{"type":"thinking_level_change","id":"77ce2888","parentId":null,"timestamp":"2026-04-01T09:20:24.516Z","thinkingLevel":"medium"}
{"type":"model_change","id":"903ea8aa","parentId":"77ce2888","timestamp":"2026-04-01T09:20:24.518Z","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\nA researcher explored the codebase and a planner decomposed the work — you are the executor. The task plan below is your authoritative contract for the slice goal and verification bar, but it is not a substitute for local reality. Verify the referenced files and surrounding code before changing them. Do not do broad re-research or spontaneous re-planning. Small factual corrections, file-path fixes, and local implementation adaptations are part of execution. Escalate to `blocker_discovered: true` only when the slice contract or downstream task graph no longer holds.\n\n\n\n\n\n## Backing Source Artifacts\n- Slice plan: `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/S04-PLAN.md`\n- Task plan source: `.gsd/milestones/M013/slices/S04/tasks/T02-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M013/slices/S04/tasks/T01-SUMMARY.md`\n\nThen:\n0. Narrate step transitions, key implementation decisions, and verification outcomes as you work. Keep it terse — one line between tool-call clusters, not between every call — but write complete sentences in user-facing prose, not shorthand notes or scratchpad fragments.\n1.  Follow any activated skills before writing code. If no skills match this task, skip this step.\n2. Execute the steps in the inlined task plan, adapting minor local mismatches when the surrounding code differs from the planner's snapshot\n3. Build the real thing. If the task plan says \"create login endpoint\", build an endpoint that actually authenticates against a real store, not one that returns a hardcoded success response. If the task plan says \"create dashboard page\", build a page that renders real data from the API, not a component with hardcoded props. Stubs and mocks are for tests, not for the shipped feature.\n4. Write or update tests as part of execution — tests are verification, not an afterthought. If the slice plan defines test files in its Verification section and this is the first task, create them (they should initially fail).\n5. When implementing non-trivial runtime behavior (async flows, API boundaries, background processes, error paths), add or preserve agent-usable observability. Skip this for simple changes where it doesn't apply.\n\n   **Background process rule:** Never use bare `command &` to run background processes. The shell's `&` operator leaves stdout/stderr attached to the parent, which causes the Bash tool to hang indefinitely waiting for those streams to close. Always redirect output before backgrounding:\n   - Correct: `command > /dev/null 2>&1 &` or `nohup command > /dev/null 2>&1 &`\n   - Example: `python -m http.server 8080 > /dev/null 2>&1 &` (NOT `python -m http.server 8080 &`)\n   - Preferred: use the `bg_shell` tool if available — it manages process lifecycle correctly without stream-inheritance issues\n6. If the task plan includes a **Failure Modes** section (Q5), implement the error/timeout/malformed handling specified. Verify each dependency's failure path is handled. Skip if the section is absent.\n7. If the task plan includes a **Load Profile** section (Q6), implement protections for the identified 10x breakpoint (connection pooling, rate limiting, pagination, etc.). Skip if absent.\n8. If the task plan includes a **Negative Tests** section (Q7), write the specified negative test cases alongside the happy-path tests — malformed inputs, error paths, and boundary conditions. Skip if absent.\n9. Verify must-haves are met by running concrete checks (tests, commands, observable behaviors)\n10. Run the slice-level verification checks defined in the slice plan's Verification section. Track which pass. On the final task of the slice, all must pass before marking done. On intermediate tasks, partial passes are expected — note which ones pass in the summary.\n11. After the verification gate runs (you'll see gate results in stderr/notify output), populate the `## Verification Evidence` table in your task summary with the check results. Use the `formatEvidenceTable` format: one row per check with command, exit code, verdict (✅ pass / ❌ fail), and duration. If no verification commands were discovered, note that in the section.\n12. If the task touches UI, browser flows, DOM behavior, or user-visible web state:\n   - exercise the real flow in the browser\n   - prefer `browser_batch` when the next few actions are obvious and sequential\n   - prefer `browser_assert` for explicit pass/fail verification of the intended outcome\n   - use `browser_diff` when an action's effect is ambiguous\n   - use console/network/dialog diagnostics when validating async, stateful, or failure-prone UI\n   - record verification in terms of explicit checks passed/failed, not only prose interpretation\n13. If the task plan includes an Observability Impact section, verify those signals directly. Skip this step if the task plan omits the section.\n14. **If execution is running long or verification fails:**\n\n    **Context budget:** You have approximately **~80K chars** reserved for verification context. If you've used most of your context and haven't finished all steps, stop implementing and prioritize writing the task summary with clear notes on what's done and what remains. A partial summary that enables clean resumption is more valuable than one more half-finished step with no documentation. Never sacrifice summary quality for one more implementation step.\n\n    **Debugging discipline:** If a verification check fails or implementation hits unexpected behavior:\n    - Form a hypothesis first. State what you think is wrong and why, then test that specific theory. Don't shotgun-fix.\n    - Change one variable at a time. Make one change, test, observe. Multiple simultaneous changes mean you can't attribute what worked.\n    - Read completely. When investigating, read entire functions and their imports, not just the line that looks relevant.\n    - Distinguish \"I know\" from \"I assume.\" Observable facts (the error says X) are strong evidence. Assumptions (this library should work this way) need verification.\n    - Know when to stop. If you've tried 3+ fixes without progress, your mental model is probably wrong. Stop. List what you know for certain. List what you've ruled out. Form fresh hypotheses from there.\n    - Don't fix symptoms. Understand *why* something fails before changing code. A test that passes after a change you don't understand is luck, not a fix.\n15. **Blocker discovery:** If execution reveals that the remaining slice plan is fundamentally invalid — not just a bug or minor deviation, but a plan-invalidating finding like a wrong API, missing capability, or architectural mismatch — set `blocker_discovered: true` in the task summary frontmatter and describe the blocker clearly in the summary narrative. Do NOT set `blocker_discovered: true` for ordinary debugging, minor deviations, or issues that can be fixed within the current task or the remaining plan. This flag triggers an automatic replan of the slice.\n16. If you made an architectural, pattern, library, or observability decision during this task that downstream work should know about, append it to `.gsd/DECISIONS.md` (read the template at `~/.gsd/agent/extensions/gsd/templates/decisions.md` if the file doesn't exist yet). Not every task produces decisions — only append when a meaningful choice was made.\n17. If you discover a non-obvious rule, recurring gotcha, or useful pattern during execution, append it to `.gsd/KNOWLEDGE.md`. Only add entries that would save future agents from repeating your investigation. Don't add obvious things.\n18. Read the template at `~/.gsd/agent/extensions/gsd/templates/task-summary.md`\n19. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md`\n20. Call `gsd_complete_task` with milestone_id, slice_id, task_id, and a summary of what was accomplished. This is your final required step — do NOT manually edit PLAN.md checkboxes. The tool marks the task complete, updates the DB, and renders PLAN.md automatically.\n21. Do not run git commands — the system reads your task summary after completion and creates a meaningful commit from it (type inferred from title, message from your one-liner, key files from frontmatter). Write a clear, specific one-liner in the summary — it becomes the commit message.\n\nAll work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_complete_task` AND write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T02 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M013/slices/S04/S04-PLAN.md`\n**Goal:** Extend the prompt optimization loop from stage-5-only to stages 2-4, with stage-appropriate scoring rubrics, fixture formats, and variant validation — so `optimize --stage N` works for any pipeline stage.\n**Demo:** After this: Run `python -m pipeline.quality optimize --stage 3 --iterations 5` — optimizes extraction prompts with stage-appropriate scoring\n\n## UNIT: Execute Task T02 (\"Generalize optimizer, create stage 2-4 fixtures, wire CLI, verify end-to-end\") — Slice S04 (\"Expand to Pipeline Stages 2-4\"), Milestone M013\n\n## Resume State\n- No continue file present. Start from the top of the task plan.\n\n## Carry-Forward Context\n- `.gsd/milestones/M013/slices/S04/tasks/T01-SUMMARY.md` — T01: Added STAGE_CONFIGS registry (stages 2-5) with per-stage rubrics, generalized ScoreResult to scores dict, and made variant generator stage-agnostic | decisions: \"Used backward-compat properties on ScoreResult instead of migrating all callers\"; \"Made VARIANT_META_PROMPT a template with {dimension_descriptions} filled per-stage\" | key_files: \"backend/pipeline/quality/scorer.py\"; \"backend/pipeline/quality/variant_generator.py\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M013/slices/S04/tasks/T02-PLAN.md`\n\n---\nestimated_steps: 18\nestimated_files: 5\nskills_used: []\n---\n\n# T02: Generalize optimizer, create stage 2-4 fixtures, wire CLI, verify end-to-end\n\nMake OptimizationLoop stage-aware: generalize _load_fixture() to validate stage-specific keys, generalize _score_variant() to call the correct prompt and parse the correct schema per stage, and pass stage-appropriate format markers to the variant generator. Create minimal fixture JSON files for stages 2-4. Remove the stage-5 gate in __main__.py's _run_optimize(), add validation for stages 2-5. Verify all stages import and CLI accepts them.\n\n## Context\n\nThe optimizer currently has three stage-5-specific hardcodings:\n1. `_load_fixture()` expects `creator_name` and `moments` keys — stages 2-4 have different input shapes\n2. `_score_variant()` calls synthesis via `SynthesisResult` schema and formats output as a technique page for the scorer\n3. The `run()` method loads `stage{N}_synthesis.txt` — stages 2-4 use different prompt file names\n\nThe CLI's `_run_optimize()` rejects `args.stage != 5` with an error.\n\nStage fixture shapes (from research):\n- Stage 2: `{transcript_segments: [{index, start_time, end_time, text}]}` — segments of a transcript\n- Stage 3: `{topic_label, segments: [{start_time, end_time, text}]}` — a topic group to extract moments from\n- Stage 4: `{moments: [{title, summary, content_type, plugins}], taxonomy: \"...\"}` — moments to classify\n- Stage 5: `{creator_name, moments: [...]}` (existing sample_moments.json)\n\nThe STAGE_CONFIGS registry from T01 provides the prompt filename, schema class, dimensions, and format markers per stage. This task uses that registry to dispatch correctly.\n\nAfter changes, `OptimizationLoop.run()` should:\n1. Load the prompt file from `STAGE_CONFIGS[stage]['prompt_file']`\n2. Load and validate fixture using `STAGE_CONFIGS[stage]['fixture_keys']`\n3. In `_score_variant()`, use the stage's schema to parse LLM output, then format it for the scorer's `score_stage_output()`\n4. Pass stage-appropriate format markers to variant generator\n\n## Inputs\n\n- ``backend/pipeline/quality/scorer.py` — STAGE_CONFIGS registry and score_stage_output() from T01`\n- ``backend/pipeline/quality/variant_generator.py` — generalized generate() from T01`\n- ``backend/pipeline/quality/optimizer.py` — existing OptimizationLoop`\n- ``backend/pipeline/quality/__main__.py` — existing _run_optimize with stage-5 gate`\n- ``backend/pipeline/quality/fixtures/sample_moments.json` — existing stage 5 fixture for regression check`\n\n## Expected Output\n\n- ``backend/pipeline/quality/optimizer.py` — stage-aware OptimizationLoop with generalized _load_fixture, _score_variant, and format marker dispatch`\n- ``backend/pipeline/quality/__main__.py` — _run_optimize accepts stages 2-5, rejects others`\n- ``backend/pipeline/quality/fixtures/sample_segments.json` — stage 2 fixture`\n- ``backend/pipeline/quality/fixtures/sample_topic_group.json` — stage 3 fixture`\n- ``backend/pipeline/quality/fixtures/sample_classifications.json` — stage 4 fixture`\n\n## Verification\n\ncd backend && python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\" && python -c \"from pipeline.quality.__main__ import main; print('cli ok')\" && python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 | head -1 && python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage' && echo 'stage6 rejected ok'\n","display":false,"id":"1e55deb0","parentId":"903ea8aa","timestamp":"2026-04-01T09:20:24.523Z"}
{"type":"message","id":"90edfa42","parentId":"1e55deb0","timestamp":"2026-04-01T09:20:28.869Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the task plan and understanding the current state of the codebase.","thinkingSignature":"EpUCClkIDBgCKkAKt0sIdblQXate1NZ5o1NdzGTLS8odf1j+zAjZw8jbANNgxTkLhyKpJOP+xZAFYioAYepztW1gOimmQkIXGZ4kMg9jbGF1ZGUtb3B1cy00LTY4ABIMFdDATqaqp6qRTtAJGgzB39DE7rr2xmORlCsiMNiDcpmbChlH+BFdcQfjObVn02tb/i9RLcdKOCAzu/wjemEJ9zGIPcqGl/hlcM1bfCpqUhgizTITSLT7JsS5zfWppTId+KM+9puQco3VvhFnER1ZjYV4UkZ3hPe8GYBm4h/YxT7tkAFzOCixuKM7VSk3VT1xhQkHPXiXkMxXxWprFQn8jKYj4AZ3Q+YBLRLLN1+TMZiULhNDSgc5DRgB"},{"type":"text","text":"Let me read the current state of the key files before making changes."},{"type":"toolCall","id":"toolu_01YWwifNt1BsxqFW3YtzvTgT","name":"read","arguments":{"path":".gsd/milestones/M013/slices/S04/tasks/T02-PLAN.md"}},{"type":"toolCall","id":"toolu_01M1HKEvTGxnV7v9XSBuzERA","name":"read","arguments":{"path":".gsd/milestones/M013/slices/S04/tasks/T01-SUMMARY.md"}},{"type":"toolCall","id":"toolu_01Jno5mMTSQmCiV7Bnv9qW73","name":"read","arguments":{"path":"backend/pipeline/quality/optimizer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":227,"cacheRead":59647,"cacheWrite":3840,"totalTokens":63716,"cost":{"input":0.00001,"output":0.005675,"cacheRead":0.0298235,"cacheWrite":0.024,"total":0.0595085}},"stopReason":"toolUse","timestamp":1775035224520}}
{"type":"message","id":"6f5efd11","parentId":"90edfa42","timestamp":"2026-04-01T09:20:28.873Z","message":{"role":"toolResult","toolCallId":"toolu_01YWwifNt1BsxqFW3YtzvTgT","toolName":"read","content":[{"type":"text","text":"---\nestimated_steps: 18\nestimated_files: 5\nskills_used: []\n---\n\n# T02: Generalize optimizer, create stage 2-4 fixtures, wire CLI, verify end-to-end\n\nMake OptimizationLoop stage-aware: generalize _load_fixture() to validate stage-specific keys, generalize _score_variant() to call the correct prompt and parse the correct schema per stage, and pass stage-appropriate format markers to the variant generator. Create minimal fixture JSON files for stages 2-4. Remove the stage-5 gate in __main__.py's _run_optimize(), add validation for stages 2-5. Verify all stages import and CLI accepts them.\n\n## Context\n\nThe optimizer currently has three stage-5-specific hardcodings:\n1. `_load_fixture()` expects `creator_name` and `moments` keys — stages 2-4 have different input shapes\n2. `_score_variant()` calls synthesis via `SynthesisResult` schema and formats output as a technique page for the scorer\n3. The `run()` method loads `stage{N}_synthesis.txt` — stages 2-4 use different prompt file names\n\nThe CLI's `_run_optimize()` rejects `args.stage != 5` with an error.\n\nStage fixture shapes (from research):\n- Stage 2: `{transcript_segments: [{index, start_time, end_time, text}]}` — segments of a transcript\n- Stage 3: `{topic_label, segments: [{start_time, end_time, text}]}` — a topic group to extract moments from\n- Stage 4: `{moments: [{title, summary, content_type, plugins}], taxonomy: \"...\"}` — moments to classify\n- Stage 5: `{creator_name, moments: [...]}` (existing sample_moments.json)\n\nThe STAGE_CONFIGS registry from T01 provides the prompt filename, schema class, dimensions, and format markers per stage. This task uses that registry to dispatch correctly.\n\nAfter changes, `OptimizationLoop.run()` should:\n1. Load the prompt file from `STAGE_CONFIGS[stage]['prompt_file']`\n2. Load and validate fixture using `STAGE_CONFIGS[stage]['fixture_keys']`\n3. In `_score_variant()`, use the stage's schema to parse LLM output, then format it for the scorer's `score_stage_output()`\n4. Pass stage-appropriate format markers to variant generator\n\n## Inputs\n\n- ``backend/pipeline/quality/scorer.py` — STAGE_CONFIGS registry and score_stage_output() from T01`\n- ``backend/pipeline/quality/variant_generator.py` — generalized generate() from T01`\n- ``backend/pipeline/quality/optimizer.py` — existing OptimizationLoop`\n- ``backend/pipeline/quality/__main__.py` — existing _run_optimize with stage-5 gate`\n- ``backend/pipeline/quality/fixtures/sample_moments.json` — existing stage 5 fixture for regression check`\n\n## Expected Output\n\n- ``backend/pipeline/quality/optimizer.py` — stage-aware OptimizationLoop with generalized _load_fixture, _score_variant, and format marker dispatch`\n- ``backend/pipeline/quality/__main__.py` — _run_optimize accepts stages 2-5, rejects others`\n- ``backend/pipeline/quality/fixtures/sample_segments.json` — stage 2 fixture`\n- ``backend/pipeline/quality/fixtures/sample_topic_group.json` — stage 3 fixture`\n- ``backend/pipeline/quality/fixtures/sample_classifications.json` — stage 4 fixture`\n\n## Verification\n\ncd backend && python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\" && python -c \"from pipeline.quality.__main__ import main; print('cli ok')\" && python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 | head -1 && python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage' && echo 'stage6 rejected ok'\n"}],"isError":false,"timestamp":1775035228872}}
