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Llm Evaluation

Checked
microck·vlatest·ai-ml

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 microck/ordinary-claude-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

microck

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

    Only contacts known or declared hosts: github.com.

    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.

    Hosts contacted

    github.comsource host

  • 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 microck/ordinary-claude-skills at 1056d293d4.

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

Commit
1056d293d4
Manifest
skills_all/llm-evaluation/SKILL.md
Scanned
2 files, 462 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

The microck-llm-evaluation skill provides a framework for evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and prompts, then assess LLM responses based on those defined parameters. The skill aims to provide structured feedback on LLM performance, enabling iterative improvement of model behavior.

When to use it

  • Benchmarking new models: Compare the performance of different LLMs on a specific task or dataset.
  • Evaluating prompt engineering efforts: Determine if changes to prompts are improving LLM output quality.
  • Identifying failure modes: Pinpoint areas where an LLM consistently struggles or produces undesirable responses.
  • Measuring improvements after fine-tuning: Quantify the impact of fine-tuning on a specific model's capabilities.

Key capabilities

  • Define evaluation criteria (e.g., accuracy, relevance, safety).
  • Create and manage prompts for LLM evaluation.
  • Assess LLM responses against defined criteria.
  • Provide structured feedback on LLM performance.

Example prompts

  • "Evaluate the following response to the prompt: '[Prompt text]' using these criteria: [Criteria list]"
  • "Run an evaluation of model X on this set of prompts: [List of prompts]"
  • "Compare the responses from Model A and Model B to the prompt 'Summarize this article:' [Article Text] based on accuracy."

Tips & gotchas

The skill's effectiveness relies heavily on clearly defined evaluation criteria. Ambiguous or poorly designed criteria will lead to unreliable results.