}\\n\")\n"}],"isError":false,"timestamp":1775034852946}}
{"type":"message","id":"00cbfc8e","parentId":"6a0a70ea","timestamp":"2026-04-01T09:14:12.951Z","message":{"role":"toolResult","toolCallId":"toolu_01JNrVzaLbGQvsZrs9Tbe5BE","toolName":"bash","content":[{"type":"text","text":"\"\"\"LLM-powered prompt variant generator for automated optimization.\n\nUses a meta-prompt to instruct the LLM to act as a prompt engineer,\nanalyzing per-dimension scores and producing targeted prompt mutations\nthat improve the weakest scoring dimensions while preserving the JSON\noutput format required by downstream parsing.\n\"\"\"\nfrom __future__ import annotations\n\nimport logging\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, ScoreResult\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Meta-prompt for variant generation ────────────────────────────────────────\n\nVARIANT_META_PROMPT = \"\"\"\\\nYou are an expert prompt engineer specializing in LLM-powered content synthesis.\n\nYour task: given a synthesis prompt and its quality evaluation scores, produce an\nimproved variant of the prompt that targets the weakest-scoring dimensions while\nmaintaining or improving the others.\n\n## Scoring Dimensions (each 0.0–1.0)\n\n- **structural** — Section naming, count (3-6), paragraph depth (2-5 per section)\n- **content_specificity** — Concrete details: frequencies, time values, ratios, plugin names, dB values\n- **voice_preservation** — Direct quotes preserved, opinions attributed to creator by name, personality retained\n- **readability** — Cohesive article flow, related info merged, no redundancy or contradiction\n- **factual_fidelity** — Every claim traceable to source material, no hallucinated specifics\n\n## Rules\n\n1. Focus your changes on the weakest 1-2 dimensions. Don't dilute the prompt by trying to fix everything.\n2. Add specific, actionable instructions — not vague encouragements.\n3. **CRITICAL: You MUST preserve the JSON output format section of the prompt EXACTLY as-is.**\n   The prompt contains instructions about outputting a JSON object with a specific schema\n   (SynthesisResult with \"pages\" containing title, summary, body_sections, etc.).\n   Do NOT modify, remove, or rephrase any part of the JSON format instructions.\n   Your changes should target the prose synthesis guidelines only.\n4. Keep the overall prompt length within 2x of the original. Don't bloat it.\n5. Make substantive changes — rewording a sentence or adding one adjective is not enough.\n\n## Output\n\nReturn ONLY the full modified prompt text. No explanation, no markdown fences, no preamble.\nJust the complete prompt that could be used directly as a system prompt.\n\"\"\"\n\n\n# Format markers that must survive variant generation — if any of these\n# are present in the base prompt, the variant must also contain them.\n_FORMAT_MARKERS = [\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]\n\n\nclass PromptVariantGenerator:\n    \"\"\"Generates prompt variants by asking an LLM to act as a prompt engineer.\n\n    Given a base prompt and its evaluation scores, produces N mutated\n    variants targeting the weakest dimensions.\n    \"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    def generate(\n        self,\n        base_prompt: str,\n        scores: ScoreResult,\n        n: int = 2,\n    ) -> list[str]:\n        \"\"\"Generate up to *n* valid prompt variants.\n\n        Each variant is produced by a separate LLM call with the meta-prompt.\n        Variants are validated: they must differ from the base by ≥50 characters\n        and must contain the JSON format instruction markers found in the base.\n\n        Invalid variants are logged and skipped.\n\n        Parameters\n        ----------\n        base_prompt:\n            The current best synthesis prompt text.\n        scores:\n            ScoreResult from the most recent evaluation of *base_prompt*.\n        n:\n            Number of variants to attempt generating.\n\n        Returns\n        -------\n        list[str]\n            Valid variant prompt strings (may be fewer than *n*).\n        \"\"\"\n        user_prompt = self._build_user_prompt(base_prompt, scores)\n        # Identify which format markers are actually present in the base\n        required_markers = [m for m in _FORMAT_MARKERS if m in base_prompt]\n\n        variants: list[str] = []\n        for i in range(n):\n            logger.info(\"Generating variant %d/%d...\", i + 1, n)\n            try:\n                raw = self.client.complete(\n                    system_prompt=VARIANT_META_PROMPT,\n                    user_prompt=user_prompt,\n                    response_model=None,  # free-form text, not JSON\n                    modality=\"chat\",\n                )\n                variant = str(raw).strip()\n            except Exception:\n                logger.exception(\"LLM error generating variant %d/%d\", i + 1, n)\n                continue\n\n            # Validate the variant\n            if not self._validate(variant, base_prompt, required_markers, i + 1):\n                continue\n\n            variants.append(variant)\n            logger.info(\"Variant %d/%d accepted (%d chars)\", i + 1, n, len(variant))\n\n        logger.info(\n            \"Generated %d valid variant(s) out of %d attempts\", len(variants), n\n        )\n        return variants\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _build_user_prompt(self, base_prompt: str, scores: ScoreResult) -> str:\n        \"\"\"Build the user message describing the current prompt and its scores.