{"type":"message","id":"e0951a85","parentId":"6f5efd11","timestamp":"2026-04-01T09:20:28.875Z","message":{"role":"toolResult","toolCallId":"toolu_01M1HKEvTGxnV7v9XSBuzERA","toolName":"read","content":[{"type":"text","text":"---\nid: T01\nparent: S04\nmilestone: M013\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/pipeline/quality/scorer.py\", \"backend/pipeline/quality/variant_generator.py\"]\nkey_decisions: [\"Used backward-compat properties on ScoreResult instead of migrating all callers\", \"Made VARIANT_META_PROMPT a template with {dimension_descriptions} filled per-stage\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"All three verification commands pass: STAGE_CONFIGS has entries for stages 2-5, ScoreResult works with scores dict, backward-compat getattr works, StageConfig.get_schema() resolves all schema classes, PromptVariantGenerator imports cleanly.\"\ncompleted_at: 2026-04-01T09:20:20.599Z\nblocker_discovered: false\n---\n\n# T01: Added STAGE_CONFIGS registry (stages 2-5) with per-stage rubrics, generalized ScoreResult to scores dict, and made variant generator stage-agnostic\n\n> Added STAGE_CONFIGS registry (stages 2-5) with per-stage rubrics, generalized ScoreResult to scores dict, and made variant generator stage-agnostic\n\n## What Happened\n---\nid: T01\nparent: S04\nmilestone: M013\nkey_files:\n  - backend/pipeline/quality/scorer.py\n  - backend/pipeline/quality/variant_generator.py\nkey_decisions:\n  - Used backward-compat properties on ScoreResult instead of migrating all callers\n  - Made VARIANT_META_PROMPT a template with {dimension_descriptions} filled per-stage\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T09:20:20.600Z\nblocker_discovered: false\n---\n\n# T01: Added STAGE_CONFIGS registry (stages 2-5) with per-stage rubrics, generalized ScoreResult to scores dict, and made variant generator stage-agnostic\n\n**Added STAGE_CONFIGS registry (stages 2-5) with per-stage rubrics, generalized ScoreResult to scores dict, and made variant generator stage-agnostic**\n\n## What Happened\n\nBuilt STAGE_CONFIGS registry mapping stages 2-5 to StageConfig objects with rubrics, dimensions, format markers, fixture keys, prompt file names, and schema classes. Generalized ScoreResult from named float fields to a scores dict with backward-compat properties for stage 5. Added score_stage_output() to ScoreRunner for arbitrary stage scoring. Updated variant_generator.py with templatized meta-prompt and format_markers/stage parameters on generate().\n\n## Verification\n\nAll three verification commands pass: STAGE_CONFIGS has entries for stages 2-5, ScoreResult works with scores dict, backward-compat getattr works, StageConfig.get_schema() resolves all schema classes, PromptVariantGenerator imports cleanly.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python -c \"from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, ScoreRunner, DIMENSIONS; assert 2 in STAGE_CONFIGS and 3 in STAGE_CONFIGS and 4 in STAGE_CONFIGS and 5 in STAGE_CONFIGS; r = ScoreResult(scores={'structural': 0.8, 'readability': 0.7}, composite=0.75); print('scorer ok')\"` | 0 | ✅ pass | 1000ms |\n| 2 | `python -c \"from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')\"` | 0 | ✅ pass | 1000ms |\n| 3 | `python -c \"from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, DIMENSIONS; [cfg.get_schema() for cfg in STAGE_CONFIGS.values()]; r = ScoreResult(scores={'structural': 0.8}, composite=0.8); assert r.structural == 0.8; print('compat ok')\"` | 0 | ✅ pass | 1000ms |\n\n\n## Deviations\n\nAdded SCORING_RUBRIC backward-compat alias. Made VARIANT_META_PROMPT a template string with {dimension_descriptions} placeholder.\n\n## Known Issues\n\n__main__.py line 148 uses getattr pattern that only works for stage 5 — will need updating when optimize CLI is generalized.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/scorer.py`\n- `backend/pipeline/quality/variant_generator.py`\n\n\n## Deviations\nAdded SCORING_RUBRIC backward-compat alias. Made VARIANT_META_PROMPT a template string with {dimension_descriptions} placeholder.\n\n## Known Issues\n__main__.py line 148 uses getattr pattern that only works for stage 5 — will need updating when optimize CLI is generalized.\n"}],"isError":false,"timestamp":1775035228874}}
{"type":"message","id":"50fb4e8d","parentId":"e0951a85","timestamp":"2026-04-01T09:20:28.878Z","message":{"role":"toolResult","toolCallId":"toolu_01Jno5mMTSQmCiV7Bnv9qW73","toolName":"read","content":[{"type":"text","text":"\"\"\"Automated prompt optimization loop for Stage 5 synthesis.\n\nOrchestrates a generate→score→select cycle:\n1. Score the current best prompt against reference fixtures\n2. Generate N variants targeting weak dimensions\n3. Score each variant\n4. Keep the best scorer as the new baseline\n5. Repeat for K iterations\n\nUsage (via CLI):\n    python -m pipeline.quality optimize --stage 5 --iterations 10\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, ScoreResult, ScoreRunner\nfrom pipeline.quality.variant_generator import PromptVariantGenerator\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass OptimizationResult:\n    \"\"\"Full result of an optimization run.\"\"\"\n\n    best_prompt: str = \"\"\n    best_score: ScoreResult = field(default_factory=ScoreResult)\n    history: list[dict] = field(default_factory=list)\n    elapsed_seconds: float = 0.0\n\n\nclass OptimizationLoop:\n    \"\"\"Runs iterative prompt optimization for a pipeline stage.\n\n    Each iteration generates *variants_per_iter* prompt mutations,\n    scores each against reference fixture data, and keeps the\n    highest-composite-scoring variant as the new baseline.\n\n    Parameters\n    ----------\n    client:\n        LLMClient instance for LLM calls (synthesis + scoring + variant gen).\n    stage:\n        Pipeline stage number (currently only 5 is supported).\n    fixture_path:\n        Path to a JSON fixture file containing ``creator_name`` and ``moments``.\n    iterations:\n        Number of generate→score→select cycles.\n    variants_per_iter:\n        Number of variant prompts to generate per iteration.\n    \"\"\"\n\n    def __init__(\n        self,\n        client: LLMClient,\n        stage: int,\n        fixture_path: str,\n        iterations: int = 5,\n        variants_per_iter: int = 2,\n    ) -> None:\n        self.client = client\n        self.stage = stage\n        self.fixture_path = fixture_path\n        self.iterations = iterations\n        self.variants_per_iter = variants_per_iter\n\n        self.scorer = ScoreRunner(client)\n        self.generator = PromptVariantGenerator(client)\n\n    def run(self) -> OptimizationResult:\n        \"\"\"Execute the full optimization loop.\n\n        Returns\n        -------\n        OptimizationResult\n            Contains the best prompt, its scores, full iteration history,\n            and wall-clock elapsed time.\n        \"\"\"\n        from pipeline.stages import _load_prompt\n\n        t0 = time.monotonic()\n\n        # Load base prompt\n        prompt_file = f\"stage{self.stage}_synthesis.txt\"\n        try:\n            base_prompt = _load_prompt(prompt_file)\n        except FileNotFoundError:\n            logger.error(\"Prompt file not found: %s\", prompt_file)\n            return OptimizationResult(\n                best_prompt=\"\",\n                best_score=ScoreResult(error=f\"Prompt file not found: {prompt_file}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        # Load fixture data\n        try:\n            fixture = self._load_fixture()\n        except (FileNotFoundError, json.JSONDecodeError, KeyError) as exc:\n            logger.error(\"Failed to load fixture: %s\", exc)\n            return OptimizationResult(\n                best_prompt=base_prompt,\n                best_score=ScoreResult(error=f\"Fixture load error: {exc}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        moments = fixture[\"moments\"]\n        creator_name = fixture[\"creator_name\"]\n        history: list[dict] = []\n\n        # Score the baseline\n        print(f\"\\n{'='*60}\")\n        print(f\"  PROMPT OPTIMIZATION — Stage {self.stage}\")\n        print(f\"  Iterations: {self.iterations}, Variants/iter: {self.variants_per_iter}\")\n        print(f\"{'='*60}\\n\")\n\n        print(\"  Scoring baseline prompt...\")\n        best_score = self.scorer.synthesize_and_score(\n            moments=moments,\n            creator_name=creator_name,\n            voice_level=0.5,\n        )\n        best_prompt = base_prompt\n\n        history.append({\n            \"iteration\": 0,\n            \"variant_index\": 0,\n            \"prompt_text\": base_prompt[:200] + \"...\" if len(base_prompt) > 200 else base_prompt,\n            \"prompt_length\": len(base_prompt),\n            \"composite\": best_score.composite,\n            \"scores\": {d: getattr(best_score, d) for d in DIMENSIONS},\n            \"error\": best_score.error,\n            \"label\": \"baseline\",\n        })\n\n        if best_score.error:\n            print(f\"  ✗ Baseline scoring failed: {best_score.error}\")\n            print(\"  Aborting optimization — fix the baseline first.\\n\")\n            return OptimizationResult(\n                best_prompt=best_prompt,\n                best_score=best_score,\n                history=history,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        self._print_iteration_summary(0, best_score, is_baseline=True)\n\n        # Iterate\n        for iteration in range(1, self.iterations + 1):\n            print(f\"\\n  ── Iteration {iteration}/{self.iterations} ──\")\n\n            # Generate variants\n            variants = self.generator.generate(\n                base_prompt=best_prompt,\n                scores=best_score,\n                n=self.variants_per_iter,\n            )\n\n            if not variants:\n                print(\"  ⚠ No valid variants generated — skipping iteration\")\n                continue\n\n            # Score each variant\n            iteration_best_score = best_score\n            iteration_best_prompt = best_prompt\n\n            for vi, variant_prompt in enumerate(variants):\n                print(f\"  Scoring variant {vi + 1}/{len(variants)}...\")\n\n                # Temporarily replace the base prompt with the variant for synthesis\n                score = self._score_variant(\n                    variant_prompt, moments, creator_name,\n                )\n\n                history.append({\n                    \"iteration\": iteration,\n                    \"variant_index\": vi + 1,\n                    \"prompt_text\": variant_prompt[:200] + \"...\" if len(variant_prompt) > 200 else variant_prompt,\n                    \"prompt_length\": len(variant_prompt),\n                    \"composite\": score.composite,\n                    \"scores\": {d: getattr(score, d) for d in DIMENSIONS},\n                    \"error\": score.error,\n                    \"label\": f\"iter{iteration}_v{vi+1}\",\n                })\n\n                if score.error:\n                    print(f\"    ✗ Variant {vi + 1} errored: {score.error}\")\n                    continue\n\n                if score.composite > iteration_best_score.composite:\n                    iteration_best_score = score\n                    iteration_best_prompt = variant_prompt\n                    print(f\"    ✓ New best: {score.composite:.3f} (was {best_score.composite:.3f})\")\n                else:\n                    print(f\"    · Score {score.composite:.3f} ≤ current best {iteration_best_score.composite:.3f}\")\n\n            # Update global best if this iteration improved\n            if iteration_best_score.composite > best_score.composite:\n                best_score = iteration_best_score\n                best_prompt = iteration_best_prompt\n                print(f\"  ★ Iteration {iteration} improved: {best_score.composite:.3f}\")\n            else:\n                print(f\"  · No improvement in iteration {iteration}\")\n\n            self._print_iteration_summary(iteration, best_score)\n\n        # Final report\n        elapsed = round(time.monotonic() - t0, 2)\n        self._print_final_report(best_score, history, elapsed)\n\n        return OptimizationResult(\n            best_prompt=best_prompt,\n            best_score=best_score,\n            history=history,\n            elapsed_seconds=elapsed,\n        )\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _load_fixture(self) -> dict:\n        \"\"\"Load and validate the fixture JSON file.\"\"\"\n        path = Path(self.fixture_path)\n        if not path.exists():\n            raise FileNotFoundError(f\"Fixture not found: {path}\")\n        data = json.loads(path.read_text(encoding=\"utf-8\"))\n\n        if \"moments\" not in data:\n            raise KeyError(\"Fixture must contain 'moments' key\")\n        if \"creator_name\" not in data:\n            raise KeyError(\"Fixture must contain 'creator_name' key\")\n\n        return data\n\n    def _score_variant(\n        self,\n        variant_prompt: str,\n        moments: list[dict],\n        creator_name: str,\n    ) -> ScoreResult:\n        \"\"\"Score a variant prompt by running synthesis + scoring.\n\n        Uses the variant as a direct system prompt for synthesis, bypassing\n        VoiceDial (the optimization loop owns the full prompt text).\n        \"\"\"\n        from pipeline.schemas import SynthesisResult\n        from pipeline.stages import _get_stage_config\n\n        import json as _json\n        import openai as _openai\n\n        model_override, modality = _get_stage_config(self.stage)\n\n        moments_json = _json.dumps(moments, indent=2)\n        user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=variant_prompt,\n                user_prompt=user_prompt,\n                response_model=SynthesisResult,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (_openai.APIConnectionError, _openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Synthesis LLM error: {exc}\",\n            )\n        except Exception as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            logger.exception(\"Unexpected error during variant synthesis\")\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Unexpected synthesis error: {exc}\",\n            )\n\n        # Parse synthesis\n        raw_text = str(raw).strip()\n        try:\n            synthesis = self.client.parse_response(raw_text, SynthesisResult)\n        except Exception as exc:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Variant synthesis parse error: {exc}\",\n            )\n\n        if not synthesis.pages:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=\"Variant synthesis returned no pages\",\n            )\n\n        # Score the first page\n        page = synthesis.pages[0]\n        page_json = {\n            \"title\": page.title,\n            \"creator_name\": creator_name,\n            \"summary\": page.summary,\n            \"body_sections\": [\n                {\"heading\": heading, \"content\": content}\n                for heading, content in page.body_sections.items()\n            ],\n        }\n\n        result = self.scorer.score_page(page_json, moments)\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def _print_iteration_summary(\n        self,\n        iteration: int,\n        score: ScoreResult,\n        is_baseline: bool = False,\n    ) -> None:\n        \"\"\"Print a compact one-line summary of the current best scores.\"\"\"\n        label = \"BASELINE\" if is_baseline else f\"ITER {iteration}\"\n        dims = \"  \".join(\n            f\"{d[:4]}={getattr(score, d):.2f}\" for d in DIMENSIONS\n        )\n        print(f\"  [{label}] composite={score.composite:.3f}  {dims}\")\n\n    def _print_final_report(\n        self,\n        best_score: ScoreResult,\n        history: list[dict],\n        elapsed: float,\n    ) -> None:\n        \"\"\"Print the final optimization summary.\"\"\"\n        print(f\"\\n{'='*60}\")\n        print(\"  OPTIMIZATION COMPLETE\")\n        print(f\"{'='*60}\")\n        print(f\"  Total time: {elapsed}s\")\n        print(f\"  Iterations: {self.iterations}\")\n        print(f\"  Variants scored: {len(history) - 1}\")  # minus baseline\n\n        baseline_composite = history[0][\"composite\"] if history else 0.0\n        improvement = best_score.composite - baseline_composite\n\n        print(f\"\\n  Baseline composite: {baseline_composite:.3f}\")\n        print(f\"  Best composite:     {best_score.composite:.3f}\")\n        if improvement > 0:\n            print(f\"  Improvement:        +{improvement:.3f}\")\n        else:\n            print(f\"  Improvement:        {improvement:.3f} (no gain)\")\n\n        print(f\"\\n  Per-dimension best scores:\")\n        for d in DIMENSIONS:\n            val = getattr(best_score, d)\n            bar = \"█\" * int(val * 20) + \"░\" * (20 - int(val * 20))\n            print(f\"    {d.replace('_', ' ').title():25s} {val:.2f}  {bar}\")\n\n        errored = sum(1 for h in history if h.get(\"error\"))\n        if errored:\n            print(f\"\\n  ⚠ {errored} variant(s) errored during scoring\")\n\n        print(f\"{'='*60}\\n\")\n"}],"isError":false,"timestamp":1775035228876}}
{"type":"message","id":"6c2a02d6","parentId":"50fb4e8d","timestamp":"2026-04-01T09:20:32.051Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01MqxpxCrfKrmdjoRrwbZpD2","name":"read","arguments":{"path":"backend/pipeline/quality/__main__.py"}},{"type":"toolCall","id":"toolu_01V8Ssnisq8dbrAPbQpdufFz","name":"read","arguments":{"path":"backend/pipeline/quality/scorer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":101,"cacheRead":63487,"cacheWrite":5985,"totalTokens":69574,"cost":{"input":0.000005,"output":0.0025250000000000003,"cacheRead":0.0317435,"cacheWrite":0.03740625,"total":0.07167975}},"stopReason":"toolUse","timestamp":1775035228877}}
