hybrid-search-implementation – Other skill for AI agents
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Install the hybrid-search-implementation skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/hybrid-search-implementation-ae7e0.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /hybrid-search-implementation – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/hybrid-search-implementation-ae7e0.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $hybrid-search-implementation – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/hybrid-search-implementation-ae7e0.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /hybrid-search-implementation – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/hybrid-search-implementation-ae7e0.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /hybrid-search-implementation – then describe the task you want done.
SKILL.md content
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 ─► Results2. 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
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