{"type":"thinking_level_change","id":"88bf234e","parentId":null,"timestamp":"2026-04-03T01:03:01.424Z","thinkingLevel":"medium"}
{"type":"model_change","id":"8b478db1","parentId":"88bf234e","timestamp":"2026-04-03T01:03:01.425Z","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/M014/slices/S02/S02-PLAN.md`\n- Task plan source: `.gsd/milestones/M014/slices/S02/tasks/T02-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M014/slices/S02/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/M014/slices/S02/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/M014/slices/S02/tasks/T02-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T02 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M014/slices/S02/S02-PLAN.md`\n**Goal:** Composition prompt (stage5_compose.txt) and test harness --compose mode enable offline testing of merging new video moments into existing technique pages, producing unified v2 output with correct citation re-indexing and deduplication.\n**Demo:** After this: Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations.\n\n## UNIT: Execute Task T02 (\"Add compose subcommand to test harness\") — Slice S02 (\"Composition Prompt + Test Harness Compose Mode\"), Milestone M014\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/M014/slices/S02/tasks/T01-SUMMARY.md` — T01: Created composition prompt with merge rules, citation re-indexing, deduplication guidance, and two valid v2 JSON examples | decisions: \"Composition prompt is self-contained — carries its own writing standards rather than importing from synthesis prompt at runtime\"; \"Offset-based citation scheme: existing keep [0]-[N-1] | key_files: \"prompts/stage5_compose.txt\"; \".gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M014/slices/S02/tasks/T02-PLAN.md`\n\n---\nestimated_steps: 22\nestimated_files: 1\nskills_used: []\n---\n\n# T02: Add compose subcommand to test harness\n\nAdd a `compose` subcommand to `backend/pipeline/test_harness.py` that loads an existing page JSON + new moments fixture, builds a compose user prompt, calls the LLM via the compose prompt, and validates the output. Also extract the compose user-prompt builder as a testable function.\n\nSteps:\n1. Read `backend/pipeline/test_harness.py` fully to understand the existing `run` and `promote` subcommand patterns.\n2. Add a `build_compose_prompt()` function that takes:\n   - `existing_page: dict` (SynthesizedPage as dict, from prior synthesis output)\n   - `existing_moments: list[tuple[MockKeyMoment, dict]]` (the original moments the page was built from)\n   - `new_moments: list[tuple[MockKeyMoment, dict]]` (new video's moments to compose in)\n   - `creator_name: str`\n   Returns: the user prompt string with `<existing_page>`, `<existing_moments>`, `<new_moments>`, and `<creator>` XML tags. Existing moments get indices [0]-[N-1], new moments get [N]-[N+M-1]. Uses `build_moments_text()` for formatting moment lists.\n3. Add a `run_compose()` function (parallel to `run_synthesis()`) that:\n   - Loads the existing page JSON and validates as SynthesizedPage\n   - Loads the new fixture via existing `load_fixture()`\n   - Filters new moments by category to match `existing_page.topic_category`\n   - Needs the existing moments for citation context — accept them as a separate fixture or extract from the existing page's moment_indices + a moments fixture\n   - Calls `build_compose_prompt()` to build the user prompt\n   - Calls LLM with compose system prompt\n   - Parses response as SynthesisResult\n   - Runs citation validation with moment_count = len(existing_moments) + len(new_moments)\n   - Logs compose-specific metrics: word count before/after, sections before/after, new sections added\n4. Register `compose` subcommand in the `main()` argparse with args: `--existing-page` (required), `--fixture` (required), `--existing-fixture` (required, original moments fixture for citation context), `--prompt`, `--output`, `--category`, `--model`, `--modality`.\n5. Wire the subcommand handler to call `run_compose()` and write output JSON.\n6. Verify: `cd backend && python -m pipeline.test_harness compose --help` exits 0.\n\n## Inputs\n\n- ``backend/pipeline/test_harness.py` — existing harness with run/promote subcommands`\n- ``backend/pipeline/schemas.py` — SynthesizedPage, SynthesisResult for validation`\n- ``backend/pipeline/citation_utils.py` — validate_citations() for output checking`\n- ``prompts/stage5_compose.txt` — default compose prompt path (from T01)`\n\n## Expected Output\n\n- ``backend/pipeline/test_harness.py` — updated with compose subcommand, build_compose_prompt() function, run_compose() function`\n\n## Verification\n\ncd backend && python -m pipeline.test_harness compose --help\n","display":false,"id":"6388a502","parentId":"8b478db1","timestamp":"2026-04-03T01:03:01.427Z"}
{"type":"message","id":"010322e9","parentId":"6388a502","timestamp":"2026-04-03T01:03:06.330Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the task plan and understanding the existing code before making changes.","thinkingSignature":"EpsCClkIDBgCKkDRPpAv0BwsE044FAKXt+KDbrfhSjXPCEsaw6qM5uX2YWs/fBXUU5oaLu/J3IX/MjhCa3rdl9CJx2QgYNYpILmuMg9jbGF1ZGUtb3B1cy00LTY4ABIM9odVBadRIEQlwPndGgz3jbkVrHcON6fg+ZUiMGw8e/3fnZVDyCJ1zPGzsfPWAJ5oxAYD+Ez0vaSllwfmQSDRStsNzItSphFzEmbL6CpwDMaBBIyyUyGLq4mXh19omfpHlHqj/F50L6yNTBUlom5wTMlT5KjyE3w2wDjjQ1SD9LubSWxUP8xyvtslfWBrxLX486QtY+0dkQShu6pd42TBtheH8bRcSYW0qav43C/RmwAuizroxif5M2eDkvRTaRgB"},{"type":"text","text":"I'll read the existing test harness and the T01 summary to understand the current patterns, then implement the compose subcommand."},{"type":"toolCall","id":"toolu_01JyJBSXBkgSgrHftZWy3FoP","name":"read","arguments":{"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01F3jEFo6nfcM8eckMSgzDPd","name":"read","arguments":{"path":".gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":181,"cacheRead":59865,"cacheWrite":3657,"totalTokens":63705,"cost":{"input":0.00001,"output":0.004525,"cacheRead":0.029932499999999997,"cacheWrite":0.02285625,"total":0.05732375}},"stopReason":"toolUse","timestamp":1775178181426}}
{"type":"message","id":"8a444355","parentId":"010322e9","timestamp":"2026-04-03T01:03:06.336Z","message":{"role":"toolResult","toolCallId":"toolu_01JyJBSXBkgSgrHftZWy3FoP","toolName":"read","content":[{"type":"text","text":"\"\"\"Offline prompt test harness for Chrysopedia synthesis.\n\nLoads a fixture JSON (exported by export_fixture.py) and a prompt file,\ncalls the LLM, and outputs the synthesized result. No Docker, no database,\nno Redis, no Celery — just prompt + fixture + LLM endpoint.\n\nUsage:\n    python -m pipeline.test_harness \\\\\n      --fixture fixtures/real_video_xyz.json \\\\\n      --prompt prompts/stage5_synthesis.txt \\\\\n      --output /tmp/result.json\n\n    # Run all categories in a fixture:\n    python -m pipeline.test_harness --fixture fixtures/video.json\n\n    # Run a specific category only:\n    python -m pipeline.test_harness --fixture fixtures/video.json --category \"Sound Design\"\n\nExit codes: 0=success, 1=LLM error, 2=parse error, 3=fixture error\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter, defaultdict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import NamedTuple\n\nfrom pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesisResult\n\n\n# ── Lightweight stand-in for KeyMoment ORM model ───────────────────────────\n\nclass _MockContentType:\n    \"\"\"Mimics KeyMomentContentType enum with a .value property.\"\"\"\n    def __init__(self, value: str) -> None:\n        self.value = value\n\n\nclass MockKeyMoment(NamedTuple):\n    \"\"\"Lightweight stand-in for the ORM KeyMoment.\n\n    Has the same attributes that _build_moments_text() accesses:\n    title, summary, content_type, start_time, end_time, plugins, raw_transcript.\n    \"\"\"\n    title: str\n    summary: str\n    content_type: object  # _MockContentType\n    start_time: float\n    end_time: float\n    plugins: list[str]\n    raw_transcript: str\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[HARNESS] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\n# ── Moment text builder (mirrors stages.py _build_moments_text) ────────────\n\ndef build_moments_text(\n    moment_group: list[tuple[MockKeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text — matches _build_moments_text in stages.py.\"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\n# ── Fixture loading ────────────────────────────────────────────────────────\n\n@dataclass\nclass FixtureData:\n    \"\"\"Parsed fixture with moments grouped by category.\"\"\"\n    creator_name: str\n    video_id: str\n    content_type: str\n    filename: str\n    # Groups: category -> list of (MockKeyMoment, cls_info_dict)\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]]\n    total_moments: int\n\n\ndef load_fixture(path: str) -> FixtureData:\n    \"\"\"Load and parse a fixture JSON file into grouped moments.\"\"\"\n    fixture_path = Path(path)\n    if not fixture_path.exists():\n        raise FileNotFoundError(f\"Fixture not found: {path}\")\n\n    raw = fixture_path.read_text(encoding=\"utf-8\")\n    size_kb = len(raw.encode(\"utf-8\")) / 1024\n    data = json.loads(raw)\n\n    moments_raw = data.get(\"moments\", [])\n    if not moments_raw:\n        raise ValueError(f\"Fixture has no moments: {path}\")\n\n    _log(\"FIXTURE\", f\"Loading: {path} ({size_kb:.1f} KB, {len(moments_raw)} moments)\")\n\n    # Build MockKeyMoment objects and group by category\n    groups: dict[str, list[tuple[MockKeyMoment, dict]]] = defaultdict(list)\n\n    for m in moments_raw:\n        cls = m.get(\"classification\", {})\n        category = cls.get(\"topic_category\", m.get(\"topic_category\", \"Uncategorized\"))\n        tags = cls.get(\"topic_tags\", m.get(\"topic_tags\", []))\n\n        mock = MockKeyMoment(\n            title=m.get(\"title\", m.get(\"summary\", \"\")[:80]),\n            summary=m.get(\"summary\", \"\"),\n            content_type=_MockContentType(m.get(\"content_type\", \"technique\")),\n            start_time=m.get(\"start_time\", 0.0),\n            end_time=m.get(\"end_time\", 0.0),\n            plugins=m.get(\"plugins\", []),\n            raw_transcript=m.get(\"raw_transcript\", m.get(\"transcript_excerpt\", \"\")),\n        )\n        cls_info = {\"topic_category\": category, \"topic_tags\": tags}\n        groups[category].append((mock, cls_info))\n\n    # Log breakdown\n    cat_counts = {cat: len(moms) for cat, moms in groups.items()}\n    counts = list(cat_counts.values())\n    _log(\n        \"FIXTURE\",\n        f\"Breakdown: {len(groups)} categories, \"\n        f\"moments per category: min={min(counts)}, max={max(counts)}, \"\n        f\"avg={sum(counts)/len(counts):.1f}\",\n    )\n    for cat, count in sorted(cat_counts.items(), key=lambda x: -x[1]):\n        _log(\"FIXTURE\", f\"  {cat}: {count} moments\")\n\n    return FixtureData(\n        creator_name=data.get(\"creator_name\", \"Unknown\"),\n        video_id=data.get(\"video_id\", \"unknown\"),\n        content_type=data.get(\"content_type\", \"tutorial\"),\n        filename=data.get(\"filename\", \"unknown\"),\n        groups=dict(groups),\n        total_moments=len(moments_raw),\n    )\n\n\n# ── Synthesis runner ───────────────────────────────────────────────────────\n\ndef run_synthesis(\n    fixture: FixtureData,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run synthesis on fixture data, returns (pages, exit_code).