hybrid-search-implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill hybrid-search-implementation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads hybrid-search-implementation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Hybrid Search Implementation
Patterns for combining vector similarity and keyword-based search.
When to Use This Skill
- Building RAG systems with improved recall
- Combining semantic understanding with exact matching
- Handling queries with specific terms (names, codes)
- Improving search for domain-specific vocabulary
- When pure vector search misses keyword matches
Core Concepts
1. Hybrid Search Architecture
Query → ┬─► Vector Search ──► Candidates ─┐
│ │
└─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results
2. Fusion Methods
| Method | Description | Best For |
|---|---|---|
| RRF | Reciprocal Rank Fusion | General purpose |
| Linear | Weighted sum of scores | Tunable balance |
| Cross-encoder | Rerank with neural model | Highest quality |
| Cascade | Filter then rerank | Efficiency |
Templates and detailed worked examples
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
Best Practices
Do's
- Tune weights empirically - Test on your data
- Use RRF for simplicity - Works well without tuning
- Add reranking - Significant quality improvement
- Log both scores - Helps with debugging
- A/B test - Measure real user impact
Don'ts
- Don't assume one size fits all - Different queries need different weights
- Don't skip keyword search - Handles exact matches better
- Don't over-fetch - Balance recall vs latency
- Don't ignore edge cases - Empty results, single word queries
FAQ
What does the hybrid-search-implementation skill do?
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
How do I install hybrid-search-implementation?
Run npx skills add https://github.com/wshobson/agents --skill hybrid-search-implementation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/hybrid-search-implementation/ (personal) or .claude/skills/hybrid-search-implementation/ (project).
Which AI agents can use hybrid-search-implementation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is hybrid-search-implementation safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket flagged items to review; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains hybrid-search-implementation?
It is published by wshobson in wshobson/agents. It has 10,867 recorded installs.
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