\"\"\"\n        # Build per-dimension score lines, sorted worst-first\n        dim_lines: list[str] = []\n        dim_scores = [(d, getattr(scores, d, 0.0)) for d in DIMENSIONS]\n        dim_scores.sort(key=lambda x: x[1])\n\n        for dim, val in dim_scores:\n            justification = scores.justifications.get(dim, \"\")\n            label = dim.replace(\"_\", \" \").title()\n            line = f\"  {label}: {val:.2f}\"\n            if justification:\n                line += f\"  — {justification}\"\n            dim_lines.append(line)\n\n        weakest = dim_scores[0][0].replace(\"_\", \" \").title()\n        second_weakest = dim_scores[1][0].replace(\"_\", \" \").title() if len(dim_scores) > 1 else weakest\n\n        return (\n            f\"## Current Prompt\\n\\n{base_prompt}\\n\\n\"\n            f\"## Evaluation Scores (sorted weakest → strongest)\\n\\n\"\n            + \"\\n\".join(dim_lines)\n            + f\"\\n\\n  Composite: {scores.composite:.3f}\\n\\n\"\n            f\"## Priority\\n\\n\"\n            f\"The weakest dimensions are **{weakest}** and **{second_weakest}**. \"\n            f\"Focus your prompt modifications on improving these.\\n\\n\"\n            f\"Return the full modified prompt now.\"\n        )\n\n    def _validate(\n        self,\n        variant: str,\n        base_prompt: str,\n        required_markers: list[str],\n        index: int,\n    ) -> bool:\n        \"\"\"Check a variant meets minimum quality gates.\"\"\"\n        if not variant:\n            logger.warning(\"Variant %d is empty — skipping\", index)\n            return False\n\n        # Must differ meaningfully from base\n        diff = abs(len(variant) - len(base_prompt))\n        # Also check actual content difference via set-symmetric-difference of lines\n        base_lines = set(base_prompt.splitlines())\n        variant_lines = set(variant.splitlines())\n        changed_lines = len(base_lines.symmetric_difference(variant_lines))\n\n        if diff < 50 and changed_lines < 3:\n            logger.warning(\n                \"Variant %d too similar to base (len_diff=%d, changed_lines=%d) — skipping\",\n                index, diff, changed_lines,\n            )\n            return False\n\n        # Must preserve format markers\n        missing = [m for m in required_markers if m not in variant]\n        if missing:\n            logger.warning(\n                \"Variant %d missing format markers %s — skipping\",\n                index, missing,\n            )\n            return False\n\n        return True\n"}],"isError":false,"timestamp":1775034852948}}
{"type":"message","id":"14b08ff7","parentId":"00cbfc8e","timestamp":"2026-04-01T09:14:12.975Z","message":{"role":"toolResult","toolCallId":"toolu_01MnAznUDpXiSed6MSBHfs5B","toolName":"bash","content":[{"type":"text","text":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness   — Run LLM fitness tests across four categories\n  score     — Score a Stage 5 technique page across 5 quality dimensions\n  optimize  — Automated prompt optimization loop with leaderboard output\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .optimizer import OptimizationLoop, OptimizationResult\nfrom .scorer import DIMENSIONS, ScoreRunner\n\n\n# ── Reporting helpers ────────────────────────────────────────────────────────\n\n\ndef print_leaderboard(result: OptimizationResult) -> None:\n    \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n    # Filter to entries that actually scored (no errors)\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if not scored:\n        print(\"\\n  No successfully scored variants to rank.\\n\")\n        return\n\n    ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n\n    print(f\"\\n{'='*72}\")\n    print(\"  LEADERBOARD — Top 5 Variants by Composite Score\")\n    print(f\"{'='*72}\")\n\n    # Header\n    dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in DIMENSIONS)\n    print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n    print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}\")\n\n    for i, entry in enumerate(ranked, 1):\n        label = entry.get(\"label\", \"?\")[:16]\n        comp = entry[\"composite\"]\n        dim_vals = \"  \".join(\n            f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in DIMENSIONS\n        )\n        bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n        print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n\n    print(f\"{'='*72}\\n\")\n\n\ndef print_trajectory(result: OptimizationResult) -> None:\n    \"\"\"Print an ASCII chart of composite score across iterations.