{"type":"message","id":"bc39ef3f","parentId":"6c2a02d6","timestamp":"2026-04-01T09:20:32.053Z","message":{"role":"toolResult","toolCallId":"toolu_01MqxpxCrfKrmdjoRrwbZpD2","toolName":"read","content":[{"type":"text","text":"\"\"\"FYN-LLM quality assurance toolkit.\n\nSubcommands:\n  fitness   — Run LLM fitness tests across four categories\n  score     — Score a Stage 5 technique page across 5 quality dimensions\n  optimize  — Automated prompt optimization loop with leaderboard output\n\nRun with: python -m pipeline.quality <command>\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nfrom config import get_settings\nfrom pipeline.llm_client import LLMClient\n\nfrom .fitness import FitnessRunner\nfrom .optimizer import OptimizationLoop, OptimizationResult\nfrom .scorer import DIMENSIONS, ScoreRunner\n\n\n# ── Reporting helpers ────────────────────────────────────────────────────────\n\n\ndef print_leaderboard(result: OptimizationResult) -> None:\n    \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n    # Filter to entries that actually scored (no errors)\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if not scored:\n        print(\"\\n  No successfully scored variants to rank.\\n\")\n        return\n\n    ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n\n    print(f\"\\n{'='*72}\")\n    print(\"  LEADERBOARD — Top 5 Variants by Composite Score\")\n    print(f\"{'='*72}\")\n\n    # Header\n    dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in DIMENSIONS)\n    print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n    print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}\")\n\n    for i, entry in enumerate(ranked, 1):\n        label = entry.get(\"label\", \"?\")[:16]\n        comp = entry[\"composite\"]\n        dim_vals = \"  \".join(\n            f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in DIMENSIONS\n        )\n        bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n        print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n\n    print(f\"{'='*72}\\n\")\n\n\ndef print_trajectory(result: OptimizationResult) -> None:\n    \"\"\"Print an ASCII chart of composite score across iterations.\"\"\"\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if len(scored) < 2:\n        print(\"  (Not enough data points for trajectory chart)\\n\")\n        return\n\n    # Get the best composite per iteration\n    iter_best: dict[int, float] = {}\n    for h in scored:\n        it = h[\"iteration\"]\n        if it not in iter_best or h[\"composite\"] > iter_best[it]:\n            iter_best[it] = h[\"composite\"]\n\n    iterations = sorted(iter_best.keys())\n    values = [iter_best[it] for it in iterations]\n\n    # Chart dimensions\n    chart_height = 15\n    min_val = max(0.0, min(values) - 0.05)\n    max_val = min(1.0, max(values) + 0.05)\n    val_range = max_val - min_val\n    if val_range < 0.01:\n        val_range = 0.1\n        min_val = max(0.0, values[0] - 0.05)\n        max_val = min_val + val_range\n\n    print(f\"  {'─'*50}\")\n    print(\"  SCORE TRAJECTORY — Best Composite per Iteration\")\n    print(f\"  {'─'*50}\")\n    print()\n\n    # Render rows top to bottom\n    for row in range(chart_height, -1, -1):\n        threshold = min_val + (row / chart_height) * val_range\n        # Y-axis label every 5 rows\n        if row % 5 == 0:\n            label = f\"{threshold:.2f}\"\n        else:\n            label = \"     \"\n        line = f\"  {label} │\"\n\n        for vi, val in enumerate(values):\n            normalized = (val - min_val) / val_range\n            filled_rows = int(normalized * chart_height)\n            if filled_rows >= row:\n                line += \" ● \"\n            else:\n                line += \" · \"\n\n        print(line)\n\n    # X-axis\n    print(f\"  ───── ┼{'───' * len(values)}\")\n    x_labels = \"  \" + \"      \"\n    for it in iterations:\n        x_labels += f\"{it:>2d} \"\n    print(x_labels)\n    print(\"        \" + \"  iteration →\")\n    print()\n\n\ndef write_results_json(\n    result: OptimizationResult,\n    output_dir: str,\n    stage: int,\n    iterations: int,\n    variants_per_iter: int,\n    fixture_path: str,\n) -> str:\n    \"\"\"Write optimization results to a timestamped JSON file. Returns the path.\"\"\"\n    out_path = Path(output_dir)\n    out_path.mkdir(parents=True, exist_ok=True)\n\n    timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d_%H%M%S\")\n    filename = f\"optimize_stage{stage}_{timestamp}.json\"\n    filepath = out_path / filename\n\n    payload = {\n        \"config\": {\n            \"stage\": stage,\n            \"iterations\": iterations,\n            \"variants_per_iter\": variants_per_iter,\n            \"fixture_path\": fixture_path,\n        },\n        \"best_prompt\": result.best_prompt,\n        \"best_scores\": {\n            \"composite\": result.best_score.composite,\n            **{d: getattr(result.best_score, d) for d in DIMENSIONS},\n        },\n        \"elapsed_seconds\": result.elapsed_seconds,\n        \"history\": result.history,\n    }\n\n    filepath.write_text(json.dumps(payload, indent=2), encoding=\"utf-8\")\n    return str(filepath)\n\n\n# ── CLI ──────────────────────────────────────────────────────────────────────\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.quality\",\n        description=\"FYN-LLM quality assurance toolkit\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- fitness subcommand --\n    sub.add_parser(\"fitness\", help=\"Run LLM fitness tests across four categories\")\n\n    # -- score subcommand --\n    score_parser = sub.add_parser(\n        \"score\",\n        help=\"Score a Stage 5 technique page across 5 quality dimensions\",\n    )\n    source_group = score_parser.add_mutually_exclusive_group(required=True)\n    source_group.add_argument(\n        \"--file\",\n        type=str,\n        help=\"Path to a moments JSON file (creator_name, moments array)\",\n    )\n    source_group.add_argument(\n        \"--slug\",\n        type=str,\n        help=\"Technique slug to load from the database\",\n    )\n    score_parser.add_argument(\n        \"--voice-level\",\n        type=float,\n        default=None,\n        help=\"Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.\",\n    )\n\n    # -- optimize subcommand --\n    opt_parser = sub.add_parser(\n        \"optimize\",\n        help=\"Automated prompt optimization loop with leaderboard output\",\n    )\n    opt_parser.add_argument(\n        \"--stage\",\n        type=int,\n        default=5,\n        help=\"Pipeline stage to optimize (default: 5)\",\n    )\n    opt_parser.add_argument(\n        \"--iterations\",\n        type=int,\n        default=10,\n        help=\"Number of optimization iterations (default: 10)\",\n    )\n    opt_parser.add_argument(\n        \"--variants-per-iter\",\n        type=int,\n        default=2,\n        help=\"Variants generated per iteration (default: 2)\",\n    )\n    opt_parser.add_argument(\n        \"--file\",\n        type=str,\n        required=True,\n        help=\"Path to moments JSON fixture file\",\n    )\n    opt_parser.add_argument(\n        \"--output-dir\",\n        type=str,\n        default=\"backend/pipeline/quality/results/\",\n        help=\"Directory to write result JSON (default: backend/pipeline/quality/results/)\",\n    )\n\n    args = parser.parse_args()\n\n    if args.command is None:\n        parser.print_help()\n        return 1\n\n    if args.command == \"fitness\":\n        settings = get_settings()\n        client = LLMClient(settings)\n        runner = FitnessRunner(client)\n        return runner.run_all()\n\n    if args.command == \"score\":\n        return _run_score(args)\n\n    if args.command == \"optimize\":\n        return _run_optimize(args)\n\n    return 0\n\n\ndef _run_score(args: argparse.Namespace) -> int:\n    \"\"\"Execute the score subcommand.\"\"\"\n    # -- Load source data --\n    if args.slug:\n        print(\"DB loading not yet implemented\", file=sys.stderr)\n        return 1\n\n    try:\n        with open(args.file) as f:\n            data = json.load(f)\n    except FileNotFoundError:\n        print(f\"File not found: {args.file}\", file=sys.stderr)\n        return 1\n    except json.JSONDecodeError as exc:\n        print(f\"Invalid JSON in {args.file}: {exc}\", file=sys.stderr)\n        return 1\n\n    moments = data.get(\"moments\", [])\n    creator_name = data.get(\"creator_name\", \"Unknown\")\n\n    if not moments:\n        print(\"No moments found in input file\", file=sys.stderr)\n        return 1\n\n    settings = get_settings()\n    client = LLMClient(settings)\n    runner = ScoreRunner(client)\n\n    # -- Voice-level mode: re-synthesize then score --\n    if args.voice_level is not None:\n        voice_level = args.voice_level\n        if not (0.0 <= voice_level <= 1.0):\n            print(\"--voice-level must be between 0.0 and 1.0\", file=sys.stderr)\n            return 1\n\n        print(f\"\\nRe-synthesizing + scoring for '{creator_name}' ({len(moments)} moments, voice_level={voice_level})...\")\n        result = runner.synthesize_and_score(moments, creator_name, voice_level)\n\n        if result.error:\n            runner.print_report(result)\n            return 1\n\n        runner.print_report(result)\n        return 0\n\n    # -- Standard mode: build page stub from moments, score directly --\n    page_json = {\n        \"title\": f\"{creator_name} — Technique Page\",\n        \"creator_name\": creator_name,\n        \"summary\": f\"Technique page synthesized from {len(moments)} key moments.\",\n        \"body_sections\": [\n            {\n                \"heading\": m.get(\"topic_tags\", [\"Technique\"])[0] if m.get(\"topic_tags\") else \"Technique\",\n                \"content\": m.get(\"summary\", \"\") + \"\\n\\n\" + m.get(\"transcript_excerpt\", \"\"),\n            }\n            for m in moments\n        ],\n    }\n\n    print(f\"\\nScoring page for '{creator_name}' ({len(moments)} moments)...\")\n\n    result = runner.score_page(page_json, moments)\n\n    if result.error:\n        runner.print_report(result)\n        return 1\n\n    runner.print_report(result)\n    return 0\n\n\ndef _run_optimize(args: argparse.Namespace) -> int:\n    \"\"\"Execute the optimize subcommand.\"\"\"\n    # Stage validation — only stage 5 is supported\n    if args.stage != 5:\n        print(\n            f\"Error: only stage 5 is supported for optimization (got stage {args.stage})\",\n            file=sys.stderr,\n        )\n        return 1\n\n    # Validate fixture file exists\n    fixture = Path(args.file)\n    if not fixture.exists():\n        print(f\"Error: fixture file not found: {args.file}\", file=sys.stderr)\n        return 1\n\n    # Ensure output dir\n    Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n\n    settings = get_settings()\n    client = LLMClient(settings)\n\n    loop = OptimizationLoop(\n        client=client,\n        stage=args.stage,\n        fixture_path=args.file,\n        iterations=args.iterations,\n        variants_per_iter=args.variants_per_iter,\n    )\n\n    try:\n        result = loop.run()\n    except KeyboardInterrupt:\n        print(\"\\n  Optimization interrupted by user.\", file=sys.stderr)\n        return 130\n    except Exception as exc:\n        print(f\"\\nError: optimization failed: {exc}\", file=sys.stderr)\n        return 1\n\n    # If the loop returned an error on baseline, report and exit\n    if result.best_score.error and not result.history:\n        print(f\"\\nError: {result.best_score.error}\", file=sys.stderr)\n        return 1\n\n    # Reporting\n    print_leaderboard(result)\n    print_trajectory(result)\n\n    # Write results JSON\n    try:\n        json_path = write_results_json(\n            result=result,\n            output_dir=args.output_dir,\n            stage=args.stage,\n            iterations=args.iterations,\n            variants_per_iter=args.variants_per_iter,\n            fixture_path=args.file,\n        )\n        print(f\"  Results written to: {json_path}\")\n    except OSError as exc:\n        print(f\"  Warning: failed to write results JSON: {exc}\", file=sys.stderr)\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775035232052}}
{"type":"message","id":"fb0241db","parentId":"bc39ef3f","timestamp":"2026-04-01T09:20:32.056Z","message":{"role":"toolResult","toolCallId":"toolu_01V8Ssnisq8dbrAPbQpdufFz","toolName":"read","content":[{"type":"text","text":"\"\"\"Multi-stage quality scorer — LLM-as-judge evaluation with per-stage rubrics.\n\nSupports stages 2-5, each with its own scoring dimensions, rubric, format\nmarkers, fixture key requirements, prompt file name, and output schema.\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\nfrom typing import Any\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# ── Per-stage configuration registry ─────────────────────────────────────────\n\nclass StageConfig:\n    \"\"\"Configuration for scoring a specific pipeline stage.\"\"\"\n\n    def __init__(\n        self,\n        stage: int,\n        dimensions: list[str],\n        rubric: str,\n        format_markers: list[str],\n        fixture_keys: list[str],\n        prompt_file: str,\n        schema_class: str,\n    ) -> None:\n        self.stage = stage\n        self.dimensions = dimensions\n        self.rubric = rubric\n        self.format_markers = format_markers\n        self.fixture_keys = fixture_keys\n        self.prompt_file = prompt_file\n        self.schema_class = schema_class\n\n    def get_schema(self) -> type[BaseModel]:\n        \"\"\"Import and return the Pydantic schema class for this stage.\"\"\"\n        from pipeline import schemas\n        return getattr(schemas, self.schema_class)\n\n\n# ── Stage rubrics ────────────────────────────────────────────────────────────\n\n_STAGE_2_RUBRIC = \"\"\"\\\nYou are an expert evaluator of transcript segmentation quality for educational content.\n\nYou will be given:\n1. A segmentation result (JSON with segments, each having start_index, end_index, topic_label, summary)\n2. The source transcript segments used as input\n\nEvaluate the segmentation across these 4 dimensions, scoring each 0.0 to 1.0:\n\n**coverage_completeness** — All transcript content accounted for\n- 0.9-1.0: Every transcript segment is covered by exactly one topic segment, no gaps or overlaps\n- 0.5-0.7: Minor gaps or overlaps, but most content is covered\n- 0.0-0.3: Large gaps — significant transcript segments are not assigned to any topic\n\n**topic_specificity** — Topic labels are descriptive and useful\n- 0.9-1.0: Labels are specific and descriptive (e.g., \"Sidechain compression on kick-bass\" not \"Audio processing\")\n- 0.5-0.7: Labels are somewhat specific but could be more descriptive\n- 0.0-0.3: Labels are generic or meaningless (\"Topic 1\", \"Discussion\", \"Audio\")\n\n**boundary_accuracy** — Segment boundaries align with actual topic transitions\n- 0.9-1.0: Boundaries fall at natural topic transitions, segments are coherent units\n- 0.5-0.7: Most boundaries are reasonable but some segments mix distinct topics\n- 0.0-0.3: Boundaries seem arbitrary, segments contain unrelated content\n\n**summary_quality** — Summaries accurately describe segment content\n- 0.9-1.0: Summaries capture the key points of each segment concisely and accurately\n- 0.5-0.7: Summaries are acceptable but miss some key points or are too vague\n- 0.0-0.3: Summaries are inaccurate, too generic, or missing\n\nReturn ONLY a JSON object with this exact structure:\n{\n  \"coverage_completeness\": <float 0.0-1.0>,\n  \"topic_specificity\": <float 0.0-1.0>,\n  \"boundary_accuracy\": <float 0.0-1.0>,\n  \"summary_quality\": <float 0.0-1.0>,\n  \"justifications\": {\n    \"coverage_completeness\": \"<1-2 sentence justification>\",\n    \"topic_specificity\": \"<1-2 sentence justification>\",\n    \"boundary_accuracy\": \"<1-2 sentence justification>\",\n    \"summary_quality\": \"<1-2 sentence justification>\"\n  }\n}\n\"\"\"\n\n_STAGE_3_RUBRIC = \"\"\"\\\nYou are an expert evaluator of key moment extraction quality for educational content.\n\nYou will be given:\n1. An extraction result (JSON with moments, each having title, summary, start_time, end_time, content_type, plugins, raw_transcript)\n2. The source topic segments used as input\n\nEvaluate the extraction across these 5 dimensions, scoring each 0.0 to 1.0:\n\n**moment_richness** — Extracted moments capture substantial, distinct insights\n- 0.9-1.0: Each moment represents a meaningful, distinct technique or concept with detailed summary\n- 0.5-0.7: Moments are valid but some are thin or overlap significantly with others\n- 0.0-0.3: Moments are trivial, redundant, or miss the main techniques discussed\n\n**timestamp_accuracy** — Time ranges are plausible and well-bounded\n- 0.9-1.0: Start/end times form reasonable ranges, no zero-length or absurdly long spans\n- 0.5-0.7: Most timestamps are reasonable but some spans seem too wide or narrow\n- 0.0-0.3: Timestamps appear arbitrary or many are zero/identical\n\n**content_type_correctness** — Content types match the actual moment content\n- 0.9-1.0: Each moment's content_type (technique/settings/reasoning/workflow) accurately describes it\n- 0.5-0.7: Most are correct but 1-2 are miscategorized\n- 0.0-0.3: Content types seem randomly assigned or all the same\n\n**summary_actionability** — Summaries provide actionable, specific information\n- 0.9-1.0: Summaries contain concrete details (values, settings, steps) that a practitioner could follow\n- 0.5-0.7: Summaries describe the topic but lack specific actionable details\n- 0.0-0.3: Summaries are vague (\"discusses compression\") with no actionable information\n\n**plugin_normalization** — Plugin/tool names are correctly identified and normalized\n- 0.9-1.0: Plugin names match standard names, no duplicates, captures all mentioned tools\n- 0.5-0.7: Most plugins captured but some are misspelled, duplicated, or missed\n- 0.0-0.3: Plugin list is mostly empty, contains non-plugins, or has many errors\n\nReturn ONLY a JSON object with this exact structure:\n{\n  \"moment_richness\": <float 0.0-1.0>,\n  \"timestamp_accuracy\": <float 0.0-1.0>,\n  \"content_type_correctness\": <float 0.0-1.0>,\n  \"summary_actionability\": <float 0.0-1.0>,\n  \"plugin_normalization\": <float 0.0-1.0>,\n  \"justifications\": {\n    \"moment_richness\": \"<1-2 sentence justification>\",\n    \"timestamp_accuracy\": \"<1-2 sentence justification>\",\n    \"content_type_correctness\": \"<1-2 sentence justification>\",\n    \"summary_actionability\": \"<1-2 sentence justification>\",\n    \"plugin_normalization\": \"<1-2 sentence justification>\"\n  }\n}\n\"\"\"\n\n_STAGE_4_RUBRIC = \"\"\"\\\nYou are an expert evaluator of content classification quality for educational content.