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Agent Evaluation

Checked
omer-metin·vlatest·agents·Apache-2.0

Evaluates text generation quality based on specified criteria like accuracy, relevance, and style for improved AI outputs.

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 omer-metin/skills-for-antigravity --skill agent-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

omer-metin

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 omer-metin/skills-for-antigravity at e8dcf4e873.

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

Commit
e8dcf4e873
Manifest
skills/agent-evaluation/SKILL.md
Scanned
4 files, 1,360 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 omer-metin-agent-evaluation skill provides a framework for evaluating the performance of AI agents. It allows users to define evaluation criteria, run agent tasks, and receive structured feedback on the agent's effectiveness against those criteria. This facilitates iterative improvement and ensures agents are meeting desired objectives.

When to use it

  • Testing new agent versions: Evaluate changes made to an agent’s logic or capabilities before deployment.
  • Comparing different agents: Determine which agent performs best for a specific task based on defined metrics.
  • Identifying areas for improvement: Pinpoint weaknesses in an agent's performance through detailed evaluation reports.
  • Validating agent alignment: Ensure the agent’s behavior aligns with intended goals and ethical guidelines.

Key capabilities

  • Define custom evaluation criteria.
  • Run predefined tasks against agents.
  • Generate structured feedback reports.
  • Compare agent performance across multiple runs.

Example prompts

  • "Evaluate Agent X on task 'Summarize this article' using the following criteria: accuracy, conciseness, and clarity."
  • "Run a benchmark test of Agents A and B against the 'Customer Service Chatbot' scenario."
  • "Generate a report comparing Agent Y’s performance across five different evaluation runs."

Tips & gotchas

The quality of the evaluation depends heavily on well-defined criteria. Ensure your evaluation metrics are specific, measurable, achievable, relevant, and time-bound (SMART) for accurate results.