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Llm Judge

Checked
existential-birds·vlatest·ai-ml·Apache-2.0

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 existential-birds/beagle --skill llm-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

existential-birds

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 existential-birds/beagle at d1a74899fb.

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

Commit
d1a74899fb
Manifest
plugins/beagle-analysis/skills/llm-judge/SKILL.md
Scanned
5 files, 742 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

The llm-judge skill enables AI agents to evaluate other models' outputs against specific criteria, acting as an automated quality control layer. It allows a primary agent to generate responses while a secondary "judge" model scores them for accuracy, tone, or adherence to constraints before final delivery. This creates a self-correcting loop that significantly improves reliability in complex workflows.

When to use it

  • Automated Grading: Use when an agent needs to score student answers, code submissions, or creative writing against rubrics without human intervention.
  • Safety Filtering: Deploy before sending sensitive data to external APIs to ensure prompts and responses comply with safety guidelines.
  • Consistency Checks: Run in parallel to verify that different agents produce consistent results on the same input task.
  • Feedback Loops: Integrate into iterative generation cycles where an agent refines its output based on a judge's critique until a score threshold is met.

Key capabilities

  • Dual-model architecture separating generator and evaluator roles.
  • Configurable scoring rubrics for custom evaluation metrics.
  • Automated feedback generation alongside numerical scores.
  • Support for iterative refinement based on judge input.

Example prompts

  1. "Generate a Python function to sort a list, then use the llm-judge skill to verify it handles edge cases like empty lists and duplicates."
  2. "Draft a customer service email response, but pass it through the judge first to ensure the tone remains empathetic before sending."
  3. "Create a multiple-choice quiz on quantum physics, then have the judge evaluate the questions for factual accuracy against a provided textbook summary."

Tips & gotchas

Ensure the judge model has access to the same context or reference materials as the generator to avoid biased evaluations due to information gaps. For high-stakes decisions, configure the system to require human review if the judge's confidence score falls below a specific threshold rather than auto-rejecting low-scoring outputs.