{"type":"thinking_level_change","id":"e2840c55","parentId":null,"timestamp":"2026-04-03T02:04:42.540Z","thinkingLevel":"medium"}
{"type":"model_change","id":"3fe9b164","parentId":"e2840c55","timestamp":"2026-04-03T02:04:42.542Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S07/S07-PLAN.md` and `.gsd/milestones/M014/slices/S07/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S07 planned.\"\n## Requirements Advanced\n\n- R012 — Stage 5 now composes new video content into existing technique pages instead of overwriting, fulfilling the incremental update aspect of R012\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\n- R006 — Technique page display now supports v2 nested sections with TOC and citations in addition to existing v1 format\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S07 (\"Search — Per-Section Embeddings + Deep Linking\") — Milestone M014\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M014/M014-ROADMAP.md`\n\n# M014: \n\n## Vision\nRestructure technique pages to be broader (per-creator+category across videos), deeper (H2/H3 nesting with TOC), multi-source (compose new video content into existing pages), with inline citations linking prose claims to source moments, admin multi-source management, and section-level search with deep linking.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Synthesis Prompt v5 — Nested Sections + Citations | high | — | ✅ | 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| S02 | Composition Prompt + Test Harness Compose Mode | high | S01 | ✅ | Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations. |\n| S03 | Data Model + Migration | low | — | ✅ | Alembic migration runs clean. API response includes body_sections_format and source_videos fields. |\n| S04 | Pipeline Compose-or-Create Logic | high | S01, S02, S03 | ✅ | Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs. |\n| S05 | Frontend — Nested Rendering, TOC, Citations | medium | S03 | ✅ | Format-2 page renders with TOC, nested sections, clickable citations. Format-1 pages unchanged. |\n| S06 | Admin UI — Multi-Source Pipeline Management | medium | S03, S04 | ✅ | Admin view for multi-source page shows source dropdown, composition history, per-video chunking inspection. |\n| S07 | Search — Per-Section Embeddings + Deep Linking | medium | S04, S05 | ⬜ | Search 'LFO grain position' → section-level result → click → navigates to page#section and scrolls. |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M014/slices/S07/S07-RESEARCH.md`\n\n# S07 Research: Search — Per-Section Embeddings + Deep Linking\n\n## Summary\n\nThis slice adds section-level search granularity and deep linking. Currently, stage 6 embeds whole technique pages and key moments as two point types in Qdrant. S07 adds a third point type (`technique_section`) that embeds individual v2 body_sections, enabling search results that link directly to a specific section within a technique page (e.g., `/techniques/lfo-modulation#grain-position-control`). The frontend already has section IDs and `scroll-margin-top` from S05 but lacks hash-based scroll handling — React Router client-side navigation doesn't auto-scroll to anchors.\n\nStraightforward extension of established patterns. No new technology, no ambiguous requirements.\n\n## Recommendation\n\nThree tasks:\n\n1. **Backend: Section embedding in stage 6 + new Qdrant upsert method** — Extend `stage6_embed_and_index` to iterate v2 `body_sections`, embed each section (heading + content + subsection content), and upsert to Qdrant with type `technique_section`. Add `upsert_technique_sections` to `QdrantManager`. Extend `SearchService._enrich_qdrant_results` to handle the new type. Update `SearchResultItem` schema with `section_anchor` field.\n\n2. **Frontend: Hash scroll handler + section result rendering** — Add `useHashScroll` hook to TechniquePage that scrolls to the hash anchor after content loads. Update `SearchResultItem` type with `section_anchor`. Update `getSearchResultLink` and `SearchResultCard` to show section context and link to `page#section-slug`.\n\n3. **Verification: End-to-end search → deep link test** — Verify stage 6 produces section points in Qdrant, search returns them with section_anchor, and clicking a result navigates to the correct section.\n\n## Implementation Landscape\n\n### Current Indexing Pipeline (stage 6)\n\n**File:** `backend/pipeline/stages.py` lines 1595–1762\n\nStage 6 loads technique pages and key moments for a video, embeds text via `EmbeddingClient`, and upserts to Qdrant via `QdrantManager`. Two point types exist:\n\n- `technique_page` — embed text: `\"{creator} {title} {category} {tags} {summary}\"`, payload: page_id, creator_id, creator_name, title, slug, topic_category, topic_tags, summary\n- `key_moment` — embed text: `\"{creator} {title} {summary}\"`, payload: moment_id, source_video_id, technique_page_id, technique_page_slug, title, creator_name, timestamps, content_type\n\nDeterministic UUIDs via `uuid5(_QDRANT_NAMESPACE, \"tp:{page_id}\")` / `\"km:{moment_id}\"`.\n\n### QdrantManager\n\n**File:** `backend/pipeline/qdrant_client.py`\n\nHas `upsert_technique_pages` and `upsert_key_moments` — both build `PointStruct` lists and call `upsert_points`. Adding `upsert_technique_sections` follows the identical pattern. Deterministic UUID would be `uuid5(_QDRANT_NAMESPACE, \"ts:{page_id}:{section_slug}\")`.\n\n### Search Service\n\n**File:** `backend/search_service.py`\n\n`search_qdrant()` returns payload dicts. `_enrich_qdrant_results()` maps payload fields to `SearchResultItem`-shaped dicts based on `payload[\"type\"]`. Currently handles `technique_page` and falls through for `key_moment`. Needs a `technique_section` branch that maps `section_heading`, `section_anchor`, and parent page slug.\n\nKeyword search (`_keyword_search_and`) queries PostgreSQL directly — no section-level keyword search needed since `body_sections` is a JSONB column and full-text indexing it would be complex with diminishing returns. Semantic search is the right tool for section-level granularity.\n\n### Search API Schema\n\n**File:** `backend/schemas.py`\n\n`SearchResultItem` needs a new optional field: `section_anchor: str = \"\"`. This carries the anchor slug (e.g., `grain-position-control`) so the frontend can build `page#anchor` links.\n\n### Frontend Search\n\n**File:** `frontend/src/pages/SearchResults.tsx`\n\n`getSearchResultLink()` already builds hash links for key_moments (`#km-{slug}`). Adding section deep links: if `item.type === \"technique_section\"`, return `/techniques/${item.technique_page_slug}#${item.section_anchor}`.\n\n**File:** `frontend/src/api/public-client.ts`\n\n`SearchResultItem` interface needs `section_anchor?: string`.\n\n### Frontend Hash Scrolling (GAP)\n\n**File:** `frontend/src/pages/TechniquePage.tsx`\n\nSections already have `id={sectionSlug}` and `scroll-margin-top: 5rem` CSS. But there is NO hash scroll handling — React Router client-side navigation won't auto-scroll to `#anchor`. Need a `useEffect` that reads `location.hash` after technique data loads and calls `document.getElementById(hash)?.scrollIntoView({ behavior: 'smooth' })`. Must run after render (when section elements exist in DOM).\n\n### Section ID Convention (from S05)\n\n`slugify()` in `frontend/src/components/TableOfContents.tsx`: lowercase, replace non-alphanumeric with hyphens, trim leading/trailing hyphens.\n\nSection IDs: `slugify(section.heading)` → e.g., `grain-position-control`\nSubsection IDs: `${sectionSlug}--${slugify(sub.heading)}` → e.g., `grain-position-control--lfo-routing`\n\nThe backend must replicate this slugify logic in Python for the `section_anchor` payload field. Python equivalent: `re.sub(r'[^a-z0-9]+', '-', heading.lower()).strip('-')`.\n\n### v2 Body Sections Structure\n\nStored as JSONB in `TechniquePage.body_sections`. Format when `body_sections_format='v2'`:\n\n```json\n[\n  {\n    \"heading\": \"Section Title\",\n    \"content\": \"Section prose...\",\n    \"subsections\": [\n      { \"heading\": \"Sub Title\", \"content\": \"Sub prose...\" }\n    ]\n  }\n]\n```\n\nPer D024: sections with subsections have empty-string content; substance lives in subsection content fields.\n\n### Embedding Text Construction for Sections\n\nFor each section, concatenate: `\"{creator_name} {page_title} — {section_heading}: {section_content} {subsection_contents}\"`. This gives the embedding model context about what page/creator the section belongs to while focusing on the section's specific content.\n\n### Volume Estimate\n\nTypical technique pages have 3-7 sections. With ~N pages in the system, this adds ~4N section points to Qdrant. The existing collection handles this without configuration changes — same vector dimensions, same distance metric.\n\n### Deduplication on Re-index\n\nDeterministic UUID `uuid5(namespace, \"ts:{page_id}:{section_slug}\")` ensures re-indexing the same page overwrites the same section points. However, if a compose operation changes section headings, orphan points from the old heading remain. Acceptable for now — a full re-index (`stage6_embed_and_index`) replaces all points for a video's pages anyway.\n\n### Risk: Section heading changes after compose\n\nWhen a page is composed with new video content, section headings may change. The old section points in Qdrant won't be deleted automatically. Mitigation: before upserting section points for a page, delete existing `technique_section` points with that `page_id`. This requires a `QdrantManager.delete_sections_by_page_id()` method using a filter on `payload.page_id`.\n\n## Constraints\n\n- Section embeddings only apply to v2 pages (`body_sections_format='v2'`). v1 pages continue with page-level embedding only.\n- The `slugify` function must be identical in Python (stage 6) and TypeScript (frontend) — any divergence breaks deep links.\n- Hash scrolling must wait for the technique page data to load and render before scrolling, otherwise the target element doesn't exist yet.\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions (compact)\nFields: id | when | scope | decision | choice | rationale | revisable | made_by\n\nD024 | M014/S01 | architecture | Content model for sections with subsections | Sections with subsections use empty-string content field; substance lives in subsections | Avoids duplication between section-level content and subsection content. The section heading serves as H2 container; all prose lives in subsection content fields. Sections without subsections use the section content field directly. | Yes | agent\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (7 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic loads a dedicated page with creator-grouped techniques and breadcrumbs.\n**Validation:** Hierarchy renders, filtering works, sub-topics have dedicated pages at /topics/:category/:subtopic with backend filtering and creator grouping. Verified: M010/S01.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n## R016 — Card Hover Animations & Staggered Entrance\n**Status:** validated\n**Description:** All technique/content cards animate on hover (scale + shadow transition). Card grids use staggered fade-in-up entrance animations on page load.\n**Validation:** Cards visually respond to hover with smooth 200ms transition. Grid cards appear sequentially on page load.\n**Primary Owner:** M011/S01\n\n## R017 — Featured Technique Visual Redesign\n**Status:** validated\n**Description:** Featured Technique section on homepage has a visually distinct treatment (gradient border, glow, or full-bleed) that differentiates it from ordinary cards.\n**Validation:** Featured technique is visually prominent and clearly distinct from regular technique cards.\n**Primary Owner:** M011/S01\n\n## R018 — Random Technique Discovery\n**Status:** validated\n**Description:** A \"Random Technique\" button exists that navigates to a randomly selected technique page.