{"type":"thinking_level_change","id":"cd394e9f","parentId":null,"timestamp":"2026-04-01T09:03:42.395Z","thinkingLevel":"medium"}
{"type":"model_change","id":"50bef1e6","parentId":"cd394e9f","timestamp":"2026-04-01T09:03:42.397Z","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/S03/S03-PLAN.md` and `.gsd/milestones/M013/slices/S03/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 S03 planned.\"\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S03 (\"Prompt Variant Generator & Automated A/B Loop\") — 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/S03/S03-RESEARCH.md`\n\n# S03 Research: Prompt Variant Generator & Automated A/B Loop\n\n## Summary\n\nThis slice builds an automated optimization loop that generates prompt variants for Stage 5 synthesis, scores each against reference articles using the existing `ScoreRunner`, and outputs a leaderboard with score trajectories. The core deliverable is `python -m pipeline.quality optimize --stage 5 --iterations 10`.\n\nThe existing infrastructure is solid: `ScoreRunner` handles scoring, `VoiceDial` demonstrates prompt modification patterns, `LLMClient` handles LLM calls with primary/fallback. The new work is: (1) an LLM-powered prompt variant generator, (2) an optimization loop orchestrator, (3) leaderboard + trajectory reporting.\n\n## Recommendation\n\nBuild in three tasks:\n\n1. **Prompt Variant Generator** — `PromptVariantGenerator` class that uses the LLM to mutate a base prompt, producing N variants per iteration. This is the riskiest piece (LLM must produce valid, meaningfully different prompts).\n2. **Optimization Loop** — `OptimizationLoop` class that orchestrates: load base prompt → generate variants → score each → select winners → repeat. Stores results in a simple JSON log file.\n3. **CLI + Reporting** — Wire the `optimize` subcommand into `__main__.py`, add leaderboard table and score trajectory ASCII chart output.\n\n## Implementation Landscape\n\n### Existing Code to Build On\n\n| File | What it provides | How S03 uses it |\n|------|-----------------|-----------------|\n| `backend/pipeline/quality/scorer.py` | `ScoreRunner.score_page()`, `ScoreRunner.synthesize_and_score()`, `ScoreResult` dataclass | Core scoring for each variant. `synthesize_and_score()` already chains synthesis → scoring. |\n| `backend/pipeline/quality/voice_dial.py` | `VoiceDial` class with band-based prompt modification | Pattern reference for prompt modification. S03's variant generator is more flexible — LLM-driven mutations vs fixed bands. |\n| `backend/pipeline/quality/__main__.py` | CLI with `fitness` and `score` subcommands | Add `optimize` subcommand here. |\n| `backend/pipeline/quality/__init__.py` | `sys.path` bootstrap for project-root execution | Already handles CWD resolution. |\n| `backend/pipeline/stages.py` | `_load_prompt()`, `_get_stage_config()` | Load the base prompt template, get model/modality config. Already used by `synthesize_and_score()`. |\n| `backend/pipeline/schemas.py` | `SynthesisResult`, `SynthesizedPage` | Parse synthesis output. Already used by `synthesize_and_score()`. |\n| `backend/pipeline/llm_client.py` | `LLMClient.complete()`, `LLMClient.parse_response()` | Drive both variant generation and synthesis calls. |\n| `backend/pipeline/quality/fixtures/sample_moments.json` | 6-moment fixture file | Default test data for optimization runs. |\n| `prompts/stage5_synthesis.txt` | 129-line Stage 5 synthesis prompt | Base prompt to optimize. Path: `./prompts/` from backend CWD or project root via symlink. |\n\n### Prompt Variant Generation Strategy\n\nThe generator should use the LLM itself to produce prompt variants. Approach:\n\n1. **Meta-prompt**: A system prompt instructing the LLM to act as a prompt engineer. Given the current best prompt and its scores, produce a variant that targets the weakest dimension(s).\n2. **Mutation types**: The meta-prompt should guide the LLM toward specific mutation strategies:\n   - **Targeted improvement**: Focus on the lowest-scoring dimension (e.g., if `structural` is 0.4, modify section naming instructions)\n   - **Balanced tweak**: Small modifications across multiple dimensions\n   - **Creative restructure**: Reorganize prompt sections or add new constraints\n3. **Constraint**: Variants must preserve the core JSON output format instructions (the synthesis must still produce valid `SynthesisResult` JSON). The meta-prompt must instruct the LLM not to modify the output format section.\n4. **Output**: The variant generator returns the full modified prompt text as a string.\n\nKey risk: The LLM might produce variants that are too similar to the base or that break the output format. Mitigation: validate that the variant differs by at least N characters from the base, and that it still contains the JSON format instructions.\n\n### Optimization Loop Design\n\n```\nOptimizationLoop:\n  __init__(client, stage, fixture_path, iterations, variants_per_iter)\n  \n  run() → OptimizationResult:\n    base_prompt = _load_prompt(f\"stage{stage}_synthesis.txt\")\n    baseline_score = synthesize_and_score(base_prompt, moments)\n    \n    best = (base_prompt, baseline_score)\n    history = [baseline_score]\n    \n    for i in range(iterations):\n      variants = generator.generate(best.prompt, best.scores, n=variants_per_iter)\n      for variant in variants:\n        score = synthesize_and_score(variant, moments)\n        history.append(score)\n        if score.composite > best.composite:\n          best = (variant, score)\n    \n    return OptimizationResult(best, history, leaderboard)\n```\n\nDesign decisions to note:\n- **Variants per iteration**: Default 2-3 (each requires a synthesis + scoring LLM call, so cost is ~2 calls per variant per iteration).\n- **Selection strategy**: Simple greedy — keep the best-scoring variant as the seed for next iteration. No population/crossover complexity.\n- **Persistence**: Write a JSON log file after each iteration with prompt text, scores, and metadata. This allows resuming and provides the data for trajectory charts.\n- **Stage parameter**: Initially only `--stage 5` works. The `--stage` flag is present for S04 expansion.\n\n### Reporting\n\n- **Leaderboard**: Top N variants by composite score, showing per-dimension breakdown. Use formatted terminal output (existing `print_report` pattern or `rich` library which is installed).\n- **Score trajectory**: ASCII line chart showing composite score across iterations. Can use a simple text-based chart (20-30 lines of code) — no need for matplotlib. The iteration count is small (10-20) so ASCII is sufficient.\n- **Output file**: Save full results to a JSON file at `backend/pipeline/quality/results/` for later analysis.\n\n### CLI Interface\n\n```\npython -m pipeline.quality optimize \\\n  --stage 5 \\\n  --iterations 10 \\\n  --variants-per-iter 2 \\\n  --file backend/pipeline/quality/fixtures/sample_moments.json \\\n  [--output-dir backend/pipeline/quality/results/]\n```\n\nAdd to `__main__.py` alongside existing `fitness` and `score` subcommands.\n\n### Dependencies\n\nNo new pip packages needed. `rich` is already installed for table output if desired, but plain formatted print (matching existing `print_report` style) is simpler and consistent.