\n\n    Returns all synthesized pages as dicts plus an exit code.\n    \"\"\"\n    # Load prompt\n    prompt_file = Path(prompt_path)\n    if not prompt_file.exists():\n        _log(\"ERROR\", f\"Prompt file not found: {prompt_path}\", level=\"ERROR\")\n        return [], 3\n\n    system_prompt = prompt_file.read_text(encoding=\"utf-8\")\n    _log(\"PROMPT\", f\"Loading: {prompt_path} ({len(system_prompt)} chars)\")\n\n    # Setup LLM\n    settings = get_settings()\n    llm = LLMClient(settings)\n\n    stage_model = model_override or settings.llm_stage5_model or settings.llm_model\n    stage_modality = modality or settings.llm_stage5_modality or \"thinking\"\n    hard_limit = settings.llm_max_tokens_hard_limit\n\n    _log(\"LLM\", f\"Model: {stage_model}, modality: {stage_modality}, hard_limit: {hard_limit}\")\n\n    # Filter categories if requested\n    categories = fixture.groups\n    if category_filter:\n        if category_filter not in categories:\n            _log(\"ERROR\", f\"Category '{category_filter}' not found. Available: {list(categories.keys())}\", level=\"ERROR\")\n            return [], 3\n        categories = {category_filter: categories[category_filter]}\n\n    all_pages: list[dict] = []\n    total_prompt_tokens = 0\n    total_completion_tokens = 0\n    total_duration_ms = 0\n    exit_code = 0\n\n    for cat_idx, (category, moment_group) in enumerate(categories.items(), 1):\n        _log(\"SYNTH\", f\"Category {cat_idx}/{len(categories)}: '{category}' ({len(moment_group)} moments)\")\n\n        # Build user prompt (same format as stages.py _synthesize_chunk)\n        moments_text, all_tags = build_moments_text(moment_group, category)\n        user_prompt = f\"<creator>{fixture.creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n        estimated_tokens = estimate_max_tokens(\n            system_prompt, user_prompt,\n            stage=\"stage5_synthesis\",\n            hard_limit=hard_limit,\n        )\n        _log(\n            \"SYNTH\",\n            f\"  Building prompt: {len(moment_group)} moments, \"\n            f\"max_tokens={estimated_tokens}, tags={sorted(all_tags)[:5]}{'...' if len(all_tags) > 5 else ''}\",\n        )\n\n        # Call LLM\n        call_start = time.monotonic()\n        _log(\"LLM\", f\"  Calling: model={stage_model}, max_tokens={estimated_tokens}, modality={stage_modality}\")\n\n        try:\n            raw = llm.complete(\n                system_prompt,\n                user_prompt,\n                response_model=SynthesisResult,\n                modality=stage_modality,\n                model_override=stage_model,\n                max_tokens=estimated_tokens,\n            )\n        except Exception as exc:\n            _log(\"ERROR\", f\"  LLM call failed: {exc}\", level=\"ERROR\")\n            exit_code = 1\n            continue\n\n        call_duration_ms = int((time.monotonic() - call_start) * 1000)\n        prompt_tokens = getattr(raw, \"prompt_tokens\", None) or 0\n        completion_tokens = getattr(raw, \"completion_tokens\", None) or 0\n        finish_reason = getattr(raw, \"finish_reason\", \"unknown\")\n\n        total_prompt_tokens += prompt_tokens\n        total_completion_tokens += completion_tokens\n        total_duration_ms += call_duration_ms\n\n        _log(\n            \"LLM\",\n            f\"  Response: {prompt_tokens} prompt + {completion_tokens} completion tokens, \"\n            f\"{call_duration_ms}ms, finish_reason={finish_reason}\",\n        )\n\n        if finish_reason == \"length\":\n            _log(\n                \"WARN\",\n                \"  finish_reason=length — output likely truncated! \"\n                \"Consider reducing fixture size or increasing max_tokens.\",\n                level=\"WARN\",\n            )\n\n        # Parse response\n        try:\n            result = SynthesisResult.model_validate_json(str(raw))\n        except (ValidationError, json.JSONDecodeError) as exc:\n            _log(\"ERROR\", f\"  Parse failed: {exc}\", level=\"ERROR\")\n            _log(\"ERROR\", f\"  Raw response (first 2000 chars): {str(raw)[:2000]}\", level=\"ERROR\")\n            exit_code = 2\n            continue\n\n        # Log per-page summary\n        _log(\"SYNTH\", f\"  Parsed: {len(result.pages)} pages synthesized\")\n        total_words = 0\n        for page in result.pages:\n            sections = page.body_sections or []\n            section_count = len(sections)\n            subsection_count = sum(len(s.subsections) for s in sections)\n            word_count = sum(\n                len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n                for s in sections\n            )\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{section_count} sections ({subsection_count} subsections), \"\n                f\"{word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            # Citation coverage reporting\n            cit = validate_citations(sections, len(page.moment_indices))\n            _log(\n                \"CITE\",\n                f\"    Citations: {cit['total_citations']}/{len(page.moment_indices)} moments cited \"\n                f\"({cit['coverage_pct']}% coverage)\"\n                + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n                + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n            )\n\n            all_pages.append(page.model_dump())\n\n    # Summary\n    _log(\"SUMMARY\", f\"Total: {len(all_pages)} pages across {len(categories)} categories\")\n    _log(\"SUMMARY\", f\"Tokens: {total_prompt_tokens} prompt + {total_completion_tokens} completion = {total_prompt_tokens + total_completion_tokens} total\")\n    _log(\"SUMMARY\", f\"Duration: {total_duration_ms}ms ({total_duration_ms / 1000:.1f}s)\")\n\n    return all_pages, exit_code\n\n\n# ── Promote: deploy a prompt to production ─────────────────────────────────\n\n_STAGE_PROMPT_MAP = {\n    2: \"stage2_segmentation.txt\",\n    3: \"stage3_extraction.txt\",\n    4: \"stage4_classification.txt\",\n    5: \"stage5_synthesis.txt\",\n}\n\n\ndef promote_prompt(prompt_path: str, stage: int, reason: str, commit: bool = False) -> int:\n    \"\"\"Copy a winning prompt to the canonical path and create a backup.\n\n    The worker reads prompts from disk at runtime — no restart needed.\n    \"\"\"\n    import hashlib\n    import shutil\n\n    if stage not in _STAGE_PROMPT_MAP:\n        _log(\"ERROR\", f\"Invalid stage {stage}. Valid: {sorted(_STAGE_PROMPT_MAP)}\", level=\"ERROR\")\n        return 1\n\n    settings = get_settings()\n    template_name = _STAGE_PROMPT_MAP[stage]\n    canonical = Path(settings.prompts_path) / template_name\n    source = Path(prompt_path)\n\n    if not source.exists():\n        _log(\"ERROR\", f\"Source prompt not found: {prompt_path}\", level=\"ERROR\")\n        return 1\n\n    new_prompt = source.read_text(encoding=\"utf-8\")\n    new_hash = hashlib.sha256(new_prompt.encode()).hexdigest()[:12]\n\n    # Backup current prompt\n    old_prompt = \"\"\n    old_hash = \"none\"\n    if canonical.exists():\n        old_prompt = canonical.read_text(encoding=\"utf-8\")\n        old_hash = hashlib.sha256(old_prompt.encode()).hexdigest()[:12]\n\n        if old_prompt.strip() == new_prompt.strip():\n            _log(\"PROMOTE\", \"No change — new prompt is identical to current prompt\")\n            return 0\n\n        archive_dir = Path(settings.prompts_path) / \"archive\"\n        archive_dir.mkdir(parents=True, exist_ok=True)\n        ts = time.strftime(\"%Y%m%d_%H%M%S\", time.gmtime())\n        backup = archive_dir / f\"{template_name.replace('.txt', '')}_{ts}.txt\"\n        shutil.copy2(canonical, backup)\n        _log(\"PROMOTE\", f\"Backed up current prompt: {old_hash} -> {backup}\")\n\n    # Write new prompt\n    canonical.write_text(new_prompt, encoding=\"utf-8\")\n\n    old_lines = old_prompt.strip().splitlines()\n    new_lines = new_prompt.strip().splitlines()\n    _log(\"PROMOTE\", f\"Installed new prompt: {new_hash} ({len(new_prompt)} chars, {len(new_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Previous: {old_hash} ({len(old_prompt)} chars, {len(old_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Reason: {reason}\")\n    _log(\"PROMOTE\", \"Worker reads prompts from disk at runtime — no restart needed\")\n\n    if commit:\n        import subprocess\n        try:\n            subprocess.run(\n                [\"git\", \"add\", str(canonical)],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            msg = f\"prompt: promote stage{stage} — {reason}\"\n            subprocess.run(\n                [\"git\", \"commit\", \"-m\", msg],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            _log(\"PROMOTE\", f\"Git commit created: {msg}\")\n        except subprocess.CalledProcessError as exc:\n            _log(\"PROMOTE\", f\"Git commit failed: {exc}\", level=\"WARN\")\n\n    return 0\n\n\n# ── CLI ────────────────────────────────────────────────────────────────────\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.test_harness\",\n        description=\"Offline prompt test harness for Chrysopedia synthesis\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- run subcommand (default behavior) --\n    run_parser = sub.add_parser(\"run\", help=\"Run synthesis against a fixture\")\n    run_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"Fixture JSON file\")\n    run_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Prompt file (default: stage5_synthesis.txt)\")\n    run_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n    run_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n    run_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n    run_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n\n    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n\n    args = parser.parse_args()\n\n    # If no subcommand, check for --fixture for backward compat\n    if args.command is None:\n        # Support running without subcommand for backward compat\n        parser.print_help()\n        return 1\n\n    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n\n    # -- run command --\n    prompt_path = args.prompt\n    if prompt_path is None:\n        settings = get_settings()\n        prompt_path = str(Path(settings.prompts_path) / \"stage5_synthesis.txt\")\n\n    overall_start = time.monotonic()\n    try:\n        fixture = load_fixture(args.fixture)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"Fixture error: {exc}\", level=\"ERROR\")\n        return 3\n\n    pages, exit_code = run_synthesis(\n        fixture=fixture,\n        prompt_path=prompt_path,\n        category_filter=args.category,\n        model_override=args.model,\n        modality=args.modality,\n    )\n\n    if not pages and exit_code != 0:\n        return exit_code\n\n    output = {\n        \"fixture_source\": args.fixture,\n        \"prompt_source\": prompt_path,\n        \"creator_name\": fixture.creator_name,\n        \"video_id\": fixture.video_id,\n        \"category_filter\": args.category,\n        \"pages\": pages,\n        \"metadata\": {\n            \"page_count\": len(pages),\n            \"total_words\": sum(\n                sum(\n                    len(s.get(\"content\", \"\").split())\n                    + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n                    for s in p.get(\"body_sections\", [])\n                )\n                for p in pages\n            ),\n            \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n        },\n    }\n\n    output_json = json.dumps(output, indent=2, ensure_ascii=False)\n\n    if args.output:\n        Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n        Path(args.output).write_text(output_json, encoding=\"utf-8\")\n        _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n    else:\n        print(output_json)\n        _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n\n    total_elapsed = time.monotonic() - overall_start\n    _log(\"DONE\", f\"Completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n\n    return exit_code\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775178186333}}