\n**Validation:** Clicking the button loads a different technique each time.\n**Primary Owner:** M011/S01\n\n## R019 — Topics Page Default-Collapsed\n**Status:** validated\n**Description:** Topics page loads with all categories collapsed, showing only category cards. Clicking a category expands its sub-topics with a smooth slide animation.\n**Validation:** Page loads collapsed. Click expands with animation. Click again collapses.\n**Primary Owner:** M011/S02\n\n## R020 — Global Search in Navigation\n**Status:** validated\n**Description:** A compact search input in the navigation bar is available on all pages except the homepage. Cmd+K (or /) keyboard shortcut focuses it from any page.\n**Validation:** Search bar visible in nav on Topics, Creators, Technique, About pages. Cmd+K focuses it.\n**Primary Owner:** M011/S03\n\n## R021 — Mobile Hamburger Menu\n**Status:** validated\n**Description:** Navigation uses a hamburger menu on viewports below 768px with generous touch targets (44×44px minimum).\n**Validation:** Mobile viewport shows hamburger icon. Tapping reveals nav items. Touch targets meet minimum size.\n**Primary Owner:** M011/S03\n\n## R022 — Heading Hierarchy Fix\n**Status:** validated\n**Description:** Every page has exactly one H1 element. Heading levels are sequential (no skips from H1 to H3).\n**Validation:** DOM inspection confirms single H1 per page and sequential heading levels.\n**Primary Owner:** M011/S04\n\n## R023 — Skip-to-Content Link\n**Status:** validated\n**Description:** A visually hidden skip link is the first focusable element on every page, visible on keyboard focus.\n**Validation:** Tab from page load shows \"Skip to content\" link. Clicking it jumps to main content area.\n**Primary Owner:** M011/S04\n\n## R024 — Text Contrast AA Compliance\n**Status:** validated\n**Description:** All text meets WCAG AA contrast ratios (4.5:1 for normal text, 3:1 for large text) against the dark background.\n**Validation:** Secondary text color passes 4.5:1 contrast check against page background.\n**Primary Owner:** M011/S04\n\n## R025 — Page-Specific Document Titles\n**Status:** validated\n**Description:** Each route sets a descriptive document title (e.g., \"Bass — Sound Design — Chrysopedia\", \"COPYCATT — Chrysopedia\").\n**Validation:** Browser tab shows distinct title per page. Navigating between pages updates the title.\n**Primary Owner:** M011/S04\n\n## R026 — Creator Stats Topic-Colored Pills\n**Status:** validated\n**Description:** Creator detail page stats line rendered as topic-colored pill badges instead of run-on text.\n**Validation:** Stats show as visually distinct colored pills grouped by topic category.\n**Primary Owner:** M011/S02\n\n## R027 — Tag Overflow Limit on Cards\n**Status:** validated\n**Description:** Technique cards show a maximum of 4 tags, with a \"+N more\" indicator for additional tags.\n**Validation:** Cards with >4 tags show exactly 4 plus a \"+N more\" badge.\n**Primary Owner:** M011/S02\n\n## R028 — Empty Subtopic Handling\n**Status:** validated\n**Description:** Sub-topics with 0 techniques either hidden by default or display a \"Coming soon\" badge.\n**Validation:** Empty subtopics are visually distinguished or hidden. No dead-end clicks.\n**Primary Owner:** M011/S02\n\n## Denied / Out of Scope\n\n## R029 — Beginner Learning Paths\n**Status:** out-of-scope\n**Description:** Curated beginner learning paths with skill-level tags and \"New to Production?\" section.\n**Validation:** n/a\n**Notes:** Denied — audience knows what they're looking for. Assessment F01.\n\n## R030 — YouTube Deep Links on Key Moments\n**Status:** out-of-scope\n**Description:** Key moment timestamps link to source YouTube videos at the correct time.\n**Validation:** n/a\n**Notes:** Denied — no video URLs in data model / don't want to link out. Assessment F02.\n\n## R031 — Hide Admin Dropdown\n**Status:** out-of-scope\n**Description:** Admin dropdown hidden from public visitors behind auth or flag.\n**Validation:** n/a\n**Notes:** Denied — fine as-is. Assessment F03.\n\n## R032 — CTA Label Changes\n**Status:** out-of-scope\n**Description:** Change \"Start Exploring\" to more descriptive CTA text.\n**Validation:** n/a\n**Notes:** Denied — skipped. Assessment F15.\n\n---\n\n[...truncated 3 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n#### S04 Summary\nSource: `.gsd/milestones/M014/slices/S04/S04-SUMMARY.md`\n\n---\nid: S04\nparent: M014\nmilestone: M014\nprovides:\n  - Compose-or-create branching in stage5_synthesis\n  - body_sections_format='v2' on all technique pages\n  - TechniquePageVideo join table populated for every page+video combination\nrequires:\n  - slice: S01\n    provides: Synthesis prompt v5 with nested sections + citations format\n  - slice: S02\n    provides: Compose prompt + test harness validation\n  - slice: S03\n    provides: Data model with body_sections_format column and TechniquePageVideo table\naffects:\n  - S06\n  - S07\nkey_files:\n  - backend/pipeline/stages.py\n  - backend/pipeline/test_compose_pipeline.py\nkey_decisions:\n  - Compose detection queries all matching pages and warns on multiple matches, uses first\n  - pg_insert with on_conflict_do_nothing for idempotent TechniquePageVideo inserts\n  - Source-code assertions for branching logic tests instead of fragile full-session mocks\npatterns_established:\n  - Compose-or-create branching: query existing pages by creator_id + LOWER(category), compose into first match, fall through to standard synthesis otherwise\n  - XML-tagged compose prompt with offset-indexed moments: existing [0]-[N-1], new [N]-[N+M-1]\nobservability_surfaces:\n  - INFO log when compose path triggered: 'Stage 5: Composing into existing page ...'\n  - WARNING log when multiple pages match creator+category\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S04/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:34:24.402Z\nblocker_discovered: false\n---\n\n# S04: Pipeline Compose-or-Create Logic\n\n**Stage 5 now detects existing technique pages by creator+category and branches to a compose path that merges new video content into them, with body_sections_format='v2' and TechniquePageVideo tracking on all pages.**\n\n## What Happened\n\nTwo tasks delivered the compose-or-create pipeline logic and its test suite.\n\nT01 added `_build_compose_user_prompt()` and `_compose_into_existing()` helper functions to stages.py, then wired compose detection into the stage5_synthesis per-category loop. The detection query uses `creator_id + LOWER(topic_category)` for case-insensitive matching. When an existing page matches, the compose path loads its linked moments, builds an XML-tagged prompt with offset-indexed moment references, and calls the LLM with the stage5_compose.txt system prompt. If no match, the existing chunked synthesis path runs unchanged. All pages now get `body_sections_format='v2'` set unconditionally, and a `TechniquePageVideo` row is inserted via `pg_insert` with `on_conflict_do_nothing` for idempotency.\n\nT02 created 12 unit tests across 4 test classes: prompt construction (5 tests for XML structure, offset indices, empty existing moments, page JSON serialization, moment content), branching logic (3 tests using source-code assertions + focused mocks), format/tracking (3 tests for v2 flag, pg_insert usage, INSERT values), and case sensitivity (1 test verifying func.lower on both sides of the category comparison). All 12 pass.\n\n## Verification\n\nAll slice-level verification checks pass:\n1. `PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\"` → exit 0\n2. `grep -q 'body_sections_format' backend/pipeline/stages.py` → exit 0\n3. `grep -q 'TechniquePageVideo' backend/pipeline/stages.py` → exit 0\n4. `grep -q 'stage5_compose' backend/pipeline/stages.py` → exit 0\n5. `PYTHONPATH=backend python -m pytest backend/pipeline/test_compose_pipeline.py -v` → 12 passed in 1.50s\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nUsed `default=str` in `json.dumps()` for page serialization in `_build_compose_user_prompt()` to handle UUID/datetime fields — not in plan but necessary for robustness. T02 replaced integration-level branching tests with source-code structure assertions + focused unit tests due to session mock fragility.\n\n## Known Limitations\n\nNone.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/stages.py` — Added _build_compose_user_prompt(), _compose_into_existing(), compose-or-create branching in stage5_synthesis, body_sections_format='v2' setting, TechniquePageVideo insertion\n- `backend/pipeline/test_compose_pipeline.py` — New file: 12 unit tests covering compose prompt construction, branching logic, format tracking, and case-insensitive matching\n\n#### S05 Summary\nSource: `.gsd/milestones/M014/slices/S05/S05-SUMMARY.md`\n\n---\nid: S05\nparent: M014\nmilestone: M014\nprovides:\n  - V2 format-aware technique page rendering (TOC, nested sections, citations) for S07 deep linking\nrequires:\n  - slice: S03\n    provides: body_sections_format and source_videos fields in API response schema\naffects:\n  - S07\nkey_files:\n  - frontend/src/pages/TechniquePage.tsx\n  - frontend/src/components/TableOfContents.tsx\n  - frontend/src/utils/citations.tsx\n  - frontend/src/api/public-client.ts\n  - frontend/src/App.css\nkey_decisions:\n  - Subsection IDs use compound slugs (sectionSlug--subSlug) to avoid anchor collision between sections and subsections\n  - D024 enforced: sections with subsections skip empty content paragraph\n  - Invalid citation indices render as plain text rather than broken links\npatterns_established:\n  - Format-discriminated rendering: body_sections_format field selects v1 (dict) or v2 (array) renderer, keeping both paths independent\n  - Citation parsing as a standalone utility (parseCitations) reusable for any component rendering technique page content\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S05/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S05/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:47:43.749Z\nblocker_discovered: false\n---\n\n# S05: Frontend — Nested Rendering, TOC, Citations\n\n**Format-2 technique pages render with nested H2/H3 sections, a clickable TOC, and citation superscript links; format-1 pages unchanged; deployed to production.**\n\n## What Happened\n\nBuilt format-aware rendering in TechniquePage.tsx that detects body_sections_format=2 (list-of-objects) vs format=1 (dict) and renders accordingly. V2 path renders a TableOfContents component with CSS-counter-numbered nested anchor links, H2 sections with slugified IDs, H3 subsections with compound slug IDs (sectionSlug--subSlug), and citation parsing. The parseCitations utility in citations.tsx converts [N] and [N,M] markers to superscript anchor links targeting key moment IDs; invalid indices render as plain text. V1 dict rendering is completely untouched.\n\nTypeScript types updated with BodySectionV2, BodySubSectionV2 interfaces, body_sections_format discriminator, and source_videos field. snapshotToOverlay updated to pass through list-format body_sections.\n\nCSS additions: TOC card with sticky positioning, subsection left-border styling, citation superscript links with hover states, scroll-margin-top on anchored sections for smooth scrolling past the fixed header.\n\nDeployed to ub01 production: pushed 11 commits, built chrysopedia-web container (56 modules, 0 Vite/TS errors), verified HTTP 200 on site root and /health. No v2 pages exist in production yet (S04 populated them on test data), so v2 rendering is verified structurally via the TypeScript build. V1 pages render unchanged.\n\n## Verification\n\nFrontend build: `cd frontend && npm run build` exits 0, 56 modules, no errors. Production: curl http://ub01:8096/ returns 200. Health: curl http://ub01:8096/health returns 200. JS bundle hash matches build output.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nDocker Compose service name is chrysopedia-web, not chrysopedia-web-8096 as listed in the slice plan verify command. The plan's verify command had shell quoting issues. T02 used the correct service name.\n\n## Known Limitations\n\nV2 rendering only verified structurally (TypeScript build) — no live v2 pages exist in production yet. Visual verification of TOC, citations, and nested sections requires S04 to populate v2 pages first. ub01 has a stashed git state with local backend edits from prior work.\n\n## Follow-ups\n\nVisual QA of v2 rendering once S04 data is in production. Review stashed edits on ub01.\n\n## Files Created/Modified\n\n- `frontend/src/pages/TechniquePage.tsx` — Format-aware rendering: v2 array → TOC + nested H2/H3 sections + citation links; v1 dict unchanged\n- `frontend/src/components/TableOfContents.tsx` — New component: CSS-counter-numbered nested anchor link list for v2 sections\n- `frontend/src/utils/citations.tsx` — New utility: parseCitations converts [N] and [N,M] markers to superscript anchor links\n- `frontend/src/api/public-client.ts` — Added BodySectionV2, BodySubSectionV2 types, body_sections_format, source_videos fields\n- `frontend/src/App.css` — TOC card styles, subsection borders, citation superscripts, scroll-margin-top for anchored sections\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Project**: `.gsd/PROJECT.md`\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M014/M014-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M014/slices/S07/S07-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"538ec567","parentId":"3fe9b164","timestamp":"2026-04-03T02:04:42.547Z"}