\"\"\"\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if len(scored) < 2:\n        print(\"  (Not enough data points for trajectory chart)\\n\")\n        return\n\n    # Get the best composite per iteration\n    iter_best: dict[int, float] = {}\n    for h in scored:\n        it = h[\"iteration\"]\n        if it not in iter_best or h[\"composite\"] > iter_best[it]:\n            iter_best[it] = h[\"composite\"]\n\n    iterations = sorted(iter_best.keys())\n    values = [iter_best[it] for it in iterations]\n\n    # Chart dimensions\n    chart_height = 15\n    min_val = max(0.0, min(values) - 0.05)\n    max_val = min(1.0, max(values) + 0.05)\n    val_range = max_val - min_val\n    if val_range < 0.01:\n        val_range = 0.1\n        min_val = max(0.0, values[0] - 0.05)\n        max_val = min_val + val_range\n\n    print(f\"  {'─'*50}\")\n    print(\"  SCORE TRAJECTORY — Best Composite per Iteration\")\n    print(f\"  {'─'*50}\")\n    print()\n\n    # Render rows top to bottom\n    for row in range(chart_height, -1, -1):\n        threshold = min_val + (row / chart_height) * val_range\n        # Y-axis label every 5 rows\n        if row % 5 == 0:\n            label = f\"{threshold:.2f}\"\n        else:\n            label = \"     \"\n        line = f\"  {label} │\"\n\n        for vi, val in enumerate(values):\n            normalized = (val - min_val) / val_range\n            filled_rows = int(normalized * chart_height)\n            if filled_rows >= row:\n                line += \" ● \"\n            else:\n                line += \" · \"\n\n        print(line)\n\n    # X-axis\n    print(f\"  ───── ┼{'───' * len(values)}\")\n    x_labels = \"  \" + \"      \"\n    for it in iterations:\n        x_labels += f\"{it:>2d} \"\n    print(x_labels)\n    print(\"        \" + \"  iteration →\")\n    print()\n\n\ndef write_results_json(\n    result: OptimizationResult,\n    output_dir: str,\n    stage: int,\n    iterations: int,\n    variants_per_iter: int,\n    fixture_path: str,\n) -> str:\n    \"\"\"Write optimization results to a timestamped JSON file. Returns the path.\"\"\"\n    out_path = Path(output_dir)\n    out_path.mkdir(parents=True, exist_ok=True)\n\n    timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d_%H%M%S\")\n    filename = f\"optimize_stage{stage}_{timestamp}.json\"\n    filepath = out_path / filename\n\n    payload = {\n        \"config\": {\n            \"stage\": stage,\n            \"iterations\": iterations,\n            \"variants_per_iter\": variants_per_iter,\n            \"fixture_path\": fixture_path,\n        },\n        \"best_prompt\": result.best_prompt,\n        \"best_scores\": {\n            \"composite\": result.best_score.composite,\n            **{d: getattr(result.best_score, d) for d in DIMENSIONS},\n        },\n        \"elapsed_seconds\": result.elapsed_seconds,\n        \"history\": result.history,\n    }\n\n    filepath.write_text(json.dumps(payload, indent=2), encoding=\"utf-8\")\n    return str(filepath)\n\n\n# ── CLI ──────────────────────────────────────────────────────────────────────\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n        default=None,\n        help=\"Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.\",\n    )\n\n    # -- optimize subcommand --\n    opt_parser = sub.add_parser(\n        \"optimize\",\n        help=\"Automated prompt optimization loop with leaderboard output\",\n    )\n    opt_parser.add_argument(\n        \"--stage\",\n        type=int,\n        default=5,\n        help=\"Pipeline stage to optimize (default: 5)\",\n    )\n    opt_parser.add_argument(\n        \"--iterations\",\n        type=int,\n        default=10,\n        help=\"Number of optimization iterations (default: 10)\",\n    )\n    opt_parser.add_argument(\n        \"--variants-per-iter\",\n        type=int,\n        default=2,\n        help=\"Variants generated per iteration (default: 2)\",\n    )\n    opt_parser.add_argument(\n        \"--file\",\n        type=str,\n        required=True,\n        help=\"Path to moments JSON fixture file\",\n    )\n    opt_parser.add_argument(\n        \"--output-dir\",\n        type=str,\n        default=\"backend/pipeline/quality/results/\",\n        help=\"Directory to write result JSON (default: backend/pipeline/quality/results/)\",\n    )\n\n    args = parser.parse_args()\n\n    if args.command is None:\n        parser.print_help()\n        return 1\n\n    if args.command == \"fitness\":\n        settings = get_settings()\n        client = LLMClient(settings)\n        runner = FitnessRunner(client)\n        return runner.run_all()\n\n    if args.command == \"score\":\n        return _run_score(args)\n\n    if args.command == \"optimize\":\n        return _run_optimize(args)\n\n    return 0\n\n\ndef _run_score(args: argparse.Namespace) -> int:\n    \"\"\"Execute the score subcommand.\"\"\"\n    # -- Load source data --\n    if args.slug:\n        print(\"DB loading not yet implemented\", file=sys.stderr)\n        return 1\n\n    try:\n        with open(args.file) as f:\n            data = json.load(f)\n    except FileNotFoundError:\n        print(f\"File not found: {args.file}\", file=sys.stderr)\n        return 1\n    except json.JSONDecodeError as exc:\n        print(f\"Invalid JSON in {args.file}: {exc}\", file=sys.stderr)\n        return 1\n\n    moments = data.get(\"moments\", [])\n    creator_name = data.get(\"creator_name\", \"Unknown\")\n\n    if not moments:\n        print(\"No moments found in input file\", file=sys.stderr)\n        return 1\n\n    settings = get_settings()\n    client = LLMClient(settings)\n    runner = ScoreRunner(client)\n\n    # -- Voice-level mode: re-synthesize then score --\n    if args.voice_level is not None:\n        voice_level = args.voice_level\n        if not (0.0 <= voice_level <= 1.0):\n            print(\"--voice-level must be between 0.0 and 1.0\", file=sys.stderr)\n            return 1\n\n        print(f\"\\nRe-synthesizing + scoring for '{creator_name}' ({len(moments)} moments, voice_level={voice_level})...