\n\n### File Plan\n\n| File | Action | Description |\n|------|--------|-------------|\n| `backend/pipeline/quality/variant_generator.py` | Create | `PromptVariantGenerator` class with LLM-powered prompt mutation |\n| `backend/pipeline/quality/optimizer.py` | Create | `OptimizationLoop` class orchestrating generate→score→select cycle |\n| `backend/pipeline/quality/__main__.py` | Modify | Add `optimize` subcommand |\n| `backend/pipeline/quality/results/` | Create dir | Output directory for optimization run results |\n\n### Verification Strategy\n\n1. `python -m pipeline.quality optimize --help` — shows --stage, --iterations, --variants-per-iter, --file args\n2. Import check: `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('ok')\"`\n3. Import check: `python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('ok')\"`\n4. Dry-run with fixture data (will fail at LLM call but should show clean error, not traceback): `python -m pipeline.quality optimize --stage 5 --iterations 1 --file backend/pipeline/quality/fixtures/sample_moments.json`\n5. Unit-level: `PromptVariantGenerator` meta-prompt construction can be tested without LLM by verifying prompt assembly\n\n### Risks\n\n1. **LLM variant quality**: The meta-prompt for generating variants is the trickiest part. If the LLM produces near-identical variants or breaks output format, the loop produces no improvement. Mitigation: validate variant diff and format preservation.\n2. **Cost per iteration**: Each variant needs synthesis + scoring = 2 LLM calls. 10 iterations × 2 variants = 40 LLM calls total. With a slow/rate-limited endpoint, this could take 10-20 minutes. Add progress output per iteration.\n3. **Prompt file path resolution**: Known issue from S02 — `_load_prompt` uses `settings.prompts_path` which defaults to `./prompts`. Works from backend/ but from project root needs the symlink. Already handled by existing symlink setup.\n\n### Natural Task Seams\n\n1. **T01: PromptVariantGenerator** — Self-contained class in new file. Testable via import + prompt assembly check. No dependency on optimizer.\n2. **T02: OptimizationLoop + CLI** — Depends on T01. Wires generator + scorer into loop, adds CLI subcommand, adds reporting/output.\n\nTwo tasks is the natural decomposition. T01 is the riskiest (meta-prompt design), T02 is integration + reporting.\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#### S02 Summary\nSource: `.gsd/milestones/M013/slices/S02/S02-SUMMARY.md`\n\n---\nid: S02\nparent: M013\nmilestone: M013\nprovides:\n  - ScoreRunner with score_page() and synthesize_and_score() methods\n  - VoiceDial class for 3-band prompt modification\n  - CLI score subcommand with --file, --slug, --voice-level\n  - 6-moment fixture file for offline testing\nrequires:\n  []\naffects:\n  - S03\nkey_files:\n  - backend/pipeline/quality/scorer.py\n  - backend/pipeline/quality/voice_dial.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/__init__.py\n  - backend/pipeline/quality/fixtures/sample_moments.json\n  - pipeline\nkey_decisions:\n  - Hardcoded scoring rubric in scorer.py rather than external prompt file — faster iteration during quality toolkit development\n  - Three discrete voice bands (low/mid/high) at 0.33/0.67 boundaries instead of continuous interpolation — simpler to reason about and test\n  - Mid band returns base prompt unmodified since existing stage5 prompt already targets ~0.6 voice preservation\n  - Project-root symlink + __init__.py sys.path bootstrap to support running from any CWD\npatterns_established:\n  - sys.path bootstrap in package __init__.py using os.path.realpath() to resolve through symlinks\n  - VoiceDial band-based prompt modification pattern reusable for other prompt dimension dials\nobservability_surfaces:\n  - Score report output with per-dimension scores and justification excerpts\n  - Band identification in voice-level output (band=low/mid/high)\n  - Clean connectivity error with endpoint URL on LLM failure\ndrill_down_paths:\n  - .gsd/milestones/M013/slices/S02/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M013/slices/S02/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T09:01:48.179Z\nblocker_discovered: false\n---\n\n# S02: Stage 5 Quality Scorer & Voice Preservation Dial\n\n**Built 5-dimension LLM-as-judge scorer with CLI `score` subcommand and 3-band voice preservation dial that modifies Stage 5 synthesis prompts, runnable from both project root and backend/ directory.**\n\n## What Happened\n\nT01 created the scoring infrastructure: `ScoreRunner` with a `ScoreResult` dataclass covering 5 quality dimensions (structural, content_specificity, voice_preservation, readability, factual_fidelity), each scored 0.0–1.0 by an LLM judge via a hardcoded rubric prompt. The `score` subcommand was added to the existing `pipeline.quality` CLI with mutually exclusive `--file`/`--slug` inputs and an optional `--voice-level` parameter. A 6-moment fixture file (`sample_moments.json`) with realistic music production content provides offline testing data.\n\nT02 added the `VoiceDial` class implementing three discrete bands (low ≤0.33, mid 0.34–0.66, high ≥0.67) that modify the Stage 5 synthesis system prompt. Low band appends voice suppression instructions (neutral third-person, no direct quotes). Mid band passes the base prompt through unmodified (it already targets ~0.6 voice preservation). High band appends amplification instructions (maximize direct quotes, preserve personality). `ScoreRunner.synthesize_and_score()` chains re-synthesis through the modified prompt followed by scoring.\n\nPost-task fix: The verification gate ran `python -m pipeline.quality score --help` from the project root, but the `pipeline` package lives under `backend/`. Added a `pipeline` symlink at project root pointing to `backend/pipeline`, and added a `sys.path` bootstrap in `backend/pipeline/quality/__init__.py` that resolves through the symlink to add `backend/` to `sys.path`. This ensures `config` and `pipeline.llm_client` imports resolve regardless of CWD.\n\n## Verification\n\nAll slice verification checks pass from both project root and backend/ directory:\n\n1. `python -m pipeline.quality score --help` — shows --file, --slug, --voice-level args (exit 0)\n2. `python -c \"from pipeline.quality.scorer import ScoreRunner, ScoreResult; print('ok')\"` — import succeeds (exit 0)\n3. `python -c \"from pipeline.quality.voice_dial import VoiceDial; vd = VoiceDial('base'); assert vd.modify(0.1) != vd.modify(0.5); assert vd.modify(0.5) != vd.modify(0.9)\"` — three distinct bands confirmed (exit 0)\n4. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json` — clean connectivity error, exit 1, no traceback\n5. `python -m pipeline.quality score --file backend/pipeline/quality/fixtures/sample_moments.json --voice-level 0.3` — re-synthesis path, clean connectivity error, exit 1, no traceback\n6. Fixture validation: 6 moments with required fields present\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nAdded project-root `pipeline` symlink and `sys.path` bootstrap in `__init__.py` to support running `python -m pipeline.quality` from the project root (not just from `backend/`). This was not in the original plan but required by the verification gate.\n\n## Known Limitations\n\n`--slug` path prints \"DB loading not yet implemented\" and exits 1 (deferred by design). Voice-level path requires `prompts/stage5_synthesis.txt` to be reachable from CWD — works in Docker (WORKDIR=/app) and from `backend/`, but from project root the prompt file path resolves differently.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/scorer.py` — New: ScoreResult dataclass + ScoreRunner with score_page() and synthesize_and_score() methods\n- `backend/pipeline/quality/voice_dial.py` — New: VoiceDial class with 3-band prompt modification (low/mid/high)\n- `backend/pipeline/quality/__main__.py` — Added score subcommand with --file, --slug, --voice-level args\n- `backend/pipeline/quality/__init__.py` — Added sys.path bootstrap for project-root execution\n- `backend/pipeline/quality/fixtures/sample_moments.json` — New: 6-moment fixture with realistic music production content\n- `backend/pipeline/quality/fixtures/__init__.py` — New: empty package marker\n- `pipeline` — New: symlink to backend/pipeline for project-root execution\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/S03/S03-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":"be373e8d","parentId":"50bef1e6","timestamp":"2026-04-01T09:03:42.400Z"}
