{"type":"thinking_level_change","id":"3c23fb03","parentId":null,"timestamp":"2026-03-31T04:55:51.334Z","thinkingLevel":"medium"}
{"type":"model_change","id":"1b824d9a","parentId":"3c23fb03","timestamp":"2026-03-31T04:55:51.335Z","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/M008/slices/S01/S01-PLAN.md` and `.gsd/milestones/M008/slices/S01/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 S01 planned.\"\n## UNIT: Plan Slice S01 (\"Fix Key Moment Search Links\") — Milestone M008\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M008/M008-ROADMAP.md`\n\n# M008: \n\n## Vision\nFix the broken and embarrassing things first. Key moment links that 404, test data in production, jargon banners, empty metrics — these erode trust before a visitor even evaluates the content. This milestone clears the credibility debt so M009/M010 improvements land on solid ground.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Fix Key Moment Search Links | high | — | ⬜ | Search 'compression', click any key moment result → lands on parent technique page with key moment visible |\n| S02 | Trust & Credibility Cleanup | low | — | ⬜ | Creators page has no TestCreator. Footer shows clean version info. Search results have no yellow jargon banner. |\n| S03 | Homepage Cards & Creator Metric Polish | low | — | ⬜ | Homepage technique cards show sub-topic tags and key moment count. Creator pages show technique count by topic instead of '0 views'. |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M008/slices/S01/S01-RESEARCH.md`\n\n# S01 Research — Fix Key Moment Search Links\n\n## Summary\n\nTwo distinct bugs cause search result links to break. Both are straightforward to fix with known patterns already in the codebase. No new technology or libraries needed.\n\n**Bug 1 — Key moment results link to nonexistent routes:** `SearchResultCard` in `SearchResults.tsx` links all results to `/techniques/${item.slug}`. For key moments, the backend returns an empty string for `slug` (keyword path) or a synthesized slug from the moment title (Qdrant path). Either way, clicking navigates to a technique page that doesn't exist → catch-all redirect to `/`.\n\n**Bug 2 — Qdrant key moment payload has no parent technique page reference:** The Qdrant payload for key moments (see `qdrant_client.py:172-185`) stores `moment_id`, `source_video_id`, `title`, timestamps, and `content_type` — but NOT `technique_page_id` or the parent technique page slug. There's no way to resolve a key moment to its parent page from Qdrant data alone.\n\n**Bug 3 (minor) — Qdrant technique page payload missing slug:** The `_enrich_results` method in `search_service.py` falls back to `title.lower().replace(\" \", \"-\")` for slug, since Qdrant payloads don't include it. DB slugs are LLM-generated in stage 5 and won't match this naive transform.\n\n## Recommendation\n\n### Backend changes\n\n1. **Add `technique_page_slug` to Qdrant key moment payloads** in `qdrant_client.py` `upsert_key_moments()`. The stage 6 caller (`stages.py:~1005`) already has access to the technique page data. Add `technique_page_slug` and `technique_page_id` to the moment dict.\n\n2. **Add `slug` to Qdrant technique page payloads** in `qdrant_client.py` `upsert_technique_pages()`. The caller already has the slug in the `page` dict — just pass it through.\n\n3. **Fix `_enrich_results` in `search_service.py`** to use the Qdrant payload slug for technique pages (now available) and to add `technique_page_slug` to key moment results.\n\n4. **Fix `keyword_search` in `search_service.py`** for key moments — currently sets `\"slug\": \"\"`. Join `TechniquePage` through `KeyMoment.technique_page_id` to get the parent slug.\n\n5. **Add `technique_page_slug` field to `SearchResultItem` schema** in `schemas.py` and `SearchResultItem` interface in `public-client.ts`.\n\n6. **Re-index existing Qdrant data** — existing points lack the new payload fields. Either re-run stage 6 for all videos, or add a migration script. Re-running stage 6 via the admin pipeline trigger is the simplest path.\n\n### Frontend changes\n\n7. **Fix `SearchResultCard` link logic in `SearchResults.tsx`:** For `type === \"key_moment\"`, link to `/techniques/${item.technique_page_slug}#km-${item.slug || item.title}` (anchor to the moment on the parent technique page). For `type === \"technique_page\"`, keep linking to `/techniques/${item.slug}`.\n\n8. **Add `id` attributes to key moment elements in `TechniquePage.tsx`:** Currently `<li key={km.id}>` but no `id` attribute for anchor targeting. Add `id={`km-${km.id}`}` to enable hash-based scrolling.\n\n9. **Add scroll-to-hash logic in `TechniquePage.tsx`:** After technique data loads, if `window.location.hash` matches a key moment ID, scroll to it. Use `useEffect` with a ref or `document.getElementById`.\n\n## Implementation Landscape\n\n### Files to modify\n\n| File | Change | Risk |\n|------|--------|------|\n| `backend/pipeline/qdrant_client.py` | Add `slug` to technique page payloads; add `technique_page_slug`/`technique_page_id` to key moment payloads | Low — additive payload change |\n| `backend/pipeline/stages.py` (~line 1005) | Pass slug and technique_page_slug in dicts sent to Qdrant upsert | Low — data already available in scope |\n| `backend/search_service.py` | Fix `_enrich_results` to use payload slug; fix `keyword_search` to join technique page for key moments; add `technique_page_slug` to result dicts | Medium — query changes |\n| `backend/schemas.py` | Add `technique_page_slug` field to `SearchResultItem` | Low |\n| `frontend/src/api/public-client.ts` | Add `technique_page_slug` to `SearchResultItem` interface | Low |\n| `frontend/src/pages/SearchResults.tsx` | Fix `SearchResultCard` link for key moments | Low |\n| `frontend/src/pages/TechniquePage.tsx` | Add `id` attrs to moment elements; add scroll-to-hash on load | Low |\n| `backend/tests/test_search.py` | Update keyword fallback test for key moment slug | Low |\n\n### Natural task boundaries\n\n**T01 — Backend: Qdrant payload + search service fixes** (est: 45min)\n- Add slug to Qdrant technique page payload\n- Add technique_page_slug to Qdrant key moment payload\n- Fix `_enrich_results` to use payload slug\n- Fix `keyword_search` to join TechniquePage for key moment results\n- Add `technique_page_slug` to `SearchResultItem` schema\n- Update/add tests in `test_search.py`\n\n**T02 — Frontend: Search result links + hash scroll** (est: 30min)\n- Add `technique_page_slug` to frontend `SearchResultItem` type\n- Fix `SearchResultCard` link logic based on result type\n- Add `id` attributes to key moment `<li>` elements in `TechniquePage.tsx`\n- Add `useEffect` scroll-to-hash after data loads\n- Verify: search → click key moment → lands on technique page at correct section\n\n**T03 — Re-index Qdrant data** (est: 15min)\n- Trigger stage 6 re-run for all processed videos via admin API\n- Verify Qdrant payloads now include slug fields\n- Note: This is operational, not code — could be folded into T01 verification if preferred\n\n### Key constraints\n\n- `KeyMoment.technique_page_id` is nullable — some moments may not be linked to a technique page yet. The search result card needs a fallback (e.g., link to creator page, or show disabled with \"unlinked\" badge).