{"type":"thinking_level_change","id":"f9ce7e76","parentId":null,"timestamp":"2026-03-31T06:04:40.116Z","thinkingLevel":"medium"}
{"type":"model_change","id":"99ed4eec","parentId":"f9ce7e76","timestamp":"2026-03-31T06:04:40.116Z","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## UNIT: Research Slice S02 (\"Related Techniques Cross-Linking\") — Milestone M010\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M010/M010-ROADMAP.md`\n\n# M010: Discovery, Navigation & Visual Identity\n\n## Vision\nChrysopedia should feel like exploring a music production library, not querying a database. This milestone adds the navigation structure (sub-topic pages, cross-links), visual identity (topic colors, creator differentiation), and interaction polish (autocomplete, transitions) that transform browsing from utilitarian to engaging. A user who reads one technique should naturally discover three more.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Dedicated Sub-Topic Pages | high | — | ✅ | Clicking 'Compression' on Topics page loads /topics/mixing/compression with techniques grouped by creator, description, breadcrumbs |\n| S02 | Related Techniques Cross-Linking | medium | — | ⬜ | Bottom of every technique page shows 3-4 related techniques from same creator or same sub-topic |\n| S03 | Topic Color Coding & Visual Polish | medium | S01 | ⬜ | Each of 7 topic categories has a distinct accent color in badges, cards, and sub-topic pages. Page transitions are smooth. Creator avatars are visually unique. |\n| S04 | Search Autocomplete & Suggestions | medium | — | ⬜ | Typing in search shows autocomplete suggestions. Empty search box shows popular search terms. |\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 6 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 to watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M010`, `slice_id: S02`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M010/slices/S02/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S02 researched.\"","display":false,"id":"3af6adc9","parentId":"99ed4eec","timestamp":"2026-03-31T06:04:40.119Z"}
{"type":"message","id":"875dc5f9","parentId":"3af6adc9","timestamp":"2026-03-31T06:04:48.518Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research the \"Related Techniques Cross-Linking\" slice. The roadmap says: \"Bottom of every technique page shows 3-4 related techniques from same creator or same sub-topic.\"\n\nThis is light-to-targeted research — it's about wiring up existing data (related techniques) into the UI. Let me check what already exists.","thinkingSignature":"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"},{"type":"text","text":"Let me explore what exists for related techniques — the data model, API, and current frontend rendering."},{"type":"toolCall","id":"toolu_01Jq88aV3ABL714D45yqfkCm","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"related\" --type py backend/ | head -20"}},{"type":"toolCall","id":"toolu_0123RRPi1zGUUJN1G2JtZn95","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"related\" --type tsx frontend/src/ 2>/dev/null || rg -l \"related\" frontend/src/ | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":284,"cacheRead":58653,"cacheWrite":6331,"totalTokens":65271,"cost":{"input":0.000015000000000000002,"output":0.0071,"cacheRead":0.0293265,"cacheWrite":0.03956875,"total":0.07601025}},"stopReason":"toolUse","timestamp":1774937080117}}
{"type":"message","id":"005ce1f7","parentId":"875dc5f9","timestamp":"2026-03-31T06:04:48.539Z","message":{"role":"toolResult","toolCallId":"toolu_01Jq88aV3ABL714D45yqfkCm","toolName":"bash","content":[{"type":"text","text":"backend/schemas.py\nbackend/tests/test_public_api.py\nbackend/tests/test_search.py\nbackend/models.py\nbackend/routers/techniques.py\n"}],"isError":false,"timestamp":1774937088539}}
{"type":"message","id":"f6706070","parentId":"005ce1f7","timestamp":"2026-03-31T06:04:48.549Z","message":{"role":"toolResult","toolCallId":"toolu_0123RRPi1zGUUJN1G2JtZn95","toolName":"bash","content":[{"type":"text","text":"frontend/src/App.css\nfrontend/src/pages/TechniquePage.tsx\nfrontend/src/api/public-client.ts\n"}],"isError":false,"timestamp":1774937088547}}
{"type":"message","id":"95bbcfec","parentId":"f6706070","timestamp":"2026-03-31T06:04:52.444Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_018fm2cKtjHxv1tYtbkNQaBT","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"related\" backend/models.py"}},{"type":"toolCall","id":"toolu_01UYD7oyFRo5ynBEKc7yes3z","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"related\" backend/schemas.py"}},{"type":"toolCall","id":"toolu_017C977X7EE7tcM2CNYXJpR1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"related\" backend/routers/techniques.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":209,"cacheRead":64984,"cacheWrite":331,"totalTokens":65525,"cost":{"input":0.000005,"output":0.0052250000000000005,"cacheRead":0.032492,"cacheWrite":0.0020687500000000003,"total":0.03979075}},"stopReason":"toolUse","timestamp":1774937088548}}
{"type":"message","id":"f80ae964","parentId":"95bbcfec","timestamp":"2026-03-31T06:04:52.460Z","message":{"role":"toolResult","toolCallId":"toolu_018fm2cKtjHxv1tYtbkNQaBT","toolName":"bash","content":[{"type":"text","text":"249:    __tablename__ = \"related_technique_links\"\n"}],"isError":false,"timestamp":1774937092460}}
{"type":"message","id":"9e26c7df","parentId":"f80ae964","timestamp":"2026-03-31T06:04:52.461Z","message":{"role":"toolResult","toolCallId":"toolu_01UYD7oyFRo5ynBEKc7yes3z","toolName":"bash","content":[{"type":"text","text":"244:    \"\"\"A related technique link with target info.\"\"\"\n262:    \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n265:    related_links: list[RelatedLinkItem] = Field(default_factory=list)\n"}],"isError":false,"timestamp":1774937092460}}
{"type":"message","id":"567a3c09","parentId":"9e26c7df","timestamp":"2026-03-31T06:04:52.464Z","message":{"role":"toolResult","toolCallId":"toolu_017C977X7EE7tcM2CNYXJpR1","toolName":"bash","content":[{"type":"text","text":"42:    # Correlated subquery for key moment count (same pattern as creators.py)\n109:    \"\"\"Get full technique page detail with key moments, creator, and related links.\"\"\"\n147:    # Build related links (outgoing + incoming)\n148:    related_links: list[RelatedLinkItem] = []\n151:            related_links.append(\n160:            related_links.append(\n181:        related_links=related_links,\n"}],"isError":false,"timestamp":1774937092463}}