{"type":"message","id":"c76fed41","parentId":"be373e8d","timestamp":"2026-04-01T09:03:51.952Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the existing codebase to ground the plan in reality before decomposing.","thinkingSignature":"EpECClkIDBgCKkBTofKe65E6RLF2rNDZnWLAWPwrlHJA4GtyegMqAQQyA0cD0TS7dyCdGtPWlZvBk7dj1Av+uQNiG7+gTcjBlHwEMg9jbGF1ZGUtb3B1cy00LTY4ABIMZAT0DNh90awp/5txGgy9Htm/k8gHWSnwYTEiMK2IoS/Zb3uBfwhpp8kdUrAcEUMRee2e1L1zOWX+t50F/DYAama4O6FdPjDW3sbpuipmbOY+LwWdnSeX2W47mgBJR+hmq414O+QxtVBpQHFIkflFvF1muJTDRekGwqiBbKuPAPzN6XIqcA2JCiFwuhqw4By1F/gMxNDd1owyOSmRxPUmqqV9m5LStpbF0lgWrJX4T+uFGXkgGAE="},{"type":"text","text":"Let me explore the existing code to verify what's in place and confirm the research findings."},{"type":"toolCall","id":"toolu_01ET25LYzpxnCfDUfLomkjKb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/scorer.py"}},{"type":"toolCall","id":"toolu_018xS94R3WUS7Q6AAbXrkaYJ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/__main__.py"}},{"type":"toolCall","id":"toolu_019MgY1wupCtQpVY3gxipJBK","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/voice_dial.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":256,"cacheRead":59647,"cacheWrite":10075,"totalTokens":69980,"cost":{"input":0.00001,"output":0.0064,"cacheRead":0.0298235,"cacheWrite":0.06296875,"total":0.09920225}},"stopReason":"toolUse","timestamp":1775034222398}}
{"type":"message","id":"0c5fc206","parentId":"c76fed41","timestamp":"2026-04-01T09:03:51.991Z","message":{"role":"toolResult","toolCallId":"toolu_01ET25LYzpxnCfDUfLomkjKb","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":1775034231990}}
{"type":"message","id":"0358fc2c","parentId":"0c5fc206","timestamp":"2026-04-01T09:03:51.992Z","message":{"role":"toolResult","toolCallId":"toolu_018xS94R3WUS7Q6AAbXrkaYJ","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\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .scorer import ScoreRunner\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n        default=None,\n        help=\"Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.\",\n    )\n\n    args = parser.parse_args()\n\n    if args.command is None:\n        parser.print_help()\n        return 1\n\n    if args.command == \"fitness\":\n        settings = get_settings()\n        client = LLMClient(settings)\n        runner = FitnessRunner(client)\n        return runner.run_all()\n\n    if args.command == \"score\":\n        return _run_score(args)\n\n    return 0\n\n\ndef _run_score(args: argparse.Namespace) -> int:\n    \"\"\"Execute the score subcommand.\"\"\"\n    # -- Load source data --\n    if args.slug:\n        print(\"DB loading not yet implemented\", file=sys.stderr)\n        return 1\n\n    try:\n        with open(args.file) as f:\n            data = json.load(f)\n    except FileNotFoundError:\n        print(f\"File not found: {args.file}\", file=sys.stderr)\n        return 1\n    except json.JSONDecodeError as exc:\n        print(f\"Invalid JSON in {args.file}: {exc}\", file=sys.stderr)\n        return 1\n\n    moments = data.get(\"moments\", [])\n    creator_name = data.get(\"creator_name\", \"Unknown\")\n\n    if not moments:\n        print(\"No moments found in input file\", file=sys.stderr)\n        return 1\n\n    settings = get_settings()\n    client = LLMClient(settings)\n    runner = ScoreRunner(client)\n\n    # -- Voice-level mode: re-synthesize then score --\n    if args.voice_level is not None:\n        voice_level = args.voice_level\n        if not (0.0 <= voice_level <= 1.0):\n            print(\"--voice-level must be between 0.0 and 1.0\", file=sys.stderr)\n            return 1\n\n        print(f\"\\nRe-synthesizing + scoring for '{creator_name}' ({len(moments)} moments, voice_level={voice_level})...\")\n        result = runner.synthesize_and_score(moments, creator_name, voice_level)\n\n        if result.error:\n            runner.print_report(result)\n            return 1\n\n        runner.print_report(result)\n        return 0\n\n    # -- Standard mode: build page stub from moments, score directly --\n    page_json = {\n        \"title\": f\"{creator_name} — Technique Page\",\n        \"creator_name\": creator_name,\n        \"summary\": f\"Technique page synthesized from {len(moments)} key moments.\",\n        \"body_sections\": [\n            {\n                \"heading\": m.get(\"topic_tags\", [\"Technique\"])[0] if m.get(\"topic_tags\") else \"Technique\",\n                \"content\": m.get(\"summary\", \"\") + \"\\n\\n\" + m.get(\"transcript_excerpt\", \"\"),\n            }\n            for m in moments\n        ],\n    }\n\n    print(f\"\\nScoring page for '{creator_name}' ({len(moments)} moments)...\")\n\n    result = runner.score_page(page_json, moments)\n\n    if result.error:\n        runner.print_report(result)\n        return 1\n\n    runner.print_report(result)\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775034231990}}
{"type":"message","id":"b38e9406","parentId":"0358fc2c","timestamp":"2026-04-01T09:03:52.007Z","message":{"role":"toolResult","toolCallId":"toolu_019MgY1wupCtQpVY3gxipJBK","toolName":"bash","content":[{"type":"text","text":"\"\"\"Voice preservation dial — modifies Stage 5 synthesis prompt by intensity band.\n\nThree bands control how much of the creator's original voice is preserved:\n  - Low  (0.0–0.33): Clinical, encyclopedic tone — suppress direct quotes\n  - Mid  (0.34–0.66): Base prompt unchanged (already ~0.6 voice preservation)\n  - High (0.67–1.0): Maximum voice — prioritize exact words, strong opinions\n\"\"\"\nfrom __future__ import annotations\n\n\n# ── Band modifier text ────────────────────────────────────────────────────────\n\n_LOW_BAND_MODIFIER = \"\"\"\n\n## Voice Suppression Override\n\nIMPORTANT — override the voice/tone guidelines above. For this synthesis:\n\n- Do NOT include any direct quotes from the creator. Rephrase all insights in neutral third-person encyclopedic style.\n- Do NOT attribute opinions or preferences to the creator by name (avoid \"he recommends\", \"she prefers\").\n- Remove all personality markers, humor, strong opinions, and conversational tone.\n- Write as a reference manual: factual, impersonal, technically precise.\n- Replace phrases like \"he warns against\" with neutral statements like \"this approach is generally avoided because.\"\n- Suppress colloquialisms and informal language entirely.\n\"\"\"\n\n_HIGH_BAND_MODIFIER = \"\"\"\n\n## Maximum Voice Preservation Override\n\nIMPORTANT — amplify the voice/tone guidelines above. For this synthesis:\n\n- Maximize the use of direct quotes from the transcript. Every memorable phrase, vivid metaphor, or strong opinion should be quoted verbatim with quotation marks.