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Llm Evaluation

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rmyndharis·vlatest·ai-ml·MIT

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

Repository

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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 rmyndharis/antigravity-skills --skill llm-evaluation -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

rmyndharis

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Install target

Live repository

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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.

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  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • Pinned to a commit: passed

    Scanned rmyndharis/antigravity-skills at 3eff4af253.

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Commit
3eff4af253
Manifest
skills/llm-evaluation/SKILL.md
Scanned
1 file, 459 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, rmyndharis-llm-evaluation, provides a framework for evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and scoring rubrics. The tool then applies these rubrics to LLM outputs, generating structured reports that highlight strengths and weaknesses based on the specified metrics.

When to use it

  • Comparing different LLMs: Evaluate several models against a common set of criteria to determine which best suits your needs.
  • Assessing model performance after fine-tuning: Measure improvements in an LLM's capabilities following fine-tuning efforts.
  • Identifying areas for improvement: Pinpoint specific weaknesses in an LLM’s responses based on defined evaluation metrics.
  • Ensuring consistent quality: Maintain a baseline level of output quality across multiple LLMs or different use cases.

Key capabilities

  • Define custom evaluation criteria.
  • Create scoring rubrics with detailed guidelines.
  • Apply rubrics to LLM outputs for structured assessment.
  • Generate reports summarizing performance against defined metrics.

Example prompts

  • "Evaluate this response from the LLM: '[LLM Response]' using the 'creative_writing' rubric."
  • "Compare the responses of Model A and Model B to the prompt '[Prompt]' based on the 'factual_accuracy' criteria."
  • “Generate a scoring rubric for evaluating code generation quality, focusing on correctness and efficiency.”

Tips & gotchas

The effectiveness of this skill depends heavily on well-defined evaluation criteria and clear scoring rubrics. Ambiguous or poorly defined metrics will lead to inconsistent and unreliable results.