{"type":"thinking_level_change","id":"76680c5b","parentId":null,"timestamp":"2026-04-03T00:52:48.339Z","thinkingLevel":"medium"}
{"type":"model_change","id":"ca3a472c","parentId":"76680c5b","timestamp":"2026-04-03T00:52:48.340Z","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/S01/S01-PLAN.md`\n- Task plan source: `.gsd/milestones/M014/slices/S01/tasks/T03-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M014/slices/S01/tasks/T01-SUMMARY.md`\n- `.gsd/milestones/M014/slices/S01/tasks/T02-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/S01/tasks/T03-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/S01/tasks/T03-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T03 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M014/slices/S01/S01-PLAN.md`\n**Goal:** Stage 5 synthesis prompt produces body_sections as a list of BodySection objects with H2/H3 nesting and inline [N] citation markers, validated by schema and test harness.\n**Demo:** After this: Run test harness with new prompt → output has list-of-objects body_sections with H2/H3 nesting, citation markers on key claims, broader page scope.\n\n## UNIT: Execute Task T03 (\"Update test harness for new body_sections structure and add citation reporting\") — Slice S01 (\"Synthesis Prompt v5 — Nested Sections + Citations\"), 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/S01/tasks/T01-SUMMARY.md` — T01: Added BodySection/BodySubSection schema models, changed SynthesizedPage.body_sections to list[BodySection] with v2 format tag, and created citation_utils with 15 passing tests | decisions: \"Citations use 0-based indices matching moment list position\"; \"validate_citations returns a dict for flexible downstream use\" | key_files: \"backend/pipeline/schemas.py\"; \"backend/pipeline/citation_utils.py\"; \"backend/pipeline/test_citation_utils.py\"\n- `.gsd/milestones/M014/slices/S01/tasks/T02-SUMMARY.md` — T02: Rewrote stage5_synthesis.txt with v2 body_sections (list-of-objects with heading/content/subsections), citation rules ([N] and [N,M] format), body_sections_format tag, and H3 subsection guidance | decisions: \"Citations placed end-of-sentence before period; matching academic convention\" | key_files: \"prompts/stage5_synthesis.txt\"; \"prompts/stage5_synthesis.20260403_005044.bak\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M014/slices/S01/tasks/T03-PLAN.md`\n\n---\nestimated_steps: 18\nestimated_files: 2\nskills_used: []\n---\n\n# T03: Update test harness for new body_sections structure and add citation reporting\n\nUpdate the test harness word-count and section-count logic to walk the new list-of-objects body_sections structure instead of dict.values(). Add citation coverage reporting using the citation_utils from T01. This closes the slice — the harness can parse and report on v2 synthesis output.\n\n## Steps\n\n1. Read `backend/pipeline/test_harness.py` fully.\n2. Read `backend/pipeline/schemas.py` (updated in T01) and `backend/pipeline/citation_utils.py` (created in T01).\n3. Update line ~283-289 (per-page summary in `run_synthesis`): replace `sections.values()` iteration with walking `BodySection` objects. Word count = sum of `section.content` words + sum of all `subsection.content` words. Section count = len(body_sections). Report subsection count too.\n4. Update line ~457 (metadata total_words): same dict→list-of-objects change for the output metadata calculation.\n5. Add citation reporting after per-page summary: import `validate_citations` from citation_utils, call it with `page.body_sections` and `len(page.moment_indices)`, log citation coverage % and any invalid indices.\n6. Create a small test script `backend/pipeline/test_harness_v2_format.py` that:\n   - Constructs a mock SynthesisResult with the new body_sections format (list of BodySection objects)\n   - Verifies word counting works correctly (known content → expected count)\n   - Verifies citation validation integrates correctly\n   - Does NOT require an LLM call\n\n## Must-Haves\n\n- [ ] Word count logic handles list[BodySection] including subsection content\n- [ ] Section count reports H2 count and subsection count\n- [ ] Citation coverage logged per-page\n- [ ] Metadata total_words uses new structure\n- [ ] Test script passes with mock v2 data\n\n## Inputs\n\n- ``backend/pipeline/test_harness.py` — current harness with dict-based word counting`\n- ``backend/pipeline/schemas.py` — updated schema from T01`\n- ``backend/pipeline/citation_utils.py` — citation utilities from T01`\n\n## Expected Output\n\n- ``backend/pipeline/test_harness.py` — updated word-count/section-count logic for list[BodySection]`\n- ``backend/pipeline/test_harness_v2_format.py` — test script validating harness handles v2 format`\n\n## Verification\n\ncd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_harness_v2_format.py -v && python -m pytest backend/pipeline/test_citation_utils.py -v\n","display":false,"id":"b945eea3","parentId":"ca3a472c","timestamp":"2026-04-03T00:52:48.342Z"}