\n- Qdrant re-indexing is required for the fix to take full effect on semantic search results. Keyword search fix works immediately.\n- The `SearchResultCard` component handles all result types — changes must not break technique_page or creator results.\n\n### Verification strategy\n\nThe roadmap demo says: \"Search 'compression', click any key moment result → lands on parent technique page with key moment visible.\" Verification:\n1. Run backend tests (`pytest backend/tests/test_search.py`)\n2. Build frontend (`cd frontend && npm run build`)\n3. On ub01: rebuild and deploy, trigger re-index\n4. Browser verify: search for a term → click a key moment result → confirm it lands on the correct technique page with the moment scrolled into view\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n\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 (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\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---\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- (no dependencies)\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/M008/M008-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M008/slices/S01/S01-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":"47944cbb","parentId":"1b824d9a","timestamp":"2026-03-31T04:55:51.337Z"}
{"type":"message","id":"fdc9cc8b","parentId":"47944cbb","timestamp":"2026-03-31T04:55:57.278Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me explore the relevant code files to ground the plan in reality before decomposing tasks.","thinkingSignature":"EpkCClkIDBgCKkAqwiU1jnXW6ZwO88viGvLDapmMExHMVecrFiMRJfJsvKFH7zMBq/KR4n3CQpQjI60dkyGNGEkgFbK66h5jNjAkMg9jbGF1ZGUtb3B1cy00LTY4ABIMm0yLcmrTIyG5uUv0Ggz2b/YS+5NVBfE80Z4iMD40PwxSSr7TaEQ5HMbcu6d4/EGemzs7DMBHk5xcpVvmImmk8gcMTxViKh/wKJ+GASpumKLlqYI25n4UdYWXM8W0Wm9o31RqHcwmmRk8N88vVO2fcyCVEGZhS5j6Z2r2dx+0WghyVTHP++MQP858KBzlmzXjPZS8cmo/b0143FQrBTqLVljCOmciIlWjHJlACUXWM4fp7h1CTqI4zWDPnMEYAQ=="},{"type":"text","text":"Let me explore the actual code to verify the research findings and understand the current state."},{"type":"toolCall","id":"toolu_01AhwkaEhP88ZkKJzTJxL8UD","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -name \"*.py\" | head -30 && echo \"---\" && find frontend/src -name \"*.tsx\" -o -name \"*.ts\" | head -30"}},{"type":"toolCall","id":"toolu_01YUMYUAm1bcb1NGFJuzRcNb","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01Ju4jrpxeenqboktVJHYYJe","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":264,"cacheRead":58653,"cacheWrite":8924,"totalTokens":67843,"cost":{"input":0.00001,"output":0.0066,"cacheRead":0.0293265,"cacheWrite":0.055775000000000005,"total":0.0917115}},"stopReason":"toolUse","timestamp":1774932951336}}
{"type":"message","id":"b7b3d72d","parentId":"fdc9cc8b","timestamp":"2026-03-31T04:55:57.290Z","message":{"role":"toolResult","toolCallId":"toolu_01AhwkaEhP88ZkKJzTJxL8UD","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/pipeline/llm_client.py\nbackend/pipeline/stages.py\nbackend/pipeline/embedding_client.py\nbackend/pipeline/__init__.py\nbackend/pipeline/qdrant_client.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\nbackend/routers/creators.py\nbackend/routers/ingest.py\nbackend/routers/search.py\nbackend/redis_client.py\nbackend/database.py\nbackend/tests/test_pipeline.py\nbackend/tests/test_search.py\nbackend/tests/test_ingest.py\nbackend/tests/__init__.py\nbackend/tests/conftest.py\nbackend/tests/test_public_api.py\nbackend/tests/fixtures/mock_llm_responses.py\nbackend/config.py\nbackend/worker.py\nbackend/watcher.py\n---\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/TechniquePage.tsx\nfrontend/src/pages/TopicsBrowse.tsx\nfrontend/src/pages/SearchResults.tsx\nfrontend/src/vite-env.d.ts\nfrontend/src/App.tsx\nfrontend/src/api/public-client.ts\nfrontend/src/components/CategoryIcons.tsx\nfrontend/src/components/CreatorAvatar.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":1774932957289}}
{"type":"message","id":"79072da1","parentId":"b7b3d72d","timestamp":"2026-03-31T04:55:57.291Z","message":{"role":"toolResult","toolCallId":"toolu_01YUMYUAm1bcb1NGFJuzRcNb","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":1774932957290}}
{"type":"message","id":"9405e4f6","parentId":"79072da1","timestamp":"2026-03-31T04:55:57.293Z","message":{"role":"toolResult","toolCallId":"toolu_01Ju4jrpxeenqboktVJHYYJe","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":1774932957290}}
{"type":"message","id":"283d790d","parentId":"9405e4f6","timestamp":"2026-03-31T04:56:04.136Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me look at the actual code files mentioned in the research to verify the current state.","thinkingSignature":"EpoCClkIDBgCKkBxAjBg49NtRN+ozU1NNED0NhzPv+26Gdzbkbq+/Ht61ytiMcwI12SrIwv4jSde7idVf9p9uXA3RbY5m+ayJ+kSMg9jbGF1ZGUtb3B1cy00LTY4ABIM8bly9tvgglUynXocGgwFF9ZLcOBoRwAiw+oiMJpoRavfOGHjDJrQd7ClCLIZpMZQzA29UNOS5qV63MkxLVb7vgxM5KUkCipMn8OZYypv1cEZJ6dPeTgqJyTH9cDuq1mm+6k5W9uogHiCbKfEFJIL94pFNCHr1fOMqISYBId5G0CrqcsdCsv9r7BYreRuNjVTXaVQruXO5CzWInD5Z5zMm59QrF+B1JkootQk1NVzgMNse8aLSFQsopISqtGiGAE="},{"type":"text","text":"Now let me verify the actual code state against the research findings."},{"type":"toolCall","id":"toolu_01NTYmVjp588r631qFu79yNz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"technique_page_slug|technique_page_id\" backend/pipeline/qdrant_client.py backend/search_service.py backend/schemas.py 2>/dev/null | head -30"}},{"type":"toolCall","id":"toolu_01PHGrRRpAXpXFFhQXrFAB5B","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"slug\" backend/pipeline/qdrant_client.py | head -20"}},{"type":"toolCall","id":"toolu_01BBQYYxBf6z83G8W5XsNUyB","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"SearchResultItem|SearchResultCard\" frontend/src/pages/SearchResults.tsx frontend/src/api/public-client.ts 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":334,"cacheRead":67577,"cacheWrite":3350,"totalTokens":71262,"cost":{"input":0.000005,"output":0.00835,"cacheRead":0.0337885,"cacheWrite":0.0209375,"total":0.063081}},"stopReason":"toolUse","timestamp":1774932957291}}