{"type":"message","id":"0348521f","parentId":"538ec567","timestamp":"2026-04-03T02:04:51.641Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the codebase to ground the plan in reality before decomposing.","thinkingSignature":"EogCClkIDBgCKkC7Y4YWOGrBVHKbpo/xSLILUWkWzOX0mpGGsf/c5mUNdf9Iv79mzW1fqCgfDGeuw8N2VrZMEvb0Ml+pPdvtCu8CMg9jbGF1ZGUtb3B1cy00LTY4ABIMnS4vKRjYcOjvWsvtGgwAWryowTJ5aGxhkz0iMKfIjpcoGffSX1e/SXWzzP2JyPTcKde1Txc6sHsCCUEncHvF9vO9lEV0F/PqRYLFXSpdbIo2EO8uapNVy1xnK6qwGx6P9nC4PIj3CYzcCVeVT8VsqPqWNpW1hDevBZ3iB0tXGowcq+RQeM39TlrcyuaKYkKSG59A1fvsw/gwirX5eTJOy0s4mfVZ1+8laRJZGAE="},{"type":"text","text":"Let me explore the current codebase to verify the research findings and understand the exact state of things."},{"type":"toolCall","id":"toolu_01QMbdFMJfY8aH9DhYXEN9MF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"technique_section\\|upsert_technique_sections\\|section_anchor\" backend/ frontend/src/ --type py --type ts --type tsx 2>/dev/null | head -30"}},{"type":"toolCall","id":"toolu_013CsM9JjfCzxdFZ5zpBjhNZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def stage6\\|def upsert_technique\\|def upsert_key_moment\\|_QDRANT_NAMESPACE\\|uuid5\" backend/pipeline/stages.py backend/pipeline/qdrant_client.py 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_01NezQXoHQLxCLfJAvwYh9PU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"section_anchor\\|technique_section\\|useHashScroll\\|location.hash\" frontend/src/ -r '' --files-with-matches 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":387,"cacheRead":59865,"cacheWrite":10784,"totalTokens":71038,"cost":{"input":0.00001,"output":0.009675000000000001,"cacheRead":0.029932499999999997,"cacheWrite":0.0674,"total":0.1070175}},"stopReason":"toolUse","timestamp":1775181882543}}
{"type":"message","id":"c33bb762","parentId":"0348521f","timestamp":"2026-04-03T02:04:51.660Z","message":{"role":"toolResult","toolCallId":"toolu_01QMbdFMJfY8aH9DhYXEN9MF","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181891659}}
{"type":"message","id":"d686f7c6","parentId":"c33bb762","timestamp":"2026-04-03T02:04:51.662Z","message":{"role":"toolResult","toolCallId":"toolu_013CsM9JjfCzxdFZ5zpBjhNZ","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181891662}}
{"type":"message","id":"de76e58a","parentId":"d686f7c6","timestamp":"2026-04-03T02:04:51.668Z","message":{"role":"toolResult","toolCallId":"toolu_01NezQXoHQLxCLfJAvwYh9PU","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775181891667}}
{"type":"message","id":"2a57f4a2","parentId":"de76e58a","timestamp":"2026-04-03T02:04:59.783Z","message":{"role":"assistant","content":[{"type":"text","text":"No existing section embedding or deep linking code. Let me check the actual files mentioned in research."},{"type":"toolCall","id":"toolu_0178Ytodk5Ykd8V3HDMgDA8Q","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"stage6\\|embed_and_index\\|_QDRANT_NAMESPACE\\|uuid5\\|upsert_technique_pages\\|upsert_key_moments\" backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_015APK2rWiMcFt1QNg7eP5X5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"upsert_technique_pages\\|upsert_key_moments\\|upsert_points\\|class QdrantManager\" backend/pipeline/qdrant_client.py | head -15"}},{"type":"toolCall","id":"toolu_011uuobLnpASdwmTXPGBSGDN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"_enrich_qdrant\\|class SearchService\\|technique_page\\|key_moment\\|SearchResultItem\" backend/search_service.py backend/schemas.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":347,"cacheRead":70649,"cacheWrite":454,"totalTokens":71451,"cost":{"input":0.000005,"output":0.008675,"cacheRead":0.0353245,"cacheWrite":0.0028375,"total":0.046842}},"stopReason":"toolUse","timestamp":1775181891667}}
{"type":"message","id":"774d432a","parentId":"2a57f4a2","timestamp":"2026-04-03T02:04:59.803Z","message":{"role":"toolResult","toolCallId":"toolu_0178Ytodk5Ykd8V3HDMgDA8Q","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181899803}}
{"type":"message","id":"747affa4","parentId":"774d432a","timestamp":"2026-04-03T02:04:59.803Z","message":{"role":"toolResult","toolCallId":"toolu_015APK2rWiMcFt1QNg7eP5X5","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181899803}}
{"type":"message","id":"3470ade1","parentId":"747affa4","timestamp":"2026-04-03T02:04:59.815Z","message":{"role":"toolResult","toolCallId":"toolu_011uuobLnpASdwmTXPGBSGDN","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181899814}}
{"type":"message","id":"984dc30a","parentId":"3470ade1","timestamp":"2026-04-03T02:05:04.352Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0143yMy1yrPnR7GXUy7DYcaZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/ -name \"*.py\" | head -30 && echo \"---\" && find frontend/src/ -name \"*.ts\" -o -name \"*.tsx\" | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":71103,"cacheWrite":438,"totalTokens":71653,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.0355515,"cacheWrite":0.0027375,"total":0.041068999999999994}},"stopReason":"toolUse","timestamp":1775181899815}}
{"type":"message","id":"4cd64adc","parentId":"984dc30a","timestamp":"2026-04-03T02:05:04.367Z","message":{"role":"toolResult","toolCallId":"toolu_0143yMy1yrPnR7GXUy7DYcaZ","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/pipeline/test_citation_utils.py\nbackend/pipeline/test_harness_compose.py\nbackend/pipeline/llm_client.py\nbackend/pipeline/test_harness.py\nbackend/pipeline/stages.py\nbackend/pipeline/export_fixture.py\nbackend/pipeline/test_compose_pipeline.py\nbackend/pipeline/citation_utils.py\nbackend/pipeline/embedding_client.py\nbackend/pipeline/__init__.py\nbackend/pipeline/test_harness_v2_format.py\nbackend/pipeline/qdrant_client.py\nbackend/pipeline/quality/optimizer.py\nbackend/pipeline/quality/__main__.py\nbackend/pipeline/quality/scorer.py\nbackend/pipeline/quality/fitness.py\nbackend/pipeline/quality/variant_generator.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/voice_dial.py\nbackend/pipeline/quality/fixtures/__init__.py\nbackend/pipeline/schemas.py\nbackend/main.py\nbackend/routers/techniques.py\nbackend/routers/topics.py\nbackend/routers/reports.py\nbackend/routers/health.py\nbackend/routers/videos.py\nbackend/routers/pipeline.py\nbackend/routers/__init__.py\n---\nfrontend/src/hooks/useSortPreference.ts\nfrontend/src/hooks/useDocumentTitle.ts\nfrontend/src/pages/AdminPipeline.tsx\nfrontend/src/pages/CreatorDetail.tsx\nfrontend/src/pages/CreatorsBrowse.tsx\nfrontend/src/pages/AdminReports.tsx\nfrontend/src/pages/Home.tsx\nfrontend/src/pages/AdminTechniquePages.tsx\nfrontend/src/pages/TechniquePage.tsx\nfrontend/src/pages/TopicsBrowse.tsx\nfrontend/src/pages/About.tsx\nfrontend/src/pages/SubTopicPage.tsx\nfrontend/src/pages/SearchResults.tsx\nfrontend/src/vite-env.d.ts\nfrontend/src/utils/citations.tsx\nfrontend/src/utils/catSlug.ts\nfrontend/src/App.tsx\nfrontend/src/api/public-client.ts\nfrontend/src/components/CategoryIcons.tsx\nfrontend/src/components/TagList.tsx\nfrontend/src/components/SearchAutocomplete.tsx\nfrontend/src/components/CreatorAvatar.tsx\nfrontend/src/components/TableOfContents.tsx\nfrontend/src/components/SortDropdown.tsx\nfrontend/src/components/AppFooter.tsx\nfrontend/src/components/CopyLinkButton.tsx\nfrontend/src/components/AdminDropdown.tsx\nfrontend/src/components/ReportIssueModal.tsx\nfrontend/src/main.tsx\n"}],"isError":false,"timestamp":1775181904366}}
{"type":"message","id":"1191acfe","parentId":"4cd64adc","timestamp":"2026-04-03T02:05:11.893Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LqzfhcgYGaVfTKJXy1mVKd","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"stage6\\|embed_and_index\\|_QDRANT_NAMESPACE\\|uuid5\\|upsert_technique\\|upsert_key\" backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_01CazMHgKAYwisJKtkTphdy1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class Qdrant\\|def upsert\\|def delete\\|_QDRANT\" backend/pipeline/qdrant_client.py | head -15"}},{"type":"toolCall","id":"toolu_01Hweq312m1a5EVjmSFoLRhZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"SearchResult\\|_enrich\\|section_anchor\\|class Search\" backend/search_service.py backend/schemas.py 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":311,"cacheRead":71541,"cacheWrite":782,"totalTokens":72635,"cost":{"input":0.000005,"output":0.007775000000000001,"cacheRead":0.0357705,"cacheWrite":0.0048875,"total":0.048438}},"stopReason":"toolUse","timestamp":1775181904367}}
{"type":"message","id":"41b6d366","parentId":"1191acfe","timestamp":"2026-04-03T02:05:11.921Z","message":{"role":"toolResult","toolCallId":"toolu_01LqzfhcgYGaVfTKJXy1mVKd","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181911920}}
{"type":"message","id":"ba50ddeb","parentId":"41b6d366","timestamp":"2026-04-03T02:05:11.921Z","message":{"role":"toolResult","toolCallId":"toolu_01CazMHgKAYwisJKtkTphdy1","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181911920}}
{"type":"message","id":"83251c5b","parentId":"ba50ddeb","timestamp":"2026-04-03T02:05:11.931Z","message":{"role":"toolResult","toolCallId":"toolu_01Hweq312m1a5EVjmSFoLRhZ","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181911929}}
{"type":"message","id":"5e6a55b7","parentId":"83251c5b","timestamp":"2026-04-03T02:05:17.313Z","message":{"role":"assistant","content":[{"type":"text","text":"Something's odd — no grep output at all. Let me check if the files exist with content."},{"type":"toolCall","id":"toolu_01F8Linu7fFWELdnWSLRsrKd","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/pipeline/stages.py backend/pipeline/qdrant_client.py backend/search_service.py backend/schemas.py 2>/dev/null || echo \"Some files missing\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":72323,"cacheWrite":402,"totalTokens":72862,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0361615,"cacheWrite":0.0025125,"total":0.042079}},"stopReason":"toolUse","timestamp":1775181911929}}
{"type":"message","id":"30f36eb6","parentId":"5e6a55b7","timestamp":"2026-04-03T02:05:17.325Z","message":{"role":"toolResult","toolCallId":"toolu_01F8Linu7fFWELdnWSLRsrKd","toolName":"bash","content":[{"type":"text","text":"  2270 backend/pipeline/stages.py\n   235 backend/pipeline/qdrant_client.py\n   603 backend/search_service.py\n   461 backend/schemas.py\n  3569 total\n"}],"isError":false,"timestamp":1775181917324}}
