ai-rag-pipeline
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/magentosh/superpowers --skill ai-rag-pipeline- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ai-rag-pipeline automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Install the belt CLI skill:
npx skills add belt-sh/cli
AI RAG Pipeline
Build RAG (Retrieval Augmented Generation) pipelines via inference.sh CLI.
Quick Start
Requires inference.sh CLI (
belt). Install instructions
belt login
# Simple RAG: Search + LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}')
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Based on this research, summarize the key trends: $SEARCH\"
}"
What is RAG?
RAG combines:
- Retrieval: Fetch relevant information from external sources
- Augmentation: Add retrieved context to the prompt
- Generation: LLM generates response using the context
This produces more accurate, up-to-date, and verifiable AI responses.
RAG Pipeline Patterns
Pattern 1: Simple Search + Answer
[User Query] -> [Web Search] -> [LLM with Context] -> [Answer]
Pattern 2: Multi-Source Research
[Query] -> [Multiple Searches] -> [Aggregate] -> [LLM Analysis] -> [Report]
Pattern 3: Extract + Process
[URLs] -> [Content Extraction] -> [Chunking] -> [LLM Summary] -> [Output]
Available Tools
Search Tools
| Tool | App ID | Best For |
|---|---|---|
| Tavily Search | tavily/search-assistant | AI-powered search with answers |
| Exa Search | exa/search | Neural search, semantic matching |
| Exa Answer | exa/answer | Direct factual answers |
Extraction Tools
| Tool | App ID | Best For |
|---|---|---|
| Tavily Extract | tavily/extract | Clean content from URLs |
| Exa Extract | exa/extract | Analyze web content |
LLM Tools
| Model | App ID | Best For |
|---|---|---|
| Claude Sonnet 4.5 | openrouter/claude-sonnet-45 | Complex analysis |
| Claude Haiku 4.5 | openrouter/claude-haiku-45 | Fast processing |
| GPT-4o | openrouter/gpt-4o | General purpose |
| Gemini 2.5 Pro | openrouter/gemini-25-pro | Long context |
Pipeline Examples
Basic RAG Pipeline
# 1. Search for information
SEARCH_RESULT=$(belt app run tavily/search-assistant --input '{
"query": "What are the latest breakthroughs in quantum computing 2024?"
}')
# 2. Generate grounded response
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"You are a research assistant. Based on the following search results, provide a comprehensive summary with citations.
Search Results:
$SEARCH_RESULT
Provide a well-structured summary with source citations.\"
}"
Multi-Source Research
# Search multiple sources
TAVILY=$(belt app run tavily/search-assistant --input '{"query": "electric vehicle market trends 2024"}')
EXA=$(belt app run exa/search --input '{"query": "EV market analysis latest reports"}')
# Combine and analyze
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Analyze these research results and identify common themes and contradictions.
Source 1 (Tavily):
$TAVILY
Source 2 (Exa):
$EXA
Provide a balanced analysis with sources.\"
}"
URL Content Analysis
# 1. Extract content from specific URLs
CONTENT=$(belt app run tavily/extract --input '{
"urls": [
"https://example.com/research-paper",
"https://example.com/industry-report"
]
}')
# 2. Analyze extracted content
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Analyze these documents and extract key insights:
$CONTENT
Provide:
1. Key findings
2. Data points
3. Recommendations\"
}"
Fact-Checking Pipeline
# Claim to verify
CLAIM="AI will replace 50% of jobs by 2030"
# 1. Search for evidence
EVIDENCE=$(belt app run tavily/search-assistant --input "{
\"query\": \"$CLAIM evidence studies research\"
}")
# 2. Verify claim
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Fact-check this claim: '$CLAIM'
Based on the following evidence:
$EVIDENCE
Provide:
1. Verdict (True/False/Partially True/Unverified)
2. Supporting evidence
3. Contradicting evidence
4. Sources\"
}"
Research Report Generator
TOPIC="Impact of generative AI on creative industries"
# 1. Initial research
OVERVIEW=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC overview\"}")
STATISTICS=$(belt app run exa/search --input "{\"query\": \"$TOPIC statistics data\"}")
OPINIONS=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC expert opinions\"}")
# 2. Generate comprehensive report
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Generate a comprehensive research report on: $TOPIC
Research Data:
== Overview ==
$OVERVIEW
== Statistics ==
$STATISTICS
== Expert Opinions ==
$OPINIONS
Format as a professional report with:
- Executive Summary
- Key Findings
- Data Analysis
- Expert Perspectives
- Conclusion
- Sources\"
}"
Quick Answer with Sources
# Use Exa Answer for direct factual questions
belt app run exa/answer --input '{
"question": "What is the current market cap of NVIDIA?"
}'
Best Practices
1. Query Optimization
# Bad: Too vague
"AI news"
# Good: Specific and contextual
"latest developments in large language models January 2024"
2. Context Management
# Summarize long search results before sending to LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "..."}')
# If too long, summarize first
SUMMARY=$(belt app run openrouter/claude-haiku-45 --input "{
\"prompt\": \"Summarize these search results in bullet points: $SEARCH\"
}")
# Then use summary for analysis
belt app run openrouter/claude-sonnet-45 --input "{
\"prompt\": \"Based on this research summary, provide insights: $SUMMARY\"
}"
3. Source Attribution
Always ask the LLM to cite sources:
belt app run openrouter/claude-sonnet-45 --input '{
"prompt": "... Always cite sources in [Source Name](URL) format."
}'
4. Iterative Research
# First pass: broad search
INITIAL=$(belt app run tavily/search-assistant --input '{"query": "topic overview"}')
# Second pass: dive deeper based on findings
DEEP=$(belt app run tavily/search-assistant --input '{"query": "specific aspect from initial search"}')
Pipeline Templates
Agent Research Tool
#!/bin/bash
# research.sh - Reusable research function
research() {
local query="$1"
# Search
local results=$(belt app run tavily/search-assistant --input "{\"query\": \"$query\"}")
# Analyze
belt app run openrouter/claude-haiku-45 --input "{
\"prompt\": \"Summarize: $results\"
}"
}
research "your query here"
Related Skills
# Web search tools
npx skills add inference-sh/skills@web-search
# LLM models
npx skills add inference-sh/skills@llm-models
# Content pipelines
npx skills add inference-sh/skills@ai-content-pipeline
# Full platform skill
npx skills add inference-sh/skills@infsh-cli
Browse all apps: belt app list
Documentation
- Adding Tools to Agents - Agent tool integration
- Building a Research Agent - Full guide
FAQ
What does the ai-rag-pipeline skill do?
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
How do I install ai-rag-pipeline?
Run npx skills add https://github.com/magentosh/superpowers --skill ai-rag-pipeline in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai-rag-pipeline/ (personal) or .claude/skills/ai-rag-pipeline/ (project).
Which AI agents can use ai-rag-pipeline?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ai-rag-pipeline safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ai-rag-pipeline?
It is published by magentosh in magentosh/superpowers. It has 41 recorded installs.
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