\n- Attribute all insights, preferences, and techniques to the creator by name — use their name frequently.\n- Preserve personality, humor, strong opinions, and conversational tone. If the creator is emphatic, the prose should feel emphatic.\n- Prioritize the creator's exact words over paraphrase. When a transcript excerpt contains a usable phrase, quote it rather than summarizing it.\n- Include warnings, caveats, and opinionated asides in the creator's own voice.\n- The resulting page should feel like the creator is speaking directly to the reader through the text.\n\"\"\"\n\n\n# ── VoiceDial class ───────────────────────────────────────────────────────────\n\nclass VoiceDial:\n    \"\"\"Modifies a Stage 5 synthesis prompt based on a voice_level parameter.\n\n    Parameters\n    ----------\n    base_prompt:\n        The original stage5_synthesis.txt system prompt content.\n    \"\"\"\n\n    # Band boundaries\n    LOW_UPPER = 0.33\n    HIGH_LOWER = 0.67\n\n    def __init__(self, base_prompt: str) -> None:\n        self.base_prompt = base_prompt\n\n    def modify(self, voice_level: float) -> str:\n        \"\"\"Return the system prompt modified for the given voice_level.\n\n        Parameters\n        ----------\n        voice_level:\n            Float 0.0–1.0. Values outside this range are clamped.\n\n        Returns\n        -------\n        str\n            Modified system prompt with band-appropriate instructions appended.\n        \"\"\"\n        voice_level = max(0.0, min(1.0, voice_level))\n\n        if voice_level <= self.LOW_UPPER:\n            return self.base_prompt + _LOW_BAND_MODIFIER\n        elif voice_level >= self.HIGH_LOWER:\n            return self.base_prompt + _HIGH_BAND_MODIFIER\n        else:\n            # Mid band — base prompt is already moderate voice preservation\n            return self.base_prompt\n\n    @staticmethod\n    def band_name(voice_level: float) -> str:\n        \"\"\"Return the human-readable band name for a voice_level value.\"\"\"\n        voice_level = max(0.0, min(1.0, voice_level))\n        if voice_level <= VoiceDial.LOW_UPPER:\n            return \"low\"\n        elif voice_level >= VoiceDial.HIGH_LOWER:\n            return \"high\"\n        return \"mid\"\n"}],"isError":false,"timestamp":1775034232005}}
{"type":"message","id":"abcefbc0","parentId":"b38e9406","timestamp":"2026-04-01T09:03:56.968Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RxW2Bzzy2SZMMADNa8PxKV","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/llm_client.py"}},{"type":"toolCall","id":"toolu_01HwfQNvcZ7yLfJSesXdBzsd","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -60 backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01XkpBoqSgykj6wqMprEAeQn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/quality/fixtures/sample_moments.json | python3 -c \"import json,sys; d=json.load(sys.stdin); print('keys:', list(d.keys())); print('creator:', d.get('creator_name')); print('moments count:', len(d.get('moments',[])))\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":268,"cacheRead":69722,"cacheWrite":6321,"totalTokens":76312,"cost":{"input":0.000005,"output":0.0067,"cacheRead":0.034860999999999996,"cacheWrite":0.03950625,"total":0.08107225}},"stopReason":"toolUse","timestamp":1775034232006}}
{"type":"message","id":"7e812449","parentId":"abcefbc0","timestamp":"2026-04-01T09:03:57.013Z","message":{"role":"toolResult","toolCallId":"toolu_01RxW2Bzzy2SZMMADNa8PxKV","toolName":"bash","content":[{"type":"text","text":"\"\"\"Synchronous LLM client with primary/fallback endpoint logic.\n\nUses the OpenAI-compatible API (works with Ollama, vLLM, OpenWebUI, etc.).\nCelery tasks run synchronously, so this uses ``openai.OpenAI`` (not Async).\n\nSupports two modalities:\n- **chat**: Standard JSON mode with ``response_format: {\"type\": \"json_object\"}``\n- **thinking**: For reasoning models that emit ``<think>...</think>`` blocks\n  before their answer. Skips ``response_format``, appends JSON instructions to\n  the system prompt, and strips think tags from the response.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport re\nfrom typing import TYPE_CHECKING, TypeVar\n\nif TYPE_CHECKING:\n    from collections.abc import Callable\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\nT = TypeVar(\"T\", bound=BaseModel)\n\n\n# ── LLM Response wrapper ─────────────────────────────────────────────────────\n\nclass LLMResponse(str):\n    \"\"\"String subclass that carries LLM response metadata.\n\n    Backward-compatible with all code that treats the response as a plain\n    string, but callers that know about it can inspect finish_reason and\n    the truncated property.\n    \"\"\"\n    finish_reason: str | None\n    prompt_tokens: int | None\n    completion_tokens: int | None\n\n    def __new__(\n        cls,\n        text: str,\n        finish_reason: str | None = None,\n        prompt_tokens: int | None = None,\n        completion_tokens: int | None = None,\n    ):\n        obj = super().__new__(cls, text)\n        obj.finish_reason = finish_reason\n        obj.prompt_tokens = prompt_tokens\n        obj.completion_tokens = completion_tokens\n        return obj\n\n    @property\n    def truncated(self) -> bool:\n        \"\"\"True if the model hit its token limit before finishing.\"\"\"\n        return self.finish_reason == \"length\"\n\n# ── Think-tag stripping ──────────────────────────────────────────────────────\n\n_THINK_PATTERN = re.compile(r\"<think>.*?</think>\", re.DOTALL)\n\n\ndef strip_think_tags(text: str) -> str:\n    \"\"\"Remove ``<think>...</think>`` blocks from LLM output.\n\n    Thinking/reasoning models often prefix their JSON with a reasoning trace\n    wrapped in ``<think>`` tags. This strips all such blocks (including\n    multiline and multiple occurrences) and returns the cleaned text.\n\n    Handles:\n    - Single ``<think>...</think>`` block\n    - Multiple blocks in one response\n    - Multiline content inside think tags\n    - Responses with no think tags (passthrough)\n    - Empty input (passthrough)\n    \"\"\"\n    if not text:\n        return text\n    cleaned = _THINK_PATTERN.sub(\"\", text)\n    return cleaned.strip()\n\n\n\n# ── Token estimation ─────────────────────────────────────────────────────────\n\n# Stage-specific output multipliers: estimated output tokens as a ratio of input tokens.\n# Tuned from actual pipeline data (KCL Ep 31 audit, April 2026):\n#   stage2: actual compl/prompt = 680/39312 = 0.017 → use 0.05 with buffer\n#   stage3: actual compl/prompt ≈ 1000/7000 = 0.14 → use 0.3 with buffer\n#   stage4: actual compl/prompt = 740/3736 = 0.20 → use 0.3 with buffer\n#   stage5: actual compl/prompt ≈ 2500/7000 = 0.36 → use 0.8 with buffer\n_STAGE_OUTPUT_RATIOS: dict[str, float] = {\n    \"stage2_segmentation\": 0.05,   # Compact topic groups — much smaller than input\n    \"stage3_extraction\": 0.3,      # Key moments with summaries — moderate\n    \"stage4_classification\": 0.3,  # Tags + categories per moment\n    \"stage5_synthesis\": 0.8,       # Full prose technique pages — heaviest output\n}\n\n# Minimum floor so we never send a trivially small max_tokens\n_MIN_MAX_TOKENS = 4096\n\n\ndef estimate_tokens(text: str) -> int:\n    \"\"\"Estimate token count from text using a chars-per-token heuristic.\n\n    Uses 3.5 chars/token which is conservative for English + JSON markup.