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 OpenRo...

Security Scan

The skill's purpose and instructions match (building RAG pipelines via the infsh CLI), but it recommends piping a remote install script from a non-standard domain and omits explicit credential guidance, which raises proportionality and install-risk concerns.

Purpose & Capability

The name, description, and SKILL.md consistently describe building RAG pipelines using the inference.sh (infsh) CLI and composing search/extraction apps with LLMs; required capabilities map to the described tools and examples.

Instruction Scope

Runtime instructions are narrowly focused on using infsh to run search/extract/LLM apps and composing outputs; they do not ask the agent to read arbitrary host files or unrelated credentials. However, the examples instruct running a remote installer (curl | sh) and expect the user/agent to authenticate via 'infsh login' without detailing where credentials are stored or how they're used.

Install Mechanism

There is no platform install spec, but SKILL.md directs users to 'curl -fsSL https://cli.inference.sh | sh' and references binaries hosted at dist.inference.sh. This is a remote-download-and-execute pattern from a domain that is not an obviously well-known release host (e.g., GitHub releases). Although the doc claims SHA-256 checksums are available, the example pipes to sh without demonstrating verification — that increases supply-chain risk.

Credentials

The skill declares no required env vars or credentials, which is consistent with being instruction-only. In practice the described workflows use OpenRouter-hosted LLMs and third-party search/extract services that will require API keys or an 'infsh' login. The skill does not enumerate or justify those credentials, making it unclear what credentials the agent/user must provide or where they are stored.

Persistence & Privilege

The skill does not request persistent privileges (always:false) and is instruction-only with no code written by the registry. It does not modify other skills or system-wide settings in its instructions.

What to consider before installing

This skill appears to do what it claims (compose RAG pipelines via the infsh CLI), but be cautious before running the suggested installer command. Prefer to: 1) Inspect the installer script at https://cli.inference.sh and the referenced dist.inference.sh host before executing; 2) download the binary and verify its SHA-256 checksum manually rather than piping to sh; 3) run the installer in an isolated environment (container or VM) if possible; 4) confirm where infsh stores API keys and only provide least-privilege credentials (use provider-specific API keys, not broad account credentials); and 5) if you need higher assurance, ask the publisher for a link to reproducible build/release artifacts (e.g., GitHub releases with signed checksums) or more details on authentication and data flows.

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

Quick Start

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\"
}"

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

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

# 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

# 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

# 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

# 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

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

# 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

# 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=$(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:

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

4. Iterative Research

# 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

#!/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

# 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

• Adding Tools to Agents - Agent tool integration
• Building a Research Agent - Full guide