Agent skill

Local RAG Search

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.

MITAuto-check passedAI & LLM Engineering

Install Local RAG Search

skills CLI

$ npx skills add nkapila6/mcp-local-rag --skill local-rag-search -a claude-code

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

GitHub CLI

$ gh skill install nkapila6/mcp-local-rag local-rag-search --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/nkapila6/mcp-local-rag.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/local-rag-search .claude/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .claude/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search

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 nkapila6/mcp-local-rag --skill local-rag-search -a codex

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

GitHub CLI

$ gh skill install nkapila6/mcp-local-rag local-rag-search --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/nkapila6/mcp-local-rag.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/local-rag-search .agents/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .agents/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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 nkapila6/mcp-local-rag --skill local-rag-search -a cursor

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

GitHub CLI

$ gh skill install nkapila6/mcp-local-rag local-rag-search --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/nkapila6/mcp-local-rag.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/local-rag-search .cursor/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .cursor/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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/nkapila6/mcp-local-rag.git --path skills/local-rag-search

--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 nkapila6/mcp-local-rag --skill local-rag-search -a gemini-cli

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

GitHub CLI

$ gh skill install nkapila6/mcp-local-rag local-rag-search --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/nkapila6/mcp-local-rag.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/local-rag-search .gemini/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .gemini/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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 nkapila6/mcp-local-rag local-rag-search

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 nkapila6/mcp-local-rag --skill local-rag-search -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/nkapila6/mcp-local-rag.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/local-rag-search .github/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .github/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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 nkapila6/mcp-local-rag --skill local-rag-search -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 nkapila6/mcp-local-rag local-rag-search --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/nkapila6/mcp-local-rag.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/local-rag-search .opencode/skills/local-rag-search && 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 "local-rag-search" agent skill from https://github.com/nkapila6/mcp-local-rag/tree/main/skills/local-rag-search into .opencode/skills/local-rag-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag-search", 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: local-rag-search
GitHub stars: 134
Used in: 1 other repo
Token cost: ~1.6k tokens
SKILL.md length: 520 words
Files: 3
Skills in repo: 1
Repo updated: Aug 31, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.

Works in 5 steps: rag_search_ddgs - DuckDuckGo Search → rag_search_google - Google Search → deep_research - Multi-Engine Deep Research → …
You need to search the web for current information
SKILL.md covers Available Tools, Best Practices, Workflow Examples and Guidelines, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Local RAG Search is an agent skill from nkapila6/mcp-local-rag. Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `README.md` and `package.json`).

It sits in AI & LLM Engineering, covering Web search, Retrieval-augmented generation and Deep research. It works with Model Context Protocol. The repository describes itself as: "primitive" RAG-like web search model context protocol (MCP) server that runs locally. ✨ no APIs ✨. The licence is MIT.

When your agent uses it

You need to search the web for current information
Research topics across multiple sources
Gather context from the internet without using external APIs

Example prompts

“/local-rag-search”

Requirements

Docker

Workflow steps

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

rag_search_ddgs - DuckDuckGo Search
rag_search_google - Google Search
deep_research - Multi-Engine Deep Research
deep_research_google - Google-Only Deep Research
deep_research_ddgs - DuckDuckGo-Only Deep Research

What it can do on your machine

Read from SKILL.md and the folder at commit aabb55d. 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

Local RAG Search loads about 1.6k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 520 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~105
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

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 nkapila6/mcp-local-rag at commit aabb55d, republished under its MIT licence (© nkapila6). 520 words, ~1,592 tokens.

Download SKILL.mdSave it as .claude/skills/local-rag-search/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.

name: local-rag-search
description: Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.

Local RAG Search Skill

This skill enables you to effectively use the mcp-local-rag MCP server for intelligent web searches with semantic ranking. The server performs RAG-like similarity scoring to prioritize the most relevant results without requiring any external APIs.

Available Tools

1. `rag_search_ddgs` - DuckDuckGo Search

Use this for privacy-focused, general web searches.

When to use:

User prefers privacy-focused searches
General information lookup
Default choice for most queries

Parameters:

query: Natural language search query
num_results: Initial results to fetch (default: 10)
top_k: Most relevant results to return (default: 5)
include_urls: Include source URLs (default: true)

2. `rag_search_google` - Google Search

Use this for comprehensive, technical, or detailed searches.