{"type":"message","id":"4a3896fa","parentId":"567a3c09","timestamp":"2026-03-31T06:04:56.695Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — there's already a `related_technique_links` model and an API that returns related links. Let me read the model and the technique detail endpoint to understand the current shape."},{"type":"toolCall","id":"toolu_01BSwN14Yswb2n1WyedhVu2N","name":"read","arguments":{"limit":40,"offset":245,"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01TZ1XPBdY9FtufTLaGxbGwn","name":"read","arguments":{"path":"backend/routers/techniques.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":169,"cacheRead":65315,"cacheWrite":473,"totalTokens":65958,"cost":{"input":0.000005,"output":0.0042250000000000005,"cacheRead":0.0326575,"cacheWrite":0.00295625,"total":0.03984375}},"stopReason":"toolUse","timestamp":1774937092463}}
{"type":"message","id":"b8fd1ec1","parentId":"4a3896fa","timestamp":"2026-03-31T06:04:56.698Z","message":{"role":"toolResult","toolCallId":"toolu_01BSwN14Yswb2n1WyedhVu2N","toolName":"read","content":[{"type":"text","text":"    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass TechniquePageVersion(Base):\n    \"\"\"Snapshot of a TechniquePage before a pipeline re-synthesis overwrites it.\"\"\"\n    __tablename__ = \"technique_page_versions\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    version_number: Mapped[int] = mapped_column(Integer, nullable=False)\n    content_snapshot: Mapped[dict] = mapped_column(JSONB, nullable=False)\n\n[116 more lines in file. Use offset=285 to continue.]"}],"isError":false,"timestamp":1774937096698}}
{"type":"message","id":"5c5e6797","parentId":"b8fd1ec1","timestamp":"2026-03-31T06:04:56.703Z","message":{"role":"toolResult","toolCallId":"toolu_01TZ1XPBdY9FtufTLaGxbGwn","toolName":"read","content":[{"type":"text","text":"\"\"\"Technique page endpoints — list and detail with eager-loaded relations.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import selectinload\n\nfrom database import get_session\nfrom models import Creator, KeyMoment, RelatedTechniqueLink, SourceVideo, TechniquePage, TechniquePageVersion\nfrom schemas import (\n    CreatorInfo,\n    KeyMomentSummary,\n    PaginatedResponse,\n    RelatedLinkItem,\n    TechniquePageDetail,\n    TechniquePageRead,\n    TechniquePageVersionDetail,\n    TechniquePageVersionListResponse,\n    TechniquePageVersionSummary,\n)\n\nlogger = logging.getLogger(\"chrysopedia.techniques\")\n\nrouter = APIRouter(prefix=\"/techniques\", tags=[\"techniques\"])\n\n\n@router.get(\"\", response_model=PaginatedResponse)\nasync def list_techniques(\n    category: Annotated[str | None, Query()] = None,\n    creator_slug: Annotated[str | None, Query()] = None,\n    sort: Annotated[str, Query()] = \"recent\",\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> PaginatedResponse:\n    \"\"\"List technique pages with optional category/creator filtering.\"\"\"\n    # Correlated subquery for key moment count (same pattern as creators.py)\n    key_moment_count_sq = (\n        select(func.count())\n        .where(KeyMoment.technique_page_id == TechniquePage.id)\n        .correlate(TechniquePage)\n        .scalar_subquery()\n    )\n\n    # Build base query with filters\n    base_stmt = select(TechniquePage.id)\n    if category:\n        base_stmt = base_stmt.where(TechniquePage.topic_category == category)\n    if creator_slug:\n        base_stmt = base_stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    # Count total before pagination\n    count_stmt = select(func.count()).select_from(base_stmt.subquery())\n    count_result = await db.execute(count_stmt)\n    total = count_result.scalar() or 0\n\n    # Main query with subquery column\n    stmt = select(\n        TechniquePage,\n        key_moment_count_sq.label(\"key_moment_count\"),\n    )\n    if category:\n        stmt = stmt.where(TechniquePage.topic_category == category)\n    if creator_slug:\n        stmt = stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    stmt = stmt.options(selectinload(TechniquePage.creator))\n    if sort == \"random\":\n        stmt = stmt.order_by(func.random())\n    else:\n        stmt = stmt.order_by(TechniquePage.created_at.desc())\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    rows = result.all()\n\n    items = []\n    for row in rows:\n        p = row[0]\n        km_count = row[1] or 0\n        item = TechniquePageRead.model_validate(p)\n        if p.creator:\n            item.creator_name = p.creator.name\n            item.creator_slug = p.creator.slug\n        item.key_moment_count = km_count\n        items.append(item)\n\n    return PaginatedResponse(\n        items=items,\n        total=total,\n        offset=offset,\n        limit=limit,\n    )\n\n\n@router.get(\"/{slug}\", response_model=TechniquePageDetail)\nasync def get_technique(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageDetail:\n    \"\"\"Get full technique page detail with key moments, creator, and related links.\"\"\"\n    stmt = (\n        select(TechniquePage)\n        .where(TechniquePage.slug == slug)\n        .options(\n            selectinload(TechniquePage.key_moments).selectinload(KeyMoment.source_video),\n            selectinload(TechniquePage.creator),\n            selectinload(TechniquePage.outgoing_links).selectinload(\n                RelatedTechniqueLink.target_page\n            ),\n            selectinload(TechniquePage.incoming_links).selectinload(\n                RelatedTechniqueLink.source_page\n            ),\n        )\n    )\n    result = await db.execute(stmt)\n    page = result.scalar_one_or_none()\n\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Build key moments (ordered by start_time)\n    key_moments = sorted(page.key_moments, key=lambda km: km.start_time)\n    key_moment_items = []\n    for km in key_moments:\n        item = KeyMomentSummary.model_validate(km)\n        item.video_filename = km.source_video.filename if km.source_video else \"\"\n        key_moment_items.append(item)\n\n    # Build creator info\n    creator_info = None\n    if page.creator:\n        creator_info = CreatorInfo(\n            name=page.creator.name,\n            slug=page.creator.slug,\n            genres=page.creator.genres,\n        )\n\n    # Build related links (outgoing + incoming)\n    related_links: list[RelatedLinkItem] = []\n    for link in page.outgoing_links:\n        if link.target_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.target_page.title,\n                    target_slug=link.target_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n    for link in page.incoming_links:\n        if link.source_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.source_page.title,\n                    target_slug=link.source_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n\n    base = TechniquePageRead.model_validate(page)\n\n    # Count versions for this page\n    version_count_stmt = select(func.count()).where(\n        TechniquePageVersion.technique_page_id == page.id\n    )\n    version_count_result = await db.execute(version_count_stmt)\n    version_count = version_count_result.scalar() or 0\n\n    return TechniquePageDetail(\n        **base.model_dump(),\n        key_moments=key_moment_items,\n        creator_info=creator_info,\n        related_links=related_links,\n        version_count=version_count,\n    )\n\n\n@router.get(\"/{slug}/versions\", response_model=TechniquePageVersionListResponse)\nasync def list_technique_versions(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageVersionListResponse:\n    \"\"\"List all version snapshots for a technique page, newest first.