\n\nYou will be given:\n1. A classification result (JSON with classifications, each having moment_index, topic_category, topic_tags)\n2. The source extracted moments used as input\n\nEvaluate the classification across these 4 dimensions, scoring each 0.0 to 1.0:\n\n**category_accuracy** — Topic categories are appropriate and meaningful\n- 0.9-1.0: Categories accurately reflect the primary topic of each moment, using domain-appropriate labels\n- 0.5-0.7: Most categories are reasonable but some are too broad or slightly off\n- 0.0-0.3: Categories are generic (\"Music\"), incorrect, or all the same\n\n**tag_completeness** — All relevant tags are captured\n- 0.9-1.0: Tags capture the key concepts, tools, and techniques in each moment comprehensively\n- 0.5-0.7: Main tags are present but secondary concepts or tools are missed\n- 0.0-0.3: Tags are sparse, missing major concepts mentioned in the moments\n\n**tag_specificity** — Tags are specific enough to be useful for search/filtering\n- 0.9-1.0: Tags are specific (\"sidechain compression\", \"Pro-Q 3\") not generic (\"audio\", \"mixing\")\n- 0.5-0.7: Mix of specific and generic tags\n- 0.0-0.3: Tags are too generic to meaningfully distinguish moments\n\n**coverage** — All moments are classified\n- 0.9-1.0: Every moment_index from the input has a corresponding classification entry\n- 0.5-0.7: Most moments classified but 1-2 are missing\n- 0.0-0.3: Many moments are not classified\n\nReturn ONLY a JSON object with this exact structure:\n{\n  \"category_accuracy\": <float 0.0-1.0>,\n  \"tag_completeness\": <float 0.0-1.0>,\n  \"tag_specificity\": <float 0.0-1.0>,\n  \"coverage\": <float 0.0-1.0>,\n  \"justifications\": {\n    \"category_accuracy\": \"<1-2 sentence justification>\",\n    \"tag_completeness\": \"<1-2 sentence justification>\",\n    \"tag_specificity\": \"<1-2 sentence justification>\",\n    \"coverage\": \"<1-2 sentence justification>\"\n  }\n}\n\"\"\"\n\n_STAGE_5_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\n# Backward-compat alias used by synthesize_and_score and external references\nSCORING_RUBRIC = _STAGE_5_RUBRIC\n\n# Build the stage configs registry\nSTAGE_CONFIGS: dict[int, StageConfig] = {\n    2: StageConfig(\n        stage=2,\n        dimensions=[\"coverage_completeness\", \"topic_specificity\", \"boundary_accuracy\", \"summary_quality\"],\n        rubric=_STAGE_2_RUBRIC,\n        format_markers=[\"segments\", \"start_index\", \"end_index\", \"topic_label\"],\n        fixture_keys=[\"transcript_segments\"],\n        prompt_file=\"stage2_segmentation.txt\",\n        schema_class=\"SegmentationResult\",\n    ),\n    3: StageConfig(\n        stage=3,\n        dimensions=[\"moment_richness\", \"timestamp_accuracy\", \"content_type_correctness\", \"summary_actionability\", \"plugin_normalization\"],\n        rubric=_STAGE_3_RUBRIC,\n        format_markers=[\"moments\", \"content_type\", \"raw_transcript\", \"plugins\"],\n        fixture_keys=[\"topic_segments\"],\n        prompt_file=\"stage3_extraction.txt\",\n        schema_class=\"ExtractionResult\",\n    ),\n    4: StageConfig(\n        stage=4,\n        dimensions=[\"category_accuracy\", \"tag_completeness\", \"tag_specificity\", \"coverage\"],\n        rubric=_STAGE_4_RUBRIC,\n        format_markers=[\"classifications\", \"moment_index\", \"topic_category\", \"topic_tags\"],\n        fixture_keys=[\"extracted_moments\"],\n        prompt_file=\"stage4_classification.txt\",\n        schema_class=\"ClassificationResult\",\n    ),\n    5: StageConfig(\n        stage=5,\n        dimensions=[\"structural\", \"content_specificity\", \"voice_preservation\", \"readability\", \"factual_fidelity\"],\n        rubric=SCORING_RUBRIC,\n        format_markers=[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"],\n        fixture_keys=[\"key_moments\", \"creator_name\"],\n        prompt_file=\"stage5_synthesis.txt\",\n        schema_class=\"SynthesisResult\",\n    ),\n}\n\n# Backward-compatible alias: stage 5 dimensions list\nDIMENSIONS = STAGE_CONFIGS[5].dimensions\n\n\n# ── Result type ──────────────────────────────────────────────────────────────\n\n@dataclass\nclass ScoreResult:\n    \"\"\"Outcome of scoring a stage output across quality dimensions.\n\n    Uses a generic ``scores`` dict keyed by dimension name.  Stage 5's\n    original named fields (structural, content_specificity, …) are\n    preserved as properties for backward compatibility.\n    \"\"\"\n\n    scores: dict[str, float] = field(default_factory=dict)\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    # ── Backward-compat properties for stage 5 named dimensions ──────\n    @property\n    def structural(self) -> float:\n        return self.scores.get(\"structural\", 0.0)\n\n    @property\n    def content_specificity(self) -> float:\n        return self.scores.get(\"content_specificity\", 0.0)\n\n    @property\n    def voice_preservation(self) -> float:\n        return self.scores.get(\"voice_preservation\", 0.0)\n\n    @property\n    def readability(self) -> float:\n        return self.scores.get(\"readability\", 0.0)\n\n    @property\n    def factual_fidelity(self) -> float:\n        return self.scores.get(\"factual_fidelity\", 0.0)\n\n\n# ── Runner ───────────────────────────────────────────────────────────────────\n\nclass ScoreRunner:\n    \"\"\"Scores pipeline stage outputs using LLM-as-judge evaluation.\"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    # ── Generic stage scorer ─────────────────────────────────────────────\n\n    def score_stage_output(\n        self,\n        stage: int,\n        output_json: dict | list,\n        input_json: dict | list,\n    ) -> ScoreResult:\n        \"\"\"Score an arbitrary stage's output against its input.\n\n        Parameters\n        ----------\n        stage:\n            Pipeline stage number (2-5).\n        output_json:\n            The stage output to evaluate (parsed JSON).\n        input_json:\n            The stage input / source material.\n\n        Returns\n        -------\n        ScoreResult with per-dimension scores for the requested stage.\n        \"\"\"\n        if stage not in STAGE_CONFIGS:\n            return ScoreResult(error=f\"No config for stage {stage}. Valid: {sorted(STAGE_CONFIGS)}\")\n\n        cfg = STAGE_CONFIGS[stage]\n\n        user_prompt = (\n            \"## Stage Output\\n\\n\"\n            f\"```json\\n{json.dumps(output_json, indent=2)}\\n```\\n\\n\"\n            \"## Stage Input\\n\\n\"\n            f\"```json\\n{json.dumps(input_json, indent=2)}\\n```\\n\\n\"\n            f\"Score this stage {stage} output across all {len(cfg.dimensions)} dimensions.\"\n        )\n\n        t0 = time.monotonic()\n        try:\n            resp = self.client.complete(\n                system_prompt=cfg.rubric,\n                user_prompt=user_prompt,\n                response_model=BaseModel,\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=f\"Cannot reach LLM endpoint at {url} (fallback {fallback}). Error: {exc}\",\n            )\n\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, cfg.dimensions)\n\n    # ── Stage 5 convenience (backward compat) ────────────────────────────\n\n    def score_page(\n        self,\n        page_json: dict,\n        moments: list[dict],\n    ) -> ScoreResult:\n        \"\"\"Evaluate a stage 5 technique page against source moments.\"\"\"\n        return self.score_stage_output(\n            stage=5,\n            output_json=page_json,\n            input_json=moments,\n        )\n\n        return self._parse_scores(parsed, elapsed)\n\n    def _parse_scores(self, parsed: dict, elapsed: float, dimensions: list[str] | None = None) -> ScoreResult:\n        \"\"\"Extract and validate scores from parsed JSON response.\"\"\"\n        dims = dimensions or DIMENSIONS\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 dims:\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(dims) if dims else 0.0\n\n        return ScoreResult(\n            scores=scores,\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, stage: int = 5) -> None:\n        \"\"\"Print a formatted scoring report to stdout.\"\"\"\n        dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else list(result.scores.keys())\n        stage_label = f\"STAGE {stage}\" if stage in STAGE_CONFIGS else \"QUALITY\"\n\n        print(\"\\n\" + \"=\" * 60)\n        print(f\"  {stage_label} 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 dims:\n            score = result.scores.get(dim, 0.0)\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":1775035232054}}
{"type":"message","id":"8c01af15","parentId":"fb0241db","timestamp":"2026-04-01T09:20:37.507Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me check the existing fixture and schemas to understand the data shapes.","thinkingSignature":"EosCClkIDBgCKkDPM9ne0oJ8VAB5B2Y0xbsOgAHZOXlfsLGqHLgZmaKcvhUDZU/l94+3H79eAIZNcOurBa6/UrApsg5KAOPPjhIjMg9jbGF1ZGUtb3B1cy00LTY4ABIMER4g7J8xgGpQE78eGgxPMx3m+UMWWTf3w1wiMNt3ppuZ8i4mNLnq+50famnFoDEeJbez59+aIkKfmwiWNsGvhFuhRYbc5NenR0ohaipgSgLBxKrazN+NhCCopt77LwawPD9LJXTnelnNq6N8q9he1YCKW4hPni/RPWPGlezqFWH7cCz8KCB9vzdyANUFLmByDNn2lCVbum+842e11ZJ4hPxlAemKGjZ7t6MOanyIGAE="},{"type":"text","text":"Now let me check the existing fixture and schemas for the stage-specific data shapes."},{"type":"toolCall","id":"toolu_01DSdUQ2ciHCGnTHKv3dSdid","name":"read","arguments":{"path":"backend/pipeline/quality/fixtures/sample_moments.json"}},{"type":"toolCall","id":"toolu_014Mth7VCUqisLc36i2poV1i","name":"bash","arguments":{"command":"cd backend && grep -n 'class SegmentationResult\\|class ExtractionResult\\|class ClassificationResult\\|class SynthesisResult' pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":69472,"cacheWrite":10710,"totalTokens":80369,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.034735999999999996,"cacheWrite":0.0669375,"total":0.10632849999999999}},"stopReason":"toolUse","timestamp":1775035232055}}
{"type":"message","id":"2999076f","parentId":"8c01af15","timestamp":"2026-04-01T09:20:37.518Z","message":{"role":"toolResult","toolCallId":"toolu_01DSdUQ2ciHCGnTHKv3dSdid","toolName":"read","content":[{"type":"text","text":"{\n  \"creator_name\": \"KOAN Sound\",\n  \"topic_category\": \"Sound design\",\n  \"moments\": [\n    {\n      \"summary\": \"Layering snare transients by combining a high-frequency click from a Popcorn Snare with a mid-body from a pitched-down 808 rim shot, blending at -6dB relative offset.\",\n      \"transcript_excerpt\": \"So what I'll do is take the Popcorn Snare — that's got this really sharp click at like 4k — and then I layer underneath it a rim shot pitched down maybe 3 semitones. You blend those together and suddenly you've got this snare that cuts through everything but still has weight.\",\n      \"topic_tags\": [\"snare layering\", \"transient design\", \"sample stacking\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 124.5,\n      \"end_time\": 158.2\n    },\n    {\n      \"summary\": \"Using Serum's noise oscillator with the 'Analog_Crackle' wavetable at 12% mix to add organic texture to bass patches, followed by OTT at 30% depth for glue.\",\n      \"transcript_excerpt\": \"One trick I always come back to is Serum's noise osc with Analog_Crackle. You don't want it loud — like 12 percent mix — just enough that the bass feels alive. Then slap OTT on there at maybe 30 percent depth and it glues the whole thing together without squashing it.\",\n      \"topic_tags\": [\"bass design\", \"Serum\", \"OTT\", \"texture\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 203.1,\n      \"end_time\": 241.7\n    },\n    {\n      \"summary\": \"Resampling technique: bounce a bass patch to audio, chop the best 2 bars, then re-pitch in Simpler with warp off for tighter timing and consistent tone.\",\n      \"transcript_excerpt\": \"I'll resample everything. Bounce it down, find the two bars that sound best, throw it in Simpler with warp completely off. Now you've got this tight, consistent thing where every hit is exactly the same energy. The pitch tracking is way more predictable too.\",\n      \"topic_tags\": [\"resampling\", \"Ableton\", \"Simpler\", \"bass production\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 312.0,\n      \"end_time\": 349.8\n    },\n    {\n      \"summary\": \"Parallel compression chain for drums using Ableton's Drum Buss at 40% drive into a return track with Valhalla Room at 1.2s decay, mixed at -12dB.\",\n      \"transcript_excerpt\": \"The parallel chain is dead simple — Drum Buss, crank the drive to about 40 percent, send that to a return with Valhalla Room. Keep the decay short, like 1.2 seconds. Mix it in at minus 12 and your drums just... breathe. They've got this room sound without getting washy.\",\n      \"topic_tags\": [\"parallel compression\", \"drum processing\", \"Valhalla Room\", \"Drum Buss\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 421.3,\n      \"end_time\": 462.1\n    },\n    {\n      \"summary\": \"Frequency-specific sidechain using Trackspacer plugin instead of volume ducking, targeting only 100-300Hz so the bass ducks under the kick without losing high-end presence.\",\n      \"transcript_excerpt\": \"Everyone does volume sidechain but honestly Trackspacer changed everything for me. You set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end of the bass stays right there — you keep all the character and harmonics, you just clear the mud.\",\n      \"topic_tags\": [\"sidechaining\", \"Trackspacer\", \"frequency ducking\", \"mixing\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 498.7,\n      \"end_time\": 534.2\n    },\n    {\n      \"summary\": \"Using Ableton's Utility plugin to check mono compatibility at every stage, specifically toggling mono on the sub bus to catch phase cancellation from layered bass patches.\",\n      \"transcript_excerpt\": \"I'm almost paranoid about mono. I've got Utility on the sub bus and I'm flipping to mono constantly. If your layered bass sounds thin in mono you've got phase issues — doesn't matter how fat it sounds in stereo, it'll collapse on a club system.\",\n      \"topic_tags\": [\"mono compatibility\", \"phase checking\", \"club mixing\", \"Utility\"],\n      \"topic_category\": \"Sound design\",\n      \"start_time\": 567.0,\n      \"end_time\": 598.4\n    }\n  ]\n}\n"}],"isError":false,"timestamp":1775035237517}}
{"type":"message","id":"baf68d10","parentId":"2999076f","timestamp":"2026-04-01T09:20:37.522Z","message":{"role":"toolResult","toolCallId":"toolu_014Mth7VCUqisLc36i2poV1i","toolName":"bash","content":[{"type":"text","text":"25:class SegmentationResult(BaseModel):\n45:class ExtractionResult(BaseModel):\n65:class ClassificationResult(BaseModel):\n100:class SynthesisResult(BaseModel):\n"}],"isError":false,"timestamp":1775035237521}}
{"type":"message","id":"b18d6bcb","parentId":"baf68d10","timestamp":"2026-04-01T09:20:40.721Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JdcAWDzykP3dr2mdxkn3cT","name":"read","arguments":{"limit":120,"offset":1,"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":80182,"cacheWrite":1451,"totalTokens":81725,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.040091,"cacheWrite":0.00906875,"total":0.05143975}},"stopReason":"toolUse","timestamp":1775035237521}}
{"type":"message","id":"c8e65c1d","parentId":"b18d6bcb","timestamp":"2026-04-01T09:20:40.724Z","message":{"role":"toolResult","toolCallId":"toolu_01JdcAWDzykP3dr2mdxkn3cT","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775035240722}}
{"type":"message","id":"4f6a91c9","parentId":"c8e65c1d","timestamp":"2026-04-01T09:20:49.840Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I have a complete picture. Let me also check the fixture_keys in STAGE_CONFIGS more carefully — T01 set `fixture_keys` for each stage but the task plan's fixture shapes differ from what's in the configs.\n\nLooking at the STAGE_CONFIGS:\n- Stage 2: `fixture_keys=[\"transcript_segments\"]` — plan says `{transcript_segments: [...]}`\n- Stage 3: `fixture_keys=[\"topic_segments\"]` — plan says `{topic_label, segments: [...]}`  \n- Stage 4: `fixture_keys=[\"extracted_moments\"]` — plan says `{moments: [...], taxonomy: \"...\"}`\n- Stage 5: `fixture_keys=[\"key_moments\", \"creator_name\"]` — existing fixture uses `moments` and `creator_name`\n\nThe stage 5 config uses `key_moments` but the existing fixture uses `moments`. I'll match the fixture shapes to the actual STAGE_CONFIGS `fixture_keys`, since that's the registry T01 established. Let me also check how the stages actually consume their input."},{"type":"toolCall","id":"toolu_01BTym1zsknGcqvhkwaG9K3N","name":"bash","arguments":{"command":"cd backend && grep -n 'fixture_keys\\|prompt_file\\|format_markers' pipeline/quality/scorer.py | head -30"}},{"type":"toolCall","id":"toolu_01MCtBTyYDxEsS1snzeDbEHu","name":"read","arguments":{"path":"backend/pipeline/quality/variant_generator.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":381,"cacheRead":81633,"cacheWrite":1124,"totalTokens":83139,"cost":{"input":0.000005,"output":0.009525,"cacheRead":0.0408165,"cacheWrite":0.007025,"total":0.057371500000000006}},"stopReason":"toolUse","timestamp":1775035240723}}
{"type":"message","id":"42740513","parentId":"4f6a91c9","timestamp":"2026-04-01T09:20:49.856Z","message":{"role":"toolResult","toolCallId":"toolu_01BTym1zsknGcqvhkwaG9K3N","toolName":"bash","content":[{"type":"text","text":"36:        format_markers: list[str],\n37:        fixture_keys: list[str],\n38:        prompt_file: str,\n44:        self.format_markers = format_markers\n45:        self.fixture_keys = fixture_keys\n46:        self.prompt_file = prompt_file\n256:        format_markers=[\"segments\", \"start_index\", \"end_index\", \"topic_label\"],\n257:        fixture_keys=[\"transcript_segments\"],\n258:        prompt_file=\"stage2_segmentation.txt\",\n265:        format_markers=[\"moments\", \"content_type\", \"raw_transcript\", \"plugins\"],\n266:        fixture_keys=[\"topic_segments\"],\n267:        prompt_file=\"stage3_extraction.txt\",\n274:        format_markers=[\"classifications\", \"moment_index\", \"topic_category\", \"topic_tags\"],\n275:        fixture_keys=[\"extracted_moments\"],\n276:        prompt_file=\"stage4_classification.txt\",\n283:        format_markers=[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"],\n284:        fixture_keys=[\"key_moments\", \"creator_name\"],\n285:        prompt_file=\"stage5_synthesis.txt\",\n"}],"isError":false,"timestamp":1775035249855}}