\")\n        result = runner.synthesize_and_score(moments, creator_name, voice_level)\n\n        if result.error:\n            runner.print_report(result)\n            return 1\n\n        runner.print_report(result)\n        return 0\n\n    # -- Standard mode: build page stub from moments, score directly --\n    page_json = {\n        \"title\": f\"{creator_name} — Technique Page\",\n        \"creator_name\": creator_name,\n        \"summary\": f\"Technique page synthesized from {len(moments)} key moments.\",\n        \"body_sections\": [\n            {\n                \"heading\": m.get(\"topic_tags\", [\"Technique\"])[0] if m.get(\"topic_tags\") else \"Technique\",\n                \"content\": m.get(\"summary\", \"\") + \"\\n\\n\" + m.get(\"transcript_excerpt\", \"\"),\n            }\n            for m in moments\n        ],\n    }\n\n    print(f\"\\nScoring page for '{creator_name}' ({len(moments)} moments)...\")\n\n    result = runner.score_page(page_json, moments)\n\n    if result.error:\n        runner.print_report(result)\n        return 1\n\n    runner.print_report(result)\n    return 0\n\n\ndef _run_optimize(args: argparse.Namespace) -> int:\n    \"\"\"Execute the optimize subcommand.\"\"\"\n    # Stage validation — only stage 5 is supported\n    if args.stage != 5:\n        print(\n            f\"Error: only stage 5 is supported for optimization (got stage {args.stage})\",\n            file=sys.stderr,\n        )\n        return 1\n\n    # Validate fixture file exists\n    fixture = Path(args.file)\n    if not fixture.exists():\n        print(f\"Error: fixture file not found: {args.file}\", file=sys.stderr)\n        return 1\n\n    # Ensure output dir\n    Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n    settings = get_settings()\n    client = LLMClient(settings)\n\n    loop = OptimizationLoop(\n        client=client,\n        stage=args.stage,\n        fixture_path=args.file,\n        iterations=args.iterations,\n        variants_per_iter=args.variants_per_iter,\n    )\n\n    try:\n        result = loop.run()\n    except KeyboardInterrupt:\n        print(\"\\n  Optimization interrupted by user.\", file=sys.stderr)\n        return 130\n    except Exception as exc:\n        print(f\"\\nError: optimization failed: {exc}\", file=sys.stderr)\n        return 1\n\n    # If the loop returned an error on baseline, report and exit\n    if result.best_score.error and not result.history:\n        print(f\"\\nError: {result.best_score.error}\", file=sys.stderr)\n        return 1\n\n    # Reporting\n    print_leaderboard(result)\n    print_trajectory(result)\n\n    # Write results JSON\n    try:\n        json_path = write_results_json(\n            result=result,\n            output_dir=args.output_dir,\n            stage=args.stage,\n            iterations=args.iterations,\n            variants_per_iter=args.variants_per_iter,\n            fixture_path=args.file,\n        )\n        print(f\"  Results written to: {json_path}\")\n    except OSError as exc:\n        print(f\"  Warning: failed to write results JSON: {exc}\", file=sys.stderr)\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775034852973}}
{"type":"message","id":"2b545937","parentId":"14b08ff7","timestamp":"2026-04-01T09:14:18.680Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the prompt files and schemas to understand the exact shapes:"},{"type":"toolCall","id":"toolu_017pNNcyDYN4wU3uqe8RVBmn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/prompts/stage*"}},{"type":"toolCall","id":"toolu_017DLYS5jdpN8tN5R8hBjsTu","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n 'class.*Result' backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_013tAurHYVgNmyyMsxyUZXu4","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/fixtures/sample_moments.json | head -30"}},{"type":"toolCall","id":"toolu_01624i7ok9eAMJ2xuLbcjDxb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/quality/fixtures/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":288,"cacheRead":69273,"cacheWrite":13290,"totalTokens":82852,"cost":{"input":0.000005,"output":0.007200000000000001,"cacheRead":0.0346365,"cacheWrite":0.0830625,"total":0.124904}},"stopReason":"toolUse","timestamp":1775034852974}}
{"type":"message","id":"0d635c22","parentId":"2b545937","timestamp":"2026-04-01T09:14:18.707Z","message":{"role":"toolResult","toolCallId":"toolu_017pNNcyDYN4wU3uqe8RVBmn","toolName":"bash","content":[{"type":"text","text":"ls: cannot access 'backend/prompts/stage*': No such file or directory\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775034858707}}