\"\"\"\n    # Resolve the technique page\n    page_stmt = select(TechniquePage).where(TechniquePage.slug == slug)\n    page_result = await db.execute(page_stmt)\n    page = page_result.scalar_one_or_none()\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Fetch versions ordered by version_number DESC\n    versions_stmt = (\n        select(TechniquePageVersion)\n        .where(TechniquePageVersion.technique_page_id == page.id)\n        .order_by(TechniquePageVersion.version_number.desc())\n    )\n    versions_result = await db.execute(versions_stmt)\n    versions = versions_result.scalars().all()\n\n    items = [TechniquePageVersionSummary.model_validate(v) for v in versions]\n    return TechniquePageVersionListResponse(items=items, total=len(items))\n\n\n@router.get(\"/{slug}/versions/{version_number}\", response_model=TechniquePageVersionDetail)\nasync def get_technique_version(\n    slug: str,\n    version_number: int,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageVersionDetail:\n    \"\"\"Get a specific version snapshot by version number.\"\"\"\n    # Resolve the technique page\n    page_stmt = select(TechniquePage).where(TechniquePage.slug == slug)\n    page_result = await db.execute(page_stmt)\n    page = page_result.scalar_one_or_none()\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Fetch the specific version\n    version_stmt = (\n        select(TechniquePageVersion)\n        .where(\n            TechniquePageVersion.technique_page_id == page.id,\n            TechniquePageVersion.version_number == version_number,\n        )\n    )\n    version_result = await db.execute(version_stmt)\n    version = version_result.scalar_one_or_none()\n    if version is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Version {version_number} not found for technique '{slug}'\",\n        )\n\n    return TechniquePageVersionDetail.model_validate(version)\n"}],"isError":false,"timestamp":1774937096700}}
{"type":"message","id":"fcd63333","parentId":"5c5e6797","timestamp":"2026-03-31T06:05:01.997Z","message":{"role":"assistant","content":[{"type":"text","text":"The backend already has full related-link infrastructure — the model, the bidirectional query (outgoing + incoming), and the schema. Now let me check the frontend rendering."},{"type":"toolCall","id":"toolu_011V2mn7XhBRBqAQLjBdSivk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n -A 3 -B 1 \"related\" frontend/src/pages/TechniquePage.tsx | head -60"}},{"type":"toolCall","id":"toolu_018UDh5EvR9DHKTYzrPiqurL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"related\" frontend/src/api/public-client.ts"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":207,"cacheRead":65788,"cacheWrite":3220,"totalTokens":69216,"cost":{"input":0.000005,"output":0.005175,"cacheRead":0.032894,"cacheWrite":0.020125,"total":0.058199}},"stopReason":"toolUse","timestamp":1774937096701}}
{"type":"message","id":"545a146c","parentId":"fcd63333","timestamp":"2026-03-31T06:05:02.013Z","message":{"role":"toolResult","toolCallId":"toolu_011V2mn7XhBRBqAQLjBdSivk","toolName":"bash","content":[{"type":"text","text":"494-      {/* Related techniques (always from live data) */}\n495:      {technique.related_links.length > 0 && (\n496:        <section className=\"technique-related\">\n497-          <h2>Related Techniques</h2>\n498:          <ul className=\"technique-related__list\">\n499:            {technique.related_links.map((link) => (\n500-              <li key={link.target_slug}>\n501-                <Link to={`/techniques/${link.target_slug}`}>\n502-                  {link.target_title}\n503-                </Link>\n504:                <span className=\"technique-related__rel\">\n505-                  ({link.relationship})\n506-                </span>\n507-              </li>\n"}],"isError":false,"timestamp":1774937102013}}
{"type":"message","id":"dece5f19","parentId":"545a146c","timestamp":"2026-03-31T06:05:02.019Z","message":{"role":"toolResult","toolCallId":"toolu_018UDh5EvR9DHKTYzrPiqurL","toolName":"bash","content":[{"type":"text","text":"71:  related_links: RelatedLinkItem[];\n"}],"isError":false,"timestamp":1774937102017}}
{"type":"message","id":"8037af8f","parentId":"dece5f19","timestamp":"2026-03-31T06:05:05.759Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what `RelatedLinkItem` looks like in the frontend and what's currently styled."},{"type":"toolCall","id":"toolu_011jNzPv91qdZ2VkevLXQUpv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"RelatedLinkItem\" frontend/src/api/public-client.ts"}},{"type":"toolCall","id":"toolu_01LmDoD9ZHjAe3sV2qgc9Z4k","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"technique-related\" frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":181,"cacheRead":69008,"cacheWrite":464,"totalTokens":69654,"cost":{"input":0.000005,"output":0.004525,"cacheRead":0.034504,"cacheWrite":0.0029000000000000002,"total":0.041934}},"stopReason":"toolUse","timestamp":1774937102018}}
{"type":"message","id":"d1bc5a23","parentId":"8037af8f","timestamp":"2026-03-31T06:05:05.774Z","message":{"role":"toolResult","toolCallId":"toolu_011jNzPv91qdZ2VkevLXQUpv","toolName":"bash","content":[{"type":"text","text":"48:export interface RelatedLinkItem {\n71:  related_links: RelatedLinkItem[];\n"}],"isError":false,"timestamp":1774937105774}}
{"type":"message","id":"5fd1cdb8","parentId":"d1bc5a23","timestamp":"2026-03-31T06:05:05.780Z","message":{"role":"toolResult","toolCallId":"toolu_01LmDoD9ZHjAe3sV2qgc9Z4k","toolName":"bash","content":[{"type":"text","text":"1774:.technique-related {\n1778:.technique-related h2 {\n1784:.technique-related__list {\n1791:.technique-related__list a {\n1797:.technique-related__list a:hover {\n1801:.technique-related__rel {\n"}],"isError":false,"timestamp":1774937105778}}
{"type":"message","id":"e98c5a86","parentId":"5fd1cdb8","timestamp":"2026-03-31T06:05:09.678Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KRbPPxk9sfMTbm5fMbw7sH","name":"read","arguments":{"limit":10,"offset":48,"path":"frontend/src/api/public-client.ts"}},{"type":"toolCall","id":"toolu_017YbHF92MNZXZDMt9eKfoVa","name":"read","arguments":{"limit":35,"offset":1774,"path":"frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":170,"cacheRead":69472,"cacheWrite":330,"totalTokens":69973,"cost":{"input":0.000005,"output":0.00425,"cacheRead":0.034735999999999996,"cacheWrite":0.0020625,"total":0.0410535}},"stopReason":"toolUse","timestamp":1774937105779}}