{"type":"message","id":"d47732d9","parentId":"30f36eb6","timestamp":"2026-04-03T02:05:21.843Z","message":{"role":"assistant","content":[{"type":"text","text":"Files exist. Let me try without pipe."},{"type":"toolCall","id":"toolu_01D65mzuX48unKmpcqZr6SGN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"stage6\\|embed_and_index\\|_QDRANT_NAMESPACE\\|uuid5\" backend/pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":117,"cacheRead":72725,"cacheWrite":201,"totalTokens":73044,"cost":{"input":0.000005,"output":0.002925,"cacheRead":0.0363625,"cacheWrite":0.00125625,"total":0.04054875}},"stopReason":"toolUse","timestamp":1775181917324}}
{"type":"message","id":"626f32a2","parentId":"d47732d9","timestamp":"2026-04-03T02:05:21.862Z","message":{"role":"toolResult","toolCallId":"toolu_01D65mzuX48unKmpcqZr6SGN","toolName":"bash","content":[{"type":"text","text":"1595:def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n1762:            _finish_run(run_id, \"complete\")  # Run is still \"complete\" — stage6 is best-effort\n1829:    \"stage6_embed_and_index\",\n1837:    \"stage6_embed_and_index\": stage6_embed_and_index,\n2265:        if stage_name == \"stage6_embed_and_index\":\n"}],"isError":false,"timestamp":1775181921859}}
{"type":"message","id":"0264e979","parentId":"626f32a2","timestamp":"2026-04-03T02:05:25.891Z","message":{"role":"assistant","content":[{"type":"text","text":"Good, now I can see the files. Let me check the key areas."},{"type":"toolCall","id":"toolu_01B7UKEHLns4HYhKXhL3FubU","name":"read","arguments":{"limit":170,"offset":1595,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":72926,"cacheWrite":256,"totalTokens":73292,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.036462999999999995,"cacheWrite":0.0016,"total":0.04079299999999999}},"stopReason":"toolUse","timestamp":1775181921860}}
{"type":"message","id":"951d8589","parentId":"0264e979","timestamp":"2026-04-03T02:05:25.897Z","message":{"role":"toolResult","toolCallId":"toolu_01B7UKEHLns4HYhKXhL3FubU","toolName":"read","content":[{"type":"text","text":"def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load technique pages created for this video's moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Get unique technique page IDs from moments\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n        pages = []\n        if page_ids:\n            pages = (\n                session.execute(\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                )\n                .scalars()\n                .all()\n            )\n\n        if not moments and not pages:\n            logger.info(\"Stage 6: No moments or pages for video_id=%s, skipping.\", video_id)\n            if run_id:\n                _finish_run(run_id, \"complete\")\n            return video_id\n\n        # Resolve creator names for enriched embedding text\n        creator_ids = {p.creator_id for p in pages}\n        creator_map: dict[str, str] = {}\n        if creator_ids:\n            creators = (\n                session.execute(\n                    select(Creator).where(Creator.id.in_(creator_ids))\n                )\n                .scalars()\n                .all()\n            )\n            creator_map = {str(c.id): c.name for c in creators}\n\n        # Resolve creator name for key moments via source_video → creator\n        video_ids = {m.source_video_id for m in moments}\n        video_creator_map: dict[str, str] = {}\n        if video_ids:\n            rows = session.execute(\n                select(SourceVideo.id, Creator.name)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .where(SourceVideo.id.in_(video_ids))\n            ).all()\n            video_creator_map = {str(r[0]): r[1] for r in rows}\n\n        embed_client = EmbeddingClient(settings)\n        qdrant = QdrantManager(settings)\n\n        # Ensure collection exists before upserting\n        qdrant.ensure_collection()\n\n        # ── Embed & upsert technique pages ───────────────────────────────\n        if pages:\n            page_texts = []\n            page_dicts = []\n            for p in pages:\n                creator_name = creator_map.get(str(p.creator_id), \"\")\n                tags_joined = \" \".join(p.topic_tags) if p.topic_tags else \"\"\n                text = f\"{creator_name} {p.title} {p.topic_category or ''} {tags_joined} {p.summary or ''}\"\n                page_texts.append(text.strip())\n                page_dicts.append({\n                    \"page_id\": str(p.id),\n                    \"creator_id\": str(p.creator_id),\n                    \"creator_name\": creator_name,\n                    \"title\": p.title,\n                    \"slug\": p.slug,\n                    \"topic_category\": p.topic_category or \"\",\n                    \"topic_tags\": p.topic_tags or [],\n                    \"summary\": p.summary or \"\",\n                })\n\n            page_vectors = embed_client.embed(page_texts)\n            if page_vectors:\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d technique page vectors for video_id=%s\",\n                    len(page_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d technique pages (video_id=%s). \"\n                    \"Skipping page upsert.\",\n                    len(page_texts), video_id,\n                )\n\n        # ── Embed & upsert key moments ───────────────────────────────────\n        if moments:\n            # Build page_id → slug mapping for linking moments to technique pages\n            page_id_to_slug: dict[str, str] = {}\n            if pages:\n                for p in pages:\n                    page_id_to_slug[str(p.id)] = p.slug\n\n            moment_texts = []\n            moment_dicts = []\n            for m in moments:\n                creator_name = video_creator_map.get(str(m.source_video_id), \"\")\n                text = f\"{creator_name} {m.title} {m.summary or ''}\"\n                moment_texts.append(text.strip())\n                tp_id = str(m.technique_page_id) if m.technique_page_id else \"\"\n                moment_dicts.append({\n                    \"moment_id\": str(m.id),\n                    \"source_video_id\": str(m.source_video_id),\n                    \"technique_page_id\": tp_id,\n                    \"technique_page_slug\": page_id_to_slug.get(tp_id, \"\"),\n                    \"title\": m.title,\n                    \"creator_name\": creator_name,\n                    \"start_time\": m.start_time,\n                    \"end_time\": m.end_time,\n                    \"content_type\": m.content_type.value,\n                })\n\n            moment_vectors = embed_client.embed(moment_texts)\n            if moment_vectors:\n                qdrant.upsert_key_moments(moment_dicts, moment_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d key moment vectors for video_id=%s\",\n                    len(moment_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d key moments (video_id=%s). \"\n                    \"Skipping moment upsert.\",\n                    len(moment_texts), video_id,\n                )\n\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 6 (embed & index) completed for video_id=%s in %.1fs — \"\n            \"%d pages, %d moments processed\",\n            video_id, elapsed, len(pages), len(moments),\n        )\n        if run_id:\n            _finish_run(run_id, \"complete\")\n        return video_id\n\n    except Exception as exc:\n        # Non-blocking: log error but don't fail the pipeline\n        logger.error(\n            \"Stage 6 failed for video_id=%s: %s. \"\n            \"Pipeline continues — embeddings can be regenerated later.\",\n            video_id, exc,\n        )\n        if run_id:\n            _finish_run(run_id, \"complete\")  # Run is still \"complete\" — stage6 is best-effort\n        return video_id\n    finally:\n\n[507 more lines in file. Use offset=1765 to continue.]"}],"isError":false,"timestamp":1775181925894}}
{"type":"message","id":"b71b0965","parentId":"951d8589","timestamp":"2026-04-03T02:05:32.991Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check QdrantManager and SearchService."},{"type":"toolCall","id":"toolu_016iPFjkRwtu8x4LAyLz46MC","name":"read","arguments":{"path":"backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01Qn5jBeM7kbbrBCcPCgF1Uh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"SearchResultItem\\|_enrich_qdrant\\|search_qdrant\\|_keyword_search\\|class Search\" backend/search_service.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":170,"cacheRead":73182,"cacheWrite":2017,"totalTokens":75370,"cost":{"input":0.000005,"output":0.00425,"cacheRead":0.036591,"cacheWrite":0.012606250000000001,"total":0.05345225}},"stopReason":"toolUse","timestamp":1775181925895}}
{"type":"message","id":"09ed029d","parentId":"b71b0965","timestamp":"2026-04-03T02:05:33.005Z","message":{"role":"toolResult","toolCallId":"toolu_016iPFjkRwtu8x4LAyLz46MC","toolName":"read","content":[{"type":"text","text":"\"\"\"Qdrant vector database manager for collection lifecycle and point upserts.\n\nHandles collection creation (idempotent) and batch upserts for technique pages\nand key moments. Connection failures are non-blocking — the pipeline continues\nwithout search indexing.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport uuid\n\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import Distance, PointStruct, VectorParams\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n# Namespace UUID for deterministic point IDs\n_QDRANT_NAMESPACE = uuid.UUID(\"a1b2c3d4-e5f6-7890-abcd-ef1234567890\")\n\n\nclass QdrantManager:\n    \"\"\"Manages a Qdrant collection for Chrysopedia technique-page and key-moment vectors.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = QdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Collection management ────────────────────────────────────────────\n\n    def ensure_collection(self) -> None:\n        \"\"\"Create the collection if it does not already exist.\n\n        Uses cosine distance and the configured embedding dimensions.\n        \"\"\"\n        try:\n            if self._client.collection_exists(self._collection):\n                logger.info(\"Qdrant collection '%s' already exists.\", self._collection)\n                return\n\n            self._client.create_collection(\n                collection_name=self._collection,\n                vectors_config=VectorParams(\n                    size=self.settings.embedding_dimensions,\n                    distance=Distance.COSINE,\n                ),\n            )\n            logger.info(\n                \"Created Qdrant collection '%s' (dim=%d, cosine).\",\n                self._collection,\n                self.settings.embedding_dimensions,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant error during ensure_collection (%s). Skipping.\",\n                exc,\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant connection failed during ensure_collection (%s: %s). Skipping.\",\n                type(exc).__name__,\n                exc,\n            )\n\n    # ── Deletion ───────────────────────────────────────────────────────────\n\n    def delete_by_video_id(self, video_id: str) -> int:\n        \"\"\"Delete all points (key moments + technique pages) associated with a video.\n\n        Key moments have source_video_id in payload.\n        Technique pages don't have direct video linkage, so only moments are deleted.\n\n        Returns the count of deleted points (best-effort — Qdrant may not report exact counts).\n        \"\"\"\n        from qdrant_client.models import Filter, FieldCondition, MatchValue\n\n        try:\n            result = self._client.delete(\n                collection_name=self._collection,\n                points_selector=Filter(\n                    must=[\n                        FieldCondition(\n                            key=\"source_video_id\",\n                            match=MatchValue(value=video_id),\n                        ),\n                    ],\n                ),\n            )\n            logger.info(\n                \"Deleted Qdrant points for video_id=%s from collection '%s'.