local-rag-search

Original：🇺🇸 English

Translated

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.

1installs

Sourcesundial-org/awesome-openclaw-skills

Added on2026-02-20

NPX Install

npx skill4agent add sundial-org/awesome-openclaw-skills local-rag-search

SKILL.md Content

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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
)

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

local-rag-search

NPX Install

Tags

SKILL.md Content

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