{"type":"message","id":"e772f0cf","parentId":"e98c5a86","timestamp":"2026-03-31T06:05:09.680Z","message":{"role":"toolResult","toolCallId":"toolu_01KRbPPxk9sfMTbm5fMbw7sH","toolName":"read","content":[{"type":"text","text":"export interface RelatedLinkItem {\n  target_title: string;\n  target_slug: string;\n  relationship: string;\n}\n\nexport interface TechniquePageDetail {\n  id: string;\n  title: string;\n  slug: string;\n\n[453 more lines in file. Use offset=58 to continue.]"}],"isError":false,"timestamp":1774937109680}}
{"type":"message","id":"cccccd44","parentId":"e772f0cf","timestamp":"2026-03-31T06:05:09.684Z","message":{"role":"toolResult","toolCallId":"toolu_017YbHF92MNZXZDMt9eKfoVa","toolName":"read","content":[{"type":"text","text":".technique-related {\n  margin-bottom: 2rem;\n}\n\n.technique-related h2 {\n  font-size: 1.25rem;\n  font-weight: 700;\n  margin-bottom: 0.5rem;\n}\n\n.technique-related__list {\n  list-style: none;\n  display: flex;\n  flex-direction: column;\n  gap: 0.375rem;\n}\n\n.technique-related__list a {\n  color: var(--color-link-accent);\n  text-decoration: none;\n  font-size: 0.9375rem;\n}\n\n.technique-related__list a:hover {\n  text-decoration: underline;\n}\n\n.technique-related__rel {\n  font-size: 0.75rem;\n  color: var(--color-text-muted);\n  margin-left: 0.375rem;\n}\n\n/* ══════════════════════════════════════════════════════════════════════════════\n   CREATORS BROWSE\n\n[1879 more lines in file. Use offset=1809 to continue.]"}],"isError":false,"timestamp":1774937109682}}
{"type":"message","id":"46e9470d","parentId":"cccccd44","timestamp":"2026-03-31T06:05:14.858Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how related links are actually populated. They come from the pipeline — let me see the stage that creates them."},{"type":"toolCall","id":"toolu_01BEraoKQZpUicQ98mD4cNU3","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"related_technique_links\\|RelatedTechniqueLink\\|outgoing_links\\|incoming_links\" backend/models.py | head -20"}},{"type":"toolCall","id":"toolu_01AitgV11UQWeC8oHAxwmUco","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"RelatedTechniqueLink\\|related_technique_links\" backend/pipeline/ | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":215,"cacheRead":69802,"cacheWrite":571,"totalTokens":70589,"cost":{"input":0.000005,"output":0.0053750000000000004,"cacheRead":0.034901,"cacheWrite":0.0035687500000000003,"total":0.04384975000000001}},"stopReason":"toolUse","timestamp":1774937109683}}
{"type":"message","id":"52cb5eef","parentId":"46e9470d","timestamp":"2026-03-31T06:05:14.870Z","message":{"role":"toolResult","toolCallId":"toolu_01BEraoKQZpUicQ98mD4cNU3","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937114870}}
{"type":"message","id":"51fecf3f","parentId":"52cb5eef","timestamp":"2026-03-31T06:05:14.876Z","message":{"role":"toolResult","toolCallId":"toolu_01AitgV11UQWeC8oHAxwmUco","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937114874}}
{"type":"message","id":"17dc7401","parentId":"51fecf3f","timestamp":"2026-03-31T06:05:19.071Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XXRiijsUwCvfNSqiqXL2hb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"RelatedTechniqueLink\\|related_technique_links\\|outgoing_links\\|incoming_links\" backend/ --type py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":70373,"cacheWrite":280,"totalTokens":70763,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.035186499999999996,"cacheWrite":0.00175,"total":0.03966649999999999}},"stopReason":"toolUse","timestamp":1774937114875}}
{"type":"message","id":"5ef1751f","parentId":"17dc7401","timestamp":"2026-03-31T06:05:19.095Z","message":{"role":"toolResult","toolCallId":"toolu_01XXRiijsUwCvfNSqiqXL2hb","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937119093}}
{"type":"message","id":"ba5f3deb","parentId":"5ef1751f","timestamp":"2026-03-31T06:05:22.509Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QS6F6kGSxBVbJVmR1AV8Yk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"related\" backend/models.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":85,"cacheRead":70653,"cacheWrite":123,"totalTokens":70862,"cost":{"input":0.000005,"output":0.002125,"cacheRead":0.0353265,"cacheWrite":0.00076875,"total":0.038225249999999995}},"stopReason":"toolUse","timestamp":1774937119094}}
{"type":"message","id":"c8030909","parentId":"ba5f3deb","timestamp":"2026-03-31T06:05:22.527Z","message":{"role":"toolResult","toolCallId":"toolu_01QS6F6kGSxBVbJVmR1AV8Yk","toolName":"bash","content":[{"type":"text","text":"    __tablename__ = \"n_technique_links\"\n"}],"isError":false,"timestamp":1774937122525}}
{"type":"message","id":"830f25f3","parentId":"c8030909","timestamp":"2026-03-31T06:05:25.774Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RDfh4yPjdrcXjqwT381izM","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"RelatedTechniqueLink\" backend/ | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":88,"cacheRead":70776,"cacheWrite":111,"totalTokens":70976,"cost":{"input":0.000005,"output":0.0022,"cacheRead":0.035387999999999996,"cacheWrite":0.00069375,"total":0.038286749999999994}},"stopReason":"toolUse","timestamp":1774937122526}}
{"type":"message","id":"d7b20bcb","parentId":"830f25f3","timestamp":"2026-03-31T06:05:25.792Z","message":{"role":"toolResult","toolCallId":"toolu_01RDfh4yPjdrcXjqwT381izM","toolName":"bash","content":[{"type":"text","text":"backend/schemas.py:# ── n ─────────────────────────────────────────────────────\nbackend/schemas.py:class nBase(BaseModel):\nbackend/schemas.py:class nCreate(nBase):\nbackend/schemas.py:class nRead(nBase):\nbackend/tests/test_public_api.py:    n,\nbackend/tests/test_public_api.py:        link = n(\nbackend/routers/techniques.py:from models import Creator, KeyMoment, n, SourceVideo, TechniquePage, TechniquePageVersion\nbackend/routers/techniques.py:                n.target_page\nbackend/routers/techniques.py:                n.source_page\nbackend/models.py:  TechniquePage, n, Tag\nbackend/models.py:    outgoing_links: Mapped[list[n]] = sa_relationship(\nbackend/models.py:        foreign_keys=\"n.source_page_id\", back_populates=\"source_page\"\nbackend/models.py:    incoming_links: Mapped[list[n]] = sa_relationship(\nbackend/models.py:        foreign_keys=\"n.target_page_id\", back_populates=\"target_page\"\nbackend/models.py:class n(Base):\n"}],"isError":false,"timestamp":1774937125790}}