{"type":"message","id":"fdf34836","parentId":"283d790d","timestamp":"2026-03-31T04:56:04.154Z","message":{"role":"toolResult","toolCallId":"toolu_01NTYmVjp588r631qFu79yNz","toolName":"bash","content":[{"type":"text","text":"backend/schemas.py:104:    technique_page_id: uuid.UUID | None = None\nbackend/schemas.py:111:    technique_page_id: uuid.UUID | None = None\n"}],"isError":false,"timestamp":1774932964154}}
{"type":"message","id":"4231f091","parentId":"fdf34836","timestamp":"2026-03-31T04:56:04.155Z","message":{"role":"toolResult","toolCallId":"toolu_01PHGrRRpAXpXFFhQXrFAB5B","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774932964154}}
{"type":"message","id":"a007efca","parentId":"4231f091","timestamp":"2026-03-31T04:56:04.161Z","message":{"role":"toolResult","toolCallId":"toolu_01BBQYYxBf6z83G8W5XsNUyB","toolName":"bash","content":[{"type":"text","text":"frontend/src/pages/SearchResults.tsx:11:import { searchApi, type SearchResultItem } from \"../api/public-client\";\nfrontend/src/pages/SearchResults.tsx:18:  const [results, setResults] = useState<SearchResultItem[]>([]);\nfrontend/src/pages/SearchResults.tsx:122:              <SearchResultCard key={`tp-${item.slug}`} item={item} />\nfrontend/src/pages/SearchResults.tsx:136:              <SearchResultCard key={`km-${item.slug}-${i}`} item={item} />\nfrontend/src/pages/SearchResults.tsx:145:function SearchResultCard({ item }: { item: SearchResultItem }) {\nfrontend/src/api/public-client.ts:10:export interface SearchResultItem {\nfrontend/src/api/public-client.ts:23:  items: SearchResultItem[];\n"}],"isError":false,"timestamp":1774932964159}}
{"type":"message","id":"88bda678","parentId":"a007efca","timestamp":"2026-03-31T04:56:08.891Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01C3bmL8vqyGLsBQyBn53ezG","name":"read","arguments":{"limit":60,"offset":160,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01CNyCwQxsegfRj6rQJkhFqw","name":"read","arguments":{"limit":30,"offset":1,"path":"/home/aux/projects/content-to-kb-automator/frontend/src/api/public-client.ts"}},{"type":"toolCall","id":"toolu_01PoDqBsAfJbLcEC2iPTp9Xh","name":"read","arguments":{"limit":40,"offset":140,"path":"/home/aux/projects/content-to-kb-automator/frontend/src/pages/SearchResults.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":298,"cacheRead":70927,"cacheWrite":643,"totalTokens":71869,"cost":{"input":0.000005,"output":0.00745,"cacheRead":0.035463499999999995,"cacheWrite":0.004018750000000001,"total":0.04693725}},"stopReason":"toolUse","timestamp":1774932964159}}
{"type":"message","id":"b045ac6f","parentId":"88bda678","timestamp":"2026-03-31T04:56:08.894Z","message":{"role":"toolResult","toolCallId":"toolu_01C3bmL8vqyGLsBQyBn53ezG","toolName":"read","content":[{"type":"text","text":"            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            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"key_moment\",\n                    \"moment_id\": moment[\"moment_id\"],\n                    \"source_video_id\": moment[\"source_video_id\"],\n                    \"title\": moment[\"title\"],\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":1774932968894}}
{"type":"message","id":"1dcc2dcb","parentId":"b045ac6f","timestamp":"2026-03-31T04:56:08.896Z","message":{"role":"toolResult","toolCallId":"toolu_01CNyCwQxsegfRj6rQJkhFqw","toolName":"read","content":[{"type":"text","text":"/**\n * Typed API client for Chrysopedia public endpoints.\n *\n * Mirrors backend schemas: SearchResponse, TechniquePageDetail, TopicCategory, CreatorBrowseItem.\n * Uses the same request<T> pattern as client.ts.\n */\n\n// ── Types ───────────────────────────────────────────────────────────────────\n\nexport 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}\n\nexport interface SearchResponse {\n  items: SearchResultItem[];\n  total: number;\n  query: string;\n  fallback_used: boolean;\n}\n\nexport interface KeyMomentSummary {\n  id: string;\n\n[462 more lines in file. Use offset=31 to continue.]"}],"isError":false,"timestamp":1774932968896}}
{"type":"message","id":"427c706e","parentId":"1dcc2dcb","timestamp":"2026-03-31T04:56:08.899Z","message":{"role":"toolResult","toolCallId":"toolu_01PoDqBsAfJbLcEC2iPTp9Xh","toolName":"read","content":[{"type":"text","text":"      )}\n    </div>\n  );\n}\n\nfunction SearchResultCard({ item }: { item: SearchResultItem }) {\n  return (\n    <Link\n      to={`/techniques/${item.slug}`}\n      className=\"search-result-card\"\n    >\n      <div className=\"search-result-card__header\">\n        <span className=\"search-result-card__title\">{item.title}</span>\n        <span className={`badge badge--type badge--type-${item.type}`}>\n          {item.type === \"technique_page\" ? \"Technique\" : \"Key Moment\"}\n        </span>\n      </div>\n      {item.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>{item.topic_category}</span>\n          </>\n        )}\n        {item.topic_tags.length > 0 && (\n          <span className=\"search-result-card__tags\">\n            {item.topic_tags.map((tag) => (\n              <span key={tag} className=\"pill\">\n                {tag}\n              </span>\n            ))}\n          </span>\n\n[6 more lines in file. Use offset=180 to continue.]"}],"isError":false,"timestamp":1774932968897}}
{"type":"message","id":"fcd30c1b","parentId":"427c706e","timestamp":"2026-03-31T04:56:12.889Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CAjiL2PQ6D3RM26AJ7gMWc","name":"read","arguments":{"limit":15,"offset":180,"path":"/home/aux/projects/content-to-kb-automator/frontend/src/pages/SearchResults.tsx"}},{"type":"toolCall","id":"toolu_01Px4iPSxw1qUsY5cYNzWwZi","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"_enrich_results|keyword_search\" backend/search_service.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":71570,"cacheWrite":1235,"totalTokens":72992,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.035785,"cacheWrite":0.007718750000000001,"total":0.04815875}},"stopReason":"toolUse","timestamp":1774932968898}}
{"type":"message","id":"8075f017","parentId":"fcd30c1b","timestamp":"2026-03-31T04:56:12.895Z","message":{"role":"toolResult","toolCallId":"toolu_01CAjiL2PQ6D3RM26AJ7gMWc","toolName":"read","content":[{"type":"text","text":"        )}\n      </div>\n    </Link>\n  );\n}\n"}],"isError":false,"timestamp":1774932972895}}
{"type":"message","id":"36614bf9","parentId":"8075f017","timestamp":"2026-03-31T04:56:12.900Z","message":{"role":"toolResult","toolCallId":"toolu_01Px4iPSxw1qUsY5cYNzWwZi","toolName":"bash","content":[{"type":"text","text":"137:    async def keyword_search(\n283:                items = await self._enrich_results(qdrant_results, db)\n287:            items = await self.keyword_search(query, scope, limit, db)\n310:    async def _enrich_results(\n"}],"isError":false,"timestamp":1774932972899}}