\n    \"\"\"\n    if not text:\n        return 0\n    return max(1, int(len(text) / 3.5))\n\n\ndef estimate_max_tokens(\n    system_prompt: str,\n    user_prompt: str,\n    stage: str | None = None,\n    hard_limit: int = 32768,\n) -> int:\n    \"\"\"Estimate the max_tokens parameter for an LLM call.\n\n    Calculates expected output size based on input size and stage-specific\n    multipliers. The result is clamped between _MIN_MAX_TOKENS and hard_limit.\n\n    Parameters\n    ----------\n    system_prompt:\n        The system prompt text.\n    user_prompt:\n        The user prompt text (transcript, moments, etc.).\n    stage:\n        Pipeline stage name (e.g. \"stage3_extraction\"). If None or unknown,\n        uses a default 1.0x multiplier.\n    hard_limit:\n        Absolute ceiling — never exceed this value.\n\n    Returns\n    -------\n    int\n        Estimated max_tokens value to pass to the LLM API.\n    \"\"\"\n    input_tokens = estimate_tokens(system_prompt) + estimate_tokens(user_prompt)\n    ratio = _STAGE_OUTPUT_RATIOS.get(stage or \"\", 1.0)\n    estimated_output = int(input_tokens * ratio)\n\n    # Add a 50% buffer for JSON overhead and variability\n    estimated_output = int(estimated_output * 1.5)\n\n    # Clamp to [_MIN_MAX_TOKENS, hard_limit]\n    result = max(_MIN_MAX_TOKENS, min(estimated_output, hard_limit))\n\n    logger.info(\n        \"Token estimate: input≈%d, stage=%s, ratio=%.2f, estimated_output=%d, max_tokens=%d (hard_limit=%d)\",\n        input_tokens, stage or \"default\", ratio, estimated_output, result, hard_limit,\n    )\n    return result\n\n\nclass LLMClient:\n    \"\"\"Sync LLM client that tries a primary endpoint and falls back on failure.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._primary = openai.OpenAI(\n            base_url=settings.llm_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._fallback = openai.OpenAI(\n            base_url=settings.llm_fallback_url,\n            api_key=settings.llm_api_key,\n        )\n\n    # ── Core completion ──────────────────────────────────────────────────\n\n    def complete(\n        self,\n        system_prompt: str,\n        user_prompt: str,\n        response_model: type[BaseModel] | None = None,\n        modality: str = \"chat\",\n        model_override: str | None = None,\n        on_complete: \"Callable | None\" = None,\n        max_tokens: int | None = None,\n    ) -> \"LLMResponse\":\n        \"\"\"Send a chat completion request, falling back on connection/timeout errors.\n\n        Parameters\n        ----------\n        system_prompt:\n            System message content.\n        user_prompt:\n            User message content.\n        response_model:\n            If provided and modality is \"chat\", ``response_format`` is set to\n            ``{\"type\": \"json_object\"}``. For \"thinking\" modality, JSON\n            instructions are appended to the system prompt instead.\n        modality:\n            Either \"chat\" (default) or \"thinking\". Thinking modality skips\n            response_format and strips ``<think>`` tags from output.\n        model_override:\n            Model name to use instead of the default. If None, uses the\n            configured default for the endpoint.\n        max_tokens:\n            Override for max_tokens on this call. If None, falls back to\n            the configured ``llm_max_tokens`` from settings.\n\n        Returns\n        -------\n        LLMResponse\n            Raw completion text (str subclass) with finish_reason metadata.\n        \"\"\"\n        kwargs: dict = {}\n        effective_system = system_prompt\n\n        if modality == \"thinking\":\n            # Thinking models often don't support response_format: json_object.\n            # Instead, append explicit JSON instructions to the system prompt.\n            if response_model is not None:\n                json_schema_hint = (\n                    \"\\n\\nYou MUST respond with ONLY valid JSON. \"\n                    \"No markdown code fences, no explanation, no preamble — \"\n                    \"just the raw JSON object.\"\n                )\n                effective_system = system_prompt + json_schema_hint\n        else:\n            # Chat modality — use standard JSON mode\n            if response_model is not None:\n                kwargs[\"response_format\"] = {\"type\": \"json_object\"}\n\n        messages = [\n            {\"role\": \"system\", \"content\": effective_system},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n        primary_model = model_override or self.settings.llm_model\n        fallback_model = self.settings.llm_fallback_model\n        effective_max_tokens = max_tokens if max_tokens is not None else self.settings.llm_max_tokens\n        effective_temperature = self.settings.llm_temperature\n\n        logger.info(\n            \"LLM request: model=%s, modality=%s, response_model=%s, max_tokens=%d, temperature=%.1f\",\n            primary_model,\n            modality,\n            response_model.__name__ if response_model else None,\n            effective_max_tokens,\n            effective_temperature,\n        )\n\n        # --- Try primary endpoint ---\n        try:\n            response = self._primary.chat.completions.create(\n                model=primary_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response: prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=primary_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Primary LLM endpoint failed (%s: %s), trying fallback at %s\",\n                type(exc).__name__,\n                exc,\n                self.settings.llm_fallback_url,\n            )\n\n        # --- Try fallback endpoint ---\n        try:\n            response = self._fallback.chat.completions.create(\n                model=fallback_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                temperature=effective_temperature,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response (fallback): prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            finish = response.choices[0].finish_reason if response.choices else None\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=fallback_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=finish,\n                        is_fallback=True,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return LLMResponse(\n                raw,\n                finish_reason=finish,\n                prompt_tokens=usage.prompt_tokens if usage else None,\n                completion_tokens=usage.completion_tokens if usage else None,\n            )\n\n        except (openai.APIConnectionError, openai.APITimeoutError, openai.APIError) as exc:\n            logger.error(\n                \"Fallback LLM endpoint also failed (%s: %s). Giving up.\",\n                type(exc).__name__,\n                exc,\n            )\n            raise\n\n    # ── Response parsing ─────────────────────────────────────────────────\n\n    def parse_response(self, text: str, model: type[T]) -> T:\n        \"\"\"Parse raw LLM output as JSON and validate against a Pydantic model.\n\n        Parameters\n        ----------\n        text:\n            Raw JSON string from the LLM.\n        model:\n            Pydantic model class to validate against.\n\n        Returns\n        -------\n        T\n            Validated Pydantic model instance.\n\n        Raises\n        ------\n        pydantic.ValidationError\n            If the JSON doesn't match the schema.\n        ValueError\n            If the text is not valid JSON.\n        \"\"\"\n        try:\n            return model.model_validate_json(text)\n        except Exception:\n            logger.error(\n                \"Failed to parse LLM response as %s. Response text: %.500s\",\n                model.__name__,\n                text,\n            )\n            raise\n"}],"isError":false,"timestamp":1775034237012}}