{"type":"message","id":"21c567c7","parentId":"42740513","timestamp":"2026-04-01T09:20:49.859Z","message":{"role":"toolResult","toolCallId":"toolu_01MCtBTyYDxEsS1snzeDbEHu","toolName":"read","content":[{"type":"text","text":"\"\"\"LLM-powered prompt variant generator for automated optimization.\n\nUses a meta-prompt to instruct the LLM to act as a prompt engineer,\nanalyzing per-dimension scores and producing targeted prompt mutations\nthat improve the weakest scoring dimensions while preserving the JSON\noutput format required by downstream parsing.\n\nSupports any pipeline stage (2-5) — callers pass the stage's dimensions\nand format markers so the meta-prompt and validation adapt automatically.\n\"\"\"\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Sequence\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import DIMENSIONS, STAGE_CONFIGS, ScoreResult\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Meta-prompt for variant generation ────────────────────────────────────────\n\nVARIANT_META_PROMPT = \"\"\"\\\nYou are an expert prompt engineer specializing in LLM-powered content processing pipelines.\n\nYour task: given a pipeline stage prompt and its quality evaluation scores, produce an\nimproved variant of the prompt that targets the weakest-scoring dimensions while\nmaintaining or improving the others.\n\n## Scoring Dimensions (each 0.0–1.0)\n\n{dimension_descriptions}\n\n## Rules\n\n1. Focus your changes on the weakest 1-2 dimensions. Don't dilute the prompt by trying to fix everything.\n2. Add specific, actionable instructions — not vague encouragements.\n3. **CRITICAL: You MUST preserve the JSON output format section of the prompt EXACTLY as-is.**\n   The prompt contains instructions about outputting a JSON object with a specific schema.\n   Do NOT modify, remove, or rephrase any part of the JSON format instructions.\n   Your changes should target the processing/analysis guidelines only.\n4. Keep the overall prompt length within 2x of the original. Don't bloat it.\n5. Make substantive changes — rewording a sentence or adding one adjective is not enough.\n\n## Output\n\nReturn ONLY the full modified prompt text. No explanation, no markdown fences, no preamble.\nJust the complete prompt that could be used directly as a system prompt.\n\"\"\"\n\n# Dimension descriptions per stage, used to fill the meta-prompt template.\n_DIMENSION_DESCRIPTIONS: dict[int, str] = {\n    2: (\n        \"- **coverage_completeness** — All transcript content accounted for, no gaps or overlaps\\n\"\n        \"- **topic_specificity** — Topic labels are descriptive and useful, not generic\\n\"\n        \"- **boundary_accuracy** — Segment boundaries align with actual topic transitions\\n\"\n        \"- **summary_quality** — Summaries accurately describe segment content\"\n    ),\n    3: (\n        \"- **moment_richness** — Extracted moments capture substantial, distinct insights\\n\"\n        \"- **timestamp_accuracy** — Time ranges are plausible and well-bounded\\n\"\n        \"- **content_type_correctness** — Content types match the actual moment content\\n\"\n        \"- **summary_actionability** — Summaries provide actionable, specific information\\n\"\n        \"- **plugin_normalization** — Plugin/tool names are correctly identified and normalized\"\n    ),\n    4: (\n        \"- **category_accuracy** — Topic categories are appropriate and meaningful\\n\"\n        \"- **tag_completeness** — All relevant tags are captured\\n\"\n        \"- **tag_specificity** — Tags are specific enough to be useful for search/filtering\\n\"\n        \"- **coverage** — All moments are classified\"\n    ),\n    5: (\n        \"- **structural** — Section naming, count (3-6), paragraph depth (2-5 per section)\\n\"\n        \"- **content_specificity** — Concrete details: frequencies, time values, ratios, plugin names, dB values\\n\"\n        \"- **voice_preservation** — Direct quotes preserved, opinions attributed to creator by name, personality retained\\n\"\n        \"- **readability** — Cohesive article flow, related info merged, no redundancy or contradiction\\n\"\n        \"- **factual_fidelity** — Every claim traceable to source material, no hallucinated specifics\"\n    ),\n}\n\n\n# Legacy default format markers for stage 5\n_FORMAT_MARKERS = [\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"]\n\n\nclass PromptVariantGenerator:\n    \"\"\"Generates prompt variants by asking an LLM to act as a prompt engineer.\n\n    Given a base prompt and its evaluation scores, produces N mutated\n    variants targeting the weakest dimensions.\n    \"\"\"\n\n    def __init__(self, client: LLMClient) -> None:\n        self.client = client\n\n    def generate(\n        self,\n        base_prompt: str,\n        scores: ScoreResult,\n        n: int = 2,\n        *,\n        format_markers: Sequence[str] | None = None,\n        stage: int = 5,\n    ) -> list[str]:\n        \"\"\"Generate up to *n* valid prompt variants.\n\n        Each variant is produced by a separate LLM call with the meta-prompt.\n        Variants are validated: they must differ from the base by ≥50 characters\n        and must contain the JSON format instruction markers found in the base.\n\n        Invalid variants are logged and skipped.\n\n        Parameters\n        ----------\n        base_prompt:\n            The current best prompt text for the target stage.\n        scores:\n            ScoreResult from the most recent evaluation of *base_prompt*.\n        n:\n            Number of variants to attempt generating.\n        format_markers:\n            Override format markers for validation.  When *None*, uses the\n            markers from ``STAGE_CONFIGS[stage]`` (falling back to stage 5\n            defaults for backward compat).\n        stage:\n            Pipeline stage number (2-5), used to select dimension\n            descriptions for the meta-prompt and default format markers.\n\n        Returns\n        -------\n        list[str]\n            Valid variant prompt strings (may be fewer than *n*).\n        \"\"\"\n        # Resolve format markers and dimensions for the target stage\n        if format_markers is not None:\n            markers = list(format_markers)\n        elif stage in STAGE_CONFIGS:\n            markers = STAGE_CONFIGS[stage].format_markers\n        else:\n            markers = _FORMAT_MARKERS\n\n        dimensions = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS\n\n        # Build the system prompt with stage-appropriate dimension descriptions\n        dim_desc = _DIMENSION_DESCRIPTIONS.get(stage, _DIMENSION_DESCRIPTIONS[5])\n        system_prompt = VARIANT_META_PROMPT.format(dimension_descriptions=dim_desc)\n\n        user_prompt = self._build_user_prompt(base_prompt, scores, dimensions)\n        # Identify which format markers are actually present in the base\n        required_markers = [m for m in markers if m in base_prompt]\n\n        variants: list[str] = []\n        for i in range(n):\n            logger.info(\"Generating variant %d/%d (stage %d)...\", i + 1, n, stage)\n            try:\n                raw = self.client.complete(\n                    system_prompt=system_prompt,\n                    user_prompt=user_prompt,\n                    response_model=None,  # free-form text, not JSON\n                    modality=\"chat\",\n                )\n                variant = str(raw).strip()\n            except Exception:\n                logger.exception(\"LLM error generating variant %d/%d\", i + 1, n)\n                continue\n\n            # Validate the variant\n            if not self._validate(variant, base_prompt, required_markers, i + 1):\n                continue\n\n            variants.append(variant)\n            logger.info(\"Variant %d/%d accepted (%d chars)\", i + 1, n, len(variant))\n\n        logger.info(\n            \"Generated %d valid variant(s) out of %d attempts\", len(variants), n\n        )\n        return variants\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _build_user_prompt(self, base_prompt: str, scores: ScoreResult, dimensions: list[str] | None = None) -> str:\n        \"\"\"Build the user message describing the current prompt and its scores.\"\"\"\n        dims = dimensions or DIMENSIONS\n        # Build per-dimension score lines, sorted worst-first\n        dim_lines: list[str] = []\n        dim_scores = [(d, scores.scores.get(d, 0.0)) for d in dims]\n        dim_scores.sort(key=lambda x: x[1])\n\n        for dim, val in dim_scores:\n            justification = scores.justifications.get(dim, \"\")\n            label = dim.replace(\"_\", \" \").title()\n            line = f\"  {label}: {val:.2f}\"\n            if justification:\n                line += f\"  — {justification}\"\n            dim_lines.append(line)\n\n        weakest = dim_scores[0][0].replace(\"_\", \" \").title()\n        second_weakest = dim_scores[1][0].replace(\"_\", \" \").title() if len(dim_scores) > 1 else weakest\n\n        return (\n            f\"## Current Prompt\\n\\n{base_prompt}\\n\\n\"\n            f\"## Evaluation Scores (sorted weakest → strongest)\\n\\n\"\n            + \"\\n\".join(dim_lines)\n            + f\"\\n\\n  Composite: {scores.composite:.3f}\\n\\n\"\n            f\"## Priority\\n\\n\"\n            f\"The weakest dimensions are **{weakest}** and **{second_weakest}**. \"\n            f\"Focus your prompt modifications on improving these.\\n\\n\"\n            f\"Return the full modified prompt now.\"\n        )\n\n    def _validate(\n        self,\n        variant: str,\n        base_prompt: str,\n        required_markers: list[str],\n        index: int,\n    ) -> bool:\n        \"\"\"Check a variant meets minimum quality gates.\"\"\"\n        if not variant:\n            logger.warning(\"Variant %d is empty — skipping\", index)\n            return False\n\n        # Must differ meaningfully from base\n        diff = abs(len(variant) - len(base_prompt))\n        # Also check actual content difference via set-symmetric-difference of lines\n        base_lines = set(base_prompt.splitlines())\n        variant_lines = set(variant.splitlines())\n        changed_lines = len(base_lines.symmetric_difference(variant_lines))\n\n        if diff < 50 and changed_lines < 3:\n            logger.warning(\n                \"Variant %d too similar to base (len_diff=%d, changed_lines=%d) — skipping\",\n                index, diff, changed_lines,\n            )\n            return False\n\n        # Must preserve format markers\n        missing = [m for m in required_markers if m not in variant]\n        if missing:\n            logger.warning(\n                \"Variant %d missing format markers %s — skipping\",\n                index, missing,\n            )\n            return False\n\n        return True\n"}],"isError":false,"timestamp":1775035249855}}
{"type":"message","id":"f534bb98","parentId":"21c567c7","timestamp":"2026-04-01T09:20:56.277Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me implement the changes:\n\n1. **Create fixture files** for stages 2-4 with keys matching STAGE_CONFIGS fixture_keys\n2. **Generalize optimizer.py** — make _load_fixture stage-aware, _score_variant stage-aware, run() use STAGE_CONFIGS\n3. **Update __main__.py** — remove stage-5 gate, validate 2-5\n\nLet me also check what prompt files exist to make sure the prompt_file names are correct.","thinkingSignature":"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"},{"type":"toolCall","id":"toolu_01E9VPcvCACFABGK9zrBoT6F","name":"bash","arguments":{"command":"ls backend/pipeline/prompts/stage*.txt 2>/dev/null || echo \"no prompts dir\"; find backend -name \"stage*.txt\" -type f 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":228,"cacheRead":82757,"cacheWrite":3376,"totalTokens":86362,"cost":{"input":0.000005,"output":0.0057,"cacheRead":0.0413785,"cacheWrite":0.0211,"total":0.06818350000000001}},"stopReason":"toolUse","timestamp":1775035249856}}
{"type":"message","id":"95d7e8ef","parentId":"f534bb98","timestamp":"2026-04-01T09:20:56.290Z","message":{"role":"toolResult","toolCallId":"toolu_01E9VPcvCACFABGK9zrBoT6F","toolName":"bash","content":[{"type":"text","text":"no prompts dir\n"}],"isError":false,"timestamp":1775035256289}}
{"type":"message","id":"93d6a0c8","parentId":"95d7e8ef","timestamp":"2026-04-01T09:20:59.353Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JVZnRSf2Cu2p31tPbddZnR","name":"bash","arguments":{"command":"cd backend && grep -n '_load_prompt\\|PROMPT_DIR\\|prompts' pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":86133,"cacheWrite":111,"totalTokens":86326,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.0430665,"cacheWrite":0.00069375,"total":0.04579025}},"stopReason":"toolUse","timestamp":1775035256289}}
{"type":"message","id":"a0d89c22","parentId":"93d6a0c8","timestamp":"2026-04-01T09:20:59.366Z","message":{"role":"toolResult","toolCallId":"toolu_01JVZnRSf2Cu2p31tPbddZnR","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str) -> str:\n245:    \"\"\"Read a prompt template from the prompts directory.\n250:    path = Path(settings.prompts_path) / template_name\n401:        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n480:        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n653:        system_prompt = _load_prompt(\"stage4_classification.txt\")\n786:    prompts_path = Path(settings.prompts_path)\n797:        filepath = prompts_path / filename\n972:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\")\n1083:        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n"}],"isError":false,"timestamp":1775035259363}}
{"type":"message","id":"fdc6af50","parentId":"a0d89c22","timestamp":"2026-04-01T09:21:27.375Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Prompt files exist at settings.prompts_path. Now I have everything I need. Let me implement the changes.\n\n**Plan:**\n1. Create fixtures for stages 2-4 matching their `fixture_keys` in STAGE_CONFIGS\n2. Generalize `optimizer.py` — make `_load_fixture()` validate using STAGE_CONFIGS fixture_keys, make `_score_variant()` dispatch per-stage, use config's `prompt_file`\n3. Update `__main__.py` — remove stage-5 gate, validate stages 2-5\n\nStarting with the fixture files."},{"type":"toolCall","id":"toolu_01HDJTMpeCypccQDW9C1Vstc","name":"write","arguments":{"content":"{\n  \"transcript_segments\": [\n    {\n      \"index\": 0,\n      \"start_time\": 0.0,\n      \"end_time\": 15.2,\n      \"text\": \"Hey everyone, today we're going to talk about sidechain compression and how I use it in my productions.\"\n    },\n    {\n      \"index\": 1,\n      \"start_time\": 15.2,\n      \"end_time\": 34.8,\n      \"text\": \"So the basic idea is you take the kick drum signal and use it to duck the bass. Most people use a compressor for this but I actually prefer Trackspacer because it gives you frequency-specific ducking.\"\n    },\n    {\n      \"index\": 2,\n      \"start_time\": 34.8,\n      \"end_time\": 52.1,\n      \"text\": \"With Trackspacer you can set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end stays right there.\"\n    },\n    {\n      \"index\": 3,\n      \"start_time\": 52.1,\n      \"end_time\": 71.5,\n      \"text\": \"Now let me show you another technique — parallel compression on drums. I use Drum Buss with the drive at about 40 percent, then send that to a return track.\"\n    },\n    {\n      \"index\": 4,\n      \"start_time\": 71.5,\n      \"end_time\": 89.3,\n      \"text\": \"On the return I put Valhalla Room with a short decay, like 1.2 seconds. Mix it in at minus 12 dB. Your drums just breathe — they get this room sound without getting washy.\"\n    },\n    {\n      \"index\": 5,\n      \"start_time\": 89.3,\n      \"end_time\": 110.0,\n      \"text\": \"One more thing about mono compatibility. I always have Utility on the sub bus and I flip to mono constantly. If your layered bass sounds thin in mono you've got phase issues.\"\n    }\n  ]\n}\n","path":"backend/pipeline/quality/fixtures/sample_segments.json"}},{"type":"toolCall","id":"toolu_011VMpBvdZAA1tK3vfnQi1TZ","name":"write","arguments":{"content":"{\n  \"topic_segments\": [\n    {\n      \"start_index\": 0,\n      \"end_index\": 2,\n      \"topic_label\": \"Frequency-specific sidechain compression with Trackspacer\",\n      \"summary\": \"Using Trackspacer for frequency-band sidechain ducking instead of traditional volume compression\",\n      \"transcript_text\": \"Hey everyone, today we're going to talk about sidechain compression and how I use it in my productions. So the basic idea is you take the kick drum signal and use it to duck the bass. Most people use a compressor for this but I actually prefer Trackspacer because it gives you frequency-specific ducking. With Trackspacer you can set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end stays right there.\"\n    },\n    {\n      \"start_index\": 3,\n      \"end_index\": 4,\n      \"topic_label\": \"Parallel drum compression with Drum Buss and Valhalla Room\",\n      \"summary\": \"Setting up a parallel compression chain using Ableton's Drum Buss and Valhalla Room reverb for drum processing\",\n      \"transcript_text\": \"Now let me show you another technique — parallel compression on drums. I use Drum Buss with the drive at about 40 percent, then send that to a return track. On the return I put Valhalla Room with a short decay, like 1.2 seconds. Mix it in at minus 12 dB. Your drums just breathe — they get this room sound without getting washy.\"\n    }\n  ]\n}\n","path":"backend/pipeline/quality/fixtures/sample_topic_group.json"}},{"type":"toolCall","id":"toolu_01Q3NDSquAj69w7XvgNEVURv","name":"write","arguments":{"content":"{\n  \"extracted_moments\": [\n    {\n      \"title\": \"Frequency-specific sidechain with Trackspacer\",\n      \"summary\": \"Using Trackspacer plugin for frequency-band sidechain compression targeting 100-300Hz, allowing bass high-end to remain present while clearing low-mid mud under the kick.\",\n      \"content_type\": \"technique\",\n      \"plugins\": [\"Trackspacer\"],\n      \"start_time\": 15.2,\n      \"end_time\": 52.1\n    },\n    {\n      \"title\": \"Parallel drum compression chain\",\n      \"summary\": \"Setting up Ableton's Drum Buss at 40% drive into a return track with Valhalla Room at 1.2s decay, mixed at -12dB for room sound without wash.\",\n      \"content_type\": \"settings\",\n      \"plugins\": [\"Drum Buss\", \"Valhalla Room\"],\n      \"start_time\": 52.1,\n      \"end_time\": 89.3\n    },\n    {\n      \"title\": \"Mono compatibility checking workflow\",\n      \"summary\": \"Using Ableton's Utility plugin on the sub bus to constantly check mono compatibility of layered bass patches, catching phase cancellation before mixdown.\",\n      \"content_type\": \"workflow\",\n      \"plugins\": [\"Utility\"],\n      \"start_time\": 89.3,\n      \"end_time\": 110.0\n    }\n  ],\n  \"taxonomy\": \"Sound Design > Mixing & Processing\"\n}\n","path":"backend/pipeline/quality/fixtures/sample_classifications.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1616,"cacheRead":86244,"cacheWrite":279,"totalTokens":88140,"cost":{"input":0.000005,"output":0.040400000000000005,"cacheRead":0.043122,"cacheWrite":0.00174375,"total":0.08527075}},"stopReason":"toolUse","timestamp":1775035259364}}
{"type":"message","id":"85a4d60a","parentId":"fdc6af50","timestamp":"2026-04-01T09:21:27.379Z","message":{"role":"toolResult","toolCallId":"toolu_01HDJTMpeCypccQDW9C1Vstc","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1583 bytes to backend/pipeline/quality/fixtures/sample_segments.json"}],"isError":false,"timestamp":1775035287378}}
{"type":"message","id":"e10de041","parentId":"85a4d60a","timestamp":"2026-04-01T09:21:27.382Z","message":{"role":"toolResult","toolCallId":"toolu_011VMpBvdZAA1tK3vfnQi1TZ","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1406 bytes to backend/pipeline/quality/fixtures/sample_topic_group.json"}],"isError":false,"timestamp":1775035287382}}
{"type":"message","id":"f635a600","parentId":"e10de041","timestamp":"2026-04-01T09:21:27.388Z","message":{"role":"toolResult","toolCallId":"toolu_01Q3NDSquAj69w7XvgNEVURv","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1183 bytes to backend/pipeline/quality/fixtures/sample_classifications.json"}],"isError":false,"timestamp":1775035287385}}
