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Rag Implementation

Checked
hermeticormus·vlatest·ai-ml·MIT

This skill provides a robust Rag (Retrieval-Augmented Generation) implementation for seamless integration of external knowledge into AI responses.

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 ormus-solutions/libreuiux-claude-code --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

hermeticormus

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 hermeticormus/libreuiux-claude-code at 2a69cbbc46.

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

Commit
2a69cbbc46
Manifest
plugins/llm-application-dev/skills/rag-implementation/SKILL.md
Scanned
1 file, 366 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

This skill provides Retrieval Augmented Generation (RAG) implementation capabilities. It allows an AI agent to access and incorporate external knowledge sources into its responses, improving accuracy and relevance. The skill leverages a vector database for efficient information retrieval and integrates this data into the generation process.

When to use it

  • Answering questions based on specific documents: Use it when you need answers derived from a particular set of files or internal documentation.
  • Generating reports with context: Generate detailed reports that incorporate information from multiple sources, ensuring all relevant details are included.
  • Providing up-to-date information: Keep responses current by retrieving the latest data from external knowledge bases.
  • Summarizing large documents: Condense lengthy texts while retaining key facts and insights using retrieved context.

Key capabilities

  • Vector database integration
  • Information retrieval based on semantic similarity
  • Contextualized text generation
  • RAG implementation

Example prompts

  • "Answer the question 'What is the company's return policy?' using the provided document."
  • "Summarize this article and include relevant statistics from the attached dataset."
  • "Generate a report on sales performance for Q3, incorporating data from both the CRM and marketing analytics platforms."

Tips & gotchas

  • Ensure your vector database is properly indexed and populated with relevant information for optimal results.
  • The quality of the retrieved context directly impacts the accuracy and relevance of generated responses.