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Rag

Checked
giuseppe-trisciuoglio·vlatest·ai-ml·MIT

Retrieval-Augmented Generation (RAG) enhances LLMs by retrieving relevant context from external data sources, boosting accuracy and knowledge.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add giuseppe-trisciuoglio/developer-kit --skill rag -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

giuseppe-trisciuoglio

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Only contacts known or declared hosts: api.openai.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.openai.commodel provider

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned giuseppe-trisciuoglio/developer-kit at fe73fb33a0.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
fe73fb33a0
Manifest
plugins/developer-kit-ai/skills/rag/SKILL.md
Scanned
8 files, 1,152 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

rag

What it does

This skill enables AI agents to retrieve and utilize information from external data sources, allowing them to answer questions based on specific documents or datasets rather than just their training knowledge. It bridges the gap between general model capabilities and domain-specific information by managing retrieval-augmented generation workflows.

When to use it

  • You need an agent to analyze custom company policies, legal contracts, or internal wikis that aren't part of its base training data.
  • Your application requires dynamic answers based on a specific knowledge base that changes frequently, such as real-time inventory logs or support tickets.
  • You want to reduce hallucinations by grounding the AI's responses in verified, retrieved context from provided files.

Key capabilities

  • Fetches relevant text chunks from external documents based on user queries.
  • Integrates with various vector databases and search engines for efficient data retrieval.
  • Formats retrieved context to enhance the accuracy of subsequent LLM generations.
  • Supports multi-document analysis by aggregating information across several sources.

Example prompts

  • "Summarize the Q3 financial report using only the attached PDF documents."
  • "What are the current return policies for products listed in this spreadsheet?"
  • "Explain the incident response procedure based on the security manual I uploaded."

Tips & gotchas

Ensure your external data sources are properly indexed and cleaned before relying on retrieval accuracy. Complex queries may require tuning the chunk size or embedding model to match specific document structures effectively.