{"type":"message","id":"987f44cf","parentId":"d7b20bcb","timestamp":"2026-03-31T06:05:30.662Z","message":{"role":"assistant","content":[{"type":"text","text":"The `rg` output is being truncated oddly. Let me check what relationship types exist and whether the pipeline populates these links."},{"type":"toolCall","id":"toolu_01U2NKsDKPjbvcPg8XEnA7vW","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"RelationshipType\" backend/models.py"}},{"type":"toolCall","id":"toolu_01QmYogKfhShpfZ3G6A1YNKy","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn \"RelatedTechniqueLink\\|related_technique\" backend/pipeline/stages.py 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":198,"cacheRead":70887,"cacheWrite":406,"totalTokens":71492,"cost":{"input":0.000005,"output":0.00495,"cacheRead":0.035443499999999996,"cacheWrite":0.0025375000000000003,"total":0.042935999999999995}},"stopReason":"toolUse","timestamp":1774937125791}}
{"type":"message","id":"5e011590","parentId":"987f44cf","timestamp":"2026-03-31T06:05:30.676Z","message":{"role":"toolResult","toolCallId":"toolu_01U2NKsDKPjbvcPg8XEnA7vW","toolName":"bash","content":[{"type":"text","text":"69:class RelationshipType(str, enum.Enum):\n261:    relationship: Mapped[RelationshipType] = mapped_column(\n262:        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n"}],"isError":false,"timestamp":1774937130676}}
{"type":"message","id":"849d6754","parentId":"5e011590","timestamp":"2026-03-31T06:05:30.680Z","message":{"role":"toolResult","toolCallId":"toolu_01QmYogKfhShpfZ3G6A1YNKy","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937130679}}
{"type":"message","id":"0b54b38f","parentId":"849d6754","timestamp":"2026-03-31T06:05:33.861Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01M8JCKBVbLq444qTqT4pfsb","name":"read","arguments":{"limit":10,"offset":69,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":71293,"cacheWrite":320,"totalTokens":71703,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.0356465,"cacheWrite":0.002,"total":0.0398765}},"stopReason":"toolUse","timestamp":1774937130679}}
{"type":"message","id":"1e319a86","parentId":"0b54b38f","timestamp":"2026-03-31T06:05:33.866Z","message":{"role":"toolResult","toolCallId":"toolu_01M8JCKBVbLq444qTqT4pfsb","toolName":"read","content":[{"type":"text","text":"class RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n\n[322 more lines in file. Use offset=79 to continue.]"}],"isError":false,"timestamp":1774937133864}}
{"type":"message","id":"cf48d732","parentId":"1e319a86","timestamp":"2026-03-31T06:05:41.637Z","message":{"role":"assistant","content":[{"type":"text","text":"So the pipeline doesn't populate `related_technique_links` — the table exists but is empty. The slice goal says: \"Bottom of every technique page shows 3-4 related techniques from same creator or same sub-topic.\" This means we need to either:\n1. Populate the links table via pipeline/background task, or\n2. Compute related techniques dynamically at query time\n\nLet me check if there's any existing data in the table on the live system and understand the TechniquePage model fields."},{"type":"toolCall","id":"toolu_015zPMtVDgS5RoSTUkMwFP4p","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"topic_category\\|topic_tags\\|creator_id\\|sub_topic\\|slug\" backend/models.py | head -20"}},{"type":"toolCall","id":"toolu_01QLVt5pqwzAnR6n381jfSCf","name":"read","arguments":{"limit":60,"offset":170,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":286,"cacheRead":71613,"cacheWrite":229,"totalTokens":72129,"cost":{"input":0.000005,"output":0.00715,"cacheRead":0.0358065,"cacheWrite":0.00143125,"total":0.04439275}},"stopReason":"toolUse","timestamp":1774937133864}}
{"type":"message","id":"30c7631e","parentId":"cf48d732","timestamp":"2026-03-31T06:05:41.650Z","message":{"role":"toolResult","toolCallId":"toolu_015zPMtVDgS5RoSTUkMwFP4p","toolName":"bash","content":[{"type":"text","text":"103:    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n124:    creator_id: Mapped[uuid.UUID] = mapped_column(\n209:    creator_id: Mapped[uuid.UUID] = mapped_column(\n213:    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n214:    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n215:    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774937141650}}
{"type":"message","id":"1e661441","parentId":"30c7631e","timestamp":"2026-03-31T06:05:41.653Z","message":{"role":"toolResult","toolCallId":"toolu_01QLVt5pqwzAnR6n381jfSCf","toolName":"read","content":[{"type":"text","text":"\nclass 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    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\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[171 more lines in file. Use offset=230 to continue.]"}],"isError":false,"timestamp":1774937141650}}
{"type":"message","id":"5617c57e","parentId":"1e661441","timestamp":"2026-03-31T06:05:45.205Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the TechniquePage relationships on the model."},{"type":"toolCall","id":"toolu_01QpLKaCmvBCkJ5rHyhHo77b","name":"read","arguments":{"limit":20,"offset":230,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":71842,"cacheWrite":1425,"totalTokens":73372,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.035921,"cacheWrite":0.008906250000000001,"total":0.04743225000000001}},"stopReason":"toolUse","timestamp":1774937141651}}
{"type":"message","id":"09878e14","parentId":"5617c57e","timestamp":"2026-03-31T06:05:45.208Z","message":{"role":"toolResult","toolCallId":"toolu_01QpLKaCmvBCkJ5rHyhHo77b","toolName":"read","content":[{"type":"text","text":"    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n\n[151 more lines in file. Use offset=250 to continue.]"}],"isError":false,"timestamp":1774937145207}}
{"type":"message","id":"b31c7e0c","parentId":"09878e14","timestamp":"2026-03-31T06:05:51.495Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the current frontend rendering location and look at the sub-topic page (S01) for context on what \"same sub-topic\" means."},{"type":"toolCall","id":"toolu_016iRnLetevqUWwtpmdx4Dvf","name":"read","arguments":{"limit":25,"offset":488,"path":"frontend/src/pages/TechniquePage.tsx"}},{"type":"toolCall","id":"toolu_01H2zTQQ94g4rkn41E7tvEfA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"sub.topic\\|subtopic\\|sub_topic\" backend/models.py backend/schemas.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":213,"cacheRead":73267,"cacheWrite":401,"totalTokens":73882,"cost":{"input":0.000005,"output":0.005325,"cacheRead":0.0366335,"cacheWrite":0.0025062500000000002,"total":0.04446975}},"stopReason":"toolUse","timestamp":1774937145207}}
{"type":"message","id":"4e6c4639","parentId":"b31c7e0c","timestamp":"2026-03-31T06:05:51.509Z","message":{"role":"toolResult","toolCallId":"toolu_016iRnLetevqUWwtpmdx4Dvf","toolName":"read","content":[{"type":"text","text":"                </div>\n              );\n            })}\n          </section>\n        )}\n\n      {/* Related techniques (always from live data) */}\n      {technique.related_links.length > 0 && (\n        <section className=\"technique-related\">\n          <h2>Related Techniques</h2>\n          <ul className=\"technique-related__list\">\n            {technique.related_links.map((link) => (\n              <li key={link.target_slug}>\n                <Link to={`/techniques/${link.target_slug}`}>\n                  {link.target_title}\n                </Link>\n                <span className=\"technique-related__rel\">\n                  ({link.relationship})\n                </span>\n              </li>\n            ))}\n          </ul>\n        </section>\n      )}\n        </div>\n\n[30 more lines in file. Use offset=513 to continue.]"}],"isError":false,"timestamp":1774937151509}}