{"type":"message","id":"fd9e9d08","parentId":"7e812449","timestamp":"2026-04-01T09:03:57.014Z","message":{"role":"toolResult","toolCallId":"toolu_01HwfQNvcZ7yLfJSesXdBzsd","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport logging\nimport subprocess\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom celery import chain as celery_chain\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, func, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient, LLMResponse, estimate_max_tokens\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n\nclass LLMTruncationError(RuntimeError):\n    \"\"\"Raised when the LLM response was truncated (finish_reason=length).\"\"\"\n    pass\n\n\n# ── Error status helper ──────────────────────────────────────────────────────\n\ndef _set_error_status(video_id: str, stage_name: str, error: Exception) -> None:\n    \"\"\"Mark a video as errored when a pipeline stage fails permanently.\"\"\"\n"}],"isError":false,"timestamp":1775034237012}}
{"type":"message","id":"bd4a0c0c","parentId":"fd9e9d08","timestamp":"2026-04-01T09:03:57.032Z","message":{"role":"toolResult","toolCallId":"toolu_01XkpBoqSgykj6wqMprEAeQn","toolName":"bash","content":[{"type":"text","text":"keys: ['creator_name', 'topic_category', 'moments']\ncreator: KOAN Sound\nmoments count: 6\n"}],"isError":false,"timestamp":1775034237031}}
{"type":"message","id":"f04c7c55","parentId":"bd4a0c0c","timestamp":"2026-04-01T09:04:02.588Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LNJ4v6nqsuDnHJPoo88wBT","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n '_load_prompt\\|_get_stage_config' backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_0194hvzb9GATLcEqyo6NatiE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la backend/pipeline/quality/results/ 2>/dev/null; echo \"---\"; ls backend/pipeline/quality/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":175,"cacheRead":76043,"cacheWrite":4565,"totalTokens":80784,"cost":{"input":0.000005,"output":0.004375,"cacheRead":0.0380215,"cacheWrite":0.02853125,"total":0.07093275}},"stopReason":"toolUse","timestamp":1775034237031}}
{"type":"message","id":"8b9a64b9","parentId":"f04c7c55","timestamp":"2026-04-01T09:04:02.609Z","message":{"role":"toolResult","toolCallId":"toolu_01LNJ4v6nqsuDnHJPoo88wBT","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]:\n401:        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n405:        model_override, modality = _get_stage_config(2)\n480:        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n482:        model_override, modality = _get_stage_config(3)\n653:        system_prompt = _load_prompt(\"stage4_classification.txt\")\n655:        model_override, modality = _get_stage_config(4)\n972:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\")\n1083:        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n1085:        model_override, modality = _get_stage_config(5)\n"}],"isError":false,"timestamp":1775034242608}}
{"type":"message","id":"578822a5","parentId":"8b9a64b9","timestamp":"2026-04-01T09:04:02.615Z","message":{"role":"toolResult","toolCallId":"toolu_0194hvzb9GATLcEqyo6NatiE","toolName":"bash","content":[{"type":"text","text":"---\nfitness.py\nfixtures\n__init__.py\n__main__.py\n__pycache__\nscorer.py\nvoice_dial.py\n"}],"isError":false,"timestamp":1775034242612}}
{"type":"message","id":"8be588ba","parentId":"578822a5","timestamp":"2026-04-01T09:04:07.142Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the templates."},{"type":"toolCall","id":"toolu_019kw2JjwaoZpVPyZSRJnNWa","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01Q1zUcNj3VhwhrnuXsKXXza","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":127,"cacheRead":80608,"cacheWrite":499,"totalTokens":81235,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.040304,"cacheWrite":0.00311875,"total":0.04660275}},"stopReason":"toolUse","timestamp":1775034242613}}
{"type":"message","id":"c6cf80c6","parentId":"8be588ba","timestamp":"2026-04-01T09:04:07.147Z","message":{"role":"toolResult","toolCallId":"toolu_019kw2JjwaoZpVPyZSRJnNWa","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":1775034247146}}
{"type":"message","id":"03a763c7","parentId":"c6cf80c6","timestamp":"2026-04-01T09:04:07.154Z","message":{"role":"toolResult","toolCallId":"toolu_01Q1zUcNj3VhwhrnuXsKXXza","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":1775034247151}}
{"type":"message","id":"0522214f","parentId":"03a763c7","timestamp":"2026-04-01T09:04:19.623Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good. I have a clear picture. The research doc's two-task decomposition is well-reasoned. Let me plan:\n\nT01: PromptVariantGenerator + OptimizationLoop (the core engine)\nT02: CLI wiring + reporting + results output\n\nActually, re-reading the research more carefully, T01 is the variant generator (self-contained), T02 is the loop + CLI + reporting. That makes sense because:\n- T01 is the riskiest piece (meta-prompt design)\n- T02 integrates everything\n\nLet me think about whether 2 tasks is right. \n\nThe variant generator is maybe 100 lines and the optimization loop with CLI and reporting is probably 200-300 lines—both fit comfortably in a single context window, so 2 tasks is the right call. For verification, I can't rely on live LLM calls in CI, so I'll focus on import checks, CLI help output, unit-testable pieces like prompt assembly and chart rendering, and a dry-run that gracefully stops at the LLM boundary.","thinkingSignature":"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"},{"type":"text","text":"Good picture of the codebase. The research's two-task decomposition maps well to the natural seams. T01 builds the variant generator and optimization loop as a self-contained engine. T02 wires the CLI subcommand and adds reporting output. Let me check the `_load_prompt` and `_get_stage_config` signatures to ensure the task plans have correct details."},{"type":"toolCall","id":"toolu_01MsSCREgAADG2EmCToJEXSw","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && sed -n '244,300p' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":446,"cacheRead":81107,"cacheWrite":2735,"totalTokens":84289,"cost":{"input":0.000005,"output":0.01115,"cacheRead":0.0405535,"cacheWrite":0.01709375,"total":0.06880225}},"stopReason":"toolUse","timestamp":1775034247152}}
