Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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Use similarity-search-patterns to design a vector-database semantic-search solution for [content corpus] that returns an implementation plan, retrieval configuration, and validation criteria.
Required inputs
[content corpus]
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SKILL.md
name: similarity-search-patternsdescription: "Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance."risk: safesource: communitydate_added: "2026-02-27"
Similarity Search Patterns
Patterns for implementing efficient similarity search in production systems.
Use this skill when
Building semantic search systems
Implementing RAG retrieval
Creating recommendation engines
Optimizing search latency
Scaling to millions of vectors
Combining semantic and keyword search
Do not use this skill when
The task is unrelated to similarity search patterns
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.
Resources
resources/implementation-playbook.md for detailed patterns and examples.
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.