{"type":"message","id":"0fafc80b","parentId":"b945eea3","timestamp":"2026-04-03T00:52:52.443Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the relevant files to understand the current state.","thinkingSignature":"EoYCClkIDBgCKkCBRIoceZRhwFgKmWLKLJLwPMfKVuy5yYH1iKsS1yeadNH4unYpOiazLhvUFPZ/pNHemxCHuo3gVxo7zwZBCSxGMg9jbGF1ZGUtb3B1cy00LTY4ABIMEcjt2bOug3cuOG9OGgwSye6oSkzEO3c7SAsiMCadaesa5FEhh7a7R4lkkdLNUPGogRhvSSS8GpzdekLsw45hrq8IzMvJZpFashS5Ripbhbh1tcDG5gTZongRI/MTryg2NtZEbvkS9ygtIuhc2VXG+aY7hc0X7to+BMdKzgXnARXG+65r87c6O8Xw60GHQxKI9WCVKkfxM/n3q31cp5hVE7ACjSWkMcWz4BgB"},{"type":"text","text":"Let me read the current files to understand what needs to change."},{"type":"toolCall","id":"toolu_01L43irbSvvyx4vXVq9AWJBa","name":"read","arguments":{"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01Xd4MgXEEhMyrvqK3PARTLQ","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_017UAxA6woBpVjQBsYbELc9N","name":"read","arguments":{"path":"backend/pipeline/citation_utils.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":186,"cacheRead":59865,"cacheWrite":3715,"totalTokens":63768,"cost":{"input":0.00001,"output":0.0046500000000000005,"cacheRead":0.029932499999999997,"cacheWrite":0.02321875,"total":0.057811249999999995}},"stopReason":"toolUse","timestamp":1775177568341}}
{"type":"message","id":"5ace2ef7","parentId":"0fafc80b","timestamp":"2026-04-03T00:52:52.447Z","message":{"role":"toolResult","toolCallId":"toolu_01L43irbSvvyx4vXVq9AWJBa","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.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            word_count = sum(len(str(v).split()) for v in sections.values())\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{len(sections)} sections, {word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\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(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\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":1775177572446}}
{"type":"message","id":"7dd9fa78","parentId":"5ace2ef7","timestamp":"2026-04-03T00:52:52.450Z","message":{"role":"toolResult","toolCallId":"toolu_01Xd4MgXEEhMyrvqK3PARTLQ","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    \"\"\"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":1775177572449}}
{"type":"message","id":"1c7a50bf","parentId":"7dd9fa78","timestamp":"2026-04-03T00:52:52.453Z","message":{"role":"toolResult","toolCallId":"toolu_017UAxA6woBpVjQBsYbELc9N","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        invalid_indices (list[int]): Cited indices that are out of range.\n        uncited_moments (list[int]): In-range moment indices that are never cited.\n        coverage_pct (float): Percentage of moments that are cited (0.0–100.0).\n    \"\"\"\n    all_indices: set[int] = set()\n\n    for section in sections:\n        all_indices.update(extract_citations(section.content))\n        for sub in section.subsections:\n            all_indices.update(extract_citations(sub.content))\n\n    valid_range = set(range(moment_count))\n    invalid_indices = sorted(all_indices - valid_range)\n    cited_in_range = all_indices & valid_range\n    uncited_moments = sorted(valid_range - cited_in_range)\n    coverage_pct = (len(cited_in_range) / moment_count * 100.0) if moment_count > 0 else 0.0\n\n    return {\n        \"valid\": len(invalid_indices) == 0 and len(uncited_moments) == 0,\n        \"total_citations\": len(cited_in_range),\n        \"invalid_indices\": invalid_indices,\n        \"uncited_moments\": uncited_moments,\n        \"coverage_pct\": round(coverage_pct, 1),\n    }\n"}],"isError":false,"timestamp":1775177572449}}
