# Send Ai Rag Pipeline to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "ai-rag-pipeline",
    "name": "Ai Rag Pipeline",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/okaris/ai-rag-pipeline",
    "canonicalUrl": "https://clawhub.ai/okaris/ai-rag-pipeline",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/ai-rag-pipeline",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ai-rag-pipeline",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/ai-rag-pipeline"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/ai-rag-pipeline",
    "downloadUrl": "https://openagent3.xyz/downloads/ai-rag-pipeline",
    "agentUrl": "https://openagent3.xyz/skills/ai-rag-pipeline/agent",
    "manifestUrl": "https://openagent3.xyz/skills/ai-rag-pipeline/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/ai-rag-pipeline/agent.md"
  }
}
```
## Documentation

### AI RAG Pipeline

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:

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.

### 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]

### Search Tools

ToolApp IDBest ForTavily Searchtavily/search-assistantAI-powered search with answersExa Searchexa/searchNeural search, semantic matchingExa Answerexa/answerDirect factual answers

### Extraction Tools

ToolApp IDBest ForTavily Extracttavily/extractClean content from URLsExa Extractexa/extractAnalyze web content

### LLM Tools

ModelApp IDBest ForClaude Sonnet 4.5openrouter/claude-sonnet-45Complex analysisClaude Haiku 4.5openrouter/claude-haiku-45Fast processingGPT-4oopenrouter/gpt-4oGeneral purposeGemini 2.5 Proopenrouter/gemini-25-proLong context

### 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?"
}'

### 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"}')

### 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
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: okaris
- Version: 0.1.5
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/ai-rag-pipeline)
- [Send to Agent page](https://openagent3.xyz/skills/ai-rag-pipeline/agent)
- [JSON manifest](https://openagent3.xyz/skills/ai-rag-pipeline/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/ai-rag-pipeline/agent.md)
- [Download page](https://openagent3.xyz/downloads/ai-rag-pipeline)