{"type":"message","id":"a51738e9","parentId":"0d635c22","timestamp":"2026-04-01T09:14:18.708Z","message":{"role":"toolResult","toolCallId":"toolu_017DLYS5jdpN8tN5R8hBjsTu","toolName":"bash","content":[{"type":"text","text":"25:class SegmentationResult(BaseModel):\n45:class ExtractionResult(BaseModel):\n65:class ClassificationResult(BaseModel):\n100:class SynthesisResult(BaseModel):\n"}],"isError":false,"timestamp":1775034858707}}
{"type":"message","id":"4315c6a3","parentId":"a51738e9","timestamp":"2026-04-01T09:14:18.708Z","message":{"role":"toolResult","toolCallId":"toolu_013tAurHYVgNmyyMsxyUZXu4","toolName":"bash","content":[{"type":"text","text":"{\n  \"creator_name\": \"KOAN Sound\",\n  \"topic_category\": \"Sound design\",\n  \"moments\": [\n    {\n      \"summary\": \"Layering snare transients by combining a high-frequency click from a Popcorn Snare with a mid-body from a pitched-down 808 rim shot, blending at -6dB relative offset.\",\n      \"transcript_excerpt\": \"So what I'll do is take the Popcorn Snare — that's got this really sharp click at like 4k — and then I layer underneath it a rim shot pitched down maybe 3 semitones. You blend those together and suddenly you've got this snare that cuts through everything but still has weight.\",\n      \"topic_tags\": [\"snare layering\", \"transient design\", \"sample stacking\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 124.5,\n      \"end_time\": 158.2\n    },\n    {\n      \"summary\": \"Using Serum's noise oscillator with the 'Analog_Crackle' wavetable at 12% mix to add organic texture to bass patches, followed by OTT at 30% depth for glue.\",\n      \"transcript_excerpt\": \"One trick I always come back to is Serum's noise osc with Analog_Crackle. You don't want it loud — like 12 percent mix — just enough that the bass feels alive. Then slap OTT on there at maybe 30 percent depth and it glues the whole thing together without squashing it.\",\n      \"topic_tags\": [\"bass design\", \"Serum\", \"OTT\", \"texture\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 203.1,\n      \"end_time\": 241.7\n    },\n    {\n      \"summary\": \"Resampling technique: bounce a bass patch to audio, chop the best 2 bars, then re-pitch in Simpler with warp off for tighter timing and consistent tone.\",\n      \"transcript_excerpt\": \"I'll resample everything. Bounce it down, find the two bars that sound best, throw it in Simpler with warp completely off. Now you've got this tight, consistent thing where every hit is exactly the same energy. The pitch tracking is way more predictable too.\",\n      \"topic_tags\": [\"resampling\", \"Ableton\", \"Simpler\", \"bass production\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 312.0,\n      \"end_time\": 349.8\n    },\n    {\n      \"summary\": \"Parallel compression chain for drums using Ableton's Drum Buss at 40% drive into a return track with Valhalla Room at 1.2s decay, mixed at -12dB.\",\n"}],"isError":false,"timestamp":1775034858708}}
{"type":"message","id":"82e8d8f6","parentId":"4315c6a3","timestamp":"2026-04-01T09:14:18.726Z","message":{"role":"toolResult","toolCallId":"toolu_01624i7ok9eAMJ2xuLbcjDxb","toolName":"bash","content":[{"type":"text","text":"__init__.py\nsample_moments.json\n"}],"isError":false,"timestamp":1775034858724}}
{"type":"message","id":"dbada19c","parentId":"82e8d8f6","timestamp":"2026-04-01T09:14:23.135Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DFjGvERDNR9nnXrFE5681p","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn 'prompts_path\\|load_prompt' backend/pipeline/stages.py | head -10"}},{"type":"toolCall","id":"toolu_015M4fHproZGfAmxgEFi1ign","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn 'def _load_prompt\\|def _get_stage_config' backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_018Yyo7Lm4EMCtQmTjdzm4NW","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":214,"cacheRead":82563,"cacheWrite":1148,"totalTokens":83926,"cost":{"input":0.000005,"output":0.005350000000000001,"cacheRead":0.0412815,"cacheWrite":0.007175,"total":0.0538115}},"stopReason":"toolUse","timestamp":1775034858725}}
{"type":"message","id":"db99b1b0","parentId":"dbada19c","timestamp":"2026-04-01T09:14:23.147Z","message":{"role":"toolResult","toolCallId":"toolu_01DFjGvERDNR9nnXrFE5681p","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str) -> str:\n250:    path = Path(settings.prompts_path) / template_name\n401:        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n480:        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n653:        system_prompt = _load_prompt(\"stage4_classification.txt\")\n786:    prompts_path = Path(settings.prompts_path)\n797:        filepath = prompts_path / filename\n972:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\")\n1083:        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n"}],"isError":false,"timestamp":1775034863147}}
{"type":"message","id":"b212a5a7","parentId":"db99b1b0","timestamp":"2026-04-01T09:14:23.162Z","message":{"role":"toolResult","toolCallId":"toolu_015M4fHproZGfAmxgEFi1ign","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str) -> str:\n262:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n"}],"isError":false,"timestamp":1775034863162}}
