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vector-database-engineer

vector-database-engineer

github.com/sickn33Data & databases48.7k395 installsUpdated today
Repository

About vector-database-engineer

A skill for engineering vector database and semantic retrieval systems, intended for teams building RAG, recommendation, or similarity-search applications. It covers database and index choices, embeddings, filtering, hybrid search, and performance tradeoffs.

Key features

  1. 01Vector database and index selection
  2. 02Embedding and chunking strategies
  3. 03Hybrid vector and keyword search
  4. 04Metadata filtering
  5. 05Latency and recall tuning
  6. 06Scaling and reindexing plans

Use cases

  1. 01Building document RAG retrieval
  2. 02Implementing semantic document search
  3. 03Creating similarity-based recommendations
  4. 04Tuning vector search performance

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill vector-database-engineer

After installing, type/vector-database-engineerin Claude Code to confirm it loaded.

Bundled filesresources/implementation-playbook.md

Details

From the project README

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.
  • If detailed examples are required, open resources/implementation-playbook.md.

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

  • Use the chosen model’s documented output dimensions; confirm database/index support and reindex on incompatible model changes
  • 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

Example

User request:

Build RAG (Retrieval Augmented Generation) systems.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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