\",\n                video_id,\n                self._collection,\n            )\n            return 0  # Qdrant delete doesn't return count\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant delete for video_id=%s failed (%s: %s). Skipping.\",\n                video_id,\n                type(exc).__name__,\n                exc,\n            )\n            return 0\n\n    # ── Low-level upsert ─────────────────────────────────────────────────\n\n    def upsert_points(self, points: list[PointStruct]) -> None:\n        \"\"\"Upsert a batch of pre-built PointStruct objects.\"\"\"\n        if not points:\n            return\n        try:\n            self._client.upsert(\n                collection_name=self._collection,\n                points=points,\n            )\n            logger.info(\n                \"Upserted %d points to Qdrant collection '%s'.\",\n                len(points),\n                self._collection,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant upsert failed (%s). %d points skipped.\",\n                exc,\n                len(points),\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant upsert connection error (%s: %s). %d points skipped.\",\n                type(exc).__name__,\n                exc,\n                len(points),\n            )\n\n    # ── High-level upserts ───────────────────────────────────────────────\n\n    def upsert_technique_pages(\n        self,\n        pages: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for technique pages.\n\n        Each page dict must contain:\n            page_id, creator_id, title, topic_category, topic_tags, summary\n\n        Parameters\n        ----------\n        pages:\n            Metadata dicts, one per technique page.\n        vectors:\n            Corresponding embedding vectors (same order as pages).\n        \"\"\"\n        if len(pages) != len(vectors):\n            logger.warning(\n                \"Technique-page count (%d) != vector count (%d). Skipping upsert.\",\n                len(pages),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for page, vector in zip(pages, vectors):\n            # Deterministic UUID: same page always gets the same point ID\n            point_id = str(uuid.uuid5(_QDRANT_NAMESPACE, f\"tp:{page['page_id']}\"))\n            point = PointStruct(\n                id=point_id,\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"creator_name\": page.get(\"creator_name\", \"\"),\n                    \"title\": page[\"title\"],\n                    \"slug\": page.get(\"slug\", \"\"),\n                    \"topic_category\": page[\"topic_category\"],\n                    \"topic_tags\": page.get(\"topic_tags\") or [],\n                    \"summary\": page.get(\"summary\") or \"\",\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n    def upsert_key_moments(\n        self,\n        moments: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for key moments.\n\n        Each moment dict must contain:\n            moment_id, source_video_id, title, start_time, end_time, content_type\n\n        Parameters\n        ----------\n        moments:\n            Metadata dicts, one per key moment.\n        vectors:\n            Corresponding embedding vectors (same order as moments).\n        \"\"\"\n        if len(moments) != len(vectors):\n            logger.warning(\n                \"Key-moment count (%d) != vector count (%d). Skipping upsert.\",\n                len(moments),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for moment, vector in zip(moments, vectors):\n            # Deterministic UUID: same moment always gets the same point ID\n            point_id = str(uuid.uuid5(_QDRANT_NAMESPACE, f\"km:{moment['moment_id']}\"))\n            point = PointStruct(\n                id=point_id,\n                vector=vector,\n                payload={\n                    \"type\": \"key_moment\",\n                    \"moment_id\": moment[\"moment_id\"],\n                    \"source_video_id\": moment[\"source_video_id\"],\n                    \"technique_page_id\": moment.get(\"technique_page_id\", \"\"),\n                    \"technique_page_slug\": moment.get(\"technique_page_slug\", \"\"),\n                    \"title\": moment[\"title\"],\n                    \"creator_name\": moment.get(\"creator_name\", \"\"),\n                    \"start_time\": moment[\"start_time\"],\n                    \"end_time\": moment[\"end_time\"],\n                    \"content_type\": moment[\"content_type\"],\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n"}],"isError":false,"timestamp":1775181933003}}
{"type":"message","id":"756851f3","parentId":"09ed029d","timestamp":"2026-04-03T02:05:33.014Z","message":{"role":"toolResult","toolCallId":"toolu_01Qn5jBeM7kbbrBCcPCgF1Uh","toolName":"bash","content":[{"type":"text","text":"35:class SearchService:\n96:    async def search_qdrant(\n267:        items = await self._keyword_search_and(tokens, scope, limit, db)\n281:    async def _keyword_search_and(\n378:            single_results = await self._keyword_search_and([token], scope, 20, db)\n434:            results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)\n435:            enriched = await self._enrich_qdrant_results(results, db)\n530:    async def _enrich_qdrant_results(\n"}],"isError":false,"timestamp":1775181933012}}
{"type":"message","id":"dd5c83ff","parentId":"756851f3","timestamp":"2026-04-03T02:05:38.430Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012yg6Qj3U1a7vxF67FDfaBM","name":"read","arguments":{"limit":75,"offset":530,"path":"backend/search_service.py"}},{"type":"toolCall","id":"toolu_011JxccARwCKseY1XBu2PMt5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"SearchResultItem\\|section_anchor\\|type.*str\\|class.*Result\" backend/schemas.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":174,"cacheRead":75199,"cacheWrite":2514,"totalTokens":77888,"cost":{"input":0.000005,"output":0.004350000000000001,"cacheRead":0.0375995,"cacheWrite":0.0157125,"total":0.057666999999999996}},"stopReason":"toolUse","timestamp":1775181933013}}
{"type":"message","id":"fe25bd9a","parentId":"dd5c83ff","timestamp":"2026-04-03T02:05:38.441Z","message":{"role":"toolResult","toolCallId":"toolu_012yg6Qj3U1a7vxF67FDfaBM","toolName":"read","content":[{"type":"text","text":"    async def _enrich_qdrant_results(\n        self,\n        qdrant_results: list[dict[str, Any]],\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Enrich Qdrant results with creator names and slugs from DB.\n\n        First reads creator_name from Qdrant payload; only hits DB for missing ones.\n        \"\"\"\n        enriched: list[dict[str, Any]] = []\n\n        # Collect creator_ids that need DB lookup\n        needs_db_lookup: set[str] = set()\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            if not payload.get(\"creator_name\") and payload.get(\"creator_id\"):\n                needs_db_lookup.add(payload[\"creator_id\"])\n\n        # Batch fetch creators from DB\n        creator_map: dict[str, dict[str, str]] = {}\n        if needs_db_lookup:\n            import uuid as uuid_mod\n            valid_ids = []\n            for cid in needs_db_lookup:\n                try:\n                    valid_ids.append(uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n            if valid_ids:\n                stmt = select(Creator).where(Creator.id.in_(valid_ids))\n                result = await db.execute(stmt)\n                for c in result.scalars().all():\n                    creator_map[str(c.id)] = {\"name\": c.name, \"slug\": c.slug}\n\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\", \"\")\n            result_type = payload.get(\"type\", \"\")\n\n            # Creator name: prefer payload, fall back to DB\n            creator_name = payload.get(\"creator_name\", \"\")\n            creator_slug = \"\"\n            if not creator_name and cid:\n                info = creator_map.get(cid, {\"name\": \"\", \"slug\": \"\"})\n                creator_name = info[\"name\"]\n                creator_slug = info[\"slug\"]\n            elif creator_name and cid:\n                # We have the name from payload but need the slug from DB\n                info = creator_map.get(cid, {})\n                creator_slug = info.get(\"slug\", \"\")\n\n            # Determine technique_page_slug based on result type\n            if result_type == \"technique_page\":\n                tp_slug = payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\"))\n            else:\n                tp_slug = payload.get(\"technique_page_slug\", \"\")\n\n            enriched.append({\n                \"type\": result_type,\n                \"title\": payload.get(\"title\", \"\"),\n                \"slug\": payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\")),\n                \"technique_page_slug\": tp_slug,\n                \"summary\": payload.get(\"summary\", \"\"),\n                \"topic_category\": payload.get(\"topic_category\", \"\"),\n                \"topic_tags\": payload.get(\"topic_tags\", []),\n                \"creator_id\": cid,\n                \"creator_name\": creator_name,\n                \"creator_slug\": creator_slug,\n                \"created_at\": payload.get(\"created_at\", \"\"),\n                \"score\": r.get(\"score\", 0.0),\n                \"match_context\": \"\",\n            })\n\n        return enriched\n"}],"isError":false,"timestamp":1775181938440}}
{"type":"message","id":"d19b255f","parentId":"fe25bd9a","timestamp":"2026-04-03T02:05:38.446Z","message":{"role":"toolResult","toolCallId":"toolu_011JxccARwCKseY1XBu2PMt5","toolName":"bash","content":[{"type":"text","text":"56:    content_type: str\n99:    content_type: str\n206:class SearchResultItem(BaseModel):\n211:    type: str = \"\"\n223:    items: list[SearchResultItem] = Field(default_factory=list)\n224:    partial_matches: list[SearchResultItem] = Field(default_factory=list)\n252:    content_type: str\n285:    content_type: str\n353:    content_type: str = Field(\n362:    report_type: str = Field(\n379:    content_type: str\n382:    report_type: str\n"}],"isError":false,"timestamp":1775181938444}}
{"type":"message","id":"5d3a6c57","parentId":"d19b255f","timestamp":"2026-04-03T02:05:43.072Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Qj4VZpdHjT84jxd9yA2a5X","name":"read","arguments":{"limit":25,"offset":206,"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":77713,"cacheWrite":1221,"totalTokens":79024,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.038856499999999995,"cacheWrite":0.00763125,"total":0.04871775}},"stopReason":"toolUse","timestamp":1775181938445}}
{"type":"message","id":"eababa24","parentId":"5d3a6c57","timestamp":"2026-04-03T02:05:43.075Z","message":{"role":"toolResult","toolCallId":"toolu_01Qj4VZpdHjT84jxd9yA2a5X","toolName":"read","content":[{"type":"text","text":"class SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    slug: str = \"\"\n    technique_page_slug: str = \"\"\n    type: str = \"\"\n    score: float = 0.0\n    summary: str = \"\"\n    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    topic_category: str = \"\"\n    topic_tags: list[str] = Field(default_factory=list)\n    match_context: str = \"\"\n\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    partial_matches: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\nclass SuggestionItem(BaseModel):\n\n[232 more lines in file. Use offset=231 to continue.]"}],"isError":false,"timestamp":1775181943073}}
