hybrid-search-implementation
Description
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems or building search engines.
How to Use
- Visit the GitHub repository to get the SKILL.md file
- Copy the file to your project root or .cursor/rules directory
- Restart your AI assistant or editor to apply the new skill
Full Skill Documentation
name
hybrid-search-implementation
description
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Tags
About hybrid-search-implementation
hybrid-search-implementation is an AI skill in the rag-search category, designed to help developers and users work more effectively with AI tools. Combine vector and keyword search for improved retrieval. Use when implementing RAG systems or building search engines.
This skill has earned 5,200 stars on GitHub, reflecting strong community adoption and trust. It is compatible with claude, codex.
Key Capabilities
Why Use hybrid-search-implementation
Adding hybrid-search-implementation to your AI workflow can significantly enhance your productivity in rag-search tasks. With pre-defined prompt templates and best practices, this skill helps AI assistants better understand your requirements and deliver more accurate responses.
Whether you use claude or codex, you can easily integrate this skill into your existing development environment.
Explore More rag-search Skills
Discover more AI skills in the rag-search category to build a comprehensive AI skill stack.
Related Skills
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search or nearest neighbor queries.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; desig
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to