When to use:

Technical or scientific queries
Need comprehensive coverage
Searching for specific documentation

3. `deep_research` - Multi-Engine Deep Research

Use this for comprehensive research across multiple search engines.

When to use:

Researching complex topics requiring broad coverage
Need diverse perspectives from multiple sources
Gathering comprehensive information on a subject

Available backends:

duckduckgo: Privacy-focused general search
google: Comprehensive technical results
bing: Microsoft's search engine
brave: Privacy-first search
wikipedia: Encyclopedia/factual content
yahoo, yandex, mojeek, grokipedia: Alternative engines

Default: ["duckduckgo", "google"]

4. `deep_research_google` - Google-Only Deep Research

Shortcut for deep research using only Google.

5. `deep_research_ddgs` - DuckDuckGo-Only Deep Research

Shortcut for deep research using only DuckDuckGo.

Best Practices

Query Formulation

Use natural language: Write queries as questions or descriptive phrases
- Good: "latest developments in quantum computing"
- Good: "how to implement binary search in Python"
- Avoid: Single keywords like "quantum" or "Python"
Be specific: Include context and details
- Good: "React hooks best practices for 2024"
- Better: "React useEffect cleanup function best practices"

Tool Selection Strategy

Single Topic, Quick Answer → Use rag_search_ddgs or rag_search_google

rag_search_ddgs(
    query="What is the capital of France?",
    top_k=3
)

Technical/Scientific Query → Use rag_search_google

rag_search_google(
    query="Docker multi-stage build optimization techniques",
    num_results=15,
    top_k=7
)

Comprehensive Research → Use deep_research with multiple search terms

deep_research(
    search_terms=[
        "machine learning fundamentals",
        "neural networks architecture",
        "deep learning best practices 2024"
    ],
    backends=["google", "duckduckgo"],
    top_k_per_term=5
)

Factual/Encyclopedia Content → Use deep_research with Wikipedia

deep_research(
    search_terms=["World War II timeline", "WWII key battles"],
    backends=["wikipedia"],
    num_results_per_term=5
)

Show full SKILL.md (222 more words)Show less

Parameter Tuning

For quick answers:

num_results=5-10, top_k=3-5

For comprehensive research:

num_results=15-20, top_k=7-10

For deep research:

num_results_per_term=10-15, top_k_per_term=3-5
Use 2-5 related search terms
Use 1-3 backends (more = more comprehensive but slower)

Workflow Examples

Example 1: Current Events

Task: "What happened at the UN climate summit last week?"

1. Use rag_search_google for recent news coverage
2. Set top_k=7 for comprehensive view
3. Present findings with source URLs

Example 2: Technical Deep Dive

Task: "How do I optimize PostgreSQL queries?"

1. Use deep_research with multiple specific terms:
   - "PostgreSQL query optimization techniques"
   - "PostgreSQL index best practices"
   - "PostgreSQL EXPLAIN ANALYZE tutorial"
2. Use backends=["google", "stackoverflow"] if available
3. Synthesize findings into actionable guide

Example 3: Multi-Perspective Research

Task: "Research the impact of remote work on productivity"

1. Use deep_research with diverse search terms:
   - "remote work productivity statistics 2024"
   - "hybrid work model effectiveness studies"
   - "work from home challenges research"
2. Use backends=["google", "duckduckgo"] for broad coverage
3. Synthesize different perspectives and studies

Guidelines

Always cite sources: When include_urls=True, reference the source URLs in your response
Verify recency: Check if the content appears current and relevant
Cross-reference: For important facts, use multiple search terms or engines
Respect privacy: Use DuckDuckGo for general queries unless specific needs require Google
Batch related queries: When researching a topic, create multiple related search terms for deep_research
Semantic relevance: Trust the RAG scoring - top results are semantically closest to the query
Explain your choice: Briefly mention which tool you're using and why

Error Handling

If a search returns insufficient results:

Try rephrasing the query with different keywords
Switch to a different backend
Increase num_results parameter
Use deep_research with multiple related search terms

Privacy Considerations

DuckDuckGo: Privacy-focused, doesn't track users
Google: Most comprehensive but tracks searches
Recommend DuckDuckGo as default unless user specifically needs Google's coverage

Performance Notes

First search may be slower (model loading)
Subsequent searches are faster (cached models)
More backends = more comprehensive but slower
Adjust num_results and top_k based on use case

Files

SKILL.md and 2 other files in skills/local-rag-search of nkapila6/mcp-local-rag.

