Skip to content
TrustedSkillsbeta
Back to all skills

Eval Audit

Checked
hamelsmu·vlatest·ai-ml·MIT

Analyzes AI outputs for bias, fairness, safety, and adherence to guidelines; generates detailed audit reports.

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 hamelsmu/evals-skills --skill eval-audit -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

hamelsmu

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 hamelsmu/evals-skills at 22418da2bf.

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

Commit
22418da2bf
Manifest
skills/eval-audit/SKILL.md
Scanned
1 file, 182 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 eval-audit skill enables AI agents to systematically review and assess evaluation datasets or criteria. It helps ensure that testing frameworks are robust, unbiased, and aligned with specific performance goals before deployment.

When to use it

  • Validating the quality and diversity of a dataset used for training or testing an AI model.
  • Checking whether evaluation metrics accurately reflect real-world success criteria.
  • Identifying potential biases or edge cases within existing test suites.
  • Auditing compliance with internal standards or external regulatory requirements for model assessment.

Key capabilities

  • Systematic review of evaluation datasets and criteria.
  • Assessment of metric alignment with performance goals.
  • Detection of bias or gaps in testing frameworks.
  • Structured reporting on audit findings.

Example prompts

  • "Run an audit on our current LLM benchmark dataset to check for representation bias."
  • "Evaluate whether our success metrics for the customer support bot align with user satisfaction surveys."
  • "Audit this set of unit tests for a code generation model to identify missing edge cases."

Tips & gotchas

Ensure you have access to the full evaluation data and clear definitions of success criteria before running an audit. The skill relies on structured input; unorganized or ambiguous datasets may yield incomplete results.