{"type":"message","id":"19ca693a","parentId":"b212a5a7","timestamp":"2026-04-01T09:14:23.166Z","message":{"role":"toolResult","toolCallId":"toolu_018Yyo7Lm4EMCtQmTjdzm4NW","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775034863164}}
{"type":"message","id":"8bce7a74","parentId":"19ca693a","timestamp":"2026-04-01T09:14:27.216Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the prompt files and the `_get_stage_config` function:"},{"type":"toolCall","id":"toolu_01DF71U3aZKMs9cNZ88qu8Za","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name 'stage*_*.txt' 2>/dev/null"}},{"type":"toolCall","id":"toolu_016ebzAKAWBcHzRHLYjC3zZV","name":"read","arguments":{"limit":40,"offset":244,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":83711,"cacheWrite":1534,"totalTokens":85426,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.0418555,"cacheWrite":0.0095875,"total":0.055948}},"stopReason":"toolUse","timestamp":1775034863165}}
{"type":"message","id":"2a149f50","parentId":"8bce7a74","timestamp":"2026-04-01T09:14:27.265Z","message":{"role":"toolResult","toolCallId":"toolu_01DF71U3aZKMs9cNZ88qu8Za","toolName":"bash","content":[{"type":"text","text":"./prompts/stage3_extraction.txt\n./prompts/stage5_synthesis.txt\n./prompts/stage2_segmentation.txt\n./prompts/stage4_classification.txt\n"}],"isError":false,"timestamp":1775034867264}}
{"type":"message","id":"063cfc22","parentId":"2a149f50","timestamp":"2026-04-01T09:14:27.269Z","message":{"role":"toolResult","toolCallId":"toolu_016ebzAKAWBcHzRHLYjC3zZV","toolName":"read","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\n[1496 more lines in file. Use offset=284 to continue.]"}],"isError":false,"timestamp":1775034867264}}
{"type":"message","id":"859aab87","parentId":"063cfc22","timestamp":"2026-04-01T09:14:32.233Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I now have a thorough understanding of the codebase. Let me read the templates and then plan."},{"type":"toolCall","id":"toolu_01EtHmU96XxCxDQ9b8nFGAGm","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_019YmwqfyXRPCX7ZzZtwWRDD","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":144,"cacheRead":85245,"cacheWrite":730,"totalTokens":86120,"cost":{"input":0.000005,"output":0.0036000000000000003,"cacheRead":0.0426225,"cacheWrite":0.004562500000000001,"total":0.05079}},"stopReason":"toolUse","timestamp":1775034867266}}
{"type":"message","id":"74d9f793","parentId":"859aab87","timestamp":"2026-04-01T09:14:32.238Z","message":{"role":"toolResult","toolCallId":"toolu_01EtHmU96XxCxDQ9b8nFGAGm","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":1775034872237}}
{"type":"message","id":"492cda33","parentId":"74d9f793","timestamp":"2026-04-01T09:14:32.244Z","message":{"role":"toolResult","toolCallId":"toolu_019YmwqfyXRPCX7ZzZtwWRDD","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":1775034872242}}
{"type":"message","id":"1da2fee2","parentId":"492cda33","timestamp":"2026-04-01T09:15:38.276Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me decompose this slice.\n\nThe work is:\n1. Generalize scorer.py with a stage config registry (rubrics, dimensions, format markers per stage)\n2. Generalize optimizer.py to dispatch per stage (fixture loading, _score_variant per stage schema)\n3. Generalize variant_generator.py (meta-prompt, format markers per stage)\n4. Create fixtures for stages 2-4\n5. Remove stage-5 gate in CLI, add stage validation (2-5)\n6. Verify end-to-end\n\nThe research doc suggests 3 natural seams. I think 2 tasks is right:\n\n\nT01 handles the scorer registry and variant generator generalization—defining stage configs with rubrics, dimensions, and format markers, then updating the variant generator to use per-stage markers and meta-prompts. T02 tackles optimizer generalization, fixture creation for stages 2-4, CLI updates to remove the stage-5 gate, and end-to-end verification. Actually, the research points to three seams: scorer and rubric registry, optimizer and variant generator, and fixtures. Let me reconsider whether I should split this differently.\n\nI think two tasks works better: T01 is the foundation layer—generalizing the scorer with a stage config registry, generalizing ScoreResult to use dicts, and generalizing the variant generator's format markers and meta-prompt. T02 is integration and fixtures—making the optimizer stage-aware for _score_variant dispatch and fixture loading, creating fixtures for stages 2-4, removing the CLI gate, and verifying everything connects properly.","thinkingSignature":"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"},{"type":"text","text":"I have a solid understanding of the codebase. The research doc's analysis is accurate. The work breaks into two natural seams: (1) the scoring/generation foundation — generalizing the rubric, dimensions, ScoreResult, and variant generator per stage; and (2) the integration layer — making the optimizer stage-aware, creating fixtures, and wiring the CLI.\n\nTwo tasks is right. The scorer registry is the foundation everything else depends on, and the optimizer/CLI/fixtures are the integration that makes it usable."