{"type":"message","id":"7ba6b264","parentId":"1c7a50bf","timestamp":"2026-04-03T00:52:59.381Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. The test harness has two places that need updating:\n\n1. **Per-page summary in `run_synthesis` (~line 283)**: `sections.values()` iteration needs to walk `BodySection` objects\n2. **Metadata total_words (~line 457)**: same dict→list change\n\nLet me also read the T01 and T02 summaries briefly for context, and the task summary template.","thinkingSignature":"EqcEClkIDBgCKkC2mBFvPYuJcJTpw/WQA0CG/SMIWQDFp45IcoEfWFS6soX8y4/ueI9HkcCH3mNLRg/q/YC0/P+G/zmfAfhWAMaiMg9jbGF1ZGUtb3B1cy00LTY4ABIMhDh6XFS7bHmTWXfgGgzTSA4HbitNledQt7giMHinbnHLBEcXjAN8TYPTISWJ7ZFee5b4r6J+DPYmZIYeCX0ZhpV+jhYhoRn84+0ErCr7Ajd8jIqHTF/U0tseW9YNgaPgN6x/Tg3W1lp/3iAjxMkHxcXjdIIj7AIyNTQqatHwmEBxzniUeCwgnFS6Z+dhJuGEr6FXIN6vZFVsC3k1m5ce/G/2EKxSiJEvd7RumIYhaBIVKECBz7IdkJJdDIzm8uvmyLMdj1FWIACyk0DUu5aa7BGgJZV2V4NFQw+gURfMcaVNsyRk1rUeAZMWT5qSuZNmOb9rCYXde4Fm0NEErBEV2PznN9dswDSDSkENA/5FkHoavL9FcSj/DUn2z3oLsvh2fAkq6mg79JAcLUc1tJ95WHCcUUvd6vHGfZhdREzN9qISqpyM7HBEn40N1kF+oX52cHHRF2zGCFEbucSRQolS5g/oNmabwKzldpzQt1JuTlnn1/ky3VDL80A94G/Isg34k4dSUs0e9knATtjMxGiTGbpbsP3uJmxZsjK6wfmzCehqax5DWeo5CcOv5ttipOzzymg21NN9GQvnVrPQHVpY/ijDZEVAlzFJUEwYAQ=="},{"type":"text","text":"Now I understand the code. Two spots in `test_harness.py` treat `body_sections` as a dict — need to walk `BodySection` objects instead. Let me make the changes."},{"type":"toolCall","id":"toolu_01Mf15RBM5Uq4p4CqDoZ5xWo","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":231,"cacheRead":63580,"cacheWrite":7621,"totalTokens":71433,"cost":{"input":0.000005,"output":0.005775000000000001,"cacheRead":0.03179,"cacheWrite":0.04763125,"total":0.08520125}},"stopReason":"toolUse","timestamp":1775177572450}}
{"type":"message","id":"b284987f","parentId":"7ba6b264","timestamp":"2026-04-03T00:52:59.386Z","message":{"role":"toolResult","toolCallId":"toolu_01Mf15RBM5Uq4p4CqDoZ5xWo","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":1775177579384}}
{"type":"message","id":"00c40fff","parentId":"b284987f","timestamp":"2026-04-03T00:53:04.821Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me implement the changes. First, update `test_harness.py` — the per-page summary section and the metadata total_words."},{"type":"toolCall","id":"toolu_01M4NDcN8GnsJwWXmbqhXXr9","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 SynthesisResult","oldText":"from pydantic import ValidationError\n\nfrom config import get_settings\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":226,"cacheRead":71201,"cacheWrite":627,"totalTokens":72055,"cost":{"input":0.000005,"output":0.0056500000000000005,"cacheRead":0.0356005,"cacheWrite":0.00391875,"total":0.04517425}},"stopReason":"toolUse","timestamp":1775177579384}}
{"type":"message","id":"c0c01e21","parentId":"00c40fff","timestamp":"2026-04-03T00:53:04.832Z","message":{"role":"toolResult","toolCallId":"toolu_01M4NDcN8GnsJwWXmbqhXXr9","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":"  32 \n  33 from pydantic import ValidationError\n  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  39 \n  40 ","firstChangedLine":36},"isError":false,"timestamp":1775177584830}}
{"type":"message","id":"82e67020","parentId":"c0c01e21","timestamp":"2026-04-03T00:53:14.746Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QwbUJUzvxiYXJBLZK77Gcf","name":"edit","arguments":{"newText":"        # 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())","oldText":"        # 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            word_count = sum(len(str(v).split()) for v in sections.values())\n            total_words += word_count\n            _log(\n                \"PAGE\",\n                f\"    '{page.title}' ({page.slug}): \"\n                f\"{len(sections)} sections, {word_count} words, \"\n                f\"{len(page.moment_indices)} moments linked, \"\n                f\"quality={page.source_quality}\",\n            )\n\n            all_pages.append(page.model_dump())","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":673,"cacheRead":71828,"cacheWrite":252,"totalTokens":72754,"cost":{"input":0.000005,"output":0.016825,"cacheRead":0.035914,"cacheWrite":0.001575,"total":0.054319}},"stopReason":"toolUse","timestamp":1775177584830}}
{"type":"message","id":"50825b11","parentId":"82e67020","timestamp":"2026-04-03T00:53:14.756Z","message":{"role":"toolResult","toolCallId":"toolu_01QwbUJUzvxiYXJBLZK77Gcf","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":" 280         # Log per-page summary\n 281         _log(\"SYNTH\", f\"  Parsed: {len(result.pages)} pages synthesized\")\n 282         total_words = 0\n 283         for page in result.pages:\n-284             sections = page.body_sections or {}\n-285             word_count = sum(len(str(v).split()) for v in sections.values())\n+284             sections = page.body_sections or []\n+285             section_count = len(sections)\n+286             subsection_count = sum(len(s.subsections) for s in sections)\n+287             word_count = sum(\n+288                 len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n+289                 