Ai Rag Pipeline
Helps with AI, pipeline automation as part of building AI and machine learning applications workflows.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add ai-rag-pipeline npx -- -y @trustedskills/ai-rag-pipeline
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"ai-rag-pipeline": {
"command": "npx",
"args": [
"-y",
"@trustedskills/ai-rag-pipeline"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The AI RAG pipeline enables an AI agent to retrieve, process, and generate responses based on external data sources. It integrates retrieval-augmented generation techniques, allowing the agent to access real-time or stored information for more accurate and context-aware outputs.
When to use it
- When answering questions that require up-to-date or domain-specific knowledge not present in the AI's training data.
- For applications like customer support where responses need to be grounded in company policies or FAQs.
- In research or analysis tasks where the agent must pull relevant information from databases, documents, or APIs.
Key capabilities
- Integration with external data sources for retrieval.
- Support for real-time and stored information processing.
- Generation of contextually accurate responses using retrieved data.
Example prompts
- "What is the latest policy on remote work?"
- "Summarize the key findings from this research paper."
- "Generate a response to customer query #12345 based on our knowledge base."
Tips & gotchas
- Ensure that external data sources are properly indexed and accessible for efficient retrieval.
- Be mindful of data privacy and access controls when integrating sensitive information into the pipeline.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.