},{"type":"toolCall","id":"toolu_01DhXpXzro86UqGxJimzQ7RZ","name":"gsd_plan_slice","arguments":{"goal":"Extend the prompt optimization loop from stage-5-only to stages 2-4, with stage-appropriate scoring rubrics, fixture formats, and variant validation — so `optimize --stage N` works for any pipeline stage.","integrationClosure":"- Upstream: `backend/pipeline/quality/scorer.py` (ScoreRunner, SCORING_RUBRIC, DIMENSIONS), `backend/pipeline/quality/optimizer.py` (OptimizationLoop), `backend/pipeline/quality/variant_generator.py` (PromptVariantGenerator, _FORMAT_MARKERS), `backend/pipeline/quality/__main__.py` (_run_optimize stage gate)\n- New wiring: STAGE_CONFIGS registry maps stage number → rubric/dimensions/format_markers/fixture_keys/prompt_file/schema. OptimizationLoop._score_variant dispatches to correct schema per stage. Variant generator reads format markers from registry.\n- What remains: nothing — after this slice, all stages 2-5 are optimizable via CLI","milestoneId":"M013","observabilityImpact":"None — this slice extends existing CLI tooling with no new runtime surfaces.","proofLevel":"Contract-level: import checks and CLI argument validation prove the wiring is correct. Full runtime proof requires a live LLM endpoint (not required for slice completion).","sliceId":"S04","successCriteria":"- `python -m pipeline.quality optimize --stage 2 --iterations 1 --file <fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 3 --iterations 1 --file <fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 4 --iterations 1 --file <fixture>` runs without error\n- `python -m pipeline.quality optimize --stage 5 --iterations 1 --file <fixture>` still works (no regression)\n- `python -m pipeline.quality optimize --stage 6` → clear error message, exit 1\n- All modified modules import cleanly: `python -c \"from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult; ...\"`","tasks":[{"description":"Build the STAGE_CONFIGS registry in scorer.py that maps each pipeline stage (2-5) to its scoring rubric, dimensions list, format markers, fixture key requirements, prompt file name, and output schema class. Generalize ScoreResult to use a `scores: dict[str, float]` field instead of 5 named fields (keep backward compat via properties). Add a `score_stage_output()` method to ScoreRunner that accepts arbitrary stage output + input and scores using the stage's rubric. Update variant_generator.py to accept format markers as a parameter rather than using the hardcoded `_FORMAT_MARKERS` list, and generalize the meta-prompt to work for any stage (not just synthesis).\n\n## Context\n\nThe existing scorer has a hardcoded `SCORING_RUBRIC` with 5 stage-5 dimensions (structural, content_specificity, voice_preservation, readability, factual_fidelity). `ScoreResult` has these as named float fields. The variant generator has hardcoded `_FORMAT_MARKERS = ['SynthesisResult', '\"pages\"', 'body_sections', 'title', 'summary']` and a `VARIANT_META_PROMPT` that references 'synthesis prompt' language.\n\nStages 2-4 need different rubrics:\n- Stage 2 (segmentation): coverage_completeness, topic_specificity, boundary_accuracy, summary_quality\n- Stage 3 (extraction): moment_richness, timestamp_accuracy, content_type_correctness, summary_actionability, plugin_normalization  \n- Stage 4 (classification): category_accuracy, tag_completeness, tag_specificity, coverage, no_overlap\n\nFormat markers per stage:\n- Stage 2: `'segments'`, `'start_index'`, `'end_index'`, `'topic_label'`\n- Stage 3: `'moments'`, `'content_type'`, `'raw_transcript'`, `'plugins'`\n- Stage 4: `'classifications'`, `'moment_index'`, `'topic_category'`, `'topic_tags'`\n- Stage 5: `'SynthesisResult'`, `'\"pages\"'`, `'body_sections'`, `'title'`, `'summary'` (existing)\n\nPrompt files: `stage2_segmentation.txt`, `stage3_extraction.txt`, `stage4_classification.txt`, `stage5_synthesis.txt`\nSchemas: `SegmentationResult`, `ExtractionResult`, `ClassificationResult`, `SynthesisResult` (all in `pipeline.schemas`)","estimate":"1.5h","expectedOutput":["`backend/pipeline/quality/scorer.py` — STAGE_CONFIGS registry, generalized ScoreResult with scores dict, score_stage_output() method on ScoreRunner","`backend/pipeline/quality/variant_generator.py` — generalized generate() accepting format_markers parameter, stage-agnostic VARIANT_META_PROMPT"],"files":["backend/pipeline/quality/scorer.py","backend/pipeline/quality/variant_generator.py"],"inputs":["`backend/pipeline/quality/scorer.py` — existing ScoreRunner, SCORING_RUBRIC, DIMENSIONS, ScoreResult","`backend/pipeline/quality/variant_generator.py` — existing PromptVariantGenerator, VARIANT_META_PROMPT, _FORMAT_MARKERS","`backend/pipeline/schemas.py` — SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult schemas"],"taskId":"T01","title":"Generalize scorer with stage config registry and update variant generator","verify":"cd backend && python -c \"from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, ScoreRunner, DIMENSIONS; assert 2 in STAGE_CONFIGS and 3 in STAGE_CONFIGS and 4 in STAGE_CONFIGS and 5 in STAGE_CONFIGS; r = ScoreResult(scores={'structural': 0.8, 'readability': 0.7}, composite=0.75); print('scorer ok')\" && python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')\""},{"description":"Make OptimizationLoop stage-aware: generalize _load_fixture() to validate stage-specific keys, generalize _score_variant() to call the correct prompt and parse the correct schema per stage, and pass stage-appropriate format markers to the variant generator. Create minimal fixture JSON files for stages 2-4. Remove the stage-5 gate in __main__.py's _run_optimize(), add validation for stages 2-5. Verify all stages import and CLI accepts them.