Similarity Search Patterns

If you're building semantic search or RAG systems, this skill gives you production-ready patterns for vector similarity search. It covers the practical stuff: optimizing nearest neighbor queries, scaling to millions of vectors, and combining semantic with keyword search when you need both. The implementation playbook has detailed examples for common scenarios like recommendation engines and retrieval pipelines. It's focused on performance and scale, so it's most useful when latency and throughput actually matter to your use case, not when you're just prototyping with a few hundred embeddings.

skills.sh installs: 188
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill similarity-search-patterns --agent claude-code

Installs into .claude/skills of the current project.

Files

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.

SKILL.md on GitHub