{"type":"message","id":"46cdad01","parentId":"0522214f","timestamp":"2026-04-01T09:04:19.644Z","message":{"role":"toolResult","toolCallId":"toolu_01MsSCREgAADG2EmCToJEXSw","toolName":"bash","content":[{"type":"text","text":"def _load_prompt(template_name: str) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    Raises FileNotFoundError if the template does not exist.\n    \"\"\"\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n    if not path.exists():\n        logger.error(\"Prompt template not found: %s\", path)\n        raise FileNotFoundError(f\"Prompt template not found: {path}\")\n    return path.read_text(encoding=\"utf-8\")\n\n\ndef _get_llm_client() -> LLMClient:\n    \"\"\"Return an LLMClient configured from settings.\"\"\"\n    return LLMClient(get_settings())\n\n\ndef _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n    \"\"\"Return (model_override, modality) for a pipeline stage.\n\n    Reads stage-specific config from Settings. If the stage-specific model\n    is None/empty, returns None (LLMClient will use its default). If the\n    stage-specific modality is unset, defaults to \"chat\".\n    \"\"\"\n    settings = get_settings()\n    model = getattr(settings, f\"llm_stage{stage_num}_model\", None) or None\n    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n    return model, modality\n\n\ndef _load_canonical_tags() -> dict:\n    \"\"\"Load canonical tag taxonomy from config/canonical_tags.yaml.\"\"\"\n    # Walk up from backend/ to find config/\n    candidates = [\n        Path(\"config/canonical_tags.yaml\"),\n        Path(\"../config/canonical_tags.yaml\"),\n    ]\n    for candidate in candidates:\n        if candidate.exists():\n            with open(candidate, encoding=\"utf-8\") as f:\n                return yaml.safe_load(f)\n    raise FileNotFoundError(\n        \"canonical_tags.yaml not found. Searched: \" + \", \".join(str(c) for c in candidates)\n    )\n\n\ndef _format_taxonomy_for_prompt(tags_data: dict) -> str:\n    \"\"\"Format the canonical tags taxonomy as readable text for the LLM prompt.\"\"\"\n    lines = []\n    for cat in tags_data.get(\"categories\", []):\n        lines.append(f\"Category: {cat['name']}\")\n        lines.append(f\"  Description: {cat['description']}\")\n        lines.append(f\"  Sub-topics: {', '.join(cat.get('sub_topics', []))}\")\n        lines.append(\"\")\n    return \"\\n\".join(lines)\n\n"}],"isError":false,"timestamp":1775034259641}}
{"type":"message","id":"cf1be476","parentId":"46cdad01","timestamp":"2026-04-01T09:05:22.492Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a solid understanding. Let me plan the slice.\n\nTwo tasks is right:\n1. T01: Build PromptVariantGenerator + OptimizationLoop classes\n2. T02: Wire CLI optimize subcommand + reporting + results output\n\nThe research suggested this exact split. T01 is the riskiest (meta-prompt design, validation logic). T02 is integration and output formatting.\n\nLet me plan this.","thinkingSignature":"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"},{"type":"text","text":"Now I have everything I need. The research decomposition of two tasks is right — the natural seam is between the engine (variant generator + optimization loop) and the interface (CLI + reporting). T01 produces the core classes; T02 wires them into the CLI and adds formatted output."},{"type":"toolCall","id":"toolu_01FP1vYBUFYTCKujgHe6dmoJ","name":"gsd_plan_slice","arguments":{"goal":"Automated prompt optimization loop that generates LLM-powered prompt variants for Stage 5 synthesis, scores each against reference articles, and outputs a ranked leaderboard with score trajectories.","integrationClosure":"Upstream surfaces consumed: `backend/pipeline/quality/scorer.py` (ScoreRunner, ScoreResult), `backend/pipeline/quality/voice_dial.py` (pattern reference), `backend/pipeline/llm_client.py` (LLMClient), `backend/pipeline/stages.py` (_load_prompt, _get_stage_config), `backend/pipeline/quality/fixtures/sample_moments.json`. New wiring: `optimize` subcommand in `__main__.py` composing PromptVariantGenerator + OptimizationLoop + ScoreRunner. What remains: S04 expands to stages 2-4.","milestoneId":"M013","observabilityImpact":"Runtime signals: per-iteration progress printed to stdout (iteration N/M, variant scores, best-so-far). Inspection surfaces: JSON results file in output directory with full prompt text, per-dimension scores, and timestamps per iteration. Failure visibility: LLM connectivity errors surface cleanly with endpoint URL; malformed variant responses logged and skipped; results file written incrementally so partial runs are recoverable.","proofLevel":"This slice proves: integration. Real runtime required: yes (LLM endpoint needed for actual optimization). Human/UAT required: no.","sliceId":"S03","successCriteria":"- `python -m pipeline.quality optimize --help` shows --stage, --iterations, --variants-per-iter, --file, --output-dir args\n- `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('ok')\"` succeeds\n- `python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('ok')\"` succeeds\n- `python -m pipeline.quality optimize --stage 5 --iterations 1 --file backend/pipeline/quality/fixtures/sample_moments.json` runs (clean LLM connectivity error if no endpoint, no traceback)\n- Results JSON written to output directory with prompt text, scores, and metadata","tasks":[{"description":"Create two new modules: `variant_generator.py` with an LLM-powered prompt mutation engine, and `optimizer.py` with an optimization loop that orchestrates generate→score→select cycles.\n\n## Context\n\nThe existing `ScoreRunner.synthesize_and_score()` already chains Stage 5 synthesis through a modified prompt and scores the result. The `VoiceDial` class demonstrates the pattern of appending modifier text to the base prompt. This task builds the higher-level automation: an LLM generates prompt variants targeting weak dimensions, and a loop selects winners across iterations.\n\n## Key Interfaces\n\n- `LLMClient.complete(system_prompt, user_prompt, response_model, modality)` — drives both variant generation and synthesis\n- `ScoreRunner.synthesize_and_score(moments, creator_name, voice_level)` — scores a variant (uses voice_level=0.5 for neutral baseline)\n- `_load_prompt('stage5_synthesis.txt')` — loads the base prompt to optimize\n- `_get_stage_config(5)` — returns (model_override, modality) for Stage 5\n\n## Steps\n\n1. Create `backend/pipeline/quality/variant_generator.py`:\n   - Define a meta-prompt constant (`VARIANT_META_PROMPT`) that instructs the LLM to act as a prompt engineer. Given the current best prompt, its per-dimension scores, and the scoring rubric summary, produce a single modified variant targeting the weakest dimension(s). The meta-prompt MUST instruct the LLM to preserve the JSON output format section of the synthesis prompt unchanged.\n   - `PromptVariantGenerator.__init__(self, client: LLMClient)` — stores the LLM client\n   - `generate(self, base_prompt: str, scores: ScoreResult, n: int = 2) -> list[str]` — calls the LLM `n` times with the meta-prompt, each time asking for a variant. Validates each variant: must differ from base by ≥50 chars, must still contain the JSON format instruction markers (e.g. `SynthesisResult` or `\"pages\"` key reference). Returns list of valid variant prompt strings. Logs and skips invalid variants.\n\n2. Create `backend/pipeline/quality/optimizer.py`:\n   - `@dataclass OptimizationResult`: best_prompt (str), best_score (ScoreResult), history (list of dicts with iteration, variant_index, prompt_text, score fields), elapsed_seconds (float)\n   - `OptimizationLoop.__init__(self, client: LLMClient, stage: int, fixture_path: str, iterations: int, variants_per_iter: int)` — stores config, creates ScoreRunner and PromptVariantGenerator internally\n   - `run(self) -> OptimizationResult` — loads base prompt via `_load_prompt(f'stage{stage}_synthesis.txt')`, loads fixture data from JSON file, runs baseline `synthesize_and_score()`, then iterates: generate N variants → score each → keep best → repeat. Prints progress per iteration. Stores full history. Returns OptimizationResult.