for s in sections\n+290             )\n 291             total_words += word_count\n 292             _log(\n 293                 \"PAGE\",\n 294                 f\"    '{page.title}' ({page.slug}): \"\n-290                 f\"{len(sections)} sections, {word_count} words, \"\n+295                 f\"{section_count} sections ({subsection_count} subsections), \"\n+296                 f\"{word_count} words, \"\n 297                 f\"{len(page.moment_indices)} moments linked, \"\n 298                 f\"quality={page.source_quality}\",\n 299             )\n 300 \n+301             # Citation coverage reporting\n+302             cit = validate_citations(sections, len(page.moment_indices))\n+303             _log(\n+304                 \"CITE\",\n+305                 f\"    Citations: {cit['total_citations']}/{len(page.moment_indices)} moments cited \"\n+306                 f\"({cit['coverage_pct']}% coverage)\"\n+307                 + (f\", invalid indices: {cit['invalid_indices']}\" if cit['invalid_indices'] else \"\")\n+308                 + (f\", uncited: {cit['uncited_moments']}\" if cit['uncited_moments'] else \"\"),\n+309             )\n+310 \n 311             all_pages.append(page.model_dump())\n 312 \n 313     # Summary\n 314     _log(\"SUMMARY\", f\"Total: {len(all_pages)} pages across {len(categories)} categories\")","firstChangedLine":284},"isError":false,"timestamp":1775177594755}}
{"type":"message","id":"50373fc4","parentId":"50825b11","timestamp":"2026-04-03T00:53:19.148Z","message":{"role":"assistant","content":[{"type":"text","text":"Now update the metadata total_words calculation:"},{"type":"toolCall","id":"toolu_01GBFM5kMEELTW8o46TEcyrJ","name":"edit","arguments":{"newText":"            \"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            ),","oldText":"            \"total_words\": sum(\n                sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n                for p in pages\n            ),","path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":233,"cacheRead":72080,"cacheWrite":699,"totalTokens":73013,"cost":{"input":0.000005,"output":0.005825,"cacheRead":0.036039999999999996,"cacheWrite":0.00436875,"total":0.046238749999999995}},"stopReason":"toolUse","timestamp":1775177594755}}
{"type":"message","id":"fed3cc4a","parentId":"50373fc4","timestamp":"2026-04-03T00:53:19.164Z","message":{"role":"toolResult","toolCallId":"toolu_01GBFM5kMEELTW8o46TEcyrJ","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/test_harness.py."}],"details":{"diff":" 470         \"pages\": pages,\n 471         \"metadata\": {\n 472             \"page_count\": len(pages),\n 473             \"total_words\": sum(\n-474                 sum(len(str(v).split()) for v in p.get(\"body_sections\", {}).values())\n+474                 sum(\n+475                     len(s.get(\"content\", \"\").split())\n+476                     + sum(len(sub.get(\"content\", \"\").split()) for sub in s.get(\"subsections\", []))\n+477                     for s in p.get(\"body_sections\", [])\n+478                 )\n 479                 for p in pages\n 480             ),\n 481             \"elapsed_seconds\": round(time.monotonic() - overall_start, 1),\n 482         },","firstChangedLine":474},"isError":false,"timestamp":1775177599162}}
{"type":"message","id":"8848ad3b","parentId":"fed3cc4a","timestamp":"2026-04-03T00:53:46.241Z","message":{"role":"assistant","content":[{"type":"text","text":"Now create the test script:"},{"type":"toolCall","id":"toolu_016ABq51JiPQWwzVhshKSTBV","name":"write","arguments":{"content":"\"\"\"Tests for test_harness compatibility with v2 body_sections format.\n\nValidates that word-counting and citation integration work correctly\nwith the list[BodySection] structure (v2) instead of the old dict format.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport pytest\n\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.schemas import BodySection, BodySubSection, SynthesizedPage, SynthesisResult\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n\ndef _make_page(\n    body_sections: list[BodySection],\n    moment_indices: list[int] | None = None,\n    title: str = \"Test Page\",\n    slug: str = \"test-page\",\n) -> SynthesizedPage:\n    return SynthesizedPage(\n        title=title,\n        slug=slug,\n        topic_category=\"Testing\",\n        summary=\"A test page.\",\n        body_sections=body_sections,\n        moment_indices=moment_indices or [],\n    )\n\n\ndef _count_words_v2(sections: list[BodySection]) -> int:\n    \"\"\"Replicate the word-counting logic from the updated test_harness.