Hybrid Search Architect
CommunityArchitect smarter hybrid retrieval pipelines.
System Documentation
What problem does it solve?
Designs a hybrid retrieval 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.
Core Features & Use Cases
- Architectural guidance for selecting a hybrid vs vector-only or BM25-only approach.
- Step-by-step configuration for reciprocal rank fusion and stack tuning across popular backends (Weaviate, Elasticsearch/OpenSearch, pgvector).
- Real-world scenarios include product documentation search, code search, and knowledge bases with mixed content.
Quick Start
Copy this file to .agents/skills/hybrid-search-architect/SKILL.md in your project root to enable the hybrid retrieval architecture.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Hybrid Search Architect Download link: https://github.com/Notysoty/openagentskills/archive/main.zip#hybrid-search-architect Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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