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Llm As A Judge

Checked
maragudk·vlatest·ai-ml·CC0-1.0

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 maragudk/evals-skills --skill llm-as-a-judge -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

maragudk

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 maragudk/evals-skills at cb4ff15894.

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

Commit
cb4ff15894
Manifest
skills/llm-as-a-judge/SKILL.md
Scanned
3 files, 521 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 allows an AI agent to evaluate text based on provided criteria. It can assess responses for qualities like helpfulness, relevance, and accuracy against a defined rubric. The LLM acts as a judge, providing scores or rankings for different outputs.

When to use it

  • Evaluating chatbot responses: Determine which response from multiple chatbots is the most appropriate given a user query.
  • Grading writing samples: Assess student essays or creative writing pieces based on specific guidelines.
  • Comparing code solutions: Evaluate different code submissions for correctness and efficiency against a set of tests.
  • Content quality assessment: Score articles or blog posts based on factors like clarity, grammar, and factual accuracy.

Key capabilities

  • Text evaluation based on provided criteria
  • Scoring outputs against a rubric
  • Ranking multiple responses
  • Assessment for helpfulness, relevance, and accuracy

Example prompts

  • "Evaluate these two chatbot responses to the query 'How do I bake a cake?' and rank them from best to worst."
  • "Score this essay on a scale of 1-5 based on grammar, clarity, and argumentation."
  • "Assess which code solution is more efficient given these test cases."

Tips & gotchas

The quality of the evaluation heavily depends on the specificity and clarity of the criteria provided. Vague or ambiguous rubrics will lead to inconsistent results.