\"\"\"\n    return sum(\n        len(s.content.split()) + sum(len(sub.content.split()) for sub in s.subsections)\n        for s in sections\n    )\n\n\ndef _count_words_metadata(pages_dicts: list[dict]) -> int:\n    \"\"\"Replicate the metadata total_words logic (operates on dicts after model_dump).\"\"\"\n    return 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_dicts\n    )\n\n\n# ── Word counting tests ─────────────────────────────────────────────────────\n\n\nclass TestWordCounting:\n    def test_flat_sections_no_subsections(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"one two three\"),\n            BodySection(heading=\"Details\", content=\"four five\"),\n        ]\n        assert _count_words_v2(sections) == 5\n\n    def test_sections_with_subsections(self):\n        sections = [\n            BodySection(\n                heading=\"Main\",\n                content=\"alpha beta\",  # 2 words\n                subsections=[\n                    BodySubSection(heading=\"Sub A\", content=\"gamma delta epsilon\"),  # 3 words\n                    BodySubSection(heading=\"Sub B\", content=\"zeta\"),  # 1 word\n                ],\n            ),\n        ]\n        assert _count_words_v2(sections) == 6\n\n    def test_empty_sections_list(self):\n        assert _count_words_v2([]) == 0\n\n    def test_section_with_empty_content(self):\n        sections = [\n            BodySection(heading=\"Empty\", content=\"\"),\n        ]\n        # \"\".split() returns [], len([]) == 0\n        assert _count_words_v2(sections) == 0\n\n    def test_metadata_word_count_matches(self):\n        \"\"\"Metadata total_words (from model_dump dicts) matches Pydantic object counting.\"\"\"\n        sections = [\n            BodySection(\n                heading=\"H2\",\n                content=\"one two three\",\n                subsections=[\n                    BodySubSection(heading=\"H3\", content=\"four five six seven\"),\n                ],\n            ),\n            BodySection(heading=\"Another\", content=\"eight nine\"),\n        ]\n        page = _make_page(sections, moment_indices=[0, 1])\n        pages_dicts = [page.model_dump()]\n\n        assert _count_words_v2(sections) == 9\n        assert _count_words_metadata(pages_dicts) == 9\n\n\n# ── Section/subsection counting ─────────────────────────────────────────────\n\n\nclass TestSectionCounting:\n    def test_section_and_subsection_counts(self):\n        sections = [\n            BodySection(heading=\"A\", content=\"text\", subsections=[\n                BodySubSection(heading=\"A.1\", content=\"sub text\"),\n            ]),\n            BodySection(heading=\"B\", content=\"more text\"),\n            BodySection(heading=\"C\", content=\"even more\", subsections=[\n                BodySubSection(heading=\"C.1\", content=\"sub1\"),\n                BodySubSection(heading=\"C.2\", content=\"sub2\"),\n            ]),\n        ]\n        section_count = len(sections)\n        subsection_count = sum(len(s.subsections) for s in sections)\n        assert section_count == 3\n        assert subsection_count == 3\n\n\n# ── Citation integration ─────────────────────────────────────────────────────\n\n\nclass TestCitationIntegration:\n    def test_full_coverage(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"First point [0]. Second point [1].\"),\n            BodySection(heading=\"Details\", content=\"More on [0] and [2].\"),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is True\n        assert result[\"coverage_pct\"] == 100.0\n        assert result[\"invalid_indices\"] == []\n        assert result[\"uncited_moments\"] == []\n\n    def test_partial_coverage(self):\n        sections = [\n            BodySection(heading=\"Intro\", content=\"Only cites [0].\"),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is False\n        assert result[\"coverage_pct\"] == pytest.approx(33.3, abs=0.1)\n        assert result[\"uncited_moments\"] == [1, 2]\n\n    def test_invalid_index(self):\n        sections = [\n            BodySection(heading=\"Bad\", content=\"Cites [0] and [99].\"),\n        ]\n        result = validate_citations(sections, moment_count=2)\n        assert result[\"invalid_indices\"] == [99]\n\n    def test_citations_in_subsections(self):\n        sections = [\n            BodySection(\n                heading=\"Main\",\n                content=\"See [0].\",\n                subsections=[\n                    BodySubSection(heading=\"Sub\", content=\"Also [1] and [2].