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
- Analyze data characteristics and query patterns
- Select appropriate embedding model
- Design chunking and preprocessing pipeline
- Choose vector database and index type
- Configure metadata schema for filtering
- Implement hybrid search if needed
- Optimize for latency/recall tradeoffs
- 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
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.
Source: sickn33/agentic-awesome-skills → skills/vector-database-engineer/SKILL.md
Also appears in: sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills/skills/vector-database-engineer/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/vector-database-engineer/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-bundle-data-engineering/skills/vector-database-engineer/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-bundle-aas-data-engineering-platform/skills/vector-database-engineer/SKILL.md
1---2name: vector-database-engineer3description: Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar4---567# Vector Database Engineer89Expert 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.1011## Do not use this skill when1213- The task is unrelated to vector database engineer14- You need a different domain or tool outside this scope1516## Instructions1718- Clarify goals, constraints, and required inputs.19- Apply relevant best practices and validate outcomes.20- Provide actionable steps and verification.21- If detailed examples are required, open `resources/implementation-playbook.md`.2223## Capabilities2425- Vector database selection and architecture26- Embedding model selection and optimization27- Index configuration (HNSW, IVF, PQ)28- Hybrid search (vector + keyword) implementation29- Chunking strategies for documents30- Metadata filtering and pre/post-filtering31- Performance tuning and scaling3233## Use this skill when3435- Building RAG (Retrieval Augmented Generation) systems36- Implementing semantic search over documents37- Creating recommendation engines38- Building image/audio similarity search39- Optimizing vector search latency and recall40- Scaling vector operations to millions of vectors4142## Workflow43441. Analyze data characteristics and query patterns452. Select appropriate embedding model463. Design chunking and preprocessing pipeline474. Choose vector database and index type485. Configure metadata schema for filtering496. Implement hybrid search if needed507. Optimize for latency/recall tradeoffs518. Set up monitoring and reindexing strategies5253## Best Practices5455- Choose embedding dimensions based on use case (384-1536)56- Implement proper chunking with overlap57- Use metadata filtering to reduce search space58- Monitor embedding drift over time59- Plan for index rebuilding60- Cache frequent queries61- Test recall vs latency tradeoffs6263## Limitations64- Use this skill only when the task clearly matches the scope described above.65- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.66- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.6768---6970**Source:** [`sickn33/agentic-awesome-skills`](https://github.com/sickn33/agentic-awesome-skills) → `skills/vector-database-engineer/SKILL.md`7172**Also appears in:** `sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills/skills/vector-database-engineer/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/vector-database-engineer/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-bundle-data-engineering/skills/vector-database-engineer/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-bundle-aas-data-engineering-platform/skills/vector-database-engineer/SKILL.md`
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