{"type":"message","id":"3b73c117","parentId":"36614bf9","timestamp":"2026-03-31T04:56:16.759Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019f851pTvnsh7VATQk2ea3R","name":"read","arguments":{"limit":60,"offset":137,"path":"/home/aux/projects/content-to-kb-automator/backend/search_service.py"}},{"type":"toolCall","id":"toolu_01Jy11rU1W1cMpS3jNBa5Bnw","name":"read","arguments":{"limit":80,"offset":280,"path":"/home/aux/projects/content-to-kb-automator/backend/search_service.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":197,"cacheRead":72805,"cacheWrite":329,"totalTokens":73332,"cost":{"input":0.000005,"output":0.0049250000000000006,"cacheRead":0.0364025,"cacheWrite":0.00205625,"total":0.04338875}},"stopReason":"toolUse","timestamp":1774932972899}}
{"type":"message","id":"3a62f008","parentId":"3b73c117","timestamp":"2026-03-31T04:56:16.761Z","message":{"role":"toolResult","toolCallId":"toolu_019f851pTvnsh7VATQk2ea3R","toolName":"read","content":[{"type":"text","text":"    async def keyword_search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"ILIKE keyword search across technique pages, key moments, and creators.\n\n        Searches title/name columns. Returns a unified list of result dicts.\n        \"\"\"\n        results: list[dict[str, Any]] = []\n        pattern = f\"%{query}%\"\n\n        if scope in (\"all\", \"topics\"):\n            stmt = (\n                select(TechniquePage)\n                .where(\n                    or_(\n                        TechniquePage.title.ilike(pattern),\n                        TechniquePage.summary.ilike(pattern),\n                    )\n                )\n                .limit(limit)\n            )\n            rows = await db.execute(stmt)\n            for tp in rows.scalars().all():\n                results.append({\n                    \"type\": \"technique_page\",\n                    \"title\": tp.title,\n                    \"slug\": tp.slug,\n                    \"summary\": tp.summary or \"\",\n                    \"topic_category\": tp.topic_category,\n                    \"topic_tags\": tp.topic_tags or [],\n                    \"creator_id\": str(tp.creator_id),\n                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\",):\n            km_stmt = (\n                select(KeyMoment, SourceVideo, Creator)\n                .join(SourceVideo, KeyMoment.source_video_id == SourceVideo.id)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .where(KeyMoment.title.ilike(pattern))\n                .limit(limit)\n            )\n            km_rows = await db.execute(km_stmt)\n            for km, sv, cr in km_rows.all():\n                results.append({\n                    \"type\": \"key_moment\",\n                    \"title\": km.title,\n                    \"slug\": \"\",\n                    \"summary\": km.summary or \"\",\n                    \"topic_category\": \"\",\n                    \"topic_tags\": [],\n                    \"creator_id\": str(cr.id),\n                    \"creator_name\": cr.name,\n                    \"creator_slug\": cr.slug,\n                    \"score\": 0.0,\n                })\n\n[167 more lines in file. Use offset=197 to continue.]"}],"isError":false,"timestamp":1774932976761}}
{"type":"message","id":"23f58481","parentId":"3a62f008","timestamp":"2026-03-31T04:56:16.764Z","message":{"role":"toolResult","toolCallId":"toolu_01Jy11rU1W1cMpS3jNBa5Bnw","toolName":"read","content":[{"type":"text","text":"            qdrant_results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)\n            if qdrant_results:\n                # Enrich Qdrant results with DB metadata\n                items = await self._enrich_results(qdrant_results, db)\n\n        # Fallback to keyword search if semantic failed or returned nothing\n        if not items:\n            items = await self.keyword_search(query, scope, limit, db)\n            fallback_used = True\n\n        elapsed_ms = (time.monotonic() - start) * 1000\n\n        logger.info(\n            \"Search query=%r scope=%s results=%d fallback=%s latency_ms=%.1f\",\n            query,\n            scope,\n            len(items),\n            fallback_used,\n            elapsed_ms,\n        )\n\n        return {\n            \"items\": items,\n            \"total\": len(items),\n            \"query\": query,\n            \"fallback_used\": fallback_used,\n        }\n\n    # ── Result enrichment ────────────────────────────────────────────────\n\n    async def _enrich_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        enriched: list[dict[str, Any]] = []\n\n        # Collect creator_ids to batch-fetch\n        creator_ids = set()\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\")\n            if cid:\n                creator_ids.add(cid)\n\n        # Batch fetch creators\n        creator_map: dict[str, dict[str, str]] = {}\n        if creator_ids:\n            from sqlalchemy.dialects.postgresql import UUID as PgUUID\n            import uuid as uuid_mod\n            valid_ids = []\n            for cid in creator_ids:\n                try:\n                    valid_ids.append(uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n\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            creator_info = creator_map.get(cid, {\"name\": \"\", \"slug\": \"\"})\n\n            enriched.append({\n                \"type\": payload.get(\"type\", \"\"),\n                \"title\": payload.get(\"title\", \"\"),\n                \"slug\": payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\")),\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_info[\"name\"],\n                \"creator_slug\": creator_info[\"slug\"],\n                \"score\": r.get(\"score\", 0.0),\n\n[4 more lines in file. Use offset=360 to continue.]"}],"isError":false,"timestamp":1774932976761}}
{"type":"message","id":"754bf27f","parentId":"23f58481","timestamp":"2026-03-31T04:56:20.399Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01VNzbb5mfWdGU6n3TbyhnyP","name":"read","arguments":{"limit":10,"offset":360,"path":"/home/aux/projects/content-to-kb-automator/backend/search_service.py"}},{"type":"toolCall","id":"toolu_01GdpKS1scaMaDS4Wy2Q1nky","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"upsert_technique_pages\" backend/pipeline/qdrant_client.py | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":73134,"cacheWrite":1701,"totalTokens":75022,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.036566999999999995,"cacheWrite":0.01063125,"total":0.05185325}},"stopReason":"toolUse","timestamp":1774932976762}}
