Best for
- Building RAG systems with improved recall
- Combining semantic understanding with exact matching
- Handling queries with specific terms (names, codes)
Source profileQuality 69/100
wshobson/agents/plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md
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.
- Source repository stars
- 38,313
- Declared platforms
- 0
- Static risk flags
- 1
- Last source update
- 2026-07-22
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
Patterns for combining vector similarity and keyword-based search.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
Source-detected install commandSource
npx skills add https://github.com/wshobson/agents --skill "plugins/llm-application-dev/skills/hybrid-search-implementation"Safe inspection promptEditorial
Inspect the Agent Skill "hybrid-search-implementation" from https://github.com/wshobson/agents/blob/c4b82b0ad771190355eb8e204b1329732a18449a/plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md at commit c4b82b0ad771190355eb8e204b1329732a18449a. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
When to Use This Skill
Building RAG systems with improved recall
Building RAG systems with improved recallCombining semantic understanding with exact matchingHandling queries with specific terms (names, codes) - 02
Core Concepts
Review the “Core Concepts” section in the pinned source before continuing.
Review and apply the “Core Concepts” source section. - 03
1. Hybrid Search Architecture
Review the “1. Hybrid Search Architecture” section in the pinned source before continuing.
Review and apply the “1. Hybrid Search Architecture” source section. - 04
2. Fusion Methods
Review the “2. Fusion Methods” section in the pinned source before continuing.
Review and apply the “2. Fusion Methods” source section.
Permission review
Static risk signals and limitations
Reads files
The documentation asks the agent to read local files, directories, or repositories.
Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.Evidence record
Why each signal appears
EvidenceSourceComputedTestedEditorial
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 69/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 38,313 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- wshobson/agents
- Skill path
- plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md
- Commit
- c4b82b0ad771190355eb8e204b1329732a18449a
- License
- MIT
- Collected
- 2026-07-28
- Default branch
- main
View the original SKILL.md
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