{"type":"message","id":"15d83ae3","parentId":"f635a600","timestamp":"2026-04-01T09:22:26.615Z","message":{"role":"assistant","content":[{"type":"text","text":"Fixtures created. Now generalizing `optimizer.py` — the core of this task. The key changes: use STAGE_CONFIGS for prompt file, fixture validation, and score dispatching."},{"type":"toolCall","id":"toolu_01Bu5R2ZtoPb9f9AcGbygkuC","name":"write","arguments":{"content":"\"\"\"Automated prompt optimization loop for pipeline stages 2-5.\n\nOrchestrates a generate→score→select cycle:\n1. Score the current best prompt against reference fixtures\n2. Generate N variants targeting weak dimensions\n3. Score each variant\n4. Keep the best scorer as the new baseline\n5. Repeat for K iterations\n\nUsage (via CLI):\n    python -m pipeline.quality optimize --stage 5 --iterations 10\n    python -m pipeline.quality optimize --stage 3 --iterations 5 --file fixtures/sample_topic_group.json\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\n\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, ScoreRunner\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass OptimizationResult:\n    \"\"\"Full result of an optimization run.\"\"\"\n\n    best_prompt: str = \"\"\n    best_score: ScoreResult = field(default_factory=ScoreResult)\n    history: list[dict] = field(default_factory=list)\n    elapsed_seconds: float = 0.0\n\n\nclass OptimizationLoop:\n    \"\"\"Runs iterative prompt optimization for a pipeline stage.\n\n    Each iteration generates *variants_per_iter* prompt mutations,\n    scores each against reference fixture data, and keeps the\n    highest-composite-scoring variant as the new baseline.\n\n    Parameters\n    ----------\n    client:\n        LLMClient instance for LLM calls (synthesis + scoring + variant gen).\n    stage:\n        Pipeline stage number (2-5).\n    fixture_path:\n        Path to a JSON fixture file matching the stage's expected keys.\n    iterations:\n        Number of generate→score→select cycles.\n    variants_per_iter:\n        Number of variant prompts to generate per iteration.\n    \"\"\"\n\n    def __init__(\n        self,\n        client: LLMClient,\n        stage: int,\n        fixture_path: str,\n        iterations: int = 5,\n        variants_per_iter: int = 2,\n    ) -> None:\n        if stage not in STAGE_CONFIGS:\n            raise ValueError(\n                f\"Unsupported stage {stage}. Valid stages: {sorted(STAGE_CONFIGS)}\"\n            )\n\n        self.client = client\n        self.stage = stage\n        self.fixture_path = fixture_path\n        self.iterations = iterations\n        self.variants_per_iter = variants_per_iter\n        self.config = STAGE_CONFIGS[stage]\n\n        self.scorer = ScoreRunner(client)\n        self.generator = PromptVariantGenerator(client)\n\n    def run(self) -> OptimizationResult:\n        \"\"\"Execute the full optimization loop.\n\n        Returns\n        -------\n        OptimizationResult\n            Contains the best prompt, its scores, full iteration history,\n            and wall-clock elapsed time.\n        \"\"\"\n        from pipeline.stages import _load_prompt\n\n        t0 = time.monotonic()\n        dimensions = self.config.dimensions\n\n        # Load base prompt using the stage's configured prompt file\n        prompt_file = self.config.prompt_file\n        try:\n            base_prompt = _load_prompt(prompt_file)\n        except FileNotFoundError:\n            logger.error(\"Prompt file not found: %s\", prompt_file)\n            return OptimizationResult(\n                best_prompt=\"\",\n                best_score=ScoreResult(error=f\"Prompt file not found: {prompt_file}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        # Load fixture data\n        try:\n            fixture = self._load_fixture()\n        except (FileNotFoundError, json.JSONDecodeError, KeyError) as exc:\n            logger.error(\"Failed to load fixture: %s\", exc)\n            return OptimizationResult(\n                best_prompt=base_prompt,\n                best_score=ScoreResult(error=f\"Fixture load error: {exc}\"),\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        history: list[dict] = []\n\n        # Score the baseline\n        print(f\"\\n{'='*60}\")\n        print(f\"  PROMPT OPTIMIZATION — Stage {self.stage}\")\n        print(f\"  Iterations: {self.iterations}, Variants/iter: {self.variants_per_iter}\")\n        print(f\"{'='*60}\\n\")\n\n        print(\"  Scoring baseline prompt...\")\n        best_score = self._score_variant(base_prompt, fixture)\n        best_prompt = base_prompt\n\n        history.append({\n            \"iteration\": 0,\n            \"variant_index\": 0,\n            \"prompt_text\": base_prompt[:200] + \"...\" if len(base_prompt) > 200 else base_prompt,\n            \"prompt_length\": len(base_prompt),\n            \"composite\": best_score.composite,\n            \"scores\": {d: best_score.scores.get(d, 0.0) for d in dimensions},\n            \"error\": best_score.error,\n            \"label\": \"baseline\",\n        })\n\n        if best_score.error:\n            print(f\"  ✗ Baseline scoring failed: {best_score.error}\")\n            print(\"  Aborting optimization — fix the baseline first.\\n\")\n            return OptimizationResult(\n                best_prompt=best_prompt,\n                best_score=best_score,\n                history=history,\n                elapsed_seconds=round(time.monotonic() - t0, 2),\n            )\n\n        self._print_iteration_summary(0, best_score, is_baseline=True)\n\n        # Iterate\n        for iteration in range(1, self.iterations + 1):\n            print(f\"\\n  ── Iteration {iteration}/{self.iterations} ──\")\n\n            # Generate variants with stage-appropriate markers\n            variants = self.generator.generate(\n                base_prompt=best_prompt,\n                scores=best_score,\n                n=self.variants_per_iter,\n                stage=self.stage,\n            )\n\n            if not variants:\n                print(\"  ⚠ No valid variants generated — skipping iteration\")\n                continue\n\n            # Score each variant\n            iteration_best_score = best_score\n            iteration_best_prompt = best_prompt\n\n            for vi, variant_prompt in enumerate(variants):\n                print(f\"  Scoring variant {vi + 1}/{len(variants)}...\")\n\n                score = self._score_variant(variant_prompt, fixture)\n\n                history.append({\n                    \"iteration\": iteration,\n                    \"variant_index\": vi + 1,\n                    \"prompt_text\": variant_prompt[:200] + \"...\" if len(variant_prompt) > 200 else variant_prompt,\n                    \"prompt_length\": len(variant_prompt),\n                    \"composite\": score.composite,\n                    \"scores\": {d: score.scores.get(d, 0.0) for d in dimensions},\n                    \"error\": score.error,\n                    \"label\": f\"iter{iteration}_v{vi+1}\",\n                })\n\n                if score.error:\n                    print(f\"    ✗ Variant {vi + 1} errored: {score.error}\")\n                    continue\n\n                if score.composite > iteration_best_score.composite:\n                    iteration_best_score = score\n                    iteration_best_prompt = variant_prompt\n                    print(f\"    ✓ New best: {score.composite:.3f} (was {best_score.composite:.3f})\")\n                else:\n                    print(f\"    · Score {score.composite:.3f} ≤ current best {iteration_best_score.composite:.3f}\")\n\n            # Update global best if this iteration improved\n            if iteration_best_score.composite > best_score.composite:\n                best_score = iteration_best_score\n                best_prompt = iteration_best_prompt\n                print(f\"  ★ Iteration {iteration} improved: {best_score.composite:.3f}\")\n            else:\n                print(f\"  · No improvement in iteration {iteration}\")\n\n            self._print_iteration_summary(iteration, best_score)\n\n        # Final report\n        elapsed = round(time.monotonic() - t0, 2)\n        self._print_final_report(best_score, history, elapsed)\n\n        return OptimizationResult(\n            best_prompt=best_prompt,\n            best_score=best_score,\n            history=history,\n            elapsed_seconds=elapsed,\n        )\n\n    # ── Internal helpers ──────────────────────────────────────────────────\n\n    def _load_fixture(self) -> dict:\n        \"\"\"Load and validate the fixture JSON file against stage-specific keys.\"\"\"\n        path = Path(self.fixture_path)\n        if not path.exists():\n            raise FileNotFoundError(f\"Fixture not found: {path}\")\n        data = json.loads(path.read_text(encoding=\"utf-8\"))\n\n        for key in self.config.fixture_keys:\n            if key not in data:\n                raise KeyError(\n                    f\"Stage {self.stage} fixture must contain '{key}' key \"\n                    f\"(required: {self.config.fixture_keys})\"\n                )\n\n        return data\n\n    def _score_variant(\n        self,\n        variant_prompt: str,\n        fixture: dict,\n    ) -> ScoreResult:\n        \"\"\"Score a variant prompt by running LLM completion + scoring.\n\n        Dispatches to stage-specific synthesis logic:\n        - Stages 2-4: call LLM with the variant prompt and fixture input,\n          parse with the stage's schema, then score via score_stage_output()\n        - Stage 5: original flow (synthesis + page scoring)\n        \"\"\"\n        from pipeline.stages import _get_stage_config\n\n        import json as _json\n        import openai as _openai\n\n        model_override, modality = _get_stage_config(self.stage)\n        schema_class = self.config.get_schema()\n\n        # Build user prompt from fixture data — stage-specific formatting\n        user_prompt = self._build_user_prompt(fixture)\n\n        t0 = time.monotonic()\n        try:\n            raw = self.client.complete(\n                system_prompt=variant_prompt,\n                user_prompt=user_prompt,\n                response_model=schema_class,\n                modality=modality,\n                model_override=model_override,\n            )\n            elapsed_synth = round(time.monotonic() - t0, 2)\n        except (_openai.APIConnectionError, _openai.APITimeoutError) as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"LLM error (stage {self.stage}): {exc}\",\n            )\n        except Exception as exc:\n            elapsed_synth = round(time.monotonic() - t0, 2)\n            logger.exception(\"Unexpected error during variant synthesis (stage %d)\", self.stage)\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Unexpected synthesis error: {exc}\",\n            )\n\n        # Parse the LLM response into the stage schema\n        raw_text = str(raw).strip()\n        try:\n            parsed = self.client.parse_response(raw_text, schema_class)\n        except Exception as exc:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Variant parse error (stage {self.stage}): {exc}\",\n            )\n\n        # Convert parsed output to JSON for the scorer\n        output_json = self._schema_to_output_json(parsed)\n        if output_json is None:\n            return ScoreResult(\n                elapsed_seconds=elapsed_synth,\n                error=f\"Stage {self.stage} produced empty output\",\n            )\n\n        # Score using the generic stage scorer\n        result = self.scorer.score_stage_output(\n            stage=self.stage,\n            output_json=output_json,\n            input_json=self._fixture_to_input_json(fixture),\n        )\n        result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)\n        return result\n\n    def _build_user_prompt(self, fixture: dict) -> str:\n        \"\"\"Build a stage-appropriate user prompt from fixture data.\"\"\"\n        if self.stage == 2:\n            segments_json = json.dumps(fixture[\"transcript_segments\"], indent=2)\n            return f\"<transcript_segments>\\n{segments_json}\\n</transcript_segments>\"\n\n        elif self.stage == 3:\n            segments_json = json.dumps(fixture[\"topic_segments\"], indent=2)\n            return f\"<topic_segments>\\n{segments_json}\\n</topic_segments>\"\n\n        elif self.stage == 4:\n            moments_json = json.dumps(fixture[\"extracted_moments\"], indent=2)\n            taxonomy = fixture.get(\"taxonomy\", \"\")\n            prompt = f\"<moments>\\n{moments_json}\\n</moments>\"\n            if taxonomy:\n                prompt += f\"\\n<taxonomy>{taxonomy}</taxonomy>\"\n            return prompt\n\n        elif self.stage == 5:\n            moments_json = json.dumps(fixture.get(\"key_moments\", fixture.get(\"moments\", [])), indent=2)\n            creator = fixture.get(\"creator_name\", \"Unknown\")\n            return f\"<creator>{creator}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n\n        else:\n            return json.dumps(fixture, indent=2)\n\n    def _schema_to_output_json(self, parsed: object) -> dict | list | None:\n        \"\"\"Convert a parsed Pydantic schema instance to JSON-serializable dict.\"\"\"\n        if hasattr(parsed, \"model_dump\"):\n            return parsed.model_dump()\n        elif hasattr(parsed, \"dict\"):\n            return parsed.dict()\n        return None\n\n    def _fixture_to_input_json(self, fixture: dict) -> dict | list:\n        \"\"\"Extract the primary input data from the fixture for scorer context.\"\"\"\n        if self.stage == 2:\n            return fixture[\"transcript_segments\"]\n        elif self.stage == 3:\n            return fixture[\"topic_segments\"]\n        elif self.stage == 4:\n            return fixture[\"extracted_moments\"]\n        elif self.stage == 5:\n            return fixture.get(\"key_moments\", fixture.get(\"moments\", []))\n        return fixture\n\n    def _print_iteration_summary(\n        self,\n        iteration: int,\n        score: ScoreResult,\n        is_baseline: bool = False,\n    ) -> None:\n        \"\"\"Print a compact one-line summary of the current best scores.\"\"\"\n        label = \"BASELINE\" if is_baseline else f\"ITER {iteration}\"\n        dimensions = self.config.dimensions\n        dims = \"  \".join(\n            f\"{d[:4]}={score.scores.get(d, 0.0):.2f}\" for d in dimensions\n        )\n        print(f\"  [{label}] composite={score.composite:.3f}  {dims}\")\n\n    def _print_final_report(\n        self,\n        best_score: ScoreResult,\n        history: list[dict],\n        elapsed: float,\n    ) -> None:\n        \"\"\"Print the final optimization summary.\"\"\"\n        dimensions = self.config.dimensions\n\n        print(f\"\\n{'='*60}\")\n        print(\"  OPTIMIZATION COMPLETE\")\n        print(f\"{'='*60}\")\n        print(f\"  Total time: {elapsed}s\")\n        print(f\"  Iterations: {self.iterations}\")\n        print(f\"  Variants scored: {len(history) - 1}\")  # minus baseline\n\n        baseline_composite = history[0][\"composite\"] if history else 0.0\n        improvement = best_score.composite - baseline_composite\n\n        print(f\"\\n  Baseline composite: {baseline_composite:.3f}\")\n        print(f\"  Best composite:     {best_score.composite:.3f}\")\n        if improvement > 0:\n            print(f\"  Improvement:        +{improvement:.3f}\")\n        else:\n            print(f\"  Improvement:        {improvement:.3f} (no gain)\")\n\n        print(f\"\\n  Per-dimension best scores:\")\n        for d in dimensions:\n            val = best_score.scores.get(d, 0.0)\n            bar = \"█\" * int(val * 20) + \"░\" * (20 - int(val * 20))\n            print(f\"    {d.replace('_', ' ').title():25s} {val:.2f}  {bar}\")\n\n        errored = sum(1 for h in history if h.get(\"error\"))\n        if errored:\n            print(f\"\\n  ⚠ {errored} variant(s) errored during scoring\")\n\n        print(f\"{'='*60}\\n\")\n\n\n# Late import to avoid circular dependency (scorer imports at module level,\n# variant_generator imports scorer)\nfrom pipeline.quality.variant_generator import PromptVariantGenerator  # noqa: E402\n","path":"backend/pipeline/quality/optimizer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":4306,"cacheRead":86523,"cacheWrite":1763,"totalTokens":92593,"cost":{"input":0.000005,"output":0.10765000000000001,"cacheRead":0.0432615,"cacheWrite":0.01101875,"total":0.16193525}},"stopReason":"toolUse","timestamp":1775035287386}}
{"type":"message","id":"ce73c04a","parentId":"15d83ae3","timestamp":"2026-04-01T09:22:26.619Z","message":{"role":"toolResult","toolCallId":"toolu_01Bu5R2ZtoPb9f9AcGbygkuC","toolName":"write","content":[{"type":"text","text":"Successfully wrote 15621 bytes to backend/pipeline/quality/optimizer.py"}],"isError":false,"timestamp":1775035346617}}
{"type":"message","id":"5efd14ae","parentId":"ce73c04a","timestamp":"2026-04-01T09:22:31.276Z","message":{"role":"assistant","content":[{"type":"text","text":"Now update `__main__.py` — remove the stage-5 gate and generalize the leaderboard/trajectory to use stage-appropriate dimensions."},{"type":"toolCall","id":"toolu_01Mqt4H1yZRvLFEAV7VgX8h2","name":"edit","arguments":{"newText":"from .scorer import DIMENSIONS, STAGE_CONFIGS, ScoreRunner","oldText":"from .scorer import DIMENSIONS, ScoreRunner","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":152,"cacheRead":88286,"cacheWrite":4335,"totalTokens":92774,"cost":{"input":0.000005,"output":0.0038,"cacheRead":0.044142999999999995,"cacheWrite":0.02709375,"total":0.07504174999999999}},"stopReason":"toolUse","timestamp":1775035346618}}
{"type":"message","id":"3e8a282f","parentId":"5efd14ae","timestamp":"2026-04-01T09:22:31.285Z","message":{"role":"toolResult","toolCallId":"toolu_01Mqt4H1yZRvLFEAV7VgX8h2","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":"  19 from pipeline.llm_client import LLMClient\n  20 \n  21 from .fitness import FitnessRunner\n  22 from .optimizer import OptimizationLoop, OptimizationResult\n- 23 from .scorer import DIMENSIONS, ScoreRunner\n+ 23 from .scorer import DIMENSIONS, STAGE_CONFIGS, ScoreRunner\n  24 \n  25 \n  26 # ── Reporting helpers ────────────────────────────────────────────────────────\n  27 ","firstChangedLine":23},"isError":false,"timestamp":1775035351284}}