{"type":"message","id":"e160a49f","parentId":"eababa24","timestamp":"2026-04-03T02:05:48.905Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the frontend search results and technique page."},{"type":"toolCall","id":"toolu_01JkieZ1aRfMgWLZ5vKK1i5X","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"getSearchResultLink\\|SearchResultItem\\|section_anchor\\|technique_page_slug\\|km-\" frontend/src/pages/SearchResults.tsx | head -20"}},{"type":"toolCall","id":"toolu_01Dn9U6Tgid152c1WMwQX9nv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"SearchResultItem\\|section_anchor\\|body_sections_format\" frontend/src/api/public-client.ts | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":211,"cacheRead":78934,"cacheWrite":331,"totalTokens":79477,"cost":{"input":0.000005,"output":0.005275,"cacheRead":0.039466999999999995,"cacheWrite":0.0020687500000000003,"total":0.046815749999999996}},"stopReason":"toolUse","timestamp":1775181943074}}
{"type":"message","id":"c77114de","parentId":"e160a49f","timestamp":"2026-04-03T02:05:48.920Z","message":{"role":"toolResult","toolCallId":"toolu_01JkieZ1aRfMgWLZ5vKK1i5X","toolName":"bash","content":[{"type":"text","text":"11:import { searchApi, type SearchResultItem } from \"../api/public-client\";\n35:  const [results, setResults] = useState<SearchResultItem[]>([]);\n36:  const [partialMatches, setPartialMatches] = useState<SearchResultItem[]>([]);\n135:              <SearchResultCard key={`km-${item.slug}-${i}`} item={item} staggerIndex={i} />\n144:function getSearchResultLink(item: SearchResultItem): string {\n146:    if (item.technique_page_slug) {\n147:      return `/techniques/${item.technique_page_slug}#km-${item.slug || item.title}`;\n155:function SearchResultCard({ item, staggerIndex }: { item: SearchResultItem; staggerIndex: number }) {\n158:      to={getSearchResultLink(item)}\n199:function PartialMatchResults({ items }: { items: SearchResultItem[] }) {\n229:              <SearchResultCard key={`partial-km-${item.slug}-${i}`} item={item} staggerIndex={i} />\n"}],"isError":false,"timestamp":1775181948920}}
{"type":"message","id":"c793c676","parentId":"c77114de","timestamp":"2026-04-03T02:05:48.929Z","message":{"role":"toolResult","toolCallId":"toolu_01Dn9U6Tgid152c1WMwQX9nv","toolName":"bash","content":[{"type":"text","text":"10:export interface SearchResultItem {\n25:  items: SearchResultItem[];\n26:  partial_matches: SearchResultItem[];\n85:  body_sections_format: string;\n752:  body_sections_format: string;\n"}],"isError":false,"timestamp":1775181948927}}
{"type":"message","id":"3e2e1cdb","parentId":"c793c676","timestamp":"2026-04-03T02:05:52.966Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01R9JWeSV9XUkN55eZUwTDxT","name":"read","arguments":{"limit":20,"offset":10,"path":"frontend/src/api/public-client.ts"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":79265,"cacheWrite":589,"totalTokens":79950,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.0396325,"cacheWrite":0.00368125,"total":0.04569375}},"stopReason":"toolUse","timestamp":1775181948928}}
{"type":"message","id":"a1ab6e14","parentId":"3e2e1cdb","timestamp":"2026-04-03T02:05:52.970Z","message":{"role":"toolResult","toolCallId":"toolu_01R9JWeSV9XUkN55eZUwTDxT","toolName":"read","content":[{"type":"text","text":"export interface SearchResultItem {\n  title: string;\n  slug: string;\n  type: string;\n  score: number;\n  summary: string;\n  creator_name: string;\n  creator_slug: string;\n  topic_category: string;\n  topic_tags: string[];\n  technique_page_slug?: string;\n  match_context?: string;\n}\n\nexport interface SearchResponse {\n  items: SearchResultItem[];\n  partial_matches: SearchResultItem[];\n  total: number;\n  query: string;\n  fallback_used: boolean;\n\n[757 more lines in file. Use offset=30 to continue.]"}],"isError":false,"timestamp":1775181952968}}
{"type":"message","id":"9a654fa7","parentId":"a1ab6e14","timestamp":"2026-04-03T02:05:59.230Z","message":{"role":"assistant","content":[{"type":"text","text":"Now check hash scrolling state in TechniquePage."},{"type":"toolCall","id":"toolu_01TxQqGAptH3q1WQaFon1dqv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"location.hash\\|useLocation\\|scrollIntoView\\|hash\\|scroll-margin\" frontend/src/pages/TechniquePage.tsx | head -15"}},{"type":"toolCall","id":"toolu_015buLHgy1D3txt9UgwMxvQo","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"slugify\\|function slug\" frontend/src/components/TableOfContents.tsx | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":201,"cacheRead":79854,"cacheWrite":257,"totalTokens":80313,"cost":{"input":0.000005,"output":0.005025,"cacheRead":0.039927,"cacheWrite":0.00160625,"total":0.04656325}},"stopReason":"toolUse","timestamp":1775181952969}}
{"type":"message","id":"ae4625fb","parentId":"9a654fa7","timestamp":"2026-04-03T02:05:59.243Z","message":{"role":"toolResult","toolCallId":"toolu_01TxQqGAptH3q1WQaFon1dqv","toolName":"bash","content":[{"type":"text","text":"6: * with pipeline metadata (prompt hashes, model config).\n174:  // Scroll to key moment if URL has a #km- hash fragment\n177:    const hash = window.location.hash;\n178:    if (hash.startsWith(\"#km-\")) {\n179:      const el = document.getElementById(hash.slice(1));\n181:        el.scrollIntoView({ behavior: \"smooth\", block: \"start\" });\n357:            {typeof versionDetail.pipeline_metadata.prompt_hashes === \"object\" &&\n358:              versionDetail.pipeline_metadata.prompt_hashes !== null && (\n360:                  <span className=\"version-metadata__key\">Prompt hashes</span>\n361:                  <div className=\"version-metadata__hashes\">\n363:                      versionDetail.pipeline_metadata.prompt_hashes as Record<\n367:                    ).map(([file, hash]) => (\n368:                      <div key={file} className=\"version-metadata__hash\">\n369:                        <span className=\"version-metadata__hash-file\">\n372:                        <code className=\"version-metadata__hash-value\">\n"}],"isError":false,"timestamp":1775181959243}}
{"type":"message","id":"5302500f","parentId":"ae4625fb","timestamp":"2026-04-03T02:05:59.248Z","message":{"role":"toolResult","toolCallId":"toolu_015buLHgy1D3txt9UgwMxvQo","toolName":"bash","content":[{"type":"text","text":"10:export function slugify(text: string): string {\n29:          const sectionSlug = slugify(section.heading);\n38:                    const subSlug = `${sectionSlug}--${slugify(sub.heading)}`;\n"}],"isError":false,"timestamp":1775181959246}}
{"type":"message","id":"87509e2b","parentId":"5302500f","timestamp":"2026-04-03T02:06:05.562Z","message":{"role":"assistant","content":[{"type":"text","text":"Interesting — there's already hash scrolling but ONLY for `#km-` fragments (key moments). It doesn't handle section anchors. Let me check lines around that."},{"type":"toolCall","id":"toolu_01Vb9PqpftC9qYr7Xt3zS5Un","name":"read","arguments":{"limit":20,"offset":170,"path":"frontend/src/pages/TechniquePage.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":80111,"cacheWrite":590,"totalTokens":80837,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.0400555,"cacheWrite":0.0036875000000000002,"total":0.047123000000000005}},"stopReason":"toolUse","timestamp":1775181959247}}
{"type":"message","id":"406b02df","parentId":"87509e2b","timestamp":"2026-04-03T02:06:05.565Z","message":{"role":"toolResult","toolCallId":"toolu_01Vb9PqpftC9qYr7Xt3zS5Un","toolName":"read","content":[{"type":"text","text":"      cancelled = true;\n    };\n  }, [slug, selectedVersion]);\n\n  // Scroll to key moment if URL has a #km- hash fragment\n  useEffect(() => {\n    if (!technique) return;\n    const hash = window.location.hash;\n    if (hash.startsWith(\"#km-\")) {\n      const el = document.getElementById(hash.slice(1));\n      if (el) {\n        el.scrollIntoView({ behavior: \"smooth\", block: \"start\" });\n      }\n    }\n  }, [technique]);\n\n  if (loading) {\n    return <div className=\"loading\">Loading technique…</div>;\n  }\n\n\n[404 more lines in file. Use offset=190 to continue.]"}],"isError":false,"timestamp":1775181965564}}
{"type":"message","id":"22ecada9","parentId":"406b02df","timestamp":"2026-04-03T02:06:12.692Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. The existing useEffect only handles `#km-` hashes. Section anchors (from slugify) won't scroll. Now let me read the templates."},{"type":"toolCall","id":"toolu_01N8izc3cZDWN2kFdkyrggUs","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01QRuvLzrbedSHrDQBDWEGsf","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":155,"cacheRead":80701,"cacheWrite":316,"totalTokens":81173,"cost":{"input":0.000005,"output":0.003875,"cacheRead":0.0403505,"cacheWrite":0.001975,"total":0.0462055}},"stopReason":"toolUse","timestamp":1775181965564}}
{"type":"message","id":"7802b40a","parentId":"22ecada9","timestamp":"2026-04-03T02:06:12.695Z","message":{"role":"toolResult","toolCallId":"toolu_01N8izc3cZDWN2kFdkyrggUs","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1775181972694}}
{"type":"message","id":"8db3a308","parentId":"7802b40a","timestamp":"2026-04-03T02:06:12.699Z","message":{"role":"toolResult","toolCallId":"toolu_01QRuvLzrbedSHrDQBDWEGsf","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1775181972697}}
{"type":"message","id":"9e5eeee4","parentId":"8db3a308","timestamp":"2026-04-03T02:06:36.963Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me plan this slice. The research is solid and matches the codebase. Here's my decomposition:\n\n**Task 1: Backend — Section embedding in stage 6 + Qdrant upsert + search enrichment**\n- Add `upsert_technique_sections` and `delete_sections_by_page_id` to QdrantManager\n- Extend stage6 to iterate v2 body_sections, embed each, upsert to Qdrant\n- Add `technique_section` handling to `_enrich_qdrant_results`\n\n\n- Add `section_anchor` field to `SearchResultItem` schema and implement a Python slugify function matching the frontend behavior\n\n**Task 2: Frontend — Hash scroll handler + section result rendering**\n- Extend hash scrolling in TechniquePage to handle any anchor, not just #km- prefixed ones\n- Update `getSearchResultLink` to handle technique_section types and add `section_anchor` to the SearchResultItem type\n- Update SearchResultCard to display section context alongside results\n\n**Task 3: Unit tests for backend section embedding + search**\n- Test the QdrantManager section upsert logic, the slugify function, enrichment for technique_section types, and stage6 section processing\n\nActually, I'm reconsidering the task structure. The research points to 3 tasks, but the verification piece feels more like an end-to-end check than a standalone task. I could fold unit tests into each task rather than separating them out. Task 1 is backend-heavy with stage 6 changes, QdrantManager, SearchService, and schema updates—all contained within the Python codebase. Task 2 is frontend-focused with hash scrolling, type updates, and result card changes. These are distinct enough to keep separate, so I'll consolidate to 2 tasks: one for backend (stage6 embedding, QdrantManager, search enrichment, schema, and unit tests) and one for frontend (hash scroll, section_anchor type, search link handling, and result card display).","thinkingSignature":"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"},{"type":"text","text":"Good — the research is accurate. Key findings from exploration:\n\n1. Stage 6 embeds whole technique pages and key moments, no section-level points exist\n2. `QdrantManager` has `upsert_technique_pages` and `upsert_key_moments` — need `upsert_technique_sections` + `delete_sections_by_page_id`\n3. `_enrich_qdrant_results` handles `technique_page` and falls through for `key_moment` — needs `technique_section` branch\n4. `SearchResultItem` schema has no `section_anchor` field\n5. Frontend hash scroll exists but ONLY for `#km-` fragments — section anchors won't scroll\n6. `slugify()` in TableOfContents.tsx needs a Python equivalent for the backend\n\nTwo tasks: backend (stage 6 + Qdrant + search service + schema + tests) and frontend (hash scroll + search result rendering). The backend is the bulk of the work; the frontend is straightforward wiring."},{"type":"toolCall","id":"toolu_01FzW6nfiDZtfe294KqrbkJp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"getSearchResultLink\" frontend/src/pages/SearchResults.tsx"}},{"type":"toolCall","id":"toolu_01XAMuJnzQsegnMFmy2nsX11","name":"read","arguments":{"limit":30,"offset":144,"path":"frontend/src/pages/SearchResults.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":947,"cacheRead":81017,"cacheWrite":2763,"totalTokens":84728,"cost":{"input":0.000005,"output":0.023675,"cacheRead":0.040508499999999996,"cacheWrite":0.01726875,"total":0.08145725}},"stopReason":"toolUse","timestamp":1775181972698}}
