Skip to content
TrustedSkillsbeta
Back to all skills

Llm Evaluation

Checked
lifangda·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 lifangda/claude-plugins --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

lifangda

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 lifangda/claude-plugins at 76d90e8304.

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

Commit
76d90e8304
Manifest
cli-tool/skills-library/llm-application-dev/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, lifangda-llm-evaluation, provides a mechanism to evaluate Large Language Models (LLMs). It allows users to submit prompts and receive evaluations based on defined criteria. The tool is designed to assess LLM performance and provide feedback for improvement or selection.

When to use it

  • Compare different LLMs: Test several models with the same prompt set to see which performs best according to your needs.
  • Assess model quality: Evaluate an existing LLM's output against specific benchmarks or desired behaviors.
  • Fine-tuning evaluation: Measure the impact of fine-tuning efforts on a particular LLM’s performance.
  • Prompt engineering validation: Test and refine prompts to optimize LLM responses for clarity, accuracy, and relevance.

Key capabilities

  • LLM Evaluation
  • Prompt Submission
  • Criteria-based assessment

Example prompts

  • "Evaluate the following prompt: 'Write a short story about a cat detective' using these criteria: creativity, coherence, grammar."
  • "Can you assess this LLM response to the prompt 'Summarize the key points of photosynthesis': [paste response here]?"
  • "Compare the outputs of Model A and Model B for the prompt 'Translate 'hello world' into Spanish'."

Tips & gotchas

The evaluation criteria are crucial for meaningful results. Ensure you define clear, measurable criteria to accurately assess LLM performance.