{"type":"message","id":"90e042e5","parentId":"754bf27f","timestamp":"2026-03-31T04:56:20.409Z","message":{"role":"toolResult","toolCallId":"toolu_01VNzbb5mfWdGU6n3TbyhnyP","toolName":"read","content":[{"type":"text","text":"            })\n\n        return enriched\n"}],"isError":false,"timestamp":1774932980409}}
{"type":"message","id":"f6862355","parentId":"90e042e5","timestamp":"2026-03-31T04:56:20.417Z","message":{"role":"toolResult","toolCallId":"toolu_01GdpKS1scaMaDS4Wy2Q1nky","toolName":"bash","content":[{"type":"text","text":"98:    def upsert_technique_pages(\n"}],"isError":false,"timestamp":1774932980415}}
{"type":"message","id":"a1f56dbe","parentId":"f6862355","timestamp":"2026-03-31T04:56:24.594Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BkPFJtRK8FGy54kKQkYAJA","name":"read","arguments":{"limit":50,"offset":98,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01TWRURjqC4ctUyoGjNtbFSF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"SearchResultItem\" backend/schemas.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":74835,"cacheWrite":267,"totalTokens":75283,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.0374175,"cacheWrite":0.00166875,"total":0.04359125}},"stopReason":"toolUse","timestamp":1774932980416}}
{"type":"message","id":"3bc1fcb9","parentId":"a1f56dbe","timestamp":"2026-03-31T04:56:24.605Z","message":{"role":"toolResult","toolCallId":"toolu_01BkPFJtRK8FGy54kKQkYAJA","toolName":"read","content":[{"type":"text","text":"    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            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"title\": page[\"title\"],\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[38 more lines in file. Use offset=148 to continue.]"}],"isError":false,"timestamp":1774932984604}}
{"type":"message","id":"2f49f6b4","parentId":"3bc1fcb9","timestamp":"2026-03-31T04:56:24.612Z","message":{"role":"toolResult","toolCallId":"toolu_01TWRURjqC4ctUyoGjNtbFSF","toolName":"bash","content":[{"type":"text","text":"203:class SearchResultItem(BaseModel):\n218:    items: list[SearchResultItem] = Field(default_factory=list)\n"}],"isError":false,"timestamp":1774932984610}}
{"type":"message","id":"929de5b1","parentId":"2f49f6b4","timestamp":"2026-03-31T04:56:28.509Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FfmmTrq9kmCzo8shBrZvB4","name":"read","arguments":{"limit":20,"offset":203,"path":"/home/aux/projects/content-to-kb-automator/backend/schemas.py"}},{"type":"toolCall","id":"toolu_01Hiw3j9oVeUUN648KYdV8KL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"km\\.\" frontend/src/pages/TechniquePage.tsx | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":75102,"cacheWrite":701,"totalTokens":75984,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.037551,"cacheWrite":0.004381250000000001,"total":0.046437250000000006}},"stopReason":"toolUse","timestamp":1774932984611}}
{"type":"message","id":"7254d67a","parentId":"929de5b1","timestamp":"2026-03-31T04:56:28.520Z","message":{"role":"toolResult","toolCallId":"toolu_01FfmmTrq9kmCzo8shBrZvB4","toolName":"read","content":[{"type":"text","text":"class SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    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\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\n[182 more lines in file. Use offset=223 to continue.]"}],"isError":false,"timestamp":1774932988519}}
{"type":"message","id":"5997a777","parentId":"7254d67a","timestamp":"2026-03-31T04:56:28.527Z","message":{"role":"toolResult","toolCallId":"toolu_01Hiw3j9oVeUUN648KYdV8KL","toolName":"bash","content":[{"type":"text","text":"260:                .map((km) => km.video_filename)\n421:              <li key={km.id} className=\"technique-moment\">\n422:                <h3 className=\"technique-moment__title\">{km.title}</h3>\n424:                  {km.video_filename && (\n426:                      {km.video_filename}\n430:                    {formatTime(km.start_time)} – {formatTime(km.end_time)}\n433:                    {km.content_type}\n436:                <p className=\"technique-moment__summary\">{km.summary}</p>\n"}],"isError":false,"timestamp":1774932988525}}
{"type":"message","id":"8a0b8a22","parentId":"5997a777","timestamp":"2026-03-31T04:56:32.517Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FXP19vyhei4KGhLA1HW8zF","name":"read","arguments":{"limit":30,"offset":415,"path":"/home/aux/projects/content-to-kb-automator/frontend/src/pages/TechniquePage.tsx"}},{"type":"toolCall","id":"toolu_01NPMz2Eo6beYU6cLhRrhR5i","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"stage_6_embed|upsert_technique_pages|upsert_key_moments\" backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":201,"cacheRead":75803,"cacheWrite":560,"totalTokens":76565,"cost":{"input":0.000005,"output":0.005025,"cacheRead":0.0379015,"cacheWrite":0.0035,"total":0.0464315}},"stopReason":"toolUse","timestamp":1774932988526}}
{"type":"message","id":"2770c2d0","parentId":"8a0b8a22","timestamp":"2026-03-31T04:56:32.525Z","message":{"role":"toolResult","toolCallId":"toolu_01FXP19vyhei4KGhLA1HW8zF","toolName":"read","content":[{"type":"text","text":"          {/* Key moments (always from live data — not versioned) */}\n      {technique.key_moments.length > 0 && (\n        <section className=\"technique-moments\">\n          <h2>Key Moments</h2>\n          <ol className=\"technique-moments__list\">\n            {technique.key_moments.map((km) => (\n              <li key={km.id} className=\"technique-moment\">\n                <h3 className=\"technique-moment__title\">{km.title}</h3>\n                <div className=\"technique-moment__meta\">\n                  {km.video_filename && (\n                    <span className=\"technique-moment__source\">\n                      {km.video_filename}\n                    </span>\n                  )}\n                  <span className=\"technique-moment__time\">\n                    {formatTime(km.start_time)} – {formatTime(km.end_time)}\n                  </span>\n                  <span className=\"badge badge--content-type\">\n                    {km.content_type}\n                  </span>\n                </div>\n                <p className=\"technique-moment__summary\">{km.summary}</p>\n              </li>\n            ))}\n          </ol>\n        </section>\n      )}\n\n      {/* Signal chains */}\n      {displayChains &&\n\n[86 more lines in file. Use offset=445 to continue.]"}],"isError":false,"timestamp":1774932992525}}
{"type":"message","id":"42161ee6","parentId":"2770c2d0","timestamp":"2026-03-31T04:56:32.534Z","message":{"role":"toolResult","toolCallId":"toolu_01NPMz2Eo6beYU6cLhRrhR5i","toolName":"bash","content":[{"type":"text","text":"975:                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n1005:                qdrant.upsert_key_moments(moment_dicts, moment_vectors)\n"}],"isError":false,"timestamp":1774932992532}}