{"type":"message","id":"9996bd72","parentId":"9e5eeee4","timestamp":"2026-04-03T02:06:36.976Z","message":{"role":"toolResult","toolCallId":"toolu_01FzW6nfiDZtfe294KqrbkJp","toolName":"bash","content":[{"type":"text","text":"144:function getSearchResultLink(item: SearchResultItem): string {\n158:      to={getSearchResultLink(item)}\n"}],"isError":false,"timestamp":1775181996976}}
{"type":"message","id":"963e39db","parentId":"9996bd72","timestamp":"2026-04-03T02:06:36.982Z","message":{"role":"toolResult","toolCallId":"toolu_01XAMuJnzQsegnMFmy2nsX11","toolName":"read","content":[{"type":"text","text":"function getSearchResultLink(item: SearchResultItem): string {\n  if (item.type === \"key_moment\") {\n    if (item.technique_page_slug) {\n      return `/techniques/${item.technique_page_slug}#km-${item.slug || item.title}`;\n    }\n    // Graceful fallback — re-search instead of 404\n    return `/search?q=${encodeURIComponent(item.title)}`;\n  }\n  return `/techniques/${item.slug}`;\n}\n\nfunction SearchResultCard({ item, staggerIndex }: { item: SearchResultItem; staggerIndex: number }) {\n  return (\n    <Link\n      to={getSearchResultLink(item)}\n      className=\"search-result-card card-stagger\"\n      style={{ '--stagger-index': staggerIndex } as React.CSSProperties}\n    >\n      <div className=\"search-result-card__header\">\n        <span className=\"search-result-card__title\">{item.title}</span>\n        <span className={`badge badge--type badge--type-${item.type}`}>\n          {item.type === \"technique_page\" ? \"Technique\" : \"Key Moment\"}\n        </span>\n      </div>\n      {item.match_context && (\n        <div className=\"search-result-card__match-context\">\n          <span className=\"match-context__icon\">⚡</span>\n          <span className=\"match-context__text\">{item.match_context}</span>\n        </div>\n      )}\n\n[64 more lines in file. Use offset=174 to continue.]"}],"isError":false,"timestamp":1775181996979}}
{"type":"message","id":"8350d482","parentId":"963e39db","timestamp":"2026-04-03T02:06:39.945Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01H9nSpvRX5EfQb6y5NuDRV2","name":"read","arguments":{"limit":30,"offset":174,"path":"frontend/src/pages/SearchResults.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":83780,"cacheWrite":876,"totalTokens":84751,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.04189,"cacheWrite":0.005475,"total":0.04972}},"stopReason":"toolUse","timestamp":1775181996980}}
{"type":"message","id":"9a9f889d","parentId":"8350d482","timestamp":"2026-04-03T02:06:39.948Z","message":{"role":"toolResult","toolCallId":"toolu_01H9nSpvRX5EfQb6y5NuDRV2","toolName":"read","content":[{"type":"text","text":"      {item.summary && (\n        <p className=\"search-result-card__summary\">\n          {item.summary.length > 200\n            ? `${item.summary.slice(0, 200)}…`\n            : item.summary}\n        </p>\n      )}\n      <div className=\"search-result-card__meta\">\n        {item.creator_name && <span className=\"search-result-card__creator\">{item.creator_name}</span>}\n        {item.topic_category && (\n          <>\n            <span className=\"queue-card__separator\">·</span>\n            <span className={`badge badge--cat-${catSlug(item.topic_category)}`}>{item.topic_category}</span>\n          </>\n        )}\n        {item.topic_tags.length > 0 && (\n          <span className=\"search-result-card__tags\">\n            <TagList tags={item.topic_tags} />\n          </span>\n        )}\n      </div>\n    </Link>\n  );\n}\n\nfunction PartialMatchResults({ items }: { items: SearchResultItem[] }) {\n  const techniqueResults = items.filter((r) => r.type === \"technique_page\");\n  const momentResults = items.filter((r) => r.type === \"key_moment\");\n\n  return (\n\n[34 more lines in file. Use offset=204 to continue.]"}],"isError":false,"timestamp":1775181999947}}
{"type":"message","id":"4285ef2d","parentId":"9a9f889d","timestamp":"2026-04-03T02:06:44.130Z","message":{"role":"assistant","content":[{"type":"text","text":"Now check the SearchAutocomplete component too — it also renders search results inline."},{"type":"toolCall","id":"toolu_01UhCGPXqMCtue93wDkcK56z","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"technique_page_slug\\|getSearchResultLink\\|key_moment\\|type.*==\" frontend/src/components/SearchAutocomplete.tsx | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":128,"cacheRead":84656,"cacheWrite":439,"totalTokens":85224,"cost":{"input":0.000005,"output":0.0032,"cacheRead":0.042328,"cacheWrite":0.00274375,"total":0.04827675}},"stopReason":"toolUse","timestamp":1775181999947}}
{"type":"message","id":"17c03e18","parentId":"4285ef2d","timestamp":"2026-04-03T02:06:44.148Z","message":{"role":"toolResult","toolCallId":"toolu_01UhCGPXqMCtue93wDkcK56z","toolName":"bash","content":[{"type":"text","text":"165:    key_moment: \"Key Moment\",\n"}],"isError":false,"timestamp":1775182004145}}
{"type":"message","id":"4e6921d8","parentId":"17c03e18","timestamp":"2026-04-03T02:06:47.961Z","message":{"role":"assistant","content":[{"type":"text","text":"The autocomplete just has a display label. Let me check how it links results."},{"type":"toolCall","id":"toolu_01XorepG6UsEqBkju6APiWsn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"to=\\|Link\\|navigate\\|href\\|/techniques/\" frontend/src/components/SearchAutocomplete.tsx | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":122,"cacheRead":85095,"cacheWrite":153,"totalTokens":85371,"cost":{"input":0.000005,"output":0.00305,"cacheRead":0.042547499999999995,"cacheWrite":0.0009562500000000001,"total":0.046558749999999996}},"stopReason":"toolUse","timestamp":1775182004146}}
{"type":"message","id":"e03bf74f","parentId":"4e6921d8","timestamp":"2026-04-03T02:06:47.980Z","message":{"role":"toolResult","toolCallId":"toolu_01XorepG6UsEqBkju6APiWsn","toolName":"bash","content":[{"type":"text","text":"11:import { Link } from \"react-router-dom\";\n219:                <Link\n221:                  to={`/techniques/${item.slug}`}\n236:                </Link>\n238:              <Link\n239:                to={`/search?q=${encodeURIComponent(query)}`}\n244:              </Link>\n"}],"isError":false,"timestamp":1775182007978}}
{"type":"message","id":"36b11170","parentId":"e03bf74f","timestamp":"2026-04-03T02:06:51.980Z","message":{"role":"assistant","content":[{"type":"text","text":"The autocomplete links to `/techniques/${item.slug}` — it'll need updating for section results too. Let me check the test situation."},{"type":"toolCall","id":"toolu_01BqBTSzGbpRjUAUyc8ibnqR","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/ -name \"test_*.py\" -o -name \"*_test.py\" | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":124,"cacheRead":85248,"cacheWrite":213,"totalTokens":85586,"cost":{"input":0.000005,"output":0.0031000000000000003,"cacheRead":0.042623999999999995,"cacheWrite":0.00133125,"total":0.04706024999999999}},"stopReason":"toolUse","timestamp":1775182007979}}
{"type":"message","id":"c8410bb4","parentId":"36b11170","timestamp":"2026-04-03T02:06:51.993Z","message":{"role":"toolResult","toolCallId":"toolu_01BqBTSzGbpRjUAUyc8ibnqR","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/test_citation_utils.py\nbackend/pipeline/test_harness_compose.py\nbackend/pipeline/test_harness.py\nbackend/pipeline/test_compose_pipeline.py\nbackend/pipeline/test_harness_v2_format.py\nbackend/tests/test_pipeline.py\nbackend/tests/test_search.py\nbackend/tests/test_ingest.py\nbackend/tests/test_public_api.py\n"}],"isError":false,"timestamp":1775182011992}}
{"type":"message","id":"29996404","parentId":"c8410bb4","timestamp":"2026-04-03T02:06:55.330Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RhJZa3cwW54N9okW96bUob","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/tests/test_search.py && head -30 backend/tests/test_search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":85461,"cacheWrite":243,"totalTokens":85800,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.0427305,"cacheWrite":0.0015187500000000001,"total":0.04662925}},"stopReason":"toolUse","timestamp":1775182011992}}
{"type":"message","id":"b08e2eca","parentId":"29996404","timestamp":"2026-04-03T02:06:55.343Z","message":{"role":"toolResult","toolCallId":"toolu_01RhJZa3cwW54N9okW96bUob","toolName":"bash","content":[{"type":"text","text":"712 backend/tests/test_search.py\n\"\"\"Integration tests for the /api/v1/search endpoint.\n\nTests run against a real PostgreSQL test database via httpx.AsyncClient.\nSearchService is mocked at the router dependency level so we can test\nendpoint behavior without requiring external embedding API or Qdrant.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom unittest.mock import AsyncMock, MagicMock, patch\n\nimport pytest\nimport pytest_asyncio\nfrom httpx import AsyncClient\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker\n\nfrom models import (\n    ContentType,\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n)\nfrom search_service import SearchService\n\nSEARCH_URL = \"/api/v1/search\"\n\n"}],"isError":false,"timestamp":1775182015341}}
{"type":"message","id":"ded2c335","parentId":"b08e2eca","timestamp":"2026-04-03T02:07:59.908Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have full picture. Let me plan this.\n\nThe slice decomposes naturally into two tasks:\n\n**T01: Backend — Section embedding in stage 6, Qdrant manager, search enrichment, schema** This adds the `technique_section` point type end-to-end on the server side: QdrantManager gets `upsert_technique_sections` + `delete_sections_by_page_id`, stage 6 iterates v2 body_sections, search service enriches section results, schema gets `section_anchor` and `section_heading`. Plus unit tests.\n\n**T02: Frontend — Hash scroll handler, search result section rendering** Extends the hash scroll useEffect to handle any anchor (not just `#km-`), updates `getSearchResultLink`, `SearchResultCard`, and `SearchAutocomplete` to handle `technique_section` type, adds `section_anchor` and `section_heading` to the TS type."},{"type":"toolCall","id":"toolu_0117R9TqBLaT8r2Crk9pqQcD","name":"gsd_plan_slice","arguments":{"goal":"Search returns section-level results for v2 technique pages, with deep links that scroll to the target section.","integrationClosure":"- Upstream: stage 6 embed pipeline (`backend/pipeline/stages.py`), QdrantManager (`backend/pipeline/qdrant_client.py`), SearchService (`backend/search_service.py`), frontend TechniquePage + SearchResults\n- New wiring: stage 6 → section embedding → Qdrant → search enrichment → API schema → frontend link + scroll\n- What remains after this slice: nothing — M014 is complete","milestoneId":"M014","observabilityImpact":"- INFO logs in stage 6 for section embedding count per video\n- WARNING if section embedding fails (non-blocking, same pattern as existing stage 6)\n- Section points in Qdrant inspectable via Qdrant dashboard filter on type=technique_section","proofLevel":"Integration — requires backend embedding+search pipeline and frontend rendering to connect end-to-end","sliceId":"S07","successCriteria":"- Stage 6 embeds individual v2 body_sections as `technique_section` points in Qdrant\n- Search results include `section_anchor` and `section_heading` for section-type results\n- Frontend navigates to `/techniques/{slug}#{section-anchor}` and auto-scrolls to the section\n- Existing technique_page and key_moment search results unchanged\n- v1 pages unaffected (no section embedding for v1)","tasks":[{"description":"Add per-section embedding to stage 6 for v2 technique pages. Extend QdrantManager with upsert_technique_sections() and delete_sections_by_page_id(). Extend SearchService._enrich_qdrant_results() with technique_section handling. Add section_anchor and section_heading to SearchResultItem schema. Write unit tests.