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Found 40 Skills
Semantic and multi-modal search across documents using LanceDB vector embeddings. Use when searching knowledge bases, finding information semantically, ingesting documents for RAG, or performing vector similarity search. Triggers on "search documents", "semantic search", "find in knowledge base", "vector search", "index documents", "LanceDB", or RAG/embedding operations.
LLM app development with RAG, prompt engineering, vector databases, and AI agents
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.
Patterns for ingesting knowledge into vector databases and RAG systems