{"type":"message","id":"2848d30d","parentId":"4e6c4639","timestamp":"2026-03-31T06:05:51.520Z","message":{"role":"toolResult","toolCallId":"toolu_01H2zTQQ94g4rkn41E7tvEfA","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937151518}}
{"type":"message","id":"baf72040","parentId":"2848d30d","timestamp":"2026-03-31T06:05:56.192Z","message":{"role":"assistant","content":[{"type":"text","text":"No explicit `sub_topic` column. The `topic_tags` array and `topic_category` are the grouping dimensions. Let me check the schema for the RelatedLinkItem and how the backend returns it."},{"type":"toolCall","id":"toolu_01HCjhaKjkqyBcmi7uGnSRQU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n -A 10 \"class RelatedLinkItem\" backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":137,"cacheRead":73668,"cacheWrite":487,"totalTokens":74293,"cost":{"input":0.000005,"output":0.003425,"cacheRead":0.036834,"cacheWrite":0.00304375,"total":0.04330775}},"stopReason":"toolUse","timestamp":1774937151518}}
{"type":"message","id":"38a7ed2c","parentId":"baf72040","timestamp":"2026-03-31T06:05:56.205Z","message":{"role":"toolResult","toolCallId":"toolu_01HCjhaKjkqyBcmi7uGnSRQU","toolName":"bash","content":[{"type":"text","text":"243:class RelatedLinkItem(BaseModel):\n244-    \"\"\"A related technique link with target info.\"\"\"\n245-    model_config = ConfigDict(from_attributes=True)\n246-\n247-    target_title: str = \"\"\n248-    target_slug: str = \"\"\n249-    relationship: str = \"\"\n250-\n251-\n252-class CreatorInfo(BaseModel):\n253-    \"\"\"Minimal creator info embedded in technique detail.\"\"\"\n"}],"isError":false,"timestamp":1774937156204}}
{"type":"message","id":"017c1eb5","parentId":"38a7ed2c","timestamp":"2026-03-31T06:06:00.468Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what the sub-topic page endpoint looks like — since S01 added it — to understand the grouping."},{"type":"toolCall","id":"toolu_01VM1fsNYfdGwnjF9jrd2iwn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/routers/"}},{"type":"toolCall","id":"toolu_01H88fTV3HtJbgE3SnCmeVWe","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"sub.topic\\|subtopic\" backend/routers/ frontend/src/ | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":179,"cacheRead":74155,"cacheWrite":257,"totalTokens":74592,"cost":{"input":0.000005,"output":0.004475,"cacheRead":0.0370775,"cacheWrite":0.00160625,"total":0.04316375}},"stopReason":"toolUse","timestamp":1774937156205}}
{"type":"message","id":"ad6503a9","parentId":"017c1eb5","timestamp":"2026-03-31T06:06:00.487Z","message":{"role":"toolResult","toolCallId":"toolu_01VM1fsNYfdGwnjF9jrd2iwn","toolName":"bash","content":[{"type":"text","text":"creators.py\nhealth.py\ningest.py\n__init__.py\npipeline.py\n__pycache__\nreports.py\nsearch.py\ntechniques.py\ntopics.py\nvideos.py\n"}],"isError":false,"timestamp":1774937160487}}
{"type":"message","id":"d5ebf1ff","parentId":"ad6503a9","timestamp":"2026-03-31T06:06:00.499Z","message":{"role":"toolResult","toolCallId":"toolu_01H88fTV3HtJbgE3SnCmeVWe","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774937160496}}
{"type":"message","id":"e5b0470d","parentId":"d5ebf1ff","timestamp":"2026-03-31T06:06:03.426Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012S3MaAobRGow6Cq8jpjBvu","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -60 backend/routers/topics.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":80,"cacheRead":74412,"cacheWrite":290,"totalTokens":74783,"cost":{"input":0.000005,"output":0.002,"cacheRead":0.037205999999999996,"cacheWrite":0.0018125,"total":0.0410235}},"stopReason":"toolUse","timestamp":1774937160497}}
{"type":"message","id":"a99b2737","parentId":"e5b0470d","timestamp":"2026-03-31T06:06:03.437Z","message":{"role":"toolResult","toolCallId":"toolu_012S3MaAobRGow6Cq8jpjBvu","toolName":"bash","content":[{"type":"text","text":"\"\"\"Topics endpoint — two-level category hierarchy with aggregated counts.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport os\nfrom typing import Annotated, Any\n\nimport yaml\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import selectinload\n\nfrom database import get_session\nfrom models import Creator, TechniquePage\nfrom schemas import (\n    PaginatedResponse,\n    TechniquePageRead,\n    TopicCategory,\n    TopicSubTopic,\n)\n\nlogger = logging.getLogger(\"chrysopedia.topics\")\n\nrouter = APIRouter(prefix=\"/topics\", tags=[\"topics\"])\n\n# Path to canonical_tags.yaml relative to the backend directory\n_TAGS_PATH = os.path.join(os.path.dirname(__file__), \"..\", \"..\", \"config\", \"canonical_tags.yaml\")\n\n\ndef _load_canonical_tags() -> list[dict[str, Any]]:\n    \"\"\"Load the canonical tag categories from YAML.\"\"\"\n    path = os.path.normpath(_TAGS_PATH)\n    try:\n        with open(path) as f:\n            data = yaml.safe_load(f)\n        return data.get(\"categories\", [])\n    except FileNotFoundError:\n        logger.warning(\"canonical_tags.yaml not found at %s\", path)\n        return []\n\n\n@router.get(\"\", response_model=list[TopicCategory])\nasync def list_topics(\n    db: AsyncSession = Depends(get_session),\n) -> list[TopicCategory]:\n    \"\"\"Return the two-level topic hierarchy with technique/creator counts per sub-topic.\n\n    Categories come from ``canonical_tags.yaml``. Counts are computed\n    from live DB data by matching ``topic_tags`` array contents.\n    \"\"\"\n    categories = _load_canonical_tags()\n\n    # Pre-fetch all technique pages with their tags and creator_ids for counting\n    tp_stmt = select(\n        TechniquePage.topic_category,\n        TechniquePage.topic_tags,\n        TechniquePage.creator_id,\n    )\n"}],"isError":false,"timestamp":1774937163434}}
{"type":"message","id":"19b17dbb","parentId":"a99b2737","timestamp":"2026-03-31T06:06:08.725Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me check if there's an existing API endpoint that can return related techniques dynamically — and whether the `topic_tags` overlap is the right signal."},{"type":"toolCall","id":"toolu_017xKYjVtKhzcKh9VQcKkhS5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"topic_tags\\|topic_category\\|creator_id\" backend/schemas.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":74702,"cacheWrite":598,"totalTokens":75436,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.037350999999999995,"cacheWrite":0.0037375000000000004,"total":0.044468499999999994}},"stopReason":"toolUse","timestamp":1774937163435}}