\n\n## Context\n\nThe optimizer currently has three stage-5-specific hardcodings:\n1. `_load_fixture()` expects `creator_name` and `moments` keys — stages 2-4 have different input shapes\n2. `_score_variant()` calls synthesis via `SynthesisResult` schema and formats output as a technique page for the scorer\n3. The `run()` method loads `stage{N}_synthesis.txt` — stages 2-4 use different prompt file names\n\nThe CLI's `_run_optimize()` rejects `args.stage != 5` with an error.\n\nStage fixture shapes (from research):\n- Stage 2: `{transcript_segments: [{index, start_time, end_time, text}]}` — segments of a transcript\n- Stage 3: `{topic_label, segments: [{start_time, end_time, text}]}` — a topic group to extract moments from\n- Stage 4: `{moments: [{title, summary, content_type, plugins}], taxonomy: \"...\"}` — moments to classify\n- Stage 5: `{creator_name, moments: [...]}` (existing sample_moments.json)\n\nThe STAGE_CONFIGS registry from T01 provides the prompt filename, schema class, dimensions, and format markers per stage. This task uses that registry to dispatch correctly.\n\nAfter changes, `OptimizationLoop.run()` should:\n1. Load the prompt file from `STAGE_CONFIGS[stage]['prompt_file']`\n2. Load and validate fixture using `STAGE_CONFIGS[stage]['fixture_keys']`\n3. In `_score_variant()`, use the stage's schema to parse LLM output, then format it for the scorer's `score_stage_output()`\n4. Pass stage-appropriate format markers to variant generator","estimate":"2h","expectedOutput":["`backend/pipeline/quality/optimizer.py` — stage-aware OptimizationLoop with generalized _load_fixture, _score_variant, and format marker dispatch","`backend/pipeline/quality/__main__.py` — _run_optimize accepts stages 2-5, rejects others","`backend/pipeline/quality/fixtures/sample_segments.json` — stage 2 fixture","`backend/pipeline/quality/fixtures/sample_topic_group.json` — stage 3 fixture","`backend/pipeline/quality/fixtures/sample_classifications.json` — stage 4 fixture"],"files":["backend/pipeline/quality/optimizer.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/fixtures/sample_segments.json","backend/pipeline/quality/fixtures/sample_topic_group.json","backend/pipeline/quality/fixtures/sample_classifications.json"],"inputs":["`backend/pipeline/quality/scorer.py` — STAGE_CONFIGS registry and score_stage_output() from T01","`backend/pipeline/quality/variant_generator.py` — generalized generate() from T01","`backend/pipeline/quality/optimizer.py` — existing OptimizationLoop","`backend/pipeline/quality/__main__.py` — existing _run_optimize with stage-5 gate","`backend/pipeline/quality/fixtures/sample_moments.json` — existing stage 5 fixture for regression check"],"taskId":"T02","title":"Generalize optimizer, create stage 2-4 fixtures, wire CLI, verify end-to-end","verify":"cd backend && python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\" && python -c \"from pipeline.quality.__main__ import main; print('cli ok')\" && python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 | head -1 && python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage' && echo 'stage6 rejected ok'"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3397,"cacheRead":85975,"cacheWrite":2752,"totalTokens":92125,"cost":{"input":0.000005,"output":0.084925,"cacheRead":0.0429875,"cacheWrite":0.0172,"total":0.1451175}},"stopReason":"toolUse","timestamp":1775034872242}}
{"type":"message","id":"ca6a30f0","parentId":"1da2fee2","timestamp":"2026-04-01T09:15:38.327Z","message":{"role":"toolResult","toolCallId":"toolu_01DhXpXzro86UqGxJimzQ7RZ","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S04 (M013)"}],"details":{"operation":"plan_slice","milestoneId":"M013","sliceId":"S04","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/S04-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1775034938325}}
{"type":"message","id":"c66024ad","parentId":"ca6a30f0","timestamp":"2026-04-01T09:15:45.209Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 planned.\n\n**Decomposition reasoning:** Two tasks with a clean dependency boundary. T01 builds the foundation — the `STAGE_CONFIGS` registry, generalized `ScoreResult`, and stage-agnostic variant generator. T02 consumes that foundation to make the optimizer dispatch correctly per stage, creates fixture data, and removes the CLI gate. This ordering ensures T02 has stable APIs to build on.\n\nThe natural seam is data model (T01) → integration (T02). Combining into one task would be ~15 files and 10+ steps, exceeding scope guidelines. Three tasks would over-split — the fixture creation, CLI wiring, and optimizer changes are tightly coupled and belong together."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":163,"cacheRead":88727,"cacheWrite":2939,"totalTokens":91830,"cost":{"input":0.000005,"output":0.0040750000000000005,"cacheRead":0.0443635,"cacheWrite":0.01836875,"total":0.06681225}},"stopReason":"stop","timestamp":1775034938326}}
