hybrid-search-architectlisted

# Hybrid Search Architect ## What this skill does This skill designs a hybrid search pipeline that combines dense vector search (semantic similarity) with BM25 sparse search (keyword matching). Hybrid search outperforms either method alone on most retrieval benchmarks because vector search handles semantic meaning while BM25 handles exact keyword matches, product names, codes, and rare terms. This skill picks the right combination and fusion strategy for your use case. ## How to use ### Claude Code / Cline Copy this file to `.agents/skills/hybrid-search-architect/SKILL.md` in your project root. Then ask: - *"Use the Hybrid Search Architect to improve our RAG pipeline's retrieval."* - *"Design a hybrid search system for our product documentation."* Provide: - What you're searching (type of documents) - What queries look like (keywords, natural language, codes/IDs) - Your current search stack (Pinecone, Weaviate, Elasticsearch, pgvector, etc.) - Latency requirements ### Cursor / Codex Describe your current retrieval setup and query patterns alongside these instructions. ## The Prompt / Instructions for the Agent ### Step 1 — Determine if hybrid search is needed | Query pattern | Pure vector | Pure BM25 | Hybrid | |---|---|---|---| | Natural language questions | ✓ | — | ✓ | | Exact product names / SKUs | — | ✓ | ✓ | | Technical codes / IDs | — | ✓ | ✓ | | Conceptual / semantic | ✓ | — | ✓ | | Mixed (most real-world) | — | — | ✓ | **Use hybrid search when:** queries