{"type":"message","id":"c9394769","parentId":"19b17dbb","timestamp":"2026-03-31T06:06:08.742Z","message":{"role":"toolResult","toolCallId":"toolu_017xKYjVtKhzcKh9VQcKkhS5","toolName":"bash","content":[{"type":"text","text":"59:    creator_id: uuid.UUID\n65:    creator_id: uuid.UUID\n121:    topic_category: str\n122:    topic_tags: list[str] | None = None\n129:    creator_id: uuid.UUID\n136:    creator_id: uuid.UUID\n183:    creator_id: uuid.UUID\n214:    topic_category: str = \"\"\n215:    topic_tags: list[str] = Field(default_factory=list)\n"}],"isError":false,"timestamp":1774937168739}}
{"type":"message","id":"3ba98f23","parentId":"c9394769","timestamp":"2026-03-31T06:06:14.442Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Let me also check what `TechniquePageRead` looks like since `TechniquePageDetail` extends it."},{"type":"toolCall","id":"toolu_01KXyrsMXMLTYqhHFYTx558R","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n -A 25 \"class TechniquePageRead\" backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":120,"cacheRead":75300,"cacheWrite":259,"totalTokens":75680,"cost":{"input":0.000005,"output":0.003,"cacheRead":0.037649999999999996,"cacheWrite":0.0016187500000000002,"total":0.04227375}},"stopReason":"toolUse","timestamp":1774937168740}}
{"type":"message","id":"70f82842","parentId":"3ba98f23","timestamp":"2026-03-31T06:06:14.458Z","message":{"role":"toolResult","toolCallId":"toolu_01KXyrsMXMLTYqhHFYTx558R","toolName":"bash","content":[{"type":"text","text":"132:class TechniquePageRead(TechniquePageBase):\n133-    model_config = ConfigDict(from_attributes=True)\n134-\n135-    id: uuid.UUID\n136-    creator_id: uuid.UUID\n137-    creator_name: str = \"\"\n138-    creator_slug: str = \"\"\n139-    source_quality: str | None = None\n140-    view_count: int = 0\n141-    key_moment_count: int = 0\n142-    created_at: datetime\n143-    updated_at: datetime\n144-\n145-\n146-# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n147-\n148-class RelatedTechniqueLinkBase(BaseModel):\n149-    source_page_id: uuid.UUID\n150-    target_page_id: uuid.UUID\n151-    relationship: str\n152-\n153-class RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n154-    pass\n155-\n156-class RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n157-    model_config = ConfigDict(from_attributes=True)\n"}],"isError":false,"timestamp":1774937174455}}
{"type":"message","id":"9b88dd50","parentId":"70f82842","timestamp":"2026-03-31T06:06:25.166Z","message":{"role":"assistant","content":[{"type":"text","text":"I have everything I need. The picture is clear:\n\n1. **Backend infrastructure exists**: `related_technique_links` table, `RelatedTechniqueLink` model, bidirectional relationship on `TechniquePage`, API returns `related_links` in technique detail.\n2. **Pipeline never populates the table** — no code in `pipeline/stages.py` writes to it.\n3. **Frontend renders whatever comes back** but the list is always empty since no links exist.\n4. **The slice goal**: \"3-4 related techniques from same creator or same sub-topic\" — this is about **computing** relatedness, not just displaying it.\n\nTwo approaches: (A) add a new endpoint that dynamically computes related techniques at query time using `topic_tags` overlap + `topic_category` + `creator_id` matching, or (B) populate the `related_technique_links` table via a background job/pipeline stage.\n\nDynamic computation at query time is simpler and always fresh. The table approach requires a population mechanism and goes stale. Given the dataset size (single-admin tool, likely <1000 technique pages), dynamic is the clear winner."},{"type":"toolCall","id":"toolu_01Eni7sbVcpT6LZfAQ6f7daA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l frontend/src/pages/TechniquePage.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":345,"cacheRead":75559,"cacheWrite":427,"totalTokens":76332,"cost":{"input":0.000005,"output":0.008625,"cacheRead":0.0377795,"cacheWrite":0.00266875,"total":0.04907825}},"stopReason":"toolUse","timestamp":1774937174456}}
{"type":"message","id":"c21c9f37","parentId":"9b88dd50","timestamp":"2026-03-31T06:06:25.181Z","message":{"role":"toolResult","toolCallId":"toolu_01Eni7sbVcpT6LZfAQ6f7daA","toolName":"bash","content":[{"type":"text","text":"541 frontend/src/pages/TechniquePage.tsx\n"}],"isError":false,"timestamp":1774937185178}}
{"type":"message","id":"78b665bc","parentId":"c21c9f37","timestamp":"2026-03-31T06:06:29.228Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check if there's a `RelatedLinkItem` that includes `topic_category` or `creator_name` — the frontend might want to show context about why something is related."},{"type":"toolCall","id":"toolu_01DEhj4MUPta1gh6PhxeoJPV","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n -A 8 \"class TechniquePageDetail\" backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":132,"cacheRead":75986,"cacheWrite":372,"totalTokens":76491,"cost":{"input":0.000005,"output":0.0033,"cacheRead":0.037993,"cacheWrite":0.0023250000000000002,"total":0.043623}},"stopReason":"toolUse","timestamp":1774937185179}}
{"type":"message","id":"7f23dd61","parentId":"78b665bc","timestamp":"2026-03-31T06:06:29.244Z","message":{"role":"toolResult","toolCallId":"toolu_01DEhj4MUPta1gh6PhxeoJPV","toolName":"bash","content":[{"type":"text","text":"261:class TechniquePageDetail(TechniquePageRead):\n262-    \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n263-    key_moments: list[KeyMomentSummary] = Field(default_factory=list)\n264-    creator_info: CreatorInfo | None = None\n265-    related_links: list[RelatedLinkItem] = Field(default_factory=list)\n266-    version_count: int = 0\n267-\n268-\n269-# ── Technique Page Versions ──────────────────────────────────────────────────\n"}],"isError":false,"timestamp":1774937189241}}
