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Rag Implementation

Checked
wshobson·vlatest·ai-ml·MIT

This skill provides a robust RAG (Retrieval-Augmented Generation) implementation for enhanced AI responses by retrieving relevant context.

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 wshobson/agents --skill rag-implementation -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

wshobson

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

    Makes no network calls.

    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.

  • 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 wshobson/agents at 46891e7e60.

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

Commit
46891e7e60
Manifest
plugins/llm-application-dev/skills/rag-implementation/SKILL.md
Scanned
2 files, 508 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

What it does

The rag-implementation skill enables AI agents to integrate and utilize Retrieval-Augmented Generation (RAG) systems. This allows agents to dynamically retrieve relevant information from external data sources and use it during the generation process, improving accuracy and contextual relevance of responses.

When to use it

  • When building an AI agent that needs access to up-to-date or domain-specific knowledge not present in its training data.
  • For applications requiring fact-checking or answering questions based on large document sets.
  • In scenarios where real-time information retrieval is necessary for generating accurate and context-aware responses.

Key capabilities

  • Integration with external databases or document repositories
  • Dynamic retrieval of relevant information during query processing
  • Enhanced response generation using retrieved data

Example prompts

  • "Find the latest research on climate change from scientific journals and summarize it."
  • "Retrieve customer support tickets related to billing issues and generate a response for the user."
  • "Search internal documentation for policies regarding data privacy and provide a concise summary."

Tips & gotchas

  • Ensure that the external data sources are properly indexed and accessible to the AI agent.
  • Be mindful of performance implications when retrieving large volumes of data, as this can impact response speed.