{"type":"message","id":"dec23c02","parentId":"8a444355","timestamp":"2026-04-03T01:03:06.339Z","message":{"role":"toolResult","toolCallId":"toolu_01F3jEFo6nfcM8eckMSgzDPd","toolName":"read","content":[{"type":"text","text":"---\nid: T01\nparent: S02\nmilestone: M014\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"prompts/stage5_compose.txt\", \".gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\"]\nkey_decisions: [\"Composition prompt is self-contained — carries its own writing standards rather than importing from synthesis prompt at runtime\", \"Offset-based citation scheme: existing keep [0]-[N-1], new get [N]-[N+M-1], no renumbering\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"Ran structural validation: file exists, 13053 chars (>2000), all 7 required sections present, both JSON blocks parse and validate with correct v2 structure (pages key, body_sections_format, moment_indices).\"\ncompleted_at: 2026-04-03T01:02:57.677Z\nblocker_discovered: false\n---\n\n# T01: Created composition prompt with merge rules, citation re-indexing, deduplication guidance, and two valid v2 JSON examples\n\n> Created composition prompt with merge rules, citation re-indexing, deduplication guidance, and two valid v2 JSON examples\n\n## What Happened\n---\nid: T01\nparent: S02\nmilestone: M014\nkey_files:\n  - prompts/stage5_compose.txt\n  - .gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\nkey_decisions:\n  - Composition prompt is self-contained — carries its own writing standards rather than importing from synthesis prompt at runtime\n  - Offset-based citation scheme: existing keep [0]-[N-1], new get [N]-[N+M-1], no renumbering\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:02:57.678Z\nblocker_discovered: false\n---\n\n# T01: Created composition prompt with merge rules, citation re-indexing, deduplication guidance, and two valid v2 JSON examples\n\n**Created composition prompt with merge rules, citation re-indexing, deduplication guidance, and two valid v2 JSON examples**\n\n## What Happened\n\nWrote prompts/stage5_compose.txt — the LLM prompt for composing new video moments into an existing technique page. Defines four XML input tags, offset-based citation re-indexing (existing [0]-[N-1], new [N]-[N+M-1]), detailed merge/enrich/dedup rules with concrete examples, workflow-ordered section placement, and v2 SynthesisResult output format. Two JSON examples included, both validated structurally.\n\n## Verification\n\nRan structural validation: file exists, 13053 chars (>2000), all 7 required sections present, both JSON blocks parse and validate with correct v2 structure (pages key, body_sections_format, moment_indices).\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python3 structural validation (sections + JSON parse)` | 0 | ✅ pass | 300ms |\n| 2 | `python3 dual JSON block validation (v2 schema)` | 0 | ✅ pass | 200ms |\n\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `prompts/stage5_compose.txt`\n- `.gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md`\n\n\n## Deviations\nNone.\n\n## Known Issues\nNone.\n"}],"isError":false,"timestamp":1775178186337}}
{"type":"message","id":"8e349578","parentId":"dec23c02","timestamp":"2026-04-03T01:03:11.893Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I understand the existing patterns. Now let me check the compose prompt and schemas to understand the data structures."},{"type":"toolCall","id":"toolu_01N2oW8gCRKa7HFC4gi3t4uq","name":"read","arguments":{"limit":40,"path":"prompts/stage5_compose.txt"}},{"type":"toolCall","id":"toolu_013fko2D3mqj1NepL5mbvdto","name":"read","arguments":{"limit":80,"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01ERekenTKijPtq14NQquQJm","name":"read","arguments":{"limit":40,"path":"backend/pipeline/citation_utils.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":216,"cacheRead":63522,"cacheWrite":6807,"totalTokens":70546,"cost":{"input":0.000005,"output":0.0054,"cacheRead":0.031761,"cacheWrite":0.042543750000000005,"total":0.07970975}},"stopReason":"toolUse","timestamp":1775178186338}}
{"type":"message","id":"1addf2df","parentId":"8e349578","timestamp":"2026-04-03T01:03:11.896Z","message":{"role":"toolResult","toolCallId":"toolu_01N2oW8gCRKa7HFC4gi3t4uq","toolName":"read","content":[{"type":"text","text":"You are updating an existing Chrysopedia technique page with new source material. A technique page already exists — it was synthesized from earlier video moments. Now new moments from additional videos are available. Your job is to compose a unified page that integrates the new material into the existing page, preserving what's already good while enriching it with genuinely new detail.\n\nThis is NOT a rewrite. This is a merge. The existing page has been reviewed and published. Treat its prose as the baseline — preserve its voice, structure, and specific details. Your job is to strengthen it with new information, not to rephrase what's already well-written.\n\n## What you receive\n\nYou receive four inputs in XML tags:\n\n1. `<existing_page>` — the current SynthesizedPage JSON (v2 format: body_sections as list of BodySection objects with heading, content, subsections)\n2. `<existing_moments>` — JSON array of the key moments that the existing page was built from (indices [0] through [N-1])\n3. `<new_moments>` — JSON array of new key moments to integrate (these will be indexed [N] through [N+M-1] in the composed output)\n4. `<creator>` — the creator name (same creator as the existing page)\n\n## Citation re-indexing — critical\n\nThe composed page uses a unified citation index space across both old and new moments:\n\n- **Existing moments** keep their original indices: `[0]`, `[1]`, ..., `[N-1]` where N is the length of `<existing_moments>`.\n- **New moments** are assigned indices starting at N: the first new moment is `[N]`, the second is `[N+1]`, ..., the last is `[N+M-1]` where M is the length of `<new_moments>`.\n- All citation markers `[N]` in the existing page prose remain valid and unchanged.\n- New citations referencing new moments use the offset indices: if there are 5 existing moments, the first new moment is cited as `[5]`, the second as `[6]`, etc.\n- When new information reinforces an existing claim, you may add a new citation alongside the existing one: `[2,7]` means the claim is supported by both existing moment 2 and new moment 7.\n\n**Never renumber existing citations.** The existing `[0]`, `[1]`, etc. in the prose must remain exactly as they are. Only add new `[N+...]` markers where new material contributes.\n\n## Merge rules\n\n### Preserve existing prose\nKeep existing section headings, prose, citations, and structure intact unless you have a specific reason to change something. Reasons to change existing prose:\n- A new moment directly contradicts an existing claim (explain the contradiction, don't silently overwrite)\n- A new moment provides a more specific value for a vague existing claim (update with the specific value and add the new citation)\n- A factual error is corrected by new information\n\n### Enrich existing sections with new detail\nWhen new moments cover the same topic as an existing section, integrate the new information INTO that section:\n- Add new paragraphs after existing ones (don't insert into the middle of existing paragraphs)\n- Add new subsections to existing sections when the new material covers a distinct sub-aspect\n- Extend existing paragraphs only by appending new sentences, never by rewriting existing sentences\n\n### Create new sections for genuinely new territory\n\n[157 more lines in file. Use offset=41 to continue.]"}],"isError":false,"timestamp":1775178191896}}
{"type":"message","id":"bf03f2e2","parentId":"1addf2df","timestamp":"2026-04-03T01:03:11.897Z","message":{"role":"toolResult","toolCallId":"toolu_013fko2D3mqj1NepL5mbvdto","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 BodySubSection(BaseModel):\n    \"\"\"An H3-level subsection within a body section.\"\"\"\n\n    heading: str = Field(description=\"H3 subsection heading\")\n    content: str = Field(description=\"Subsection body text (may contain [N] citation markers)\")\n\n\nclass BodySection(BaseModel):\n\n[46 more lines in file. Use offset=81 to continue.]"}],"isError":false,"timestamp":1775178191896}}
{"type":"message","id":"fdacd467","parentId":"bf03f2e2","timestamp":"2026-04-03T01:03:11.901Z","message":{"role":"toolResult","toolCallId":"toolu_01ERekenTKijPtq14NQquQJm","toolName":"read","content":[{"type":"text","text":"\"\"\"Citation extraction and validation utilities for synthesized technique pages.\n\nUsed by stage 5 synthesis and the test harness to verify that [N] citation\nmarkers in body sections reference valid source moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport re\n\nfrom pipeline.schemas import BodySection\n\n# Matches [N] or [N,M] or [N,M,P] style citation markers where N,M,P are integers.\n_CITATION_RE = re.compile(r\"\\[(\\d+(?:,\\s*\\d+)*)\\]\")\n\n\ndef extract_citations(text: str) -> list[int]:\n    \"\"\"Extract all citation indices from ``[N]`` and ``[N,M,...]`` markers in *text*.\n\n    Returns a sorted list of unique integer indices.\n    \"\"\"\n    indices: set[int] = set()\n    for match in _CITATION_RE.finditer(text):\n        for part in match.group(1).split(\",\"):\n            indices.add(int(part.strip()))\n    return sorted(indices)\n\n\ndef validate_citations(\n    sections: list[BodySection],\n    moment_count: int,\n) -> dict:\n    \"\"\"Validate citation markers across all *sections* against *moment_count* source moments.\n\n    Moments are expected to be referenced as 0-based indices ``[0]`` through\n    ``[moment_count - 1]``.\n\n    Returns a dict with:\n        valid (bool): True when every cited index is in range and every moment is cited.\n        total_citations (int): Count of unique cited indices.\n\n[25 more lines in file. Use offset=41 to continue.]"}],"isError":false,"timestamp":1775178191899}}
{"type":"message","id":"ed6bbb4b","parentId":"fdacd467","timestamp":"2026-04-03T01:03:15.903Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me see the rest of schemas.py for the SynthesizedPage model."},{"type":"toolCall","id":"toolu_01KNvFkq6HZgncreHkVLBGZi","name":"read","arguments":{"offset":81,"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":70329,"cacheWrite":2276,"totalTokens":72699,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.0351645,"cacheWrite":0.014225000000000002,"total":0.0517195}},"stopReason":"toolUse","timestamp":1775178191899}}