SKILL.md
README.md
package.json

Open the folder on GitHubat commit aabb55d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in nkapila6/mcp-local-rag, which our catalogue first saw on October 7, 2026.

sundial-org/awesome-openclaw-skillsskills/local-rag-search

Compare with similar skills

Local RAG Search 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.

Local RAG Search compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Local RAG Search this skillnkapila6/mcp-local-rag	134	1 repos	~1.6k	Automated safety check: Pass	MIT	1 mo ago
Exa Neural Search via MCPaffaan-m/ECC	277k	4 repos	~1.1k	Automated safety check: Pass	MIT	today
Sciverseopendatalab/Sciverse-Agent-Tools	120	—	~3k	Automated safety check: Pass	Custom licence	21 days ago
Deep Research with Firecrawl and Exaaffaan-m/ECC	276k	—	~150	Automated safety check: Pass	MIT	yesterday
MCP Local RAGshinpr/mcp-local-rag	412	—	~4.4k	Automated safety check: Pass	MIT	today
AutoRAG Setup and RepairMarker-Inc-Korea/AutoRAG	5.1k	—	~5.6k	Automated safety check: Pass	MIT	today

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Works with

Model Context Protocol

Questions about Local RAG Search

What does Local RAG Search do?

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Local RAG Search is an agent skill from nkapila6/mcp-local-rag. Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.

When should I use Local RAG Search?

Local RAG Search fits situations like: you need to search the web for current information; research topics across multiple sources; gather context from the internet without using external APIs.

How do I install Local RAG Search in Claude Code?

Run `npx skills add nkapila6/mcp-local-rag --skill local-rag-search -a claude-code`. Or copy the skill folder (skills/local-rag-search in nkapila6/mcp-local-rag) into .claude/skills/local-rag-search in your project. Claude Code loads it when a task matches its description.

How do I install Local RAG Search in Codex?

Run `npx skills add nkapila6/mcp-local-rag --skill local-rag-search -a codex`. Or copy the skill folder (skills/local-rag-search in nkapila6/mcp-local-rag) into .agents/skills/local-rag-search in your project. Codex loads it when a task matches its description.

Can I use Local RAG Search 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 nkapila6/mcp-local-rag --skill local-rag-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/local-rag-search, .gemini/skills/local-rag-search, .github/skills/local-rag-search and .opencode/skills/local-rag-search in your project.

What does Local RAG Search need to run?

SKILL.md names no scripts, command-line tools or credentials: Local RAG Search is instructions for the agent only. Our summary lists: Docker.

Does Local RAG Search 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 Local RAG Search 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 Local RAG Search use?

Local RAG Search 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 Local RAG Search use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Local RAG Search?

Skills that share tags, products or a category with Local RAG Search: Exa Neural Search via MCP (affaan-m/ECC, 277k stars), Sciverse (opendatalab/Sciverse-Agent-Tools, 120 stars), Deep Research with Firecrawl and Exa (affaan-m/ECC, 276k stars) and MCP Local RAG (shinpr/mcp-local-rag, 412 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Local RAG Search?

nkapila6 (a GitHub user) maintains it in nkapila6/mcp-local-rag, which has 134 GitHub stars. The repository was last updated on August 31, 2026.

Source: nkapila6/mcp-local-rag on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Local RAG Search

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Local RAG Search Skill

Available Tools

1. rag_search_ddgs - DuckDuckGo Search

2. rag_search_google - Google Search

3. deep_research - Multi-Engine Deep Research

4. deep_research_google - Google-Only Deep Research

5. deep_research_ddgs - DuckDuckGo-Only Deep Research

Best Practices

Query Formulation

Tool Selection Strategy

Parameter Tuning

Workflow Examples

Example 1: Current Events

Example 2: Technical Deep Dive

Example 3: Multi-Perspective Research

Guidelines

Error Handling

Privacy Considerations

Performance Notes

Files

Used in 1 other repository

Compare with similar skills

Similar skills

Exa Neural Search via MCP

Sciverse

Deep Research with Firecrawl and Exa

MCP Local RAG

AutoRAG Setup and Repair

Digoal Read Think Writer

Related

Questions about Local RAG Search

1. `rag_search_ddgs` - DuckDuckGo Search

2. `rag_search_google` - Google Search

3. `deep_research` - Multi-Engine Deep Research

4. `deep_research_google` - Google-Only Deep Research

5. `deep_research_ddgs` - DuckDuckGo-Only Deep Research