{"type":"message","id":"be646db2","parentId":"3e8a282f","timestamp":"2026-04-01T09:22:44.307Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JSBffE28EcNEAXinr3HHMj","name":"edit","arguments":{"newText":"def print_leaderboard(result: OptimizationResult, stage: int = 5) -> None:\n    \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n    dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS\n\n    # Filter to entries that actually scored (no errors)\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if not scored:\n        print(\"\\n  No successfully scored variants to rank.\\n\")\n        return\n\n    ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n\n    print(f\"\\n{'='*72}\")\n    print(f\"  LEADERBOARD — Top 5 Variants by Composite Score (Stage {stage})\")\n    print(f\"{'='*72}\")\n\n    # Header\n    dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in dims)\n    sep_segments = \"  \".join(\"─\" * 5 for _ in dims)\n    print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n    print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {sep_segments}\")\n\n    for i, entry in enumerate(ranked, 1):\n        label = entry.get(\"label\", \"?\")[:16]\n        comp = entry[\"composite\"]\n        dim_vals = \"  \".join(\n            f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in dims\n        )\n        bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n        print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n\n    print(f\"{'='*72}\\n\")","oldText":"def print_leaderboard(result: OptimizationResult) -> None:\n    \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n    # Filter to entries that actually scored (no errors)\n    scored = [h for h in result.history if not h.get(\"error\")]\n    if not scored:\n        print(\"\\n  No successfully scored variants to rank.\\n\")\n        return\n\n    ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n\n    print(f\"\\n{'='*72}\")\n    print(\"  LEADERBOARD — Top 5 Variants by Composite Score\")\n    print(f\"{'='*72}\")\n\n    # Header\n    dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in DIMENSIONS)\n    print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n    print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}\")\n\n    for i, entry in enumerate(ranked, 1):\n        label = entry.get(\"label\", \"?\")[:16]\n        comp = entry[\"composite\"]\n        dim_vals = \"  \".join(\n            f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in DIMENSIONS\n        )\n        bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n        print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n\n    print(f\"{'='*72}\\n\")","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1032,"cacheRead":92621,"cacheWrite":177,"totalTokens":93831,"cost":{"input":0.000005,"output":0.0258,"cacheRead":0.0463105,"cacheWrite":0.00110625,"total":0.07322175}},"stopReason":"toolUse","timestamp":1775035351284}}
{"type":"message","id":"c754ff3d","parentId":"be646db2","timestamp":"2026-04-01T09:22:44.321Z","message":{"role":"toolResult","toolCallId":"toolu_01JSBffE28EcNEAXinr3HHMj","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":"  25 \n  26 # ── Reporting helpers ────────────────────────────────────────────────────────\n  27 \n  28 \n- 29 def print_leaderboard(result: OptimizationResult) -> None:\n+ 29 def print_leaderboard(result: OptimizationResult, stage: int = 5) -> None:\n  30     \"\"\"Print a formatted leaderboard of top 5 variants by composite score.\"\"\"\n+ 31     dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS\n+ 32 \n  33     # Filter to entries that actually scored (no errors)\n  34     scored = [h for h in result.history if not h.get(\"error\")]\n  35     if not scored:\n  36         print(\"\\n  No successfully scored variants to rank.\\n\")\n...\n  38 \n  39     ranked = sorted(scored, key=lambda h: h[\"composite\"], reverse=True)[:5]\n  40 \n  41     print(f\"\\n{'='*72}\")\n- 40     print(\"  LEADERBOARD — Top 5 Variants by Composite Score\")\n+ 42     print(f\"  LEADERBOARD — Top 5 Variants by Composite Score (Stage {stage})\")\n  43     print(f\"{'='*72}\")\n  44 \n  45     # Header\n- 44     dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in DIMENSIONS)\n+ 46     dim_headers = \"  \".join(f\"{d[:5]:>5s}\" for d in dims)\n+ 47     sep_segments = \"  \".join(\"─\" * 5 for _ in dims)\n  48     print(f\"  {'#':>2s}  {'Label':<16s}  {'Comp':>5s}  {dim_headers}\")\n- 46     print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}  {'─'*5}\")\n+ 49     print(f\"  {'─'*2}  {'─'*16}  {'─'*5}  {sep_segments}\")\n  50 \n  51     for i, entry in enumerate(ranked, 1):\n  52         label = entry.get(\"label\", \"?\")[:16]\n  53         comp = entry[\"composite\"]\n  54         dim_vals = \"  \".join(\n- 52             f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in DIMENSIONS\n+ 55             f\"{entry['scores'].get(d, 0.0):5.2f}\" for d in dims\n  56         )\n  57         bar = \"█\" * int(comp * 20) + \"░\" * (20 - int(comp * 20))\n  58         print(f\"  {i:>2d}  {label:<16s}  {comp:5.3f}  {dim_vals}  {bar}\")\n  59 ","firstChangedLine":29},"isError":false,"timestamp":1775035364317}}
{"type":"message","id":"780bb686","parentId":"c754ff3d","timestamp":"2026-04-01T09:22:50.027Z","message":{"role":"assistant","content":[{"type":"text","text":"Now update `write_results_json` to use stage-appropriate dimensions:"},{"type":"toolCall","id":"toolu_01RuJUgWAsVsxZGu12VRkpzd","name":"edit","arguments":{"newText":"    dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS\n\n    payload = {\n        \"config\": {\n            \"stage\": stage,\n            \"iterations\": iterations,\n            \"variants_per_iter\": variants_per_iter,\n            \"fixture_path\": fixture_path,\n        },\n        \"best_prompt\": result.best_prompt,\n        \"best_scores\": {\n            \"composite\": result.best_score.composite,\n            **{d: result.best_score.scores.get(d, 0.0) for d in dims},\n        },","oldText":"    payload = {\n        \"config\": {\n            \"stage\": stage,\n            \"iterations\": iterations,\n            \"variants_per_iter\": variants_per_iter,\n            \"fixture_path\": fixture_path,\n        },\n        \"best_prompt\": result.best_prompt,\n        \"best_scores\": {\n            \"composite\": result.best_score.composite,\n            **{d: getattr(result.best_score, d) for d in DIMENSIONS},\n        },","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":365,"cacheRead":92798,"cacheWrite":1057,"totalTokens":94221,"cost":{"input":0.000005,"output":0.009125000000000001,"cacheRead":0.046398999999999996,"cacheWrite":0.00660625,"total":0.062135249999999996}},"stopReason":"toolUse","timestamp":1775035364319}}
{"type":"message","id":"d51e4e63","parentId":"780bb686","timestamp":"2026-04-01T09:22:50.039Z","message":{"role":"toolResult","toolCallId":"toolu_01RuJUgWAsVsxZGu12VRkpzd","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":" 137     timestamp = datetime.now(timezone.utc).strftime(\"%Y%m%d_%H%M%S\")\n 138     filename = f\"optimize_stage{stage}_{timestamp}.json\"\n 139     filepath = out_path / filename\n 140 \n+141     dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS\n+142 \n 143     payload = {\n 144         \"config\": {\n 145             \"stage\": stage,\n 146             \"iterations\": iterations,\n...\n 149         },\n 150         \"best_prompt\": result.best_prompt,\n 151         \"best_scores\": {\n 152             \"composite\": result.best_score.composite,\n-151             **{d: getattr(result.best_score, d) for d in DIMENSIONS},\n+153             **{d: result.best_score.scores.get(d, 0.0) for d in dims},\n 154         },\n 155         \"elapsed_seconds\": result.elapsed_seconds,\n 156         \"history\": result.history,\n 157     }","firstChangedLine":141},"isError":false,"timestamp":1775035370036}}
{"type":"message","id":"40e9f59c","parentId":"d51e4e63","timestamp":"2026-04-01T09:22:56.976Z","message":{"role":"assistant","content":[{"type":"text","text":"Now remove the stage-5 gate in `_run_optimize`:"},{"type":"toolCall","id":"toolu_0146NkqJRuMucyqg1WQWKDpr","name":"edit","arguments":{"newText":"def _run_optimize(args: argparse.Namespace) -> int:\n    \"\"\"Execute the optimize subcommand.\"\"\"\n    # Stage validation — stages 2-5 are supported\n    if args.stage not in STAGE_CONFIGS:\n        print(\n            f\"Error: unsupported stage {args.stage}. Valid stages: {sorted(STAGE_CONFIGS)}\",\n            file=sys.stderr,\n        )\n        return 1","oldText":"def _run_optimize(args: argparse.Namespace) -> int:\n    \"\"\"Execute the optimize subcommand.\"\"\"\n    # Stage validation — only stage 5 is supported\n    if args.stage != 5:\n        print(\n            f\"Error: only stage 5 is supported for optimization (got stage {args.stage})\",\n            file=sys.stderr,\n        )\n        return 1","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":307,"cacheRead":93855,"cacheWrite":390,"totalTokens":94553,"cost":{"input":0.000005,"output":0.007675,"cacheRead":0.0469275,"cacheWrite":0.0024375,"total":0.057045}},"stopReason":"toolUse","timestamp":1775035370037}}
{"type":"message","id":"15efd42a","parentId":"40e9f59c","timestamp":"2026-04-01T09:22:56.986Z","message":{"role":"toolResult","toolCallId":"toolu_0146NkqJRuMucyqg1WQWKDpr","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":" 325 \n 326 \n 327 def _run_optimize(args: argparse.Namespace) -> int:\n 328     \"\"\"Execute the optimize subcommand.\"\"\"\n-329     # Stage validation — only stage 5 is supported\n-330     if args.stage != 5:\n+329     # Stage validation — stages 2-5 are supported\n+330     if args.stage not in STAGE_CONFIGS:\n 331         print(\n-332             f\"Error: only stage 5 is supported for optimization (got stage {args.stage})\",\n+332             f\"Error: unsupported stage {args.stage}. Valid stages: {sorted(STAGE_CONFIGS)}\",\n 333             file=sys.stderr,\n 334         )\n 335         return 1\n 336 ","firstChangedLine":329},"isError":false,"timestamp":1775035376984}}
{"type":"message","id":"5ce6b922","parentId":"15efd42a","timestamp":"2026-04-01T09:23:00.761Z","message":{"role":"assistant","content":[{"type":"text","text":"Update the leaderboard call to pass stage:"},{"type":"toolCall","id":"toolu_014vYQ92HvfQpNBWoon8Wb39","name":"edit","arguments":{"newText":"    # Reporting\n    print_leaderboard(result, stage=args.stage)","oldText":"    # Reporting\n    print_leaderboard(result)","path":"backend/pipeline/quality/__main__.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":94245,"cacheWrite":332,"totalTokens":94714,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0471225,"cacheWrite":0.002075,"total":0.052602499999999996}},"stopReason":"toolUse","timestamp":1775035376985}}
{"type":"message","id":"7efbf9f5","parentId":"5ce6b922","timestamp":"2026-04-01T09:23:00.773Z","message":{"role":"toolResult","toolCallId":"toolu_014vYQ92HvfQpNBWoon8Wb39","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/__main__.py."}],"details":{"diff":" 368         print(f\"\\nError: {result.best_score.error}\", file=sys.stderr)\n 369         return 1\n 370 \n 371     # Reporting\n-372     print_leaderboard(result)\n+372     print_leaderboard(result, stage=args.stage)\n 373     print_trajectory(result)\n 374 \n 375     # Write results JSON\n 376     try:","firstChangedLine":372},"isError":false,"timestamp":1775035380770}}
{"type":"message","id":"fc523b4e","parentId":"7efbf9f5","timestamp":"2026-04-01T09:23:04.682Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let's run the verification commands from the task plan."},{"type":"toolCall","id":"toolu_017xRSLk5v8fgWH88TJniDjF","name":"bash","arguments":{"command":"cd backend && python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":94577,"cacheWrite":161,"totalTokens":94833,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.0472885,"cacheWrite":0.00100625,"total":0.05064975}},"stopReason":"toolUse","timestamp":1775035380771}}
{"type":"message","id":"88782c88","parentId":"fc523b4e","timestamp":"2026-04-01T09:23:05.217Z","message":{"role":"toolResult","toolCallId":"toolu_017xRSLk5v8fgWH88TJniDjF","toolName":"bash","content":[{"type":"text","text":"optimizer ok\n"}],"isError":false,"timestamp":1775035385214}}
{"type":"message","id":"152a3e3f","parentId":"88782c88","timestamp":"2026-04-01T09:23:08.013Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XgR8usvv7vRLx44cAivsdb","name":"bash","arguments":{"command":"cd backend && python -c \"from pipeline.quality.__main__ import main; print('cli ok')\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":78,"cacheRead":94738,"cacheWrite":109,"totalTokens":94926,"cost":{"input":0.000005,"output":0.0019500000000000001,"cacheRead":0.047368999999999994,"cacheWrite":0.00068125,"total":0.050005249999999994}},"stopReason":"toolUse","timestamp":1775035385215}}
{"type":"message","id":"f1870765","parentId":"152a3e3f","timestamp":"2026-04-01T09:23:08.512Z","message":{"role":"toolResult","toolCallId":"toolu_01XgR8usvv7vRLx44cAivsdb","toolName":"bash","content":[{"type":"text","text":"cli ok\n"}],"isError":false,"timestamp":1775035388510}}
{"type":"message","id":"634b2995","parentId":"f1870765","timestamp":"2026-04-01T09:23:11.818Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017fBPb4ca8GgYDhD7dNckLH","name":"bash","arguments":{"command":"cd backend && python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 | head -1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":94847,"cacheWrite":93,"totalTokens":95036,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.0474235,"cacheWrite":0.0005812500000000001,"total":0.05038475}},"stopReason":"toolUse","timestamp":1775035388511}}
{"type":"message","id":"83593dd5","parentId":"634b2995","timestamp":"2026-04-01T09:23:12.328Z","message":{"role":"toolResult","toolCallId":"toolu_017fBPb4ca8GgYDhD7dNckLH","toolName":"bash","content":[{"type":"text","text":"usage: pipeline.quality optimize [-h] [--stage STAGE]\n"}],"isError":false,"timestamp":1775035392325}}
{"type":"message","id":"124ab262","parentId":"83593dd5","timestamp":"2026-04-01T09:23:16.506Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LiwcRYgQzy5HzCJgbjuHc4","name":"bash","arguments":{"command":"cd backend && python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage' && echo 'stage6 rejected ok'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":94940,"cacheWrite":123,"totalTokens":95156,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.04747,"cacheWrite":0.00076875,"total":0.05054375}},"stopReason":"toolUse","timestamp":1775035392326}}
{"type":"message","id":"bc684dec","parentId":"124ab262","timestamp":"2026-04-01T09:23:16.995Z","message":{"role":"toolResult","toolCallId":"toolu_01LiwcRYgQzy5HzCJgbjuHc4","toolName":"bash","content":[{"type":"text","text":"stage6 rejected ok\n"}],"isError":false,"timestamp":1775035396993}}
{"type":"message","id":"6d8cfe7a","parentId":"bc684dec","timestamp":"2026-04-01T09:23:23.755Z","message":{"role":"assistant","content":[{"type":"text","text":"All four verification commands pass. Let me also verify that each fixture loads correctly through the optimizer's validation:"},{"type":"toolCall","id":"toolu_01VUUF9GZyzLLyirwJXBfu7K","name":"bash","arguments":{"command":"cd backend && python -c \"\nfrom pipeline.quality.optimizer import OptimizationLoop\nfrom pipeline.llm_client import LLMClient\n\n# Test fixture validation for each stage\nfor stage, fixture in [\n    (2, 'pipeline/quality/fixtures/sample_segments.json'),\n    (3, 'pipeline/quality/fixtures/sample_topic_group.json'),\n    (4, 'pipeline/quality/fixtures/sample_classifications.json'),\n    (5, 'pipeline/quality/fixtures/sample_moments.json'),\n]:\n    loop = OptimizationLoop.__new__(OptimizationLoop)\n    from pipeline.quality.scorer import STAGE_CONFIGS\n    loop.config = STAGE_CONFIGS[stage]\n    loop.stage = stage\n    loop.fixture_path = fixture\n    data = loop._load_fixture()\n    print(f'Stage {stage}: fixture loaded ok, keys={list(data.keys())}')\n\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":303,"cacheRead":95063,"cacheWrite":110,"totalTokens":95477,"cost":{"input":0.000005,"output":0.007575,"cacheRead":0.0475315,"cacheWrite":0.0006875000000000001,"total":0.055798999999999994}},"stopReason":"toolUse","timestamp":1775035396994}}
{"type":"message","id":"c680594a","parentId":"6d8cfe7a","timestamp":"2026-04-01T09:23:24.281Z","message":{"role":"toolResult","toolCallId":"toolu_01VUUF9GZyzLLyirwJXBfu7K","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 17, in <module>\n  File \"/home/aux/projects/content-to-kb-automator/backend/pipeline/quality/optimizer.py\", line 233, in _load_fixture\n    raise KeyError(\nKeyError: \"Stage 5 fixture must contain 'key_moments' key (required: ['key_moments', 'creator_name'])\"\nStage 2: fixture loaded ok, keys=['transcript_segments']\nStage 3: fixture loaded ok, keys=['topic_segments']\nStage 4: fixture loaded ok, keys=['extracted_moments', 'taxonomy']\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775035404278}}
{"type":"message","id":"b550c8d4","parentId":"c680594a","timestamp":"2026-04-01T09:23:33.440Z","message":{"role":"assistant","content":[{"type":"text","text":"Stage 5's config expects `key_moments` but the existing fixture uses `moments`. The existing fixture is the established one — I need to handle this in the user prompt builder (already does with `fixture.get(\"key_moments\", fixture.get(\"moments\", []))`), but the _load_fixture validation is strict. The cleanest fix: update the stage 5 fixture_keys to match reality, or make the fixture loader accept `moments` as an alias for `key_moments`.\n\nThe existing fixture is the established contract for stage 5 and uses `moments`. The STAGE_CONFIGS was built in T01 with `key_moments` — that's a mismatch. I'll update the stage 5 config to use `moments` since that's what the real fixture and the rest of the codebase use."},{"type":"toolCall","id":"toolu_011bKj3uu5XsEm6SRU9JeXR4","name":"edit","arguments":{"newText":"        fixture_keys=[\"moments\", \"creator_name\"],","oldText":"        fixture_keys=[\"key_moments\", \"creator_name\"],","path":"backend/pipeline/quality/scorer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":303,"cacheRead":95173,"cacheWrite":482,"totalTokens":95959,"cost":{"input":0.000005,"output":0.007575,"cacheRead":0.0475865,"cacheWrite":0.0030125,"total":0.058178999999999995}},"stopReason":"toolUse","timestamp":1775035404279}}
{"type":"message","id":"e9db4f10","parentId":"b550c8d4","timestamp":"2026-04-01T09:23:33.457Z","message":{"role":"toolResult","toolCallId":"toolu_011bKj3uu5XsEm6SRU9JeXR4","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/scorer.py."}],"details":{"diff":" 280         stage=5,\n 281         dimensions=[\"structural\", \"content_specificity\", \"voice_preservation\", \"readability\", \"factual_fidelity\"],\n 282         rubric=SCORING_RUBRIC,\n 283         format_markers=[\"SynthesisResult\", '\"pages\"', \"body_sections\", \"title\", \"summary\"],\n-284         fixture_keys=[\"key_moments\", \"creator_name\"],\n+284         fixture_keys=[\"moments\", \"creator_name\"],\n 285         prompt_file=\"stage5_synthesis.txt\",\n 286         schema_class=\"SynthesisResult\",\n 287     ),\n 288 }","firstChangedLine":284},"isError":false,"timestamp":1775035413454}}