{"type":"message","id":"a267b0de","parentId":"42161ee6","timestamp":"2026-03-31T04:56:35.699Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GVvc8Kf7AqPfi6h8EVt2ex","name":"read","arguments":{"limit":50,"offset":950,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":107,"cacheRead":76363,"cacheWrite":640,"totalTokens":77111,"cost":{"input":0.000005,"output":0.0026750000000000003,"cacheRead":0.0381815,"cacheWrite":0.004,"total":0.0448615}},"stopReason":"toolUse","timestamp":1774932992532}}
{"type":"message","id":"ba4888ae","parentId":"a267b0de","timestamp":"2026-03-31T04:56:35.705Z","message":{"role":"toolResult","toolCallId":"toolu_01GVvc8Kf7AqPfi6h8EVt2ex","toolName":"read","content":[{"type":"text","text":"\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                text = f\"{p.title} {p.summary or ''} {p.topic_category 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                    \"title\": p.title,\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            moment_texts = []\n            moment_dicts = []\n            for m in moments:\n                text = f\"{m.title} {m.summary or ''}\"\n                moment_texts.append(text.strip())\n                moment_dicts.append({\n                    \"moment_id\": str(m.id),\n                    \"source_video_id\": str(m.source_video_id),\n                    \"title\": m.title,\n                    \"start_time\": m.start_time,\n                    \"end_time\": m.end_time,\n\n[189 more lines in file. Use offset=1000 to continue.]"}],"isError":false,"timestamp":1774932995703}}
{"type":"message","id":"f253a195","parentId":"ba4888ae","timestamp":"2026-03-31T04:56:39.483Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LWZJpkLD4j5veKKYsZZMS5","name":"read","arguments":{"limit":15,"offset":1000,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01U3nMbzEfaSz4JuqdG8Cy2X","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"technique_page_id\" backend/models.py | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":179,"cacheRead":77003,"cacheWrite":680,"totalTokens":77863,"cost":{"input":0.000005,"output":0.004475,"cacheRead":0.0385015,"cacheWrite":0.00425,"total":0.047231499999999996}},"stopReason":"toolUse","timestamp":1774932995704}}
{"type":"message","id":"8c8fd914","parentId":"f253a195","timestamp":"2026-03-31T04:56:39.492Z","message":{"role":"toolResult","toolCallId":"toolu_01LWZJpkLD4j5veKKYsZZMS5","toolName":"read","content":[{"type":"text","text":"                    \"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[174 more lines in file. Use offset=1015 to continue.]"}],"isError":false,"timestamp":1774932999492}}
{"type":"message","id":"112ca0d3","parentId":"8c8fd914","timestamp":"2026-03-31T04:56:39.500Z","message":{"role":"toolResult","toolCallId":"toolu_01U3nMbzEfaSz4JuqdG8Cy2X","toolName":"bash","content":[{"type":"text","text":"177:    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n200:        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n234:        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n279:    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n"}],"isError":false,"timestamp":1774932999498}}
{"type":"message","id":"e953c5d9","parentId":"112ca0d3","timestamp":"2026-03-31T04:56:44.306Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what the stage 6 caller has in scope — specifically whether `pages` have a `.slug` attribute:"},{"type":"toolCall","id":"toolu_01NAQiESPSpyRLePDfg9DMBr","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class TechniquePage\" backend/models.py | head -3"}},{"type":"toolCall","id":"toolu_01VRZB9euyEjTvKwCDwnsuj9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"\\.slug\" backend/models.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":184,"cacheRead":77683,"cacheWrite":524,"totalTokens":78392,"cost":{"input":0.000005,"output":0.0046,"cacheRead":0.0388415,"cacheWrite":0.003275,"total":0.0467215}},"stopReason":"toolUse","timestamp":1774932999499}}
{"type":"message","id":"36c184a7","parentId":"e953c5d9","timestamp":"2026-03-31T04:56:44.323Z","message":{"role":"toolResult","toolCallId":"toolu_01NAQiESPSpyRLePDfg9DMBr","toolName":"bash","content":[{"type":"text","text":"204:class TechniquePage(Base):\n274:class TechniquePageVersion(Base):\n"}],"isError":false,"timestamp":1774933004323}}
{"type":"message","id":"a912d7b5","parentId":"36c184a7","timestamp":"2026-03-31T04:56:44.329Z","message":{"role":"toolResult","toolCallId":"toolu_01VRZB9euyEjTvKwCDwnsuj9","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774933004326}}
{"type":"message","id":"8105ad05","parentId":"a912d7b5","timestamp":"2026-03-31T04:56:48.453Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SyjdPh4pwMkv75hJvRBEe6","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"slug\" backend/models.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":84,"cacheRead":78207,"cacheWrite":271,"totalTokens":78563,"cost":{"input":0.000005,"output":0.0021000000000000003,"cacheRead":0.0391035,"cacheWrite":0.0016937500000000002,"total":0.04290225}},"stopReason":"toolUse","timestamp":1774933004327}}
{"type":"message","id":"8c459d6b","parentId":"8105ad05","timestamp":"2026-03-31T04:56:48.470Z","message":{"role":"toolResult","toolCallId":"toolu_01SyjdPh4pwMkv75hJvRBEe6","toolName":"bash","content":[{"type":"text","text":"103:    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n212:    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n"}],"isError":false,"timestamp":1774933008468}}
{"type":"message","id":"c100dc11","parentId":"8c459d6b","timestamp":"2026-03-31T04:56:53.977Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — `TechniquePage` has `.slug` at line 212 and `Creator` has `.slug` at line 103. The `pages` variable in stage 6 is a list of `TechniquePage` ORM objects with `.slug` available.\n\nNow check if `KeyMoment` has `technique_page_id` accessible and if technique pages are loaded at stage 6:"},{"type":"toolCall","id":"toolu_01577bqAXcmfigRLeURbUph8","name":"read","arguments":{"limit":40,"offset":920,"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":197,"cacheRead":78207,"cacheWrite":423,"totalTokens":78828,"cost":{"input":0.000005,"output":0.0049250000000000006,"cacheRead":0.0391035,"cacheWrite":0.0026437500000000003,"total":0.04667725}},"stopReason":"toolUse","timestamp":1774933008468}}
{"type":"message","id":"04bedfdb","parentId":"c100dc11","timestamp":"2026-03-31T04:56:53.983Z","message":{"role":"toolResult","toolCallId":"toolu_01577bqAXcmfigRLeURbUph8","toolName":"read","content":[{"type":"text","text":"\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            return video_id\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\n[229 more lines in file. Use offset=960 to continue.]"}],"isError":false,"timestamp":1774933013980}}