{"type":"message","id":"dc64561b","parentId":"ed6bbb4b","timestamp":"2026-04-03T01:03:15.908Z","message":{"role":"toolResult","toolCallId":"toolu_01KNvFkq6HZgncreHkVLBGZi","toolName":"read","content":[{"type":"text","text":"    \"\"\"An H2-level section of a technique page body.\"\"\"\n\n    heading: str = Field(description=\"H2 section heading\")\n    content: str = Field(description=\"Section body text (may contain [N] citation markers)\")\n    subsections: list[BodySubSection] = Field(\n        default_factory=list,\n        description=\"Optional H3-level subsections\",\n    )\n\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: list[BodySection] = Field(\n        default_factory=list,\n        description=\"Structured body content as H2 sections with optional H3 subsections\",\n    )\n    body_sections_format: str = Field(\n        default=\"v2\",\n        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\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":1775178195906}}
{"type":"message","id":"fbed2657","parentId":"dc64561b","timestamp":"2026-04-03T01:03:20.635Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have all the context I need. Let me implement the compose subcommand. The pattern is clear from `run_synthesis()` — I need `build_compose_prompt()`, `run_compose()`, and the CLI wiring."},{"type":"toolCall","id":"toolu_01Wbu2UMpjsk8E5S5Y9jayKh","name":"read","arguments":{"offset":280,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":72605,"cacheWrite":573,"totalTokens":73309,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.0363025,"cacheWrite":0.0035812500000000002,"total":0.043138750000000003}},"stopReason":"toolUse","timestamp":1775178195906}}
{"type":"message","id":"ae80db93","parentId":"fbed2657","timestamp":"2026-04-03T01:03:20.640Z","message":{"role":"toolResult","toolCallId":"toolu_01Wbu2UMpjsk8E5S5Y9jayKh","toolName":"read","content":[{"type":"text","text":"        # Log per-page summary\n        _log(\"SYNTH\", f\"  Parsed: {len(result.pages)} pages synthesized\")\n        total_words = 0\n        for page in result.pages:\n            sections = page.body_sections or []\n            section_count = len(sections)\n            subsection_count = sum(len(s.subsections) for s in sections)\n            word_count = sum(\n                len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n                for s in sections\n            )\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{section_count} sections ({subsection_count} subsections), \"\n                f\"{word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            # Citation coverage reporting\n            cit = validate_citations(sections, len(page.moment_indices))\n            _log(\n                \"CITE\",\n                f\"    Citations: {cit['total_citations']}/{len(page.moment_indices)} moments cited \"\n                f\"({cit['coverage_pct']}% coverage)\"\n                + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n                + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n            )\n\n            all_pages.append(page.model_dump())\n\n    # Summary\n    _log(\"SUMMARY\", f\"Total: {len(all_pages)} pages across {len(categories)} categories\")\n    _log(\"SUMMARY\", f\"Tokens: {total_prompt_tokens} prompt + {total_completion_tokens} completion = {total_prompt_tokens + total_completion_tokens} total\")\n    _log(\"SUMMARY\", f\"Duration: {total_duration_ms}ms ({total_duration_ms / 1000:.1f}s)\")\n\n    return all_pages, exit_code\n\n\n# ── Promote: deploy a prompt to production ─────────────────────────────────\n\n_STAGE_PROMPT_MAP = {\n    2: \"stage2_segmentation.txt\",\n    3: \"stage3_extraction.txt\",\n    4: \"stage4_classification.txt\",\n    5: \"stage5_synthesis.txt\",\n}\n\n\ndef promote_prompt(prompt_path: str, stage: int, reason: str, commit: bool = False) -> int:\n    \"\"\"Copy a winning prompt to the canonical path and create a backup.\n\n    The worker reads prompts from disk at runtime — no restart needed.\n    \"\"\"\n    import hashlib\n    import shutil\n\n    if stage not in _STAGE_PROMPT_MAP:\n        _log(\"ERROR\", f\"Invalid stage {stage}. Valid: {sorted(_STAGE_PROMPT_MAP)}\", level=\"ERROR\")\n        return 1\n\n    settings = get_settings()\n    template_name = _STAGE_PROMPT_MAP[stage]\n    canonical = Path(settings.prompts_path) / template_name\n    source = Path(prompt_path)\n\n    if not source.exists():\n        _log(\"ERROR\", f\"Source prompt not found: {prompt_path}\", level=\"ERROR\")\n        return 1\n\n    new_prompt = source.read_text(encoding=\"utf-8\")\n    new_hash = hashlib.sha256(new_prompt.encode()).hexdigest()[:12]\n\n    # Backup current prompt\n    old_prompt = \"\"\n    old_hash = \"none\"\n    if canonical.exists():\n        old_prompt = canonical.read_text(encoding=\"utf-8\")\n        old_hash = hashlib.sha256(old_prompt.encode()).hexdigest()[:12]\n\n        if old_prompt.strip() == new_prompt.strip():\n            _log(\"PROMOTE\", \"No change — new prompt is identical to current prompt\")\n            return 0\n\n        archive_dir = Path(settings.prompts_path) / \"archive\"\n        archive_dir.mkdir(parents=True, exist_ok=True)\n        ts = time.strftime(\"%Y%m%d_%H%M%S\", time.gmtime())\n        backup = archive_dir / f\"{template_name.replace('.txt', '')}_{ts}.txt\"\n        shutil.copy2(canonical, backup)\n        _log(\"PROMOTE\", f\"Backed up current prompt: {old_hash} -> {backup}\")\n\n    # Write new prompt\n    canonical.write_text(new_prompt, encoding=\"utf-8\")\n\n    old_lines = old_prompt.strip().splitlines()\n    new_lines = new_prompt.strip().splitlines()\n    _log(\"PROMOTE\", f\"Installed new prompt: {new_hash} ({len(new_prompt)} chars, {len(new_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Previous: {old_hash} ({len(old_prompt)} chars, {len(old_lines)} lines)\")\n    _log(\"PROMOTE\", f\"Reason: {reason}\")\n    _log(\"PROMOTE\", \"Worker reads prompts from disk at runtime — no restart needed\")\n\n    if commit:\n        import subprocess\n        try:\n            subprocess.run(\n                [\"git\", \"add\", str(canonical)],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            msg = f\"prompt: promote stage{stage} — {reason}\"\n            subprocess.run(\n                [\"git\", \"commit\", \"-m\", msg],\n                cwd=str(canonical.parent.parent),\n                check=True, capture_output=True,\n            )\n            _log(\"PROMOTE\", f\"Git commit created: {msg}\")\n        except subprocess.CalledProcessError as exc:\n            _log(\"PROMOTE\", f\"Git commit failed: {exc}\", level=\"WARN\")\n\n    return 0\n\n\n# ── CLI ────────────────────────────────────────────────────────────────────\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(\n        prog=\"pipeline.test_harness\",\n        description=\"Offline prompt test harness for Chrysopedia synthesis\",\n    )\n    sub = parser.add_subparsers(dest=\"command\")\n\n    # -- run subcommand (default behavior) --\n    run_parser = sub.add_parser(\"run\", help=\"Run synthesis against a fixture\")\n    run_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"Fixture JSON file\")\n    run_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Prompt file (default: stage5_synthesis.txt)\")\n    run_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n    run_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n    run_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n    run_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n\n    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n\n    args = parser.parse_args()\n\n    # If no subcommand, check for --fixture for backward compat\n    if args.command is None:\n        # Support running without subcommand for backward compat\n        parser.print_help()\n        return 1\n\n    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n\n    # -- run command --\n    prompt_path = args.prompt\n    if prompt_path is None:\n        settings = get_settings()\n        prompt_path = str(Path(settings.prompts_path) / \"stage5_synthesis.txt\")\n\n    overall_start = time.monotonic()\n    try:\n        fixture = load_fixture(args.fixture)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"Fixture error: {exc}\", level=\"ERROR\")\n        return 3\n\n    pages, exit_code = run_synthesis(\n        fixture=fixture,\n        prompt_path=prompt_path,\n        category_filter=args.category,\n        model_override=args.model,\n        modality=args.modality,\n    )\n\n    if not pages and exit_code != 0:\n        return exit_code\n\n    output = {\n        \"fixture_source\": args.fixture,\n        \"prompt_source\": prompt_path,\n        \"creator_name\": fixture.creator_name,\n        \"video_id\": fixture.video_id,\n        \"category_filter\": args.category,\n        \"pages\": pages,\n        \"metadata\": {\n            \"page_count\": len(pages),\n            \"total_words\": sum(\n                sum(\n                    len(s.get(\"content\", \"\").split())\n                    + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n                    for s in p.get(\"body_sections\", [])\n                )\n                for p in pages\n            ),\n            \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n        },\n    }\n\n    output_json = json.dumps(output, indent=2, ensure_ascii=False)\n\n    if args.output:\n        Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n        Path(args.output).write_text(output_json, encoding=\"utf-8\")\n        _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n    else:\n        print(output_json)\n        _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n\n    total_elapsed = time.monotonic() - overall_start\n    _log(\"DONE\", f\"Completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n\n    return exit_code\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1775178200637}}
{"type":"message","id":"65e08ba3","parentId":"ae80db93","timestamp":"2026-04-03T01:03:26.581Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I'll add the compose functionality. I need to insert `build_compose_prompt()` and `run_compose()` between the synthesis runner and the promote section, then wire the CLI."},{"type":"toolCall","id":"toolu_01YHqWe1EVQyzWtghs1DFKPL","name":"edit","arguments":{"newText":"from pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesizedPage, SynthesisResult","oldText":"from pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesisResult","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":252,"cacheRead":73178,"cacheWrite":2794,"totalTokens":76225,"cost":{"input":0.000005,"output":0.0063,"cacheRead":0.036588999999999997,"cacheWrite":0.017462500000000002,"total":0.060356499999999993}},"stopReason":"toolUse","timestamp":1775178200638}}
{"type":"message","id":"cfc58aba","parentId":"65e08ba3","timestamp":"2026-04-03T01:03:26.592Z","message":{"role":"toolResult","toolCallId":"toolu_01YHqWe1EVQyzWtghs1DFKPL","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":"  34 \n  35 from config import get_settings\n  36 from pipeline.citation_utils import validate_citations\n  37 from pipeline.llm_client import LLMClient, estimate_max_tokens\n- 38 from pipeline.schemas import SynthesisResult\n+ 38 from pipeline.schemas import SynthesizedPage, SynthesisResult\n  39 \n  40 \n  41 # ── Lightweight stand-in for KeyMoment ORM model ───────────────────────────\n  42 ","firstChangedLine":38},"isError":false,"timestamp":1775178206591}}
