ai-vector-brain

# AI Vector Brain Build a vector brain implementation. V1 default: Postgres + pgvector. Pick a corpus playbook. Use this skill when the user asks to: - build a vector brain, RAG brain, LLM brain, repo brain, docs brain, or compliance brain - turn a repo, docs hub, policy corpus, guide set, or generated context artifacts into a repeatable retrieval layer - choose a vector-brain backend and produce a concrete implementation path - create SQL, manifests, ingestion scripts, eval seeds, or an agent retrieval tool contract ## Boundary Contract | Skill | Owns | Does Not Own | |---|---|---| | `ai-context-layer` | Where context lives: memory vs retrieval vs tools, grounding, provenance, lifecycle, app context architecture | Paste-ready SQL, vector DB schemas, embedding pipelines, ingest scripts | | `ai-rag` | Retrieval theory: chunking principles, hybrid fusion concepts, reranking concepts, eval theory, when retrieval is wrong | Operational DDL, backend-specific SQL, concrete ingest scripts | | `ai-agents` | Agent topology, tool use, planner/critic flows, agent vs workflow decisions | Retrieval backend implementation | | `ai-bot-builder` | Bot UX, conversation flows, escalation, channel integration, KB use in a bot surface | KB/vector-brain construction | | `ai-vector-brain` | How to build: corpus inventory, manifests, DDL, ingest scripts, embeddings, hybrid search, eval seeds, backend recipes | Broad RAG theory, app context strategy, agent topology, bot UX | ## Quick Reference ...