{"type":"message","id":"952ca40c","parentId":"04bedfdb","timestamp":"2026-03-31T04:56:57.604Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a complete picture. Let me check the test file:"},{"type":"toolCall","id":"toolu_01D8UqauuyDRvdPsdQB5GJJW","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/tests/test_search.py && rg -n \"key_moment|keyword\" backend/tests/test_search.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":121,"cacheRead":78630,"cacheWrite":565,"totalTokens":79317,"cost":{"input":0.000005,"output":0.0030250000000000003,"cacheRead":0.039314999999999996,"cacheWrite":0.00353125,"total":0.04587624999999999}},"stopReason":"toolUse","timestamp":1774933013981}}
{"type":"message","id":"22f37f75","parentId":"952ca40c","timestamp":"2026-03-31T04:56:57.621Z","message":{"role":"toolResult","toolCallId":"toolu_01D8UqauuyDRvdPsdQB5GJJW","toolName":"bash","content":[{"type":"text","text":"341 backend/tests/test_search.py\n243:async def test_search_keyword_fallback(client, db_engine):\n244:    \"\"\"When embedding fails, search uses keyword fallback and sets fallback_used=true.\"\"\"\n"}],"isError":false,"timestamp":1774933017619}}
{"type":"message","id":"d09690b7","parentId":"22f37f75","timestamp":"2026-03-31T04:57:06.155Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Research is accurate. Now let me understand the task decomposition.\n\nThe research suggests 3 tasks but T03 (re-indexing) is operational and happens on ub01 — it can't be verified from this dev machine. The natural split is:\n\n- **T01 — Backend**: Fix Qdrant payloads + search service + schema + tests. This is the core fix.\n- **T02 — Frontend**: Fix search result links + hash scroll on technique page.\n\nTwo tasks is the right grain. The re-index step is operational and belongs in the deployment notes, not as a separate code task.\n\nLet me check one more thing — how key moments map to technique pages, since `technique_page_id` is nullable:"},{"type":"toolCall","id":"toolu_01Dqgt1ZqjZJhhgi25waqpkd","name":"read","arguments":{"limit":35,"offset":170,"path":"/home/aux/projects/content-to-kb-automator/backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":271,"cacheRead":79195,"cacheWrite":188,"totalTokens":79655,"cost":{"input":0.000005,"output":0.006775000000000001,"cacheRead":0.0395975,"cacheWrite":0.001175,"total":0.047552500000000004}},"stopReason":"toolUse","timestamp":1774933017620}}
{"type":"message","id":"b64e0c11","parentId":"d09690b7","timestamp":"2026-03-31T04:57:06.159Z","message":{"role":"toolResult","toolCallId":"toolu_01Dqgt1ZqjZJhhgi25waqpkd","toolName":"read","content":[{"type":"text","text":"class KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n\n[195 more lines in file. Use offset=205 to continue.]"}],"isError":false,"timestamp":1774933026157}}
{"type":"message","id":"89c0def3","parentId":"b64e0c11","timestamp":"2026-03-31T04:57:54.409Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — `KeyMoment.technique_page_id` is nullable with FK to `technique_pages.id`, and there's a `technique_page` relationship. In stage 6, moments are queried and pages are fetched by `page_ids` — the technique page slug is accessible from the `pages` list.\n\nFor the keyword search fix in `search_service.py`, the key moment query joins `SourceVideo` and `Creator` but not `TechniquePage`. We need to add a join to `TechniquePage` to get the parent slug. Since `technique_page_id` is nullable, it needs an outer join.\n\nNow I have enough to plan. Two tasks:\n\n**T01 (Backend)**: Add `slug` to Qdrant technique page payload, add `technique_page_slug`/`technique_page_id` to key moment payload, fix `_enrich_results`, fix `keyword_search`, add `technique_page_slug` to `SearchResultItem` schema, update tests.\n\n**T02 (Frontend)**: Add `technique_page_slug` to TS interface, fix `SearchResultCard` links, add `id` attrs to key moments in `TechniquePage.tsx`, add scroll-to-hash logic."},{"type":"toolCall","id":"toolu_01FDZeFabaT6CjnbzMN3badR","name":"gsd_plan_slice","arguments":{"goal":"Key moment search results link to the correct parent technique page and scroll to the specific moment, instead of 404ing.","integrationClosure":"Upstream: Qdrant payload structure (qdrant_client.py), search service (search_service.py), pipeline stage 6 (stages.py). New wiring: technique_page_slug flows from Qdrant/DB → search API → frontend SearchResultCard → URL navigation → TechniquePage hash scroll. After this slice, existing Qdrant data still lacks the new fields until re-indexing on ub01 — keyword search works immediately, semantic search needs re-index.","milestoneId":"M008","observabilityImpact":"Search results now include technique_page_slug in the API response. Missing parent technique page for a key moment is visible as empty technique_page_slug in the response (graceful fallback — links to search instead of 404).","proofLevel":"integration — requires backend tests exercising the search path and frontend build verification","sliceId":"S01","successCriteria":"- Search for a term that matches key moments → results display with type badge \"Key Moment\"\n- Click a key moment result → navigates to `/techniques/{parent-slug}#km-{moment-id}`\n- Technique page loads and scrolls to the referenced key moment\n- Technique page results still link correctly to `/techniques/{slug}`\n- Backend `SearchResultItem` includes `technique_page_slug` field\n- Keyword search returns parent technique page slug for key moment results\n- Qdrant payloads include `slug` (technique pages) and `technique_page_slug` (key moments)\n- All existing search tests pass, new tests cover key moment slug resolution","tasks":[{"description":"Fix the backend so key moment search results include the parent technique page slug. Three areas need changes:\n\n1. **Qdrant payload enrichment** (`backend/pipeline/qdrant_client.py` + `backend/pipeline/stages.py`): Add `slug` to technique page payloads and `technique_page_slug`/`technique_page_id` to key moment payloads. The stage 6 code in `stages.py` (~line 960) builds `page_dicts` and `moment_dicts` — add the missing fields there. For moments, build a `page_id_to_slug` mapping from the `pages` list, then look up each moment's `technique_page_id` to get the slug.\n\n2. **Search service** (`backend/search_service.py`):\n   - `_enrich_results()` (~line 310): Currently falls back to `title.lower().replace(' ', '-')` for slug. Add `technique_page_slug` to the enriched dict, reading from `payload.get('technique_page_slug', '')`. For technique pages, use `payload.get('slug', '')` directly (no longer need the naive fallback once re-indexed, but keep it as fallback for old data).