{"type":"message","id":"34003ec5","parentId":"cfc58aba","timestamp":"2026-04-03T01:04:11.166Z","message":{"role":"assistant","content":[{"type":"text","text":"Now add `build_compose_prompt()` and `run_compose()` after the `run_synthesis()` function and before the promote section:"},{"type":"toolCall","id":"toolu_019Zua3ap74DkW8MokQpbvzK","name":"edit","arguments":{"newText":"# ── Compose: merge new moments into existing page ──────────────────────────\n\ndef _count_page_words(page_dict: dict) -> int:\n    \"\"\"Count total words in a page's body sections.\"\"\"\n    return sum(\n        len(s.get(\"content\", \"\").split())\n        + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n        for s in page_dict.get(\"body_sections\", [])\n    )\n\n\ndef build_compose_prompt(\n    existing_page: dict,\n    existing_moments: list[tuple[MockKeyMoment, dict]],\n    new_moments: list[tuple[MockKeyMoment, dict]],\n    creator_name: str,\n) -> str:\n    \"\"\"Build the user prompt for composition (merging new moments into an existing page).\n\n    Existing moments keep indices [0]-[N-1].\n    New moments get indices [N]-[N+M-1].\n    Uses build_moments_text() for formatting, with index offsets applied for new moments.\n    \"\"\"\n    category = existing_page.get(\"topic_category\", \"Uncategorized\")\n\n    # Format existing moments [0]-[N-1]\n    existing_text, _ = build_moments_text(existing_moments, category)\n\n    # Format new moments with offset indices [N]-[N+M-1]\n    n = len(existing_moments)\n    new_lines = []\n    for i, (m, cls_info) in enumerate(new_moments):\n        tags = cls_info.get(\"topic_tags\", [])\n        new_lines.append(\n            f\"[{n + i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    new_text = \"\\n\\n\".join(new_lines)\n\n    page_json = json.dumps(existing_page, indent=2, ensure_ascii=False)\n\n    return (\n        f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n        f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n        f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n        f\"<creator>{creator_name}</creator>\"\n    )\n\n\ndef run_compose(\n    existing_page_path: str,\n    existing_fixture_path: str,\n    new_fixture_path: str,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run composition: merge new fixture moments into an existing page.\n\n    Returns (pages, exit_code) — same shape as run_synthesis().\n    \"\"\"\n    # Load existing page JSON\n    existing_page_file = Path(existing_page_path)\n    if not existing_page_file.exists():\n        _log(\"ERROR\", f\"Existing page not found: {existing_page_path}\", level=\"ERROR\")\n        return [], 3\n\n    try:\n        existing_raw = json.loads(existing_page_file.read_text(encoding=\"utf-8\"))\n    except json.JSONDecodeError as exc:\n        _log(\"ERROR\", f\"Invalid JSON in existing page: {exc}\", level=\"ERROR\")\n        return [], 3\n\n    # The existing page file might be a harness output (with .pages[]) or a raw SynthesizedPage\n    if \"pages\" in existing_raw and isinstance(existing_raw[\"pages\"], list):\n        page_dicts = existing_raw[\"pages\"]\n        _log(\"COMPOSE\", f\"Loaded harness output with {len(page_dicts)} pages\")\n    elif \"title\" in existing_raw and \"body_sections\" in existing_raw:\n        page_dicts = [existing_raw]\n        _log(\"COMPOSE\", \"Loaded single SynthesizedPage\")\n    else:\n        _log(\"ERROR\", \"Existing page JSON must be a SynthesizedPage or harness output with 'pages' key\", level=\"ERROR\")\n        return [], 3\n\n    # Validate each page against SynthesizedPage\n    validated_pages: list[dict] = []\n    for pd in page_dicts:\n        try:\n            SynthesizedPage.model_validate(pd)\n            validated_pages.append(pd)\n        except ValidationError as exc:\n            _log(\"WARN\", f\"Skipping invalid page '{pd.get('title', '?')}': {exc}\", level=\"WARN\")\n\n    if not validated_pages:\n        _log(\"ERROR\", \"No valid SynthesizedPage found in existing page file\", level=\"ERROR\")\n        return [], 3\n\n    # Apply category filter\n    if category_filter:\n        validated_pages = [p for p in validated_pages if p.get(\"topic_category\") == category_filter]\n        if not validated_pages:\n            _log(\"ERROR\", f\"No pages match category '{category_filter}'\", level=\"ERROR\")\n            return [], 3\n\n    # Load existing moments fixture (the original moments the page was built from)\n    try:\n        existing_fixture = load_fixture(existing_fixture_path)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"Existing fixture error: {exc}\", level=\"ERROR\")\n        return [], 3\n\n    # Load new moments fixture\n    try:\n        new_fixture = load_fixture(new_fixture_path)\n    except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n        _log(\"ERROR\", f\"New fixture error: {exc}\", level=\"ERROR\")\n        return [], 3\n\n    # Load prompt\n    prompt_file = Path(prompt_path)\n    if not prompt_file.exists():\n        _log(\"ERROR\", f\"Prompt file not found: {prompt_path}\", level=\"ERROR\")\n        return [], 3\n    system_prompt = prompt_file.read_text(encoding=\"utf-8\")\n    _log(\"PROMPT\", f\"Loading compose prompt: {prompt_path} ({len(system_prompt)} chars)\")\n\n    # Setup LLM\n    settings = get_settings()\n    llm = LLMClient(settings)\n    stage_model = model_override or settings.llm_stage5_model or settings.llm_model\n    stage_modality = modality or settings.llm_stage5_modality or \"thinking\"\n    hard_limit = settings.llm_max_tokens_hard_limit\n    _log(\"LLM\", f\"Model: {stage_model}, modality: {stage_modality}, hard_limit: {hard_limit}\")\n\n    all_pages: list[dict] = []\n    exit_code = 0\n\n    for page_idx, existing_page in enumerate(validated_pages, 1):\n        page_category = existing_page.get(\"topic_category\", \"Uncategorized\")\n        page_title = existing_page.get(\"title\", \"Untitled\")\n        _log(\"COMPOSE\", f\"Page {page_idx}/{len(validated_pages)}: '{page_title}' ({page_category})\")\n\n        # Get existing moments for this page's category\n        existing_moments = existing_fixture.groups.get(page_category, [])\n        if not existing_moments:\n            _log(\"WARN\", f\"  No existing moments found for category '{page_category}' — skipping\", level=\"WARN\")\n            continue\n\n        # Get new moments for this page's category\n        new_moments = new_fixture.groups.get(page_category, [])\n        if not new_moments:\n            _log(\"WARN\", f\"  No new moments for category '{page_category}' — nothing to compose\", level=\"WARN\")\n            all_pages.append(existing_page)\n            continue\n\n        n_existing = len(existing_moments)\n        n_new = len(new_moments)\n        total_moments = n_existing + n_new\n\n        # Before metrics\n        before_words = _count_page_words(existing_page)\n        before_sections = len(existing_page.get(\"body_sections\", []))\n\n        _log(\n            \"COMPOSE\",\n            f\"  Existing: {n_existing} moments, {before_sections} sections, {before_words} words | \"\n            f\"New: {n_new} moments | Total citation space: [0]-[{total_moments - 1}]\",\n        )\n\n        # Build compose prompt\n        user_prompt = build_compose_prompt(\n            existing_page=existing_page,\n            existing_moments=existing_moments,\n            new_moments=new_moments,\n            creator_name=existing_fixture.creator_name,\n        )\n\n        estimated_tokens = estimate_max_tokens(\n            system_prompt, user_prompt,\n            stage=\"stage5_synthesis\",\n            hard_limit=hard_limit,\n        )\n        _log(\"COMPOSE\", f\"  Prompt built: {len(user_prompt)} chars, max_tokens={estimated_tokens}\")\n\n        # Call LLM\n        call_start = time.monotonic()\n        _log(\"LLM\", f\"  Calling: model={stage_model}, max_tokens={estimated_tokens}, modality={stage_modality}\")\n\n        try:\n            raw = llm.complete(\n                system_prompt,\n                user_prompt,\n                response_model=SynthesisResult,\n                modality=stage_modality,\n                model_override=stage_model,\n                max_tokens=estimated_tokens,\n            )\n        except Exception as exc:\n            _log(\"ERROR\", f\"  LLM call failed: {exc}\", level=\"ERROR\")\n            exit_code = 1\n            continue\n\n        call_duration_ms = int((time.monotonic() - call_start) * 1000)\n        prompt_tokens = getattr(raw, \"prompt_tokens\", None) or 0\n        completion_tokens = getattr(raw, \"completion_tokens\", None) or 0\n        finish_reason = getattr(raw, \"finish_reason\", \"unknown\")\n\n        _log(\n            \"LLM\",\n            f\"  Response: {prompt_tokens} prompt + {completion_tokens} completion tokens, \"\n            f\"{call_duration_ms}ms, finish_reason={finish_reason}\",\n        )\n\n        if finish_reason == \"length\":\n            _log(\"WARN\", \"  finish_reason=length — output likely truncated!\", level=\"WARN\")\n\n        # Parse response\n        try:\n            result = SynthesisResult.model_validate_json(str(raw))\n        except (ValidationError, json.JSONDecodeError) as exc:\n            _log(\"ERROR\", f\"  Parse failed: {exc}\", level=\"ERROR\")\n            _log(\"ERROR\", f\"  Raw response (first 2000 chars): {str(raw)[:2000]}\", level=\"ERROR\")\n            exit_code = 2\n            continue\n\n        # Log compose-specific metrics\n        for page in result.pages:\n            page_dict = page.model_dump()\n            after_words = _count_page_words(page_dict)\n            after_sections = len(page.body_sections or [])\n\n            # Identify new sections (headings not in the original)\n            existing_headings = {s.get(\"heading\", \"\") for s in existing_page.get(\"body_sections\", [])}\n            new_section_headings = [\n                s.heading for s in (page.body_sections or []) if s.heading not in existing_headings\n            ]\n\n            _log(\n                \"COMPOSE\",\n                f\"  Result: '{page.title}' — \"\n                f\"words {before_words}→{after_words} ({after_words - before_words:+d}), \"\n                f\"sections {before_sections}→{after_sections} ({after_sections - before_sections:+d})\"\n                + (f\", new sections: {new_section_headings}\" if new_section_headings else \"\"),\n            )\n\n            # Citation validation with unified moment count\n            cit = validate_citations(page.body_sections or [], total_moments)\n            _log(\n                \"CITE\",\n                f\"  Citations: {cit['total_citations']}/{total_moments} moments cited \"\n                f\"({cit['coverage_pct']}% coverage)\"\n                + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n                + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n            )\n\n            all_pages.append(page_dict)\n\n    _log(\"SUMMARY\", f\"Compose complete: {len(all_pages)} pages\")\n    return all_pages, exit_code\n\n\n# ── Promote: deploy a prompt to production ─────────────────────────────────","oldText":"# ── Promote: deploy a prompt to production ─────────────────────────────────","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3412,"cacheRead":75972,"cacheWrite":278,"totalTokens":79663,"cost":{"input":0.000005,"output":0.0853,"cacheRead":0.037986,"cacheWrite":0.0017375000000000001,"total":0.12502850000000001}},"stopReason":"toolUse","timestamp":1775178206591}}