\"),\n                ],\n            ),\n        ]\n        result = validate_citations(sections, moment_count=3)\n        assert result[\"valid\"] is True\n        assert result[\"total_citations\"] == 3\n\n    def test_multi_citation_markers(self):\n        sections = [\n            BodySection(heading=\"X\", content=\"Both sources agree [0,1].\"),\n        ]\n        result = validate_citations(sections, moment_count=2)\n        assert result[\"valid\"] is True\n        assert result[\"total_citations\"] == 2\n\n    def test_no_sections(self):\n        result = validate_citations([], moment_count=0)\n        assert result[\"valid\"] is True\n        assert result[\"coverage_pct\"] == 0.0\n\n\n# ── End-to-end: SynthesisResult with v2 body_sections ───────────────────────\n\n\nclass TestSynthesisResultV2:\n    def test_round_trip_model_dump(self):\n        \"\"\"SynthesisResult with v2 body_sections round-trips through model_dump/validate.\"\"\"\n        sections = [\n            BodySection(\n                heading=\"Overview\",\n                content=\"This technique [0] is fundamental.\",\n                subsections=[\n                    BodySubSection(heading=\"Key Concept\", content=\"Detail [1].\"),\n                ],\n            ),\n        ]\n        page = _make_page(sections, moment_indices=[0, 1])\n        result = SynthesisResult(pages=[page])\n\n        dumped = result.model_dump()\n        restored = SynthesisResult.model_validate(dumped)\n\n        assert len(restored.pages) == 1\n        restored_page = restored.pages[0]\n        assert len(restored_page.body_sections) == 1\n        assert restored_page.body_sections[0].heading == \"Overview\"\n        assert len(restored_page.body_sections[0].subsections) == 1\n        assert restored_page.body_sections_format == \"v2\"\n","path":"backend/pipeline/test_harness_v2_format.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2145,"cacheRead":72779,"cacheWrite":259,"totalTokens":75184,"cost":{"input":0.000005,"output":0.053625000000000006,"cacheRead":0.0363895,"cacheWrite":0.0016187500000000002,"total":0.09163825}},"stopReason":"toolUse","timestamp":1775177599162}}
{"type":"message","id":"5a059f70","parentId":"8848ad3b","timestamp":"2026-04-03T00:53:46.246Z","message":{"role":"toolResult","toolCallId":"toolu_016ABq51JiPQWwzVhshKSTBV","toolName":"write","content":[{"type":"text","text":"Successfully wrote 7400 bytes to backend/pipeline/test_harness_v2_format.py"}],"isError":false,"timestamp":1775177626244}}
{"type":"message","id":"dee02b8c","parentId":"5a059f70","timestamp":"2026-04-03T00:53:51.192Z","message":{"role":"assistant","content":[{"type":"text","text":"Now run verification:"},{"type":"toolCall","id":"toolu_014isC1HZMc1Q3PrxCMyMM8C","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/pipeline/test_harness_v2_format.py -v && python -m pytest backend/pipeline/test_citation_utils.py -v","timeout":30}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":129,"cacheRead":73038,"cacheWrite":2180,"totalTokens":75348,"cost":{"input":0.000005,"output":0.003225,"cacheRead":0.036518999999999996,"cacheWrite":0.013625,"total":0.05337399999999999}},"stopReason":"toolUse","timestamp":1775177626245}}
{"type":"message","id":"28dbb822","parentId":"dee02b8c","timestamp":"2026-04-03T00:53:51.840Z","message":{"role":"toolResult","toolCallId":"toolu_014isC1HZMc1Q3PrxCMyMM8C","toolName":"bash","content":[{"type":"text","text":"============================= test session starts ==============================\nplatform linux -- Python 3.12.3, pytest-8.4.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /home/aux/projects/content-to-kb-automator/backend\nconfigfile: pytest.ini\nplugins: asyncio-0.24.0, anyio-4.13.0\nasyncio: mode=Mode.AUTO, default_loop_scope=None\ncollecting ... collected 13 items\n\nbackend/pipeline/test_harness_v2_format.py::TestWordCounting::test_flat_sections_no_subsections PASSED [  7%]\nbackend/pipeline/test_harness_v2_format.py::TestWordCounting::test_sections_with_subsections PASSED [ 15%]\nbackend/pipeline/test_harness_v2_format.py::TestWordCounting::test_empty_sections_list PASSED [ 23%]\nbackend/pipeline/test_harness_v2_format.py::TestWordCounting::test_section_with_empty_content PASSED [ 30%]\nbackend/pipeline/test_harness_v2_format.py::TestWordCounting::test_metadata_word_count_matches PASSED [ 38%]\nbackend/pipeline/test_harness_v2_format.py::TestSectionCounting::test_section_and_subsection_counts PASSED [ 46%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_full_coverage PASSED [ 53%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_partial_coverage PASSED [ 61%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_invalid_index PASSED [ 69%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_citations_in_subsections PASSED [ 76%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_multi_citation_markers PASSED [ 84%]\nbackend/pipeline/test_harness_v2_format.py::TestCitationIntegration::test_no_sections PASSED [ 92%]\nbackend/pipeline/test_harness_v2_format.py::TestSynthesisResultV2::test_round_trip_model_dump PASSED [100%]\n\n============================== 13 passed in 0.08s ==============================\n============================= test session starts ==============================\nplatform linux -- Python 3.12.3, pytest-8.4.