\n   - `keyword_search()` (~line 137): Key moment results currently set `slug: ''`. Add an outerjoin to `TechniquePage` via `KeyMoment.technique_page_id == TechniquePage.id` to get the parent slug. Add `technique_page_slug` to the result dict.\n   - Also add `technique_page_slug` to technique_page keyword results (set to same as `slug` — a technique page IS its own parent).\n\n3. **Schema** (`backend/schemas.py`): Add `technique_page_slug: str = ''` to `SearchResultItem`.\n\n4. **Tests** (`backend/tests/test_search.py`): Update the keyword fallback test to verify `technique_page_slug` is present in results. Add a test for key moment keyword search returning the correct parent technique page slug.","estimate":"45m","expectedOutput":["`backend/pipeline/stages.py` — page_dicts include 'slug', moment_dicts include 'technique_page_slug' and 'technique_page_id'","`backend/pipeline/qdrant_client.py` — upsert payloads include slug fields","`backend/search_service.py` — _enrich_results returns technique_page_slug; keyword_search joins TechniquePage for key moments","`backend/schemas.py` — SearchResultItem has technique_page_slug field","`backend/tests/test_search.py` — tests verify technique_page_slug in search results"],"files":["backend/pipeline/stages.py","backend/pipeline/qdrant_client.py","backend/search_service.py","backend/schemas.py","backend/tests/test_search.py"],"inputs":["`backend/pipeline/stages.py` — stage 6 embed_and_index, builds page_dicts and moment_dicts (~line 950-1010)","`backend/pipeline/qdrant_client.py` — upsert_technique_pages() and upsert_key_moments() payload structure","`backend/search_service.py` — _enrich_results() and keyword_search() methods","`backend/schemas.py` — SearchResultItem Pydantic model (line ~203)","`backend/tests/test_search.py` — existing search tests","`backend/models.py` — KeyMoment.technique_page_id FK, TechniquePage.slug"],"taskId":"T01","title":"Add technique page slug to Qdrant payloads, search service, and schema","verify":"cd /home/aux/projects/content-to-kb-automator && python -m pytest backend/tests/test_search.py -v 2>&1 | tail -30"},{"description":"Fix the frontend so clicking a key moment search result navigates to the parent technique page and scrolls to the specific moment.\n\n1. **TypeScript interface** (`frontend/src/api/public-client.ts`): Add `technique_page_slug?: string` to the `SearchResultItem` interface (line ~10).\n\n2. **Search result card** (`frontend/src/pages/SearchResults.tsx`): In `SearchResultCard` (~line 145), the `<Link>` currently always goes to `/techniques/${item.slug}`. Change to:\n   - If `item.type === 'key_moment'` and `item.technique_page_slug`: link to `/techniques/${item.technique_page_slug}#km-${item.slug || item.title}`\n   - If `item.type === 'key_moment'` but no `technique_page_slug`: link to `/search?q=${encodeURIComponent(item.title)}` (graceful fallback — don't 404)\n   - Otherwise (technique_page): keep `/techniques/${item.slug}`\n\n3. **Key moment anchors** (`frontend/src/pages/TechniquePage.tsx`): The `<li key={km.id}>` at ~line 421 has no `id` attribute. Add `id={`km-${km.id}`}` to the `<li>` element so hash fragments can target specific moments.\n\n4. **Scroll-to-hash** (`frontend/src/pages/TechniquePage.tsx`): Add a `useEffect` that runs after technique data loads. If `window.location.hash` starts with `#km-`, find the element by ID and call `scrollIntoView({ behavior: 'smooth', block: 'start' })`. The effect should depend on `technique` (the loaded data) so it fires after render.\n\n5. **Build verification**: Run `npm run build` in the frontend to ensure no TypeScript errors.","estimate":"30m","expectedOutput":["`frontend/src/api/public-client.ts` — SearchResultItem includes technique_page_slug","`frontend/src/pages/SearchResults.tsx` — SearchResultCard links key moments to parent technique page with hash anchor","`frontend/src/pages/TechniquePage.tsx` — key moment <li> elements have id attributes; useEffect scrolls to hash on load"],"files":["frontend/src/api/public-client.ts","frontend/src/pages/SearchResults.tsx","frontend/src/pages/TechniquePage.tsx"],"inputs":["`frontend/src/api/public-client.ts` — SearchResultItem interface (line ~10)","`frontend/src/pages/SearchResults.tsx` — SearchResultCard component (line ~145)","`frontend/src/pages/TechniquePage.tsx` — key moments list rendering (line ~421)","`backend/schemas.py` — SearchResultItem with technique_page_slug (from T01)"],"taskId":"T02","title":"Fix frontend search result links and add hash-scroll to technique page","verify":"cd /home/aux/projects/content-to-kb-automator/frontend && npm run build 2>&1 | tail -20"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2658,"cacheRead":79383,"cacheWrite":783,"totalTokens":82825,"cost":{"input":0.000005,"output":0.06645000000000001,"cacheRead":0.0396915,"cacheWrite":0.0048937500000000005,"total":0.11104025000000001}},"stopReason":"toolUse","timestamp":1774933026158}}
{"type":"message","id":"e7ff1b7e","parentId":"89c0def3","timestamp":"2026-03-31T04:57:54.450Z","message":{"role":"toolResult","toolCallId":"toolu_01FDZeFabaT6CjnbzMN3badR","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S01 (M008)"}],"details":{"operation":"plan_slice","milestoneId":"M008","sliceId":"S01","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M008/slices/S01/S01-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M008/slices/S01/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M008/slices/S01/tasks/T02-PLAN.md"]},"isError":false,"timestamp":1774933074448}}
{"type":"message","id":"6506494b","parentId":"e7ff1b7e","timestamp":"2026-03-31T04:58:01.401Z","message":{"role":"assistant","content":[{"type":"text","text":"Two tasks, clean split at the backend/frontend boundary. T01 is the riskier half — three interacting changes (Qdrant payloads, search service, schema) that all need to agree. T02 is mechanical frontend wiring that depends on T01's schema being right.\n\nRequirement coverage: This slice primarily addresses bugs in R005 (search) and R009 (Qdrant vector search) — both already validated but broken for key moment results. No new requirements owned; this is a fix slice. R015 (30-second retrieval) benefits indirectly since key moment clicks currently 404.\n\nSlice S01 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":148,"cacheRead":80166,"cacheWrite":2644,"totalTokens":82959,"cost":{"input":0.000005,"output":0.0037,"cacheRead":0.040083,"cacheWrite":0.016525,"total":0.060313000000000005}},"stopReason":"stop","timestamp":1774933074449}}