{"type":"message","id":"d00232fd","parentId":"34003ec5","timestamp":"2026-04-03T01:04:11.188Z","message":{"role":"toolResult","toolCallId":"toolu_019Zua3ap74DkW8MokQpbvzK","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":" 317 \n 318     return all_pages, exit_code\n 319 \n 320 \n+321 # ── Compose: merge new moments into existing page ──────────────────────────\n+322 \n+323 def _count_page_words(page_dict: dict) -> int:\n+324     \"\"\"Count total words in a page's body sections.\"\"\"\n+325     return sum(\n+326         len(s.get(\"content\", \"\").split())\n+327         + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n+328         for s in page_dict.get(\"body_sections\", [])\n+329     )\n+330 \n+331 \n+332 def build_compose_prompt(\n+333     existing_page: dict,\n+334     existing_moments: list[tuple[MockKeyMoment, dict]],\n+335     new_moments: list[tuple[MockKeyMoment, dict]],\n+336     creator_name: str,\n+337 ) -> str:\n+338     \"\"\"Build the user prompt for composition (merging new moments into an existing page).\n+339 \n+340     Existing moments keep indices [0]-[N-1].\n+341     New moments get indices [N]-[N+M-1].\n+342     Uses build_moments_text() for formatting, with index offsets applied for new moments.\n+343     \"\"\"\n+344     category = existing_page.get(\"topic_category\", \"Uncategorized\")\n+345 \n+346     # Format existing moments [0]-[N-1]\n+347     existing_text, _ = build_moments_text(existing_moments, category)\n+348 \n+349     # Format new moments with offset indices [N]-[N+M-1]\n+350     n = len(existing_moments)\n+351     new_lines = []\n+352     for i, (m, cls_info) in enumerate(new_moments):\n+353         tags = cls_info.get(\"topic_tags\", [])\n+354         new_lines.append(\n+355             f\"[{n + i}] Title: {m.title}\\n\"\n+356             f\"    Summary: {m.summary}\\n\"\n+357             f\"    Content type: {m.content_type.value}\\n\"\n+358             f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n+359             f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n+360             f\"    Category: {category}\\n\"\n+361             f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n+362             f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n+363         )\n+364     new_text = \"\\n\\n\".join(new_lines)\n+365 \n+366     page_json = json.dumps(existing_page, indent=2, ensure_ascii=False)\n+367 \n+368     return (\n+369         f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n+370         f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n+371         f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n+372         f\"<creator>{creator_name}</creator>\"\n+373     )\n+374 \n+375 \n+376 def run_compose(\n+377     existing_page_path: str,\n+378     existing_fixture_path: str,\n+379     new_fixture_path: str,\n+380     prompt_path: str,\n+381     category_filter: str | None = None,\n+382     model_override: str | None = None,\n+383     modality: str | None = None,\n+384 ) -> tuple[list[dict], int]:\n+385     \"\"\"Run composition: merge new fixture moments into an existing page.\n+386 \n+387     Returns (pages, exit_code) — same shape as run_synthesis().\n+388     \"\"\"\n+389     # Load existing page JSON\n+390     existing_page_file = Path(existing_page_path)\n+391     if not existing_page_file.exists():\n+392         _log(\"ERROR\", f\"Existing page not found: {existing_page_path}\", level=\"ERROR\")\n+393         return [], 3\n+394 \n+395     try:\n+396         existing_raw = json.loads(existing_page_file.read_text(encoding=\"utf-8\"))\n+397     except json.JSONDecodeError as exc:\n+398         _log(\"ERROR\", f\"Invalid JSON in existing page: {exc}\", level=\"ERROR\")\n+399         return [], 3\n+400 \n+401     # The existing page file might be a harness output (with .pages[]) or a raw SynthesizedPage\n+402     if \"pages\" in existing_raw and isinstance(existing_raw[\"pages\"], list):\n+403         page_dicts = existing_raw[\"pages\"]\n+404         _log(\"COMPOSE\", f\"Loaded harness output with {len(page_dicts)} pages\")\n+405     elif \"title\" in existing_raw and \"body_sections\" in existing_raw:\n+406         page_dicts = [existing_raw]\n+407         _log(\"COMPOSE\", \"Loaded single SynthesizedPage\")\n+408     else:\n+409         _log(\"ERROR\", \"Existing page JSON must be a SynthesizedPage or harness output with 'pages' key\", level=\"ERROR\")\n+410         return [], 3\n+411 \n+412     # Validate each page against SynthesizedPage\n+413     validated_pages: list[dict] = []\n+414     for pd in page_dicts:\n+415         try:\n+416             SynthesizedPage.model_validate(pd)\n+417             validated_pages.append(pd)\n+418         except ValidationError as exc:\n+419             _log(\"WARN\", f\"Skipping invalid page '{pd.get('title', '?')}': {exc}\", level=\"WARN\")\n+420 \n+421     if not validated_pages:\n+422         _log(\"ERROR\", \"No valid SynthesizedPage found in existing page file\", level=\"ERROR\")\n+423         return [], 3\n+424 \n+425     # Apply category filter\n+426     if category_filter:\n+427         validated_pages = [p for p in validated_pages if p.get(\"topic_category\") == category_filter]\n+428         if not validated_pages:\n+429             _log(\"ERROR\", f\"No pages match category '{category_filter}'\", level=\"ERROR\")\n+430             return [], 3\n+431 \n+432     # Load existing moments fixture (the original moments the page was built from)\n+433     try:\n+434         existing_fixture = load_fixture(existing_fixture_path)\n+435     except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n+436         _log(\"ERROR\", f\"Existing fixture error: {exc}\", level=\"ERROR\")\n+437         return [], 3\n+438 \n+439     # Load new moments fixture\n+440     try:\n+441         new_fixture = load_fixture(new_fixture_path)\n+442     except (FileNotFoundError, ValueError, json.JSONDecodeError) as exc:\n+443         _log(\"ERROR\", f\"New fixture error: {exc}\", level=\"ERROR\")\n+444         return [], 3\n+445 \n+446     # Load prompt\n+447     prompt_file = Path(prompt_path)\n+448     if not prompt_file.exists():\n+449         _log(\"ERROR\", f\"Prompt file not found: {prompt_path}\", level=\"ERROR\")\n+450         return [], 3\n+451     system_prompt = prompt_file.read_text(encoding=\"utf-8\")\n+452     _log(\"PROMPT\", f\"Loading compose prompt: {prompt_path} ({len(system_prompt)} chars)\")\n+453 \n+454     # Setup LLM\n+455     settings = get_settings()\n+456     llm = LLMClient(settings)\n+457     stage_model = model_override or settings.llm_stage5_model or settings.llm_model\n+458     stage_modality = modality or settings.llm_stage5_modality or \"thinking\"\n+459     hard_limit = settings.llm_max_tokens_hard_limit\n+460     _log(\"LLM\", f\"Model: {stage_model}, modality: {stage_modality}, hard_limit: {hard_limit}\")\n+461 \n+462     all_pages: list[dict] = []\n+463     exit_code = 0\n+464 \n+465     for page_idx, existing_page in enumerate(validated_pages, 1):\n+466         page_category = existing_page.get(\"topic_category\", \"Uncategorized\")\n+467         page_title = existing_page.get(\"title\", \"Untitled\")\n+468         _log(\"COMPOSE\", f\"Page {page_idx}/{len(validated_pages)}: '{page_title}' ({page_category})\")\n+469 \n+470         # Get existing moments for this page's category\n+471         existing_moments = existing_fixture.groups.get(page_category, [])\n+472         if not existing_moments:\n+473             _log(\"WARN\", f\"  No existing moments found for category '{page_category}' — skipping\", level=\"WARN\")\n+474             continue\n+475 \n+476         # Get new moments for this page's category\n+477         new_moments = new_fixture.groups.get(page_category, [])\n+478         if not new_moments:\n+479             _log(\"WARN\", f\"  No new moments for category '{page_category}' — nothing to compose\", level=\"WARN\")\n+480             all_pages.append(existing_page)\n+481             continue\n+482 \n+483         n_existing = len(existing_moments)\n+484         n_new = len(new_moments)\n+485         total_moments = n_existing + n_new\n+486 \n+487         # Before metrics\n+488         before_words = _count_page_words(existing_page)\n+489         before_sections = len(existing_page.get(\"body_sections\", []))\n+490 \n+491         _log(\n+492             \"COMPOSE\",\n+493             f\"  Existing: {n_existing} moments, {before_sections} sections, {before_words} words | \"\n+494             f\"New: {n_new} moments | Total citation space: [0]-[{total_moments - 1}]\",\n+495         )\n+496 \n+497         # Build compose prompt\n+498         user_prompt = build_compose_prompt(\n+499             existing_page=existing_page,\n+500             existing_moments=existing_moments,\n+501             new_moments=new_moments,\n+502             creator_name=existing_fixture.creator_name,\n+503         )\n+504 \n+505         estimated_tokens = estimate_max_tokens(\n+506             system_prompt, user_prompt,\n+507             stage=\"stage5_synthesis\",\n+508             hard_limit=hard_limit,\n+509         )\n+510         _log(\"COMPOSE\", f\"  Prompt built: {len(user_prompt)} chars, max_tokens={estimated_tokens}\")\n+511 \n+512         # Call LLM\n+513         call_start = time.monotonic()\n+514         _log(\"LLM\", f\"  Calling: model={stage_model}, max_tokens={estimated_tokens}, modality={stage_modality}\")\n+515 \n+516         try:\n+517             raw = llm.complete(\n+518                 system_prompt,\n+519                 user_prompt,\n+520                 response_model=SynthesisResult,\n+521                 modality=stage_modality,\n+522                 model_override=stage_model,\n+523                 max_tokens=estimated_tokens,\n+524             )\n+525         except Exception as exc:\n+526             _log(\"ERROR\", f\"  LLM call failed: {exc}\", level=\"ERROR\")\n+527             exit_code = 1\n+528             continue\n+529 \n+530         call_duration_ms = int((time.monotonic() - call_start) * 1000)\n+531         prompt_tokens = getattr(raw, \"prompt_tokens\", None) or 0\n+532         completion_tokens = getattr(raw, \"completion_tokens\", None) or 0\n+533         finish_reason = getattr(raw, \"finish_reason\", \"unknown\")\n+534 \n+535         _log(\n+536             \"LLM\",\n+537             f\"  Response: {prompt_tokens} prompt + {completion_tokens} completion tokens, \"\n+538             f\"{call_duration_ms}ms, finish_reason={finish_reason}\",\n+539         )\n+540 \n+541         if finish_reason == \"length\":\n+542             _log(\"WARN\", \"  finish_reason=length — output likely truncated!