\n   - Handle LLM errors gracefully: if a variant's synthesis/scoring returns a ScoreResult with `error`, log it and skip that variant (don't abort the loop).\n\n3. Verify both modules import cleanly from project root.\n\n## Must-Haves\n\n- Meta-prompt instructs LLM to preserve JSON output format section\n- Variant validation: minimum diff from base, format markers present\n- Invalid/errored variants are skipped, not fatal\n- Progress output per iteration\n- OptimizationResult contains full history for downstream reporting","estimate":"1.5h","expectedOutput":["`backend/pipeline/quality/variant_generator.py` — PromptVariantGenerator class with LLM-powered prompt mutation","`backend/pipeline/quality/optimizer.py` — OptimizationLoop class with generate→score→select cycle and OptimizationResult dataclass"],"files":["backend/pipeline/quality/variant_generator.py","backend/pipeline/quality/optimizer.py"],"inputs":["`backend/pipeline/quality/scorer.py` — ScoreRunner and ScoreResult classes used by optimizer","`backend/pipeline/quality/voice_dial.py` — pattern reference for prompt modification","`backend/pipeline/llm_client.py` — LLMClient used by both generator and optimizer","`backend/pipeline/stages.py` — _load_prompt() and _get_stage_config() used by optimizer","`backend/pipeline/quality/fixtures/sample_moments.json` — fixture format reference"],"taskId":"T01","title":"Build PromptVariantGenerator and OptimizationLoop engine","verify":"python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')\" && python -c \"from pipeline.quality.optimizer import OptimizationLoop, OptimizationResult; print('optimizer ok')\""},{"description":"Add the `optimize` subcommand to `__main__.py` that connects PromptVariantGenerator + OptimizationLoop to the CLI, and add formatted reporting: a leaderboard table and an ASCII score trajectory chart. Write results to a JSON file.\n\n## Context\n\nT01 produced `PromptVariantGenerator` and `OptimizationLoop` with `OptimizationResult`. This task wires them into the existing CLI (which already has `fitness` and `score` subcommands) and adds human-readable output.\n\n## Steps\n\n1. Add `optimize` subparser to `__main__.py`:\n   - `--stage` (int, default 5) — pipeline stage to optimize\n   - `--iterations` (int, default 10) — number of optimization iterations\n   - `--variants-per-iter` (int, default 2) — variants generated per iteration\n   - `--file` (str, required) — path to moments JSON fixture\n   - `--output-dir` (str, default `backend/pipeline/quality/results/`) — where to write result JSON\n   - Validate --stage is 5 (others not yet supported, print message and exit 1 for other values)\n   - Create output dir if it doesn't exist\n   - Instantiate `OptimizationLoop` with parsed args, call `run()`, handle result\n\n2. Add reporting functions (can be in `__main__.py` or a small `reporting.py`):\n   - `print_leaderboard(result: OptimizationResult)` — formatted table showing top 5 variants by composite score, with per-dimension breakdown. Use the same visual style as `ScoreRunner.print_report()` (plain print with alignment, score bars).\n   - `print_trajectory(result: OptimizationResult)` — ASCII line chart of composite score across iterations. ~20 rows height, iteration index on x-axis. Simple text rendering (no external deps).\n   - Print both after the optimization loop completes.\n\n3. Write results JSON:\n   - Save `OptimizationResult` to `{output_dir}/optimize_stage{N}_{timestamp}.json` with: best_prompt, best_scores, full history, config (stage, iterations, variants_per_iter, fixture_path), elapsed_seconds.\n   - Create `backend/pipeline/quality/results/.gitkeep` so the directory is tracked.\n\n4. Verify the full CLI flow:\n   - `python -m pipeline.quality optimize --help` shows all args\n   - `python -m pipeline.quality optimize --stage 5 --iterations 1 --file backend/pipeline/quality/fixtures/sample_moments.json` runs and either produces output (if LLM reachable) or shows clean connectivity error\n   - `python -m pipeline.quality optimize --stage 3` prints 'only stage 5 supported' and exits 1\n\n## Must-Haves\n\n- --stage, --iterations, --variants-per-iter, --file, --output-dir CLI args\n- Stage validation (only 5 supported currently)\n- Leaderboard table printed after loop\n- ASCII trajectory chart printed after loop\n- Results JSON written to output directory\n- Clean error handling (no tracebacks on LLM connectivity failure)","estimate":"1h","expectedOutput":["`backend/pipeline/quality/__main__.py` — updated with optimize subcommand, leaderboard, trajectory chart, results JSON output","`backend/pipeline/quality/results/.gitkeep` — tracked output directory"],"files":["backend/pipeline/quality/__main__.py","backend/pipeline/quality/results/.gitkeep"],"inputs":["`backend/pipeline/quality/variant_generator.py` — PromptVariantGenerator from T01","`backend/pipeline/quality/optimizer.py` — OptimizationLoop and OptimizationResult from T01","`backend/pipeline/quality/__main__.py` — existing CLI with fitness and score subcommands","`backend/pipeline/quality/scorer.py` — ScoreRunner.print_report() style reference for formatting"],"taskId":"T02","title":"Wire optimize CLI subcommand with leaderboard and trajectory output","verify":"python -m pipeline.quality optimize --help && python -m pipeline.quality optimize --stage 3 --iterations 1 --file backend/pipeline/quality/fixtures/sample_moments.json 2>&1 | grep -q 'stage 5'"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3214,"cacheRead":83842,"cacheWrite":813,"totalTokens":87870,"cost":{"input":0.000005,"output":0.08035,"cacheRead":0.041921,"cacheWrite":0.00508125,"total":0.12735725}},"stopReason":"toolUse","timestamp":1775034259643}}
{"type":"message","id":"b80aa8e2","parentId":"cf1be476","timestamp":"2026-04-01T09:05:22.543Z","message":{"role":"toolResult","toolCallId":"toolu_01FP1vYBUFYTCKujgHe6dmoJ","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S03 (M013)"}],"details":{"operation":"plan_slice","milestoneId":"M013","sliceId":"S03","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S03/S03-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S03/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S03/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1775034322542}}
{"type":"message","id":"2e8db242","parentId":"b80aa8e2","timestamp":"2026-04-01T09:05:30.291Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n\n- **Completion semantics**: T01 produces the engine (generator + optimizer), T02 wires CLI + reporting + results output. Together they deliver the demo: `python -m pipeline.quality optimize --stage 5 --iterations 10` runs, scores, outputs leaderboard + trajectory.\n- **Requirement coverage**: No active requirements owned by this slice. All must-haves map to tasks.\n- **Task completeness**: Both tasks have concrete steps, must-haves, verification commands, and backtick-wrapped file paths for inputs/outputs.\n- **Dependency correctness**: T02 depends on T01 outputs. No circular refs.\n- **Scope**: T01 ~2 files, 3 steps. T02 ~2 files, 4 steps. Well within limits.\n- **Feature completeness**: Both tasks produce user-facing progress — T01 is the engine, T02 is the interface.\n\nSlice S03 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":226,"cacheRead":84655,"cacheWrite":3158,"totalTokens":88040,"cost":{"input":0.000005,"output":0.0056500000000000005,"cacheRead":0.0423275,"cacheWrite":0.0197375,"total":0.06772}},"stopReason":"stop","timestamp":1775034322542}}