\n\n## Steps\n\n1. **Add `section_anchor` and `section_heading` to SearchResultItem** in `backend/schemas.py`. Both optional strings defaulting to empty.\n\n2. **Add `_slugify_heading()` helper** to `backend/pipeline/stages.py` — Python equivalent of the frontend's slugify: `re.sub(r'[^a-z0-9]+', '-', heading.lower()).strip('-')`. This must produce identical output to `frontend/src/components/TableOfContents.tsx` slugify.\n\n3. **Add `delete_sections_by_page_id()` to QdrantManager** in `backend/pipeline/qdrant_client.py`. Uses a Qdrant filter on `payload.page_id` AND `payload.type == 'technique_section'` to delete stale section points before re-upserting. Non-blocking (logs warning on failure).\n\n4. **Add `upsert_technique_sections()` to QdrantManager** — follows the same pattern as `upsert_technique_pages()`. Deterministic UUID: `uuid5(_QDRANT_NAMESPACE, f'ts:{page_id}:{section_slug}')`. Payload includes: type='technique_section', page_id, creator_id, creator_name, title (page title), slug (page slug), section_heading, section_anchor, topic_category, topic_tags, summary (section content truncated to 200 chars).\n\n5. **Extend stage 6 to embed v2 sections**. After the existing technique page embedding block, add a new block that iterates pages where `body_sections_format == 'v2'` and `body_sections` is a list. For each section: build embed text as `'{creator_name} {page_title} — {section_heading}: {section_content} {subsection_contents}'`, build metadata dict with section_anchor from `_slugify_heading(section.heading)`. Before upserting, call `delete_sections_by_page_id()` for each page to remove stale points. Then batch embed and upsert. Log count of section points.\n\n6. **Add `technique_section` branch to `_enrich_qdrant_results()`** in `backend/search_service.py`. Map payload fields: type='technique_section', title=section_heading, slug=page_slug, technique_page_slug=page_slug, section_anchor from payload, section_heading from payload. Creator resolution same as other types.\n\n7. **Write unit tests** in `backend/pipeline/test_section_embedding.py`. Test: (a) _slugify_heading produces correct slugs for various headings, (b) section embed text construction includes creator/page/section context, (c) delete_sections_by_page_id is called before upsert, (d) v1 pages are skipped, (e) upsert_technique_sections builds correct payloads with deterministic UUIDs.\n\n## Must-Haves\n\n- [ ] _slugify_heading output matches frontend slugify for: 'Grain Position Control' → 'grain-position-control', 'LFO Routing & Modulation' → 'lfo-routing---modulation' (consecutive special chars become single hyphen — verify actual frontend behavior)\n- [ ] Deterministic UUIDs: same page+section always overwrites the same Qdrant point\n- [ ] delete_sections_by_page_id called before upsert to prevent orphan points from heading renames\n- [ ] v1 pages (body_sections_format != 'v2') produce zero section points\n- [ ] SearchResultItem.section_anchor populated for technique_section results\n- [ ] Existing technique_page and key_moment enrichment unchanged\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Qdrant | Log WARNING, skip section upsert (non-blocking) | Same — stage 6 is best-effort | Skip point, continue |\n| Embedding API | Log WARNING, skip section embedding | Same | Skip batch, continue |\n| body_sections JSONB | Skip page if not a list or missing heading field | N/A | Log WARNING, skip malformed section |\n\n## Negative Tests\n\n- Page with body_sections_format='v1' → zero section points\n- Page with body_sections=None → zero section points\n- Section with empty heading → skipped\n- Section with subsections but no direct content → subsection content still embedded","estimate":"2h","expectedOutput":["`backend/schemas.py` — SearchResultItem with section_anchor and section_heading fields","`backend/pipeline/stages.py` — _slugify_heading helper + section embedding block in stage6_embed_and_index","`backend/pipeline/qdrant_client.py` — upsert_technique_sections() and delete_sections_by_page_id() methods","`backend/search_service.py` — technique_section branch in _enrich_qdrant_results","`backend/pipeline/test_section_embedding.py` — unit tests for section embedding pipeline"],"files":["backend/schemas.py","backend/pipeline/stages.py","backend/pipeline/qdrant_client.py","backend/search_service.py","backend/pipeline/test_section_embedding.py"],"inputs":["`backend/pipeline/stages.py` — existing stage6_embed_and_index function","`backend/pipeline/qdrant_client.py` — existing QdrantManager with upsert_technique_pages pattern","`backend/search_service.py` — existing _enrich_qdrant_results with technique_page/key_moment handling","`backend/schemas.py` — existing SearchResultItem class","`frontend/src/components/TableOfContents.tsx` — reference slugify implementation (lines 10-15) to match in Python"],"taskId":"T01","title":"Backend — Section embedding, Qdrant upsert, search enrichment, schema","verify":"PYTHONPATH=backend python -m pytest backend/pipeline/test_section_embedding.py -v && PYTHONPATH=backend python -c \"from pipeline.stages import _slugify_heading; assert _slugify_heading('Grain Position Control') == 'grain-position-control'; print('slugify OK')\" && grep -q 'section_anchor' backend/schemas.py && grep -q 'technique_section' backend/search_service.py"},{"description":"Extend TechniquePage hash scrolling to handle section anchors (not just #km-). Update search result components to display and link technique_section results. Add section_anchor and section_heading to the TypeScript SearchResultItem type.\n\n## Steps\n\n1. **Add `section_anchor` and `section_heading` to SearchResultItem** in `frontend/src/api/public-client.ts`. Both optional strings.\n\n2. **Generalize hash scroll in TechniquePage.tsx**. The existing useEffect (line ~175) only scrolls for `#km-` hashes. Change it to scroll to ANY hash fragment after technique data loads: `const hash = window.location.hash.slice(1); if (hash) { const el = document.getElementById(hash); if (el) el.scrollIntoView({ behavior: 'smooth', block: 'start' }); }`. This handles both key moment hashes and section anchors.\n\n3. **Add `technique_section` branch to `getSearchResultLink()`** in `frontend/src/pages/SearchResults.tsx`. If `item.type === 'technique_section'`, return `/techniques/${item.technique_page_slug}#${item.section_anchor}`. Place this before the existing `key_moment` branch.\n\n4. **Update `SearchResultCard` badge display** — add a 'Section' label for technique_section type. Show section_heading as subtitle context if present.\n\n5. **Update `SearchAutocomplete` autocomplete results** in `frontend/src/components/SearchAutocomplete.tsx`. The type label map (line ~165) needs `technique_section: 'Section'`. The link at line ~221 (`/techniques/${item.slug}`) needs the same section-aware logic: if type is technique_section, link to `/techniques/${item.technique_page_slug}#${item.section_anchor}`.\n\n6. **Update result filtering** in SearchResults.tsx PartialMatchResults — add technique_section to the filter groups (currently only filters technique_page and key_moment).\n\n7. **Build and verify** — `cd frontend && npm run build` must pass with zero errors.\n\n## Must-Haves\n\n- [ ] Hash scroll works for section anchors (e.g., #grain-position-control)\n- [ ] Hash scroll still works for key moment anchors (e.g., #km-some-moment)\n- [ ] technique_section results link to /techniques/{slug}#{anchor}\n- [ ] Badge shows 'Section' for technique_section results\n- [ ] Autocomplete links section results correctly\n- [ ] Frontend builds with zero TypeScript errors","estimate":"1h","expectedOutput":["`frontend/src/api/public-client.ts` — SearchResultItem with section_anchor and section_heading","`frontend/src/pages/TechniquePage.tsx` — generalized hash scroll handling for any anchor","`frontend/src/pages/SearchResults.tsx` — technique_section link routing, badge, and filter handling","`frontend/src/components/SearchAutocomplete.tsx` — technique_section type label and link routing"],"files":["frontend/src/api/public-client.ts","frontend/src/pages/TechniquePage.tsx","frontend/src/pages/SearchResults.tsx","frontend/src/components/SearchAutocomplete.tsx"],"inputs":["`frontend/src/api/public-client.ts` — existing SearchResultItem interface","`frontend/src/pages/TechniquePage.tsx` — existing #km- hash scroll useEffect at line ~175","`frontend/src/pages/SearchResults.tsx` — existing getSearchResultLink and SearchResultCard","`frontend/src/components/SearchAutocomplete.tsx` — existing type label map and result links","`backend/schemas.py` — T01 output: section_anchor and section_heading fields on SearchResultItem"],"taskId":"T02","title":"Frontend — Hash scroll handler + section search result rendering","verify":"cd frontend && npm run build 2>&1 | tail -5 && echo 'Build OK'"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3481,"cacheRead":85704,"cacheWrite":315,"totalTokens":89501,"cost":{"input":0.000005,"output":0.087025,"cacheRead":0.042852,"cacheWrite":0.00196875,"total":0.13185075}},"stopReason":"toolUse","timestamp":1775182015342}}
{"type":"message","id":"fae89a81","parentId":"ded2c335","timestamp":"2026-04-03T02:07:59.966Z","message":{"role":"toolResult","toolCallId":"toolu_0117R9TqBLaT8r2Crk9pqQcD","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S07 (M014)"}],"details":{"operation":"plan_slice","milestoneId":"M014","sliceId":"S07","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S07/S07-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S07/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S07/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1775182079964}}
{"type":"message","id":"1aea7d77","parentId":"fae89a81","timestamp":"2026-04-03T02:08:24.254Z","message":{"role":"assistant","content":[{"type":"text","text":"Self-audit:\n\n1. **Completion semantics**: T01 delivers backend section embedding + search enrichment + tests. T02 delivers frontend hash scroll + result rendering. Together they close the loop: search returns section results → user clicks → navigates to page#anchor → scrolls to section. ✓\n2. **Requirement coverage**: R006 (technique page display with v2 sections) advanced by deep linking. R005 (search) advanced by section-level results. All must-haves mapped to tasks. ✓\n3. **Task completeness**: Both tasks have steps, must-haves, verification, inputs with file paths, expected outputs with file paths. ✓\n4. **Dependency correctness**: T02 depends on T01's schema output (section_anchor field). T01 runs first. ✓\n5. **Key links planned**: Python slugify matches frontend slugify (explicit step + test). Backend schema field → frontend type. Search enrichment → API response → frontend rendering. ✓\n6. **Scope sanity**: T01: 7 steps, 5 files. T02: 7 steps, 4 files. Both within bounds. ✓\n7. **Feature completeness**: Both tasks produce user-facing progress — real search results with working deep links. ✓\n\nSlice S07 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":307,"cacheRead":86019,"cacheWrite":3516,"totalTokens":89843,"cost":{"input":0.000005,"output":0.007675,"cacheRead":0.0430095,"cacheWrite":0.021975,"total":0.07266449999999999}},"stopReason":"stop","timestamp":1775182079965}}
