Agent skill · research science · rmyndharis
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
Why this skill is useful
Provides specialized knowledge on vector databases and embedding strategies that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-02. 1,255 stars on the source repository.
What this skill does
Vector Database Engineer Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems. Do not use this skill when The task is unrelated to vector database engineer You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. Capabilities Vector database selection and architecture Embedding model selection and optimization Index configuration (HNSW, IVF, PQ) Hybrid search (vector + keyword) implementation Chunking strategies for documents Metadata filtering and pre/post-filtering Performance tuning and scaling Use this skill when Building RAG (Retrieval Augmented Generation) systems Implementing semantic search over documents Creating recommendation engines Building image/audio similarity search Optimizing vector search latency and recall Scaling vector operations to millions of vectors Workflow 1. Analyze data characteristics and query patterns 2. Select appropriate embedding model 3. Design chunking and preprocessing pipeline 4. Choose vector database and index type 5. Configure metadata schema for filtering 6. Implement hybrid search if needed 7. Optimize for latency/recall tradeoffs 8. Set up monitoring and reindexing strategies Best Practices Choose embedding dimensions based on use case (384-1536) Implement proper chunking with overlap Use metadata filtering to reduce search space Monitor embedding drift over time Plan for index rebuilding Cache frequent queries Test recall vs latency tradeoffs
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills rmyndharis/vector-database-engineer