Agent skill

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

View SKILL.md on GitHub Repository
Stars 232
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/inference-sh/ai-rag-pipeline

SKILL.md

AI RAG Pipeline

Build RAG (Retrieval Augmented Generation) pipelines via inference.sh CLI.

Quick Start

bash
curl -fsSL https://cli.inference.sh | sh && infsh login

# Simple RAG: Search + LLM
SEARCH=$(infsh app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}')
infsh app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Based on this research, summarize the key trends: $SEARCH\"
}"

What is RAG?

RAG combines:

  1. Retrieval: Fetch relevant information from external sources
  2. Augmentation: Add retrieved context to the prompt
  3. 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

bash
# 1. Search for information
SEARCH_RESULT=$(infsh app run tavily/search-assistant --input '{
  "query": "What are the latest breakthroughs in quantum computing 2024?"
}')

# 2. Generate grounded response
infsh 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

bash
# Search multiple sources
TAVILY=$(infsh app run tavily/search-assistant --input '{"query": "electric vehicle market trends 2024"}')
EXA=$(infsh app run exa/search --input '{"query": "EV market analysis latest reports"}')

# Combine and analyze
infsh 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

bash
# 1. Extract content from specific URLs
CONTENT=$(infsh app run tavily/extract --input '{
  "urls": [
    "https://example.com/research-paper",
    "https://example.com/industry-report"
  ]
}')

# 2. Analyze extracted content
infsh 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

bash
# Claim to verify
CLAIM="AI will replace 50% of jobs by 2030"

# 1. Search for evidence
EVIDENCE=$(infsh app run tavily/search-assistant --input "{
  \"query\": \"$CLAIM evidence studies research\"
}")

# 2. Verify claim
infsh 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

bash
TOPIC="Impact of generative AI on creative industries"

# 1. Initial research
OVERVIEW=$(infsh app run tavily/search-assistant --input "{\"query\": \"$TOPIC overview\"}")
STATISTICS=$(infsh app run exa/search --input "{\"query\": \"$TOPIC statistics data\"}")
OPINIONS=$(infsh app run tavily/search-assistant --input "{\"query\": \"$TOPIC expert opinions\"}")

# 2. Generate comprehensive report
infsh 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

bash
# Use Exa Answer for direct factual questions
infsh app run exa/answer --input '{
  "question": "What is the current market cap of NVIDIA?"
}'

Best Practices

1. Query Optimization

bash
# Bad: Too vague
"AI news"

# Good: Specific and contextual
"latest developments in large language models January 2024"

2. Context Management

bash
# Summarize long search results before sending to LLM
SEARCH=$(infsh app run tavily/search-assistant --input '{"query": "..."}')

# If too long, summarize first
SUMMARY=$(infsh app run openrouter/claude-haiku-45 --input "{
  \"prompt\": \"Summarize these search results in bullet points: $SEARCH\"
}")

# Then use summary for analysis
infsh 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:

bash
infsh app run openrouter/claude-sonnet-45 --input '{
  "prompt": "... Always cite sources in [Source Name](URL) format."
}'

4. Iterative Research

bash
# First pass: broad search
INITIAL=$(infsh app run tavily/search-assistant --input '{"query": "topic overview"}')

# Second pass: dive deeper based on findings
DEEP=$(infsh app run tavily/search-assistant --input '{"query": "specific aspect from initial search"}')

Pipeline Templates

Agent Research Tool

bash
#!/bin/bash
# research.sh - Reusable research function

research() {
  local query="$1"

  # Search
  local results=$(infsh app run tavily/search-assistant --input "{\"query\": \"$query\"}")

  # Analyze
  infsh app run openrouter/claude-haiku-45 --input "{
    \"prompt\": \"Summarize: $results\"
  }"
}

research "your query here"

Related Skills

bash
# 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@inference-sh

Browse all apps: infsh app list

Documentation

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

aiskillstore/marketplace

perigon-backend

Perigon ASP.NET Core + EF Core + Aspire conventions

232 15
Explore
aiskillstore/marketplace

perigon-agent

Pointers for Copilot/agents to apply Perigon conventions

232 15
Explore
aiskillstore/marketplace

perigon-angular

Angular 21+ standalone/Material/signal conventions for Perigon WebApp

232 15
Explore
aiskillstore/marketplace

fastapi-mastery

Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.

232 15
Explore
aiskillstore/marketplace

context7-efficient

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.

232 15
Explore
aiskillstore/marketplace

browser-use

Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction.

232 15
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results