Agent-Skills.md

Agent Skills: 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.

UncategorizedID: wshobson/agents/hybrid-search-implementation

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wshobson/agents
wshobsonLicense: MIT
32,1353,507

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pnpm dlx add-skill https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/hybrid-search-implementation

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plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md

Skill Metadata

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.

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