{"type":"message","id":"47104c6e","parentId":"e9db4f10","timestamp":"2026-04-01T09:23:38.977Z","message":{"role":"assistant","content":[{"type":"text","text":"And update the user prompt builder for stage 5 to match:"},{"type":"toolCall","id":"toolu_016pe7CGQBhJkdtMZLCz59ck","name":"edit","arguments":{"newText":"        elif self.stage == 5:\n            moments_json = json.dumps(fixture[\"moments\"], indent=2)\n            creator = fixture.get(\"creator_name\", \"Unknown\")\n            return f\"<creator>{creator}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"","oldText":"        elif self.stage == 5:\n            moments_json = json.dumps(fixture.get(\"key_moments\", fixture.get(\"moments\", [])), indent=2)\n            creator = fixture.get(\"creator_name\", \"Unknown\")\n            return f\"<creator>{creator}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"","path":"backend/pipeline/quality/optimizer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":256,"cacheRead":95655,"cacheWrite":328,"totalTokens":96240,"cost":{"input":0.000005,"output":0.0064,"cacheRead":0.047827499999999995,"cacheWrite":0.00205,"total":0.0562825}},"stopReason":"toolUse","timestamp":1775035413455}}
{"type":"message","id":"98c5b46d","parentId":"47104c6e","timestamp":"2026-04-01T09:23:38.983Z","message":{"role":"toolResult","toolCallId":"toolu_016pe7CGQBhJkdtMZLCz59ck","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/optimizer.py."}],"details":{"diff":" 329                 prompt += f\"\\n<taxonomy>{taxonomy}</taxonomy>\"\n 330             return prompt\n 331 \n 332         elif self.stage == 5:\n-333             moments_json = json.dumps(fixture.get(\"key_moments\", fixture.get(\"moments\", [])), indent=2)\n+333             moments_json = json.dumps(fixture[\"moments\"], indent=2)\n 334             creator = fixture.get(\"creator_name\", \"Unknown\")\n 335             return f\"<creator>{creator}</creator>\\n<moments>\\n{moments_json}\\n</moments>\"\n 336 \n 337         else:","firstChangedLine":333},"isError":false,"timestamp":1775035418982}}
{"type":"message","id":"0fe345f9","parentId":"98c5b46d","timestamp":"2026-04-01T09:23:43.168Z","message":{"role":"assistant","content":[{"type":"text","text":"Also update _fixture_to_input_json for stage 5:"},{"type":"toolCall","id":"toolu_01SQRsjeqmfBnauYsykBb64d","name":"edit","arguments":{"newText":"        elif self.stage == 5:\n            return fixture[\"moments\"]","oldText":"        elif self.stage == 5:\n            return fixture.get(\"key_moments\", fixture.get(\"moments\", []))","path":"backend/pipeline/quality/optimizer.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":95983,"cacheWrite":281,"totalTokens":96418,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.0479915,"cacheWrite":0.00175625,"total":0.05357775}},"stopReason":"toolUse","timestamp":1775035418982}}
{"type":"message","id":"6733da4e","parentId":"0fe345f9","timestamp":"2026-04-01T09:23:43.180Z","message":{"role":"toolResult","toolCallId":"toolu_01SQRsjeqmfBnauYsykBb64d","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/quality/optimizer.py."}],"details":{"diff":" 353             return fixture[\"topic_segments\"]\n 354         elif self.stage == 4:\n 355             return fixture[\"extracted_moments\"]\n 356         elif self.stage == 5:\n-357             return fixture.get(\"key_moments\", fixture.get(\"moments\", []))\n+357             return fixture[\"moments\"]\n 358         return fixture\n 359 \n 360     def _print_iteration_summary(\n 361         self,","firstChangedLine":357},"isError":false,"timestamp":1775035423177}}
{"type":"message","id":"f5b40cbd","parentId":"6733da4e","timestamp":"2026-04-01T09:23:47.525Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Eygt1DuEgyb9SUSuuSua5Z","name":"bash","arguments":{"command":"cd backend && python -c \"\nfrom pipeline.quality.optimizer import OptimizationLoop\nfrom pipeline.quality.scorer import STAGE_CONFIGS\n\nfor stage, fixture in [\n    (2, 'pipeline/quality/fixtures/sample_segments.json'),\n    (3, 'pipeline/quality/fixtures/sample_topic_group.json'),\n    (4, 'pipeline/quality/fixtures/sample_classifications.json'),\n    (5, 'pipeline/quality/fixtures/sample_moments.json'),\n]:\n    loop = OptimizationLoop.__new__(OptimizationLoop)\n    loop.config = STAGE_CONFIGS[stage]\n    loop.stage = stage\n    loop.fixture_path = fixture\n    data = loop._load_fixture()\n    print(f'Stage {stage}: fixture loaded ok, keys={list(data.keys())}')\nprint('All fixtures validated')\n\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":266,"cacheRead":96264,"cacheWrite":178,"totalTokens":96709,"cost":{"input":0.000005,"output":0.0066500000000000005,"cacheRead":0.048132,"cacheWrite":0.0011125,"total":0.055899500000000005}},"stopReason":"toolUse","timestamp":1775035423178}}
{"type":"message","id":"932356ed","parentId":"f5b40cbd","timestamp":"2026-04-01T09:23:48.054Z","message":{"role":"toolResult","toolCallId":"toolu_01Eygt1DuEgyb9SUSuuSua5Z","toolName":"bash","content":[{"type":"text","text":"Stage 2: fixture loaded ok, keys=['transcript_segments']\nStage 3: fixture loaded ok, keys=['topic_segments']\nStage 4: fixture loaded ok, keys=['extracted_moments', 'taxonomy']\nStage 5: fixture loaded ok, keys=['creator_name', 'topic_category', 'moments']\nAll fixtures validated\n"}],"isError":false,"timestamp":1775035428051}}
{"type":"message","id":"e294083a","parentId":"932356ed","timestamp":"2026-04-01T09:23:52.308Z","message":{"role":"assistant","content":[{"type":"text","text":"Re-run all four verification commands:"},{"type":"toolCall","id":"toolu_0122V5naSAE8U8TXMsZYWjAM","name":"bash","arguments":{"command":"cd backend && python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\" && python -c \"from pipeline.quality.__main__ import main; print('cli ok')\" && python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 | head -1 && python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage' && echo 'stage6 rejected ok'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":189,"cacheRead":96442,"cacheWrite":356,"totalTokens":96988,"cost":{"input":0.000005,"output":0.004725,"cacheRead":0.048221,"cacheWrite":0.002225,"total":0.055175999999999996}},"stopReason":"toolUse","timestamp":1775035428052}}
{"type":"message","id":"5557ee2d","parentId":"e294083a","timestamp":"2026-04-01T09:23:54.356Z","message":{"role":"toolResult","toolCallId":"toolu_0122V5naSAE8U8TXMsZYWjAM","toolName":"bash","content":[{"type":"text","text":"optimizer ok\ncli ok\nusage: pipeline.quality optimize [-h] [--stage STAGE]\nstage6 rejected ok\n"}],"isError":false,"timestamp":1775035434353}}
{"type":"message","id":"8692859f","parentId":"5557ee2d","timestamp":"2026-04-01T09:23:57.924Z","message":{"role":"assistant","content":[{"type":"text","text":"All pass. Let me also verify stage 1 is rejected:"},{"type":"toolCall","id":"toolu_01ETe5cy9GQA2dFe3GZi5os1","name":"bash","arguments":{"command":"cd backend && python -m pipeline.quality optimize --stage 1 --file x 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":96798,"cacheWrite":229,"totalTokens":97116,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.048399,"cacheWrite":0.00143125,"total":0.05203525}},"stopReason":"toolUse","timestamp":1775035434355}}
{"type":"message","id":"854cea2b","parentId":"8692859f","timestamp":"2026-04-01T09:23:58.416Z","message":{"role":"toolResult","toolCallId":"toolu_01ETe5cy9GQA2dFe3GZi5os1","toolName":"bash","content":[{"type":"text","text":"Error: unsupported stage 1. Valid stages: [2, 3, 4, 5]\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775035438412}}
{"type":"message","id":"4f00c44d","parentId":"854cea2b","timestamp":"2026-04-01T09:24:02.391Z","message":{"role":"assistant","content":[{"type":"text","text":"Now read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_01D7Hka4VcdVZHK62svi4S4T","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":84,"cacheRead":97027,"cacheWrite":140,"totalTokens":97252,"cost":{"input":0.000005,"output":0.0021000000000000003,"cacheRead":0.0485135,"cacheWrite":0.000875,"total":0.051493500000000005}},"stopReason":"toolUse","timestamp":1775035438414}}
{"type":"message","id":"a74944d5","parentId":"4f00c44d","timestamp":"2026-04-01T09:24:02.397Z","message":{"role":"toolResult","toolCallId":"toolu_01D7Hka4VcdVZHK62svi4S4T","toolName":"read","content":[{"type":"text","text":"---\nid: {{taskId}}\nparent: {{sliceId}}\nmilestone: {{milestoneId}}\nprovides:\n  - {{whatThisTaskProvides}}\nkey_files:\n  - {{filePath}}\nkey_decisions:\n  - {{decision}}\npatterns_established:\n  - {{pattern}}\nobservability_surfaces:\n  - {{status endpoint, structured log, persisted failure state, diagnostic command, or none}}\nduration: {{duration}}\nverification_result: passed\ncompleted_at: {{date}}\n# Set blocker_discovered: true only if execution revealed the remaining slice plan\n# is fundamentally invalid (wrong API, missing capability, architectural mismatch).\n# Do NOT set true for ordinary bugs, minor deviations, or fixable issues.\nblocker_discovered: false\n---\n\n# {{taskId}}: {{taskTitle}}\n\n<!-- One-liner must say what actually shipped, not just that work completed.\n     Good: \"Added retry-aware worker status logging\"\n     Bad: \"Implemented logging improvements\" -->\n\n**{{oneLiner}}**\n\n## What Happened\n\n{{narrative}}\n\n## Verification\n\n{{whatWasVerifiedAndHow — commands run, tests passed, behavior confirmed}}\n\n## Verification Evidence\n\n<!-- Populated from verification gate output. If the gate ran, fill in the table below.\n     If no gate ran (e.g., no verification commands discovered), note that. -->\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| {{row}} | {{command}} | {{exitCode}} | {{verdict}} | {{duration}} |\n\n## Diagnostics\n\n{{howToInspectWhatThisTaskBuiltLater — status surfaces, logs, error shapes, failure artifacts, or none}}\n\n## Deviations\n\n<!-- Deviations are unplanned changes to the written task plan, not ordinary debugging during implementation. -->\n\n{{deviationsFromPlan_OR_none}}\n\n## Known Issues\n\n{{issuesDiscoveredButNotFixed_OR_none}}\n\n## Files Created/Modified\n\n- `{{filePath}}` — {{description}}\n- `{{filePath}}` — {{description}}\n"}],"isError":false,"timestamp":1775035442394}}
{"type":"message","id":"7907bb1d","parentId":"a74944d5","timestamp":"2026-04-01T09:24:27.794Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019EYQwNR9EN1qjbYRiJk2zo","name":"write","arguments":{"content":"---\nid: T02\nparent: S04\nmilestone: M013\nprovides:\n  - stage-aware OptimizationLoop accepting stages 2-5\n  - fixture files for stages 2-4\n  - CLI stage validation for stages 2-5\nkey_files:\n  - backend/pipeline/quality/optimizer.py\n  - backend/pipeline/quality/__main__.py\n  - backend/pipeline/quality/fixtures/sample_segments.json\n  - backend/pipeline/quality/fixtures/sample_topic_group.json\n  - backend/pipeline/quality/fixtures/sample_classifications.json\nkey_decisions:\n  - \"Fixed stage 5 fixture_keys from ['key_moments', 'creator_name'] to ['moments', 'creator_name'] to match existing fixture\"\n  - \"Stage-specific user prompt building via _build_user_prompt() dispatch rather than a shared format\"\npatterns_established:\n  - \"Fixture-to-input-json extraction pattern: _fixture_to_input_json() maps fixture keys to scorer input per stage\"\nobservability_surfaces:\n  - \"Error messages include stage number for disambiguation: 'LLM error (stage N)', 'Variant parse error (stage N)'\"\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-01T05:57:00.000Z\nblocker_discovered: false\n---\n\n# T02: Generalized optimizer to stages 2-5, created stage 2-4 fixtures, wired CLI validation\n\n**Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI**\n\n## What Happened\n\nRewrote `optimizer.py` to be fully stage-aware. Key changes:\n- Constructor validates stage against STAGE_CONFIGS, stores config reference\n- `_load_fixture()` validates against `config.fixture_keys` instead of hardcoded `moments`/`creator_name`\n- `_score_variant()` dispatches per-stage: builds stage-appropriate user prompts via `_build_user_prompt()`, parses LLM output with the stage's schema class, and scores via `score_stage_output()`\n- `run()` loads prompt file from `config.prompt_file` instead of hardcoded `stage5_synthesis.txt`\n- `_print_iteration_summary()` and `_print_final_report()` use `config.dimensions` instead of the global `DIMENSIONS`\n\nUpdated `__main__.py`: replaced `args.stage != 5` gate with `args.stage not in STAGE_CONFIGS`, updated `print_leaderboard()` to accept stage parameter and use stage-appropriate dimensions, updated `write_results_json()` similarly.\n\nCreated three fixture files matching STAGE_CONFIGS fixture_keys:\n- Stage 2: `sample_segments.json` with `transcript_segments` array\n- Stage 3: `sample_topic_group.json` with `topic_segments` array\n- Stage 4: `sample_classifications.json` with `extracted_moments` array + taxonomy\n\nFixed stage 5 config: `fixture_keys` was `[\"key_moments\", \"creator_name\"]` but the existing fixture uses `moments` — corrected to `[\"moments\", \"creator_name\"]`.\n\n## Verification\n\nAll four verification commands from the task plan pass. Additionally verified fixture loading for all four stages, and that stage 1 and 6 are both rejected with informative error messages.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\"` | 0 | ✅ pass | 500ms |\n| 2 | `python -c \"from pipeline.quality.__main__ import main; print('cli ok')\"` | 0 | ✅ pass | 500ms |\n| 3 | `python -m pipeline.quality optimize --stage 2 --iterations 1 --file pipeline/quality/fixtures/sample_segments.json --help 2>&1 \\| head -1` | 0 | ✅ pass | 500ms |\n| 4 | `python -m pipeline.quality optimize --stage 6 --file x 2>&1 \\| grep -q 'stage' && echo 'stage6 rejected ok'` | 0 | ✅ pass | 500ms |\n| 5 | `Fixture validation for all 4 stages (2-5)` | 0 | ✅ pass | 500ms |\n\n## Diagnostics\n\nRun `python -m pipeline.quality optimize --stage N --file <fixture> --iterations 1` for any stage 2-5. Errors include stage number for disambiguation. Invalid stages produce: `Error: unsupported stage N. Valid stages: [2, 3, 4, 5]`.\n\n## Deviations\n\nFixed stage 5 `fixture_keys` from `[\"key_moments\", \"creator_name\"]` to `[\"moments\", \"creator_name\"]` — the T01-established config didn't match the existing fixture file.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/quality/optimizer.py` — Rewritten to be fully stage-aware (fixture validation, schema dispatch, user prompt building, dimensions)\n- `backend/pipeline/quality/__main__.py` — Removed stage-5 gate, generalized leaderboard/results to use stage-appropriate dimensions\n- `backend/pipeline/quality/scorer.py` — Fixed stage 5 fixture_keys to match existing fixture\n- `backend/pipeline/quality/fixtures/sample_segments.json` — New stage 2 fixture\n- `backend/pipeline/quality/fixtures/sample_topic_group.json` — New stage 3 fixture\n- `backend/pipeline/quality/fixtures/sample_classifications.json` — New stage 4 fixture\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1532,"cacheRead":97167,"cacheWrite":594,"totalTokens":99294,"cost":{"input":0.000005,"output":0.0383,"cacheRead":0.048583499999999995,"cacheWrite":0.0037125,"total":0.09060099999999999}},"stopReason":"toolUse","timestamp":1775035442395}}
{"type":"message","id":"fad23689","parentId":"7907bb1d","timestamp":"2026-04-01T09:24:27.800Z","message":{"role":"toolResult","toolCallId":"toolu_019EYQwNR9EN1qjbYRiJk2zo","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4778 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md"}],"isError":false,"timestamp":1775035467797}}
{"type":"message","id":"6643511e","parentId":"fad23689","timestamp":"2026-04-01T09:24:39.814Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Sn4HwTHcsD5NBDRWgPV4Us","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"Fixed stage 5 fixture_keys from ['key_moments', 'creator_name'] to ['moments', 'creator_name'] — T01 config didn't match existing fixture.","keyDecisions":["Fixed stage 5 fixture_keys to match existing fixture (moments not key_moments)","Stage-specific user prompt building via _build_user_prompt() dispatch"],"keyFiles":["backend/pipeline/quality/optimizer.py","backend/pipeline/quality/__main__.py","backend/pipeline/quality/scorer.py","backend/pipeline/quality/fixtures/sample_segments.json","backend/pipeline/quality/fixtures/sample_topic_group.json","backend/pipeline/quality/fixtures/sample_classifications.json"],"knownIssues":"None.","milestoneId":"M013","narrative":"Rewrote optimizer.py to be fully stage-aware: constructor validates stage against STAGE_CONFIGS, _load_fixture() validates against config.fixture_keys, _score_variant() dispatches per-stage with stage-appropriate user prompts and schema parsing, run() uses config.prompt_file. Updated __main__.py to accept stages 2-5 and use stage-appropriate dimensions in leaderboard/results. Created three fixture files for stages 2-4. Fixed stage 5 fixture_keys mismatch from T01.","oneLiner":"Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI","sliceId":"S04","taskId":"T02","verification":"All four task plan verification commands pass: optimizer imports ok, CLI imports ok, --help output works for stage 2, stage 6 is rejected. Additionally verified fixture loading for all stages 2-5 and that stage 1 is also rejected.","verificationEvidence":[{"command":"python -c \"from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"python -c \"from pipeline.quality.__main__ import main; print('cli ok')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"python -m pipeline.quality optimize --stage 2 --iterations 1 --file ... --help 2>&1 | head -1","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage'","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"Fixture validation for all stages 2-5","durationMs":500,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":896,"cacheRead":97761,"cacheWrite":1593,"totalTokens":100251,"cost":{"input":0.000005,"output":0.0224,"cacheRead":0.0488805,"cacheWrite":0.00995625,"total":0.08124175}},"stopReason":"toolUse","timestamp":1775035467798}}
{"type":"message","id":"1f0df953","parentId":"6643511e","timestamp":"2026-04-01T09:24:39.855Z","message":{"role":"toolResult","toolCallId":"toolu_01Sn4HwTHcsD5NBDRWgPV4Us","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T02 (S04/M013)"}],"details":{"operation":"complete_task","taskId":"T02","sliceId":"S04","milestoneId":"M013","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md"},"isError":false,"timestamp":1775035479853}}
{"type":"message","id":"64275e51","parentId":"1f0df953","timestamp":"2026-04-01T09:24:42.611Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T02 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":99354,"cacheWrite":929,"totalTokens":100293,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.049677,"cacheWrite":0.005806250000000001,"total":0.05571325}},"stopReason":"stop","timestamp":1775035479854}}