{"type":"message","id":"07fe3f98","parentId":"7f23dd61","timestamp":"2026-03-31T06:07:14.249Z","message":{"role":"assistant","content":[{"type":"text","text":"That's all I need. Let me write the research doc."},{"type":"toolCall","id":"toolu_01NCDvrQ9qC1YbQKuHjoaXLt","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S02 Research — Related Techniques Cross-Linking\n\n## Summary\n\nThe data model, API plumbing, and frontend rendering for related technique links all exist but are **empty** — the pipeline never populates the `related_technique_links` table. This slice needs to compute relatedness dynamically at query time rather than pre-populating a join table.\n\nThe technique page already renders `related_links` when non-empty (simple list with relationship label). The slice upgrades this to a visually richer card-based display showing 3-4 related techniques with context (creator name, topic category, relationship reason).\n\n**Approach:** Dynamic computation in the `GET /techniques/{slug}` endpoint, replacing the current join-table-based approach. No new tables, no pipeline changes, no background jobs.\n\n## Recommendation\n\nCompute related techniques dynamically in the technique detail endpoint using a scored query: same-creator same-category pages rank highest, same-category different-creator next, then shared `topic_tags` overlap. Limit to 4 results. Return enriched `RelatedLinkItem` with `creator_name`, `topic_category`, and a human-readable `reason` string. Upgrade the frontend section from a plain link list to cards matching the existing design system.\n\n## Implementation Landscape\n\n### Backend — What Exists\n\n| File | Role | Relevant Code |\n|------|------|---------------|\n| `backend/models.py:69-74` | `RelationshipType` enum | `same_technique_other_creator`, `same_creator_adjacent`, `general_cross_reference` |\n| `backend/models.py:249-274` | `RelatedTechniqueLink` model | Fully defined, bidirectional FK to `TechniquePage` |\n| `backend/models.py:232-247` | `TechniquePage` relationships | `outgoing_links`, `incoming_links` mapped |\n| `backend/routers/techniques.py:109-181` | `get_technique()` | Eager-loads both link directions, builds `related_links` list |\n| `backend/schemas.py:243-249` | `RelatedLinkItem` | `target_title`, `target_slug`, `relationship` |\n\n### Backend — What's Needed\n\n1. **New query in `get_technique()`**: After loading the technique page, run a second query to find related pages. Scoring logic:\n   - Same `creator_id` AND same `topic_category`: weight 3 (same creator, adjacent technique)\n   - Same `topic_category`, different creator: weight 2 (same topic area)\n   - Overlapping `topic_tags` (using PostgreSQL `&&` array overlap operator): weight 1 per shared tag\n   - Exclude the current page itself\n   - ORDER BY score DESC, LIMIT 4\n\n2. **Enrich `RelatedLinkItem`**: Add `creator_name: str = \"\"`, `topic_category: str = \"\"`, `reason: str = \"\"` fields. The `reason` field provides display text like \"Same creator\" or \"Also about compression\".\n\n3. **The existing `related_technique_links` table and its eager-loading can stay** — if manually curated links exist in the future, they should take priority. The dynamic query fills in when the table is empty.\n\n### Frontend — What Exists\n\n| File | Role | Relevant Code |\n|------|------|---------------|\n| `frontend/src/pages/TechniquePage.tsx:494-511` | Related section | Renders `related_links` as `<ul>` with link + relationship label |\n| `frontend/src/App.css:1774-1808` | Styles | Basic list styling with muted relationship text |\n| `frontend/src/api/public-client.ts:48-52` | `RelatedLinkItem` type | `target_title`, `target_slug`, `relationship` |\n\n### Frontend — What's Needed\n\n1. **Update `RelatedLinkItem` interface** in `public-client.ts`: add `creator_name`, `topic_category`, `reason` fields.\n\n2. **Replace plain list with card layout** in `TechniquePage.tsx`: Each related technique becomes a small card showing title (linked), creator name, topic category badge, and reason text. Use existing CSS variables and badge patterns from the codebase.\n\n3. **Update CSS** in `App.css`: Replace the `.technique-related__list` vertical list with a responsive grid of cards (2 columns on desktop, 1 on mobile). Follow existing card patterns (e.g., technique cards on sub-topic pages from S01).\n\n### Key Constraints\n\n- **PostgreSQL array overlap**: The `topic_tags` column is `ARRAY(String)`. Use `TechniquePage.topic_tags.overlap(current_tags)` in SQLAlchemy for the `&&` operator. This is standard PostgreSQL — no extensions needed.\n- **Performance**: The dynamic query hits `technique_pages` table with filters on `creator_id`, `topic_category`, and array overlap. For datasets under 10K rows, this is fast without additional indexes. A GIN index on `topic_tags` would help at scale but isn't needed now.\n- **The `related_technique_links` table stays empty** — no migration needed. The dynamic query supplements or replaces it at query time.\n- **Existing tests**: `backend/tests/test_public_api.py` has technique detail tests. The related links section currently returns `[]`. Tests need updating to verify dynamic results.\n\n### Natural Seams (Task Decomposition)\n\n1. **Backend: dynamic related-techniques query** — modify `get_technique()` in `routers/techniques.py`, enrich `RelatedLinkItem` schema in `schemas.py`. Verify with a test that creates 3+ technique pages sharing tags/category and confirms the endpoint returns related links.\n\n2. **Frontend: card-based related section** — update `RelatedLinkItem` type, replace the list rendering in `TechniquePage.tsx` with cards, add/update CSS. Verify with `npm run build` (zero TS errors) and visual check.\n\nThese two tasks are independent (frontend works with the existing empty array until backend is deployed, and the backend change is backward-compatible since it only adds fields).\n\n### What to Build First\n\nThe backend query is the riskier piece — it involves SQLAlchemy array operators and scoring logic. Build and test that first. The frontend is straightforward CSS/JSX using established patterns.\n\n### Verification\n\n- **Backend**: Integration test — create 4+ technique pages with overlapping tags/categories, call `GET /techniques/{slug}`, assert `related_links` contains expected entries with correct `reason` strings and scores ≤ 4 results.\n- **Frontend**: `npm run build` passes with zero errors. Visual verification that the related section renders cards (not a plain list) with creator name and category badge.\n- **End-to-end**: On the live system, navigate to any technique page and confirm 3-4 related techniques appear at the bottom.\n","milestone_id":"M010","slice_id":"S02"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1757,"cacheRead":76358,"cacheWrite":291,"totalTokens":78407,"cost":{"input":0.000005,"output":0.043925,"cacheRead":0.038179,"cacheWrite":0.00181875,"total":0.08392774999999998}},"stopReason":"toolUse","timestamp":1774937189242}}
{"type":"message","id":"aeb87955","parentId":"07fe3f98","timestamp":"2026-03-31T06:07:14.254Z","message":{"role":"toolResult","toolCallId":"toolu_01NCDvrQ9qC1YbQKuHjoaXLt","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M010/slices/S02/S02-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M010/slices/S02/S02-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774937234251}}
{"type":"message","id":"e181b0bd","parentId":"aeb87955","timestamp":"2026-03-31T06:07:17.799Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S02 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":76649,"cacheWrite":1795,"totalTokens":78456,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.0383245,"cacheWrite":0.011218750000000001,"total":0.04982325}},"stopReason":"stop","timestamp":1774937234252}}
