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Llm Evaluation

Checked
hermeticormus·vlatest·ai-ml·MIT

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 ormus-solutions/libreuiux-claude-code --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

hermeticormus

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 hermeticormus/libreuiux-claude-code at 2a69cbbc46.

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

Commit
2a69cbbc46
Manifest
plugins/llm-application-dev/skills/llm-evaluation/SKILL.md
Scanned
1 file, 448 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, hermeticormus-llm-evaluation, provides automated evaluation of Large Language Model (LLM) outputs. It assesses responses based on pre-defined criteria and generates structured reports with scores for various aspects like helpfulness, accuracy, and safety. The tool can be used to benchmark LLMs or identify areas where a model needs improvement.

When to use it

  • Benchmarking different LLMs: Compare the performance of several models on a standardized set of prompts and evaluation criteria.
  • Evaluating prompt engineering efforts: Assess how changes to prompts impact the quality and safety of LLM responses.
  • Identifying model weaknesses: Pinpoint areas where an LLM consistently struggles, such as factual accuracy or avoiding harmful content.
  • Automated regression testing: Ensure that updates to an LLM don’t degrade its performance on critical tasks.

Key capabilities

  • Automated evaluation of LLM responses
  • Scoring based on predefined criteria (helpfulness, accuracy, safety)
  • Generation of structured reports with scores
  • Benchmarking across multiple models

Example prompts

  • "Evaluate the following response from an LLM: [LLM Response] against these criteria: helpfulness, accuracy, and safety."
  • "Compare the responses to this prompt '[Prompt]' between Model A and Model B, focusing on factual correctness."
  • "Run a regression test on the model's ability to answer questions about historical events. Provide a detailed report of scores."

Tips & gotchas

  • The quality of evaluation depends heavily on the clarity and specificity of the criteria provided. Well-defined scoring rubrics are essential for reliable results.