Agent skill

Hybrid Search Implementation

Shows how to run vector and keyword search side by side and merge their results, so retrieval catches both meaning and exact terms in RAG and search systems.

MITAuto-check passedAI & LLM Engineering

Install Hybrid Search Implementation

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .claude/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .claude/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .agents/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .agents/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .cursor/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .cursor/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/wshobson/agents.git --path plugins/llm-application-dev/skills/hybrid-search-implementation

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .gemini/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .gemini/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .github/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .github/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wshobson/agents --skill hybrid-search-implementation -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install wshobson/agents hybrid-search-implementation --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/llm-application-dev/skills/hybrid-search-implementation .opencode/skills/hybrid-search-implementation && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "hybrid-search-implementation" agent skill from https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/hybrid-search-implementation into .opencode/skills/hybrid-search-implementation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hybrid-search-implementation", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: hybrid-search-implementation
GitHub stars: 40k
Used in: 9 other repos
Token cost: ~497 tokens
SKILL.md length: 179 words
Files: 2 (incl. references)
Skills in repo: 142
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Shows how to run vector and keyword search side by side and merge their results, so retrieval catches both meaning and exact terms in RAG and search systems.

Works in 2 steps: Hybrid Search Architecture → Fusion Methods
Building a RAG system whose recall is too low with embeddings alone
SKILL.md covers When to Use This Skill, Core Concepts, Templates and detailed worked… and Best Practices
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill covers search setups that run vector similarity and keyword matching in parallel, then fuse the two candidate lists into one ranking. Its architecture sketch shows both searches feeding a fusion step that produces the final results.

A table compares four fusion methods: Reciprocal Rank Fusion (RRF), a weighted linear sum, cross-encoder reranking, and a cascade that filters before reranking. Advice includes tuning weights on your own data, logging both scores for debugging, A/B testing the effect on users and not over-fetching candidates. Templates and worked examples are kept in references/details.md.

When your agent uses it

Building a RAG system whose recall is too low with embeddings alone
Searching content full of product codes, names or domain vocabulary
Choosing a fusion or reranking method for combined search results
Debugging queries where vector search misses exact keyword matches

Example prompts

“Our RAG retrieval misses queries that contain SKU codes. Add keyword search alongside the vectors.”
“Implement reciprocal rank fusion to merge keyword hits and embedding hits into one list.”
“Add a cross-encoder reranking step after the combined search.”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

Hybrid Search Architecture
Fusion Methods

What it can do on your machine

Read from SKILL.md and the folder at commit 46891e7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hybrid Search Implementation loads about 497 tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 51 tokens; SKILL.md has 179 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~51
When it runs · the whole SKILL.md, loaded when a task matches: ~497
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 179 words, ~497 tokens.

Download SKILL.mdSave it as .claude/skills/hybrid-search-implementation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

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

Files

SKILL.md and 1 other file (references) in plugins/llm-application-dev/skills/hybrid-search-implementation of wshobson/agents.

SKILL.md
references/details.md

Open the folder on GitHubat commit 46891e7

Used in 9 other repositories

We found 19 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 9 other GitHub owners. This page covers the copy in wshobson/agents, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Hybrid Search Implementation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hybrid Search Implementation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Hybrid Search Implementation this skillwshobson/agents	40k	9 repos	~497	Automated safety check: Pass	MIT	5 days ago
Postgrestimescale/pg-aiguide	1.9k	—	~941	Automated safety check: Pass	Apache-2.0	3 days ago
Searching DocumentsGAIK-project/gaik-toolkit	100	—	~4.2k	Automated safety check: Pass	MIT	yesterday
Qdrant Search Qualitygithub/awesome-copilot	40k	1 repos	~336	Automated safety check: Pass	MIT	yesterday
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs	13k	7 repos	~2.3k	Automated safety check: Pass	MIT	3 mo ago
Embeddings via 9Routerdecolua/9router	31k	—	~604	Automated safety check: Pass	MIT	2 days ago

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Questions about Hybrid Search Implementation

What does Hybrid Search Implementation do?

Shows how to run vector and keyword search side by side and merge their results, so retrieval catches both meaning and exact terms in RAG and search systems. The skill covers search setups that run vector similarity and keyword matching in parallel, then fuse the two candidate lists into one ranking. Its architecture sketch shows both searches feeding a fusion step that produces the final results.

When should I use Hybrid Search Implementation?

Hybrid Search Implementation fits situations like: building a RAG system whose recall is too low with embeddings alone; searching content full of product codes, names or domain vocabulary; choosing a fusion or reranking method for combined search results; debugging queries where vector search misses exact keyword matches.

How do I install Hybrid Search Implementation in Claude Code?

Run `npx skills add wshobson/agents --skill hybrid-search-implementation -a claude-code`. Or copy the skill folder (plugins/llm-application-dev/skills/hybrid-search-implementation in wshobson/agents) into .claude/skills/hybrid-search-implementation in your project. Claude Code loads it when a task matches its description.

How do I install Hybrid Search Implementation in Codex?

Run `npx skills add wshobson/agents --skill hybrid-search-implementation -a codex`. Or copy the skill folder (plugins/llm-application-dev/skills/hybrid-search-implementation in wshobson/agents) into .agents/skills/hybrid-search-implementation in your project. Codex loads it when a task matches its description.

Can I use Hybrid Search Implementation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wshobson/agents --skill hybrid-search-implementation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hybrid-search-implementation, .gemini/skills/hybrid-search-implementation, .github/skills/hybrid-search-implementation and .opencode/skills/hybrid-search-implementation in your project.

What does Hybrid Search Implementation need to run?

SKILL.md names no scripts, command-line tools or credentials: Hybrid Search Implementation is instructions for the agent only.

Does Hybrid Search Implementation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hybrid Search Implementation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hybrid Search Implementation use?

Hybrid Search Implementation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hybrid Search Implementation use?

About 497 tokens (SKILL.md is roughly 2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4k tokens, read only when the agent opens those files.

What are the alternatives to Hybrid Search Implementation?

Skills that share tags, products or a category with Hybrid Search Implementation: Postgres (timescale/pg-aiguide, 1.9k stars), Searching Documents (GAIK-project/gaik-toolkit, 100 stars), Qdrant Search Quality (github/awesome-copilot, 40k stars) and Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hybrid Search Implementation?

wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,314 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.

Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Hybrid Search Implementation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Hybrid Search Implementation

When to Use This Skill

Core Concepts

1. Hybrid Search Architecture

2. Fusion Methods

Templates and detailed worked examples

Best Practices

Do's

Don'ts

Files

Used in 9 other repositories

Compare with similar skills

Similar skills

More from wshobson/agents

Related

Questions about Hybrid Search Implementation