\", level=\"WARN\")\n+543 \n+544         # Parse response\n+545         try:\n+546             result = SynthesisResult.model_validate_json(str(raw))\n+547         except (ValidationError, json.JSONDecodeError) as exc:\n+548             _log(\"ERROR\", f\"  Parse failed: {exc}\", level=\"ERROR\")\n+549             _log(\"ERROR\", f\"  Raw response (first 2000 chars): {str(raw)[:2000]}\", level=\"ERROR\")\n+550             exit_code = 2\n+551             continue\n+552 \n+553         # Log compose-specific metrics\n+554         for page in result.pages:\n+555             page_dict = page.model_dump()\n+556             after_words = _count_page_words(page_dict)\n+557             after_sections = len(page.body_sections or [])\n+558 \n+559             # Identify new sections (headings not in the original)\n+560             existing_headings = {s.get(\"heading\", \"\") for s in existing_page.get(\"body_sections\", [])}\n+561             new_section_headings = [\n+562                 s.heading for s in (page.body_sections or []) if s.heading not in existing_headings\n+563             ]\n+564 \n+565             _log(\n+566                 \"COMPOSE\",\n+567                 f\"  Result: '{page.title}' — \"\n+568                 f\"words {before_words}→{after_words} ({after_words - before_words:+d}), \"\n+569                 f\"sections {before_sections}→{after_sections} ({after_sections - before_sections:+d})\"\n+570                 + (f\", new sections: {new_section_headings}\" if new_section_headings else \"\"),\n+571             )\n+572 \n+573             # Citation validation with unified moment count\n+574             cit = validate_citations(page.body_sections or [], total_moments)\n+575             _log(\n+576                 \"CITE\",\n+577                 f\"  Citations: {cit['total_citations']}/{total_moments} moments cited \"\n+578                 f\"({cit['coverage_pct']}% coverage)\"\n+579                 + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n+580                 + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n+581             )\n+582 \n+583             all_pages.append(page_dict)\n+584 \n+585     _log(\"SUMMARY\", f\"Compose complete: {len(all_pages)} pages\")\n+586     return all_pages, exit_code\n+587 \n+588 \n 589 # ── Promote: deploy a prompt to production ─────────────────────────────────\n 590 \n 591 _STAGE_PROMPT_MAP = {\n 592     2: \"stage2_segmentation.txt\",","firstChangedLine":321},"isError":false,"timestamp":1775178251185}}
{"type":"message","id":"52c7470b","parentId":"d00232fd","timestamp":"2026-04-03T01:04:23.054Z","message":{"role":"assistant","content":[{"type":"text","text":"Now wire the compose subcommand in the CLI:"},{"type":"toolCall","id":"toolu_01Y4QunujFp7qSUNWCsqi6E6","name":"edit","arguments":{"newText":"    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n\n    # -- compose subcommand --\n    compose_parser = sub.add_parser(\"compose\", help=\"Merge new moments into an existing page\")\n    compose_parser.add_argument(\"--existing-page\", type=str, required=True, help=\"Existing page JSON (harness output or raw SynthesizedPage)\")\n    compose_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"New moments fixture JSON\")\n    compose_parser.add_argument(\"--existing-fixture\", type=str, required=True, help=\"Original moments fixture JSON (for citation context)\")\n    compose_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Compose prompt file (default: stage5_compose.txt)\")\n    compose_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n    compose_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n    compose_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n    compose_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])","oldText":"    # -- promote subcommand --\n    promo_parser = sub.add_parser(\"promote\", help=\"Deploy a winning prompt to production\")\n    promo_parser.add_argument(\"--prompt\", \"-p\", type=str, required=True, help=\"Path to the winning prompt file\")\n    promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n    promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n    promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":733,"cacheRead":79688,"cacheWrite":0,"totalTokens":80422,"cost":{"input":0.000005,"output":0.018325,"cacheRead":0.039844,"cacheWrite":0,"total":0.058174000000000003}},"stopReason":"toolUse","timestamp":1775178251186}}
{"type":"message","id":"1d4d14ac","parentId":"52c7470b","timestamp":"2026-04-03T01:04:23.085Z","message":{"role":"toolResult","toolCallId":"toolu_01Y4QunujFp7qSUNWCsqi6E6","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":" 693     promo_parser.add_argument(\"--stage\", \"-s\", type=int, default=5, help=\"Stage number (default: 5)\")\n 694     promo_parser.add_argument(\"--reason\", \"-r\", type=str, required=True, help=\"Why this prompt is being promoted\")\n 695     promo_parser.add_argument(\"--commit\", action=\"store_true\", help=\"Also create a git commit\")\n 696 \n+697     # -- compose subcommand --\n+698     compose_parser = sub.add_parser(\"compose\", help=\"Merge new moments into an existing page\")\n+699     compose_parser.add_argument(\"--existing-page\", type=str, required=True, help=\"Existing page JSON (harness output or raw SynthesizedPage)\")\n+700     compose_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"New moments fixture JSON\")\n+701     compose_parser.add_argument(\"--existing-fixture\", type=str, required=True, help=\"Original moments fixture JSON (for citation context)\")\n+702     compose_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Compose prompt file (default: stage5_compose.txt)\")\n+703     compose_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n+704     compose_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n+705     compose_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n+706     compose_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n+707 \n 708     args = parser.parse_args()\n 709 \n 710     # If no subcommand, check for --fixture for backward compat\n 711     if args.command is None:","firstChangedLine":697},"isError":false,"timestamp":1775178263083}}
{"type":"message","id":"9be1de1d","parentId":"1d4d14ac","timestamp":"2026-04-03T01:04:33.926Z","message":{"role":"assistant","content":[{"type":"text","text":"Now wire the compose handler in the command dispatch:"},{"type":"toolCall","id":"toolu_01H681PQmYyknLxqiTPSyBg4","name":"edit","arguments":{"newText":"    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n\n    if args.command == \"compose\":\n        # Resolve default compose prompt\n        prompt_path = args.prompt\n        if prompt_path is None:\n            settings = get_settings()\n            prompt_path = str(Path(settings.prompts_path) / \"stage5_compose.txt\")\n\n        overall_start = time.monotonic()\n        pages, exit_code = run_compose(\n            existing_page_path=args.existing_page,\n            existing_fixture_path=args.existing_fixture,\n            new_fixture_path=args.fixture,\n            prompt_path=prompt_path,\n            category_filter=args.category,\n            model_override=args.model,\n            modality=args.modality,\n        )\n\n        if not pages and exit_code != 0:\n            return exit_code\n\n        output = {\n            \"existing_page_source\": args.existing_page,\n            \"existing_fixture_source\": args.existing_fixture,\n            \"new_fixture_source\": args.fixture,\n            \"prompt_source\": prompt_path,\n            \"category_filter\": args.category,\n            \"pages\": pages,\n            \"metadata\": {\n                \"page_count\": len(pages),\n                \"total_words\": sum(_count_page_words(p) for p in pages),\n                \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n            },\n        }\n\n        output_json = json.dumps(output, indent=2, ensure_ascii=False)\n\n        if args.output:\n            Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n            Path(args.output).write_text(output_json, encoding=\"utf-8\")\n            _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n        else:\n            print(output_json)\n            _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n\n        total_elapsed = time.monotonic() - overall_start\n        _log(\"DONE\", f\"Compose completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n        return exit_code","oldText":"    if args.command == \"promote\":\n        return promote_prompt(args.prompt, args.stage, args.reason, args.commit)","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":710,"cacheRead":79688,"cacheWrite":759,"totalTokens":81158,"cost":{"input":0.000005,"output":0.017750000000000002,"cacheRead":0.039844,"cacheWrite":0.004743750000000001,"total":0.062342749999999995}},"stopReason":"toolUse","timestamp":1775178263083}}
{"type":"message","id":"507fd8ec","parentId":"9be1de1d","timestamp":"2026-04-03T01:04:33.958Z","message":{"role":"toolResult","toolCallId":"toolu_01H681PQmYyknLxqiTPSyBg4","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":" 715 \n 716     if args.command == \"promote\":\n 717         return promote_prompt(args.prompt, args.stage, args.reason, args.commit)\n 718 \n+719     if args.command == \"compose\":\n+720         # Resolve default compose prompt\n+721         prompt_path = args.prompt\n+722         if prompt_path is None:\n+723             settings = get_settings()\n+724             prompt_path = str(Path(settings.prompts_path) / \"stage5_compose.txt\")\n+725 \n+726         overall_start = time.monotonic()\n+727         pages, exit_code = run_compose(\n+728             existing_page_path=args.existing_page,\n+729             existing_fixture_path=args.existing_fixture,\n+730             new_fixture_path=args.fixture,\n+731             prompt_path=prompt_path,\n+732             category_filter=args.category,\n+733             model_override=args.model,\n+734             modality=args.modality,\n+735         )\n+736 \n+737         if not pages and exit_code != 0:\n+738             return exit_code\n+739 \n+740         output = {\n+741             \"existing_page_source\": args.existing_page,\n+742             \"existing_fixture_source\": args.existing_fixture,\n+743             \"new_fixture_source\": args.fixture,\n+744             \"prompt_source\": prompt_path,\n+745             \"category_filter\": args.category,\n+746             \"pages\": pages,\n+747             \"metadata\": {\n+748                 \"page_count\": len(pages),\n+749                 \"total_words\": sum(_count_page_words(p) for p in pages),\n+750                 \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n+751             },\n+752         }\n+753 \n+754         output_json = json.dumps(output, indent=2, ensure_ascii=False)\n+755 \n+756         if args.output:\n+757             Path(args.output).parent.mkdir(parents=True, exist_ok=True)\n+758             Path(args.output).write_text(output_json, encoding=\"utf-8\")\n+759             _log(\"OUTPUT\", f\"Written to: {args.output} ({len(output_json) / 1024:.1f} KB)\")\n+760         else:\n+761             print(output_json)\n+762             _log(\"OUTPUT\", f\"Printed to stdout ({len(output_json) / 1024:.1f} KB)\")\n+763 \n+764         total_elapsed = time.monotonic() - overall_start\n+765         _log(\"DONE\", f\"Compose completed in {total_elapsed:.1f}s (exit_code={exit_code})\")\n+766         return exit_code\n+767 \n 768     # -- run command --\n 769     prompt_path = args.prompt\n 770     if prompt_path is None:\n 771         settings = get_settings()","firstChangedLine":719},"isError":false,"timestamp":1775178273956}}
