Skip to content
TrustedSkillsbeta
Back to all skills

Llm Evaluation

Checked
ovachiever·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 ovachiever/droid-tings --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

ovachiever

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 ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
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, ovachiever-llm-evaluation, provides a framework for evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and then assesses LLM responses against those criteria, providing structured feedback. The tool focuses on quantitative assessment of LLM outputs based on user-defined metrics.

When to use it

  • Benchmarking different LLMs: Compare the performance of various models on specific tasks or datasets using consistent evaluation metrics.
  • Improving prompt engineering: Assess how changes to prompts affect an LLM's output quality and refine prompts accordingly.
  • Measuring model progress: Track improvements in an LLM’s performance over time as it is fine-tuned or updated.
  • Validating new models: Quickly determine if a newly deployed LLM meets the required standards before wider adoption.

Key capabilities

  • Definable evaluation criteria
  • Quantitative assessment of LLM responses
  • Structured feedback reporting
  • Framework for benchmarking LLMs

Example prompts

  • "Evaluate this LLM response: '[Response text]' against these criteria: [Criteria list]."
  • "Compare the performance of Model A and Model B on this prompt: '[Prompt text]' using evaluation metrics [Metric names]."
  • "Assess how changing the prompt from '[Original Prompt]' to '[New Prompt]' affects the LLM's response quality."

Tips & gotchas

The effectiveness of this skill relies heavily on clearly defined and measurable evaluation criteria. Ambiguous or subjective criteria will lead to inconsistent results.