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Production Rag Checklist

Checked
latestaiagents·vlatest·ai-ml·MIT

This skill generates a detailed checklist based on Production Readiness Guidelines (PRG) to ensure comprehensive documentation and mitigate risks.

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 latestaiagents/agent-skills --skill production-rag-checklist -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

latestaiagents

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 latestaiagents/agent-skills at 6120c5539d.

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

Commit
6120c5539d
Manifest
skills/rag-architect/production-rag-checklist/SKILL.md
Scanned
2 files, 720 lines
Copies in repo
2 — all of them scanned
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 a comprehensive checklist to ensure Retrieval Augmented Generation (RAG) systems are production-ready. It helps evaluate and improve the quality, reliability, and performance of RAG pipelines, covering aspects from data preparation to evaluation metrics. The checklist aims to reduce errors and optimize for real-world usage scenarios.

When to use it

  • Pre-launch validation: Before deploying a new RAG application or significant updates to an existing one.
  • Troubleshooting performance issues: When a RAG system is exhibiting unexpected behavior or inaccurate results.
  • Regular maintenance: As part of routine checks to maintain the health and accuracy of a production RAG pipeline.
  • Auditing for compliance: To ensure adherence to quality standards and best practices in RAG development.

Key capabilities

  • Provides a structured checklist format.
  • Covers data preparation, indexing, retrieval, generation, and evaluation stages.
  • Offers guidance on common pitfalls and potential solutions.
  • Focuses on production-level considerations for RAG systems.

Example prompts

  • "Run the RAG production readiness checklist."
  • "Evaluate my RAG pipeline using the production checklist; I'm seeing inconsistent results."
  • "What are the key data preparation steps in the production RAG checklist?"

Tips & gotchas

The skill assumes a basic understanding of RAG architecture and components. It’s most effective when used by individuals familiar with the specific RAG implementation being assessed.