{"type":"message","id":"88af0fda","parentId":"507fd8ec","timestamp":"2026-04-03T01:04:38.120Z","message":{"role":"assistant","content":[{"type":"text","text":"Now verify the `--help` exits 0:"},{"type":"toolCall","id":"toolu_013TtriM4hLDbQprVKVmgmt2","name":"bash","arguments":{"command":"cd backend && python -m pipeline.test_harness compose --help 2>&1; echo \"EXIT: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":80447,"cacheWrite":736,"totalTokens":81273,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.040223499999999995,"cacheWrite":0.0046,"total":0.0470535}},"stopReason":"toolUse","timestamp":1775178273957}}
{"type":"message","id":"a3da960d","parentId":"88af0fda","timestamp":"2026-04-03T01:04:38.663Z","message":{"role":"toolResult","toolCallId":"toolu_013TtriM4hLDbQprVKVmgmt2","toolName":"bash","content":[{"type":"text","text":"usage: pipeline.test_harness compose [-h] --existing-page EXISTING_PAGE\n                                     --fixture FIXTURE --existing-fixture\n                                     EXISTING_FIXTURE [--prompt PROMPT]\n                                     [--output OUTPUT] [--category CATEGORY]\n                                     [--model MODEL]\n                                     [--modality {chat,thinking}]\n\noptions:\n  -h, --help            show this help message and exit\n  --existing-page EXISTING_PAGE\n                        Existing page JSON (harness output or raw\n                        SynthesizedPage)\n  --fixture FIXTURE, -f FIXTURE\n                        New moments fixture JSON\n  --existing-fixture EXISTING_FIXTURE\n                        Original moments fixture JSON (for citation context)\n  --prompt PROMPT, -p PROMPT\n                        Compose prompt file (default: stage5_compose.txt)\n  --output OUTPUT, -o OUTPUT\n                        Output file path\n  --category CATEGORY, -c CATEGORY\n                        Filter to a specific category\n  --model MODEL         Override LLM model\n  --modality {chat,thinking}\nEXIT: 0\n"}],"isError":false,"timestamp":1775178278661}}
{"type":"message","id":"cdc1bcf8","parentId":"a3da960d","timestamp":"2026-04-03T01:04:48.348Z","message":{"role":"assistant","content":[{"type":"text","text":"Verification passes. Let me also confirm `build_compose_prompt` is importable and produces correct output structure with a quick smoke test:"},{"type":"toolCall","id":"toolu_01QgLWGMMV6A5DMsbnGDg8Ms","name":"bash","arguments":{"command":"cd backend && python -c \"\nfrom pipeline.test_harness import build_compose_prompt, MockKeyMoment, _MockContentType\n\npage = {'topic_category': 'Sound Design', 'title': 'Test', 'body_sections': []}\nexisting = [(MockKeyMoment('E1', 'sum', _MockContentType('technique'), 0.0, 5.0, [], 'transcript'), {'topic_tags': ['tag1']})]\nnew = [(MockKeyMoment('N1', 'new sum', _MockContentType('technique'), 10.0, 15.0, ['Serum'], 'new transcript'), {'topic_tags': ['tag2']})]\n\nprompt = build_compose_prompt(page, existing, new, 'TestCreator')\n\n# Verify XML tags present\nassert '<existing_page>' in prompt\nassert '</existing_page>' in prompt\nassert '<existing_moments>' in prompt\nassert '<new_moments>' in prompt\nassert '<creator>TestCreator</creator>' in prompt\n\n# Verify index offsets: existing is [0], new is [1]\nassert '[0] Title: E1' in prompt\nassert '[1] Title: N1' in prompt\n\nprint('build_compose_prompt: all assertions passed')\n\" 2>&1; echo \"EXIT: $?\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":393,"cacheRead":81183,"cacheWrite":365,"totalTokens":81942,"cost":{"input":0.000005,"output":0.009825,"cacheRead":0.040591499999999996,"cacheWrite":0.0022812500000000003,"total":0.05270274999999999}},"stopReason":"toolUse","timestamp":1775178278662}}
{"type":"message","id":"74c28ea9","parentId":"cdc1bcf8","timestamp":"2026-04-03T01:04:48.865Z","message":{"role":"toolResult","toolCallId":"toolu_01QgLWGMMV6A5DMsbnGDg8Ms","toolName":"bash","content":[{"type":"text","text":"build_compose_prompt: all assertions passed\nEXIT: 0\n"}],"isError":false,"timestamp":1775178288863}}
{"type":"message","id":"16516145","parentId":"74c28ea9","timestamp":"2026-04-03T01:04:53.349Z","message":{"role":"assistant","content":[{"type":"text","text":"Both the CLI help and the `build_compose_prompt` function work correctly. Now let me read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_017sEwKm1eJKJXa2bWUzs21B","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":100,"cacheRead":81548,"cacheWrite":420,"totalTokens":82069,"cost":{"input":0.000005,"output":0.0025,"cacheRead":0.040774,"cacheWrite":0.002625,"total":0.045904}},"stopReason":"toolUse","timestamp":1775178288864}}
{"type":"message","id":"ce98f5c7","parentId":"16516145","timestamp":"2026-04-03T01:04:53.354Z","message":{"role":"toolResult","toolCallId":"toolu_017sEwKm1eJKJXa2bWUzs21B","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":1775178293352}}
{"type":"message","id":"93c34b6e","parentId":"ce98f5c7","timestamp":"2026-04-03T01:05:12.869Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SVoYdiUFFFSnZqkqQgTUdg","name":"write","arguments":{"content":"---\nid: T02\nparent: S02\nmilestone: M014\nprovides:\n  - compose subcommand in test harness\n  - build_compose_prompt() function for offline compose testing\nkey_files:\n  - backend/pipeline/test_harness.py\nkey_decisions:\n  - Compose accepts both harness output (with .pages[]) and raw SynthesizedPage JSON for the existing page input\n  - New moment indices built manually with offset rather than reusing build_moments_text() to ensure correct [N+i] numbering\npatterns_established:\n  - Compose output JSON mirrors synthesis output structure with additional source metadata (existing_page_source, existing_fixture_source, new_fixture_source)\nobservability_surfaces:\n  - Compose-specific metrics logged to stderr: word count before/after, sections before/after, new section headings, citation coverage with unified moment count\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T00:42:00.000Z\nblocker_discovered: false\n---\n\n# T02: Added compose subcommand and build_compose_prompt() to test harness\n\n**Added compose subcommand with build_compose_prompt(), run_compose(), and CLI wiring for offline composition testing**\n\n## What Happened\n\nAdded three pieces to `backend/pipeline/test_harness.py`:\n\n1. **`build_compose_prompt()`** — takes existing page dict, existing moments, new moments, and creator name; builds the user prompt with `<existing_page>`, `<existing_moments>`, `<new_moments>`, and `<creator>` XML tags. Existing moments keep indices [0]-[N-1], new moments get offset indices [N]-[N+M-1].\n\n2. **`run_compose()`** — loads existing page JSON (supports both harness output format and raw SynthesizedPage), loads existing and new fixtures, filters by category, calls LLM via compose prompt, validates output as SynthesisResult, logs compose-specific metrics (word count delta, section count delta, new section headings, citation coverage against unified moment count).\n\n3. **CLI `compose` subcommand** — registered with `--existing-page` (required), `--fixture` (required), `--existing-fixture` (required), plus optional `--prompt`, `--output`, `--category`, `--model`, `--modality`. Defaults compose prompt to `stage5_compose.txt`.\n\n## Verification\n\n- `cd backend && python -m pipeline.test_harness compose --help` exits 0 with correct argument listing\n- `build_compose_prompt()` smoke test: verified XML tag presence, correct index offsets ([0] for existing, [1] for new with 1 existing moment), creator tag content\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -m pipeline.test_harness compose --help` | 0 | ✅ pass | 500ms |\n| 2 | `python -c build_compose_prompt assertions` | 0 | ✅ pass | 400ms |\n\n## Diagnostics\n\nRun `cd backend && python -m pipeline.test_harness compose --help` to see available options. Compose logs metrics to stderr with `[HARNESS] [INFO] COMPOSE:` prefix — grep for word count deltas and new section headings.\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/test_harness.py` — added build_compose_prompt(), run_compose(), _count_page_words(), compose CLI subcommand, SynthesizedPage import\n","path":".gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":964,"cacheRead":81968,"cacheWrite":610,"totalTokens":83543,"cost":{"input":0.000005,"output":0.0241,"cacheRead":0.040984,"cacheWrite":0.0038125000000000004,"total":0.0689015}},"stopReason":"toolUse","timestamp":1775178293352}}
{"type":"message","id":"a6dc70a0","parentId":"93c34b6e","timestamp":"2026-04-03T01:05:12.872Z","message":{"role":"toolResult","toolCallId":"toolu_01SVoYdiUFFFSnZqkqQgTUdg","toolName":"write","content":[{"type":"text","text":"Successfully wrote 3188 bytes to .gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md"}],"isError":false,"timestamp":1775178312870}}
{"type":"message","id":"9d74f52b","parentId":"a6dc70a0","timestamp":"2026-04-03T01:05:22.032Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01NQyQK1fa4YzrMsAMNYtmE4","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"None.","keyDecisions":["Compose accepts both harness output (with .pages[]) and raw SynthesizedPage JSON","New moment indices built manually with offset to ensure correct [N+i] numbering"],"keyFiles":["backend/pipeline/test_harness.py"],"knownIssues":"None.","milestoneId":"M014","narrative":"Added build_compose_prompt() (builds user prompt with XML tags and offset-based citation indices), run_compose() (loads existing page + two fixtures, calls LLM, validates output, logs compose-specific metrics), and the compose CLI subcommand with --existing-page, --fixture, --existing-fixture args plus optional prompt/output/category/model/modality overrides.","oneLiner":"Added compose subcommand with build_compose_prompt(), run_compose(), and CLI wiring for offline composition testing","sliceId":"S02","taskId":"T02","verification":"compose --help exits 0 with correct argument listing. build_compose_prompt() smoke test verified XML tag presence, correct index offsets, and creator tag content.","verificationEvidence":[{"command":"cd backend && python -m pipeline.test_harness compose --help","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"python -c build_compose_prompt assertions (XML tags, index offsets, creator)","durationMs":400,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":550,"cacheRead":82578,"cacheWrite":1010,"totalTokens":84139,"cost":{"input":0.000005,"output":0.01375,"cacheRead":0.041289,"cacheWrite":0.0063125,"total":0.061356499999999994}},"stopReason":"toolUse","timestamp":1775178312871}}
{"type":"message","id":"f7a27b57","parentId":"9d74f52b","timestamp":"2026-04-03T01:05:22.075Z","message":{"role":"toolResult","toolCallId":"toolu_01NQyQK1fa4YzrMsAMNYtmE4","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T02 (S02/M014)"}],"details":{"operation":"complete_task","taskId":"T02","sliceId":"S02","milestoneId":"M014","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md"},"isError":false,"timestamp":1775178322073}}
{"type":"message","id":"266741bb","parentId":"f7a27b57","timestamp":"2026-04-03T01:05:25.378Z","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":83588,"cacheWrite":577,"totalTokens":84175,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.041794,"cacheWrite":0.00360625,"total":0.04563025}},"stopReason":"stop","timestamp":1775178322074}}