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /home/aux/projects/content-to-kb-automator/backend\nconfigfile: pytest.ini\nplugins: asyncio-0.24.0, anyio-4.13.0\nasyncio: mode=Mode.AUTO, default_loop_scope=None\ncollecting ... collected 15 items\n\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_single_markers PASSED [  6%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_multi_marker PASSED [ 13%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_multi_marker_with_spaces PASSED [ 20%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_no_citations PASSED [ 26%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_duplicate_indices_deduplicated PASSED [ 33%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_returns_sorted PASSED [ 40%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_adjacent_markers PASSED [ 46%]\nbackend/pipeline/test_citation_utils.py::TestExtractCitations::test_does_not_match_non_numeric_brackets PASSED [ 53%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_all_moments_cited PASSED [ 60%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_out_of_range_index PASSED [ 66%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_multi_citation_markers PASSED [ 73%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_no_citations_at_all PASSED [ 80%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_empty_sections PASSED [ 86%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_subsection_citations_counted PASSED [ 93%]\nbackend/pipeline/test_citation_utils.py::TestValidateCitations::test_zero_moment_count_with_citations PASSED [100%]\n\n============================== 15 passed in 0.08s ==============================\n"}],"isError":false,"timestamp":1775177631839}}
{"type":"message","id":"c16fb5cb","parentId":"28dbb822","timestamp":"2026-04-03T00:54:15.714Z","message":{"role":"assistant","content":[{"type":"text","text":"All 28 tests pass. Now write the task summary and complete."},{"type":"toolCall","id":"toolu_01QzUo4sMKYPRgXQjmog8iFs","name":"write","arguments":{"content":"---\nid: T03\nparent: S01\nmilestone: M014\nprovides:\n  - Test harness compatible with v2 body_sections (list[BodySection]) format\n  - Citation coverage reporting in harness output\n  - 13-test suite validating v2 format handling\nkey_files:\n  - backend/pipeline/test_harness.py\n  - backend/pipeline/test_harness_v2_format.py\nkey_decisions:\n  - \"Word count includes both section.content and subsection.content; heading text excluded\"\n  - \"Citation report uses CITE log tag for grep-friendly structured output\"\npatterns_established:\n  - \"Harness per-page log now reports H2 count and subsection count separately\"\nobservability_surfaces:\n  - \"[HARNESS] [INFO] CITE: per-page citation coverage, invalid indices, uncited moments\"\nduration: ~8min\nverification_result: passed\ncompleted_at: 2026-04-03\nblocker_discovered: false\n---\n\n# T03: Update test harness for new body_sections structure and add citation reporting\n\n**Updated test_harness.py word-count/section-count logic for list[BodySection] v2 format and added per-page citation coverage reporting with 13 new tests passing**\n\n## What Happened\n\nTwo locations in `test_harness.py` treated `body_sections` as a `dict` (calling `.values()` and `.keys()`). Updated both to walk `BodySection` objects:\n\n1. **Per-page summary (run_synthesis):** Replaced `sections.values()` iteration with `BodySection` field access. Word count now sums `s.content` words plus all `sub.content` words per subsection. Reports H2 section count and subsection count separately. Added citation coverage reporting via `validate_citations()` — logs coverage %, invalid indices, and uncited moments under a `CITE` tag.\n\n2. **Metadata total_words (output dict):** Same dict→list-of-objects change. Since `pages` is already `model_dump()`'d at this point, uses dict `.get()` access on the serialized structure.\n\nCreated `test_harness_v2_format.py` with 13 tests covering word counting (flat sections, nested subsections, empty content, metadata round-trip), section/subsection counting, citation integration (full coverage, partial, invalid indices, subsections, multi-markers, empty), and SynthesisResult v2 round-trip.\n\n## Verification\n\nRan both test suites — 28 tests total, all passing:\n- `test_harness_v2_format.py`: 13 passed\n- `test_citation_utils.py`: 15 passed\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python -m pytest backend/pipeline/test_harness_v2_format.py -v` | 0 | ✅ pass | 0.08s |\n| 2 | `python -m pytest backend/pipeline/test_citation_utils.py -v` | 0 | ✅ pass | 0.08s |\n\n## Diagnostics\n\n- Citation coverage visible in harness output: `grep CITE` on stderr when running the harness against a fixture\n- Invalid citation indices logged explicitly so prompt iteration can target uncited moments\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/test_harness.py` — Updated word-count/section-count for list[BodySection], added citation coverage reporting via validate_citations\n- `backend/pipeline/test_harness_v2_format.py` — New test suite (13 tests) validating v2 format handling\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1022,"cacheRead":75218,"cacheWrite":1430,"totalTokens":77671,"cost":{"input":0.000005,"output":0.02555,"cacheRead":0.037609,"cacheWrite":0.008937500000000001,"total":0.0721015}},"stopReason":"toolUse","timestamp":1775177631839}}
{"type":"message","id":"1d9ecf3c","parentId":"c16fb5cb","timestamp":"2026-04-03T00:54:15.717Z","message":{"role":"toolResult","toolCallId":"toolu_01QzUo4sMKYPRgXQjmog8iFs","toolName":"write","content":[{"type":"text","text":"Successfully wrote 3145 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md"}],"isError":false,"timestamp":1775177655716}}
{"type":"message","id":"f419453b","parentId":"1d9ecf3c","timestamp":"2026-04-03T00:54:24.345Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QP29L6Geab55d5RajhDMmH","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"None.","keyDecisions":["Word count includes both section.content and subsection.content; heading text excluded","Citation report uses CITE log tag for grep-friendly structured output"],"keyFiles":["backend/pipeline/test_harness.py","backend/pipeline/test_harness_v2_format.py"],"knownIssues":"None.","milestoneId":"M014","narrative":"Two locations in test_harness.py treated body_sections as a dict. Updated both to walk BodySection objects: per-page summary now counts words from section.content + subsection.content, reports H2/subsection counts separately, and logs citation coverage via validate_citations(). Metadata total_words uses the same structure on serialized dicts. Created test_harness_v2_format.py with 13 tests covering word counting, section counting, citation integration, and SynthesisResult v2 round-trip.","oneLiner":"Updated test_harness.py word-count/section-count logic for list[BodySection] v2 format and added per-page citation coverage reporting with 13 new tests passing","sliceId":"S01","taskId":"T03","verification":"Ran both test suites — 28 tests total, all passing: test_harness_v2_format.py (13 passed), test_citation_utils.py (15 passed).","verificationEvidence":[{"command":"python -m pytest backend/pipeline/test_harness_v2_format.py -v","durationMs":80,"exitCode":0,"verdict":"✅ pass"},{"command":"python -m pytest backend/pipeline/test_citation_utils.py -v","durationMs":80,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":624,"cacheRead":76648,"cacheWrite":1083,"totalTokens":78356,"cost":{"input":0.000005,"output":0.015600000000000001,"cacheRead":0.038324,"cacheWrite":0.0067687500000000005,"total":0.060697749999999995}},"stopReason":"toolUse","timestamp":1775177655717}}
{"type":"message","id":"811e6273","parentId":"f419453b","timestamp":"2026-04-03T00:54:24.389Z","message":{"role":"toolResult","toolCallId":"toolu_01QP29L6Geab55d5RajhDMmH","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T03 (S01/M014)"}],"details":{"operation":"complete_task","taskId":"T03","sliceId":"S01","milestoneId":"M014","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md"},"isError":false,"timestamp":1775177664387}}
{"type":"message","id":"c1e3ad64","parentId":"811e6273","timestamp":"2026-04-03T00:54:27.202Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T03 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":77731,"cacheWrite":651,"totalTokens":78392,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.0388655,"cacheWrite":0.00406875,"total":0.04316425}},"stopReason":"stop","timestamp":1775177664388}}
