Skip to content
TrustedSkillsbeta
Back to all skills

Agentic Eval

Official
github·vlatest·agents·MIT

Agentic Eval assesses an agent’s performance across multiple runs, identifying strengths and weaknesses for improved efficiency and reliability.

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 github/awesome-copilot --skill agentic-eval -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

github

Matched to the vendor's own GitHub organisation.

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 github/awesome-copilot at 6c4d33b9cf.

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

Commit
6c4d33b9cf
Manifest
skills/agentic-eval/SKILL.md
Scanned
1 file, 174 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 agentic-eval skill enables AI agents to evaluate their own performance or that of other agents through structured, goal-oriented assessments. It supports defining evaluation criteria, executing test scenarios, and providing feedback based on predefined metrics.

When to use it

  • To assess the accuracy and reliability of an agent's responses in a controlled environment.
  • When developing and refining AI agents for complex tasks like coding or content generation.
  • For benchmarking multiple agents against each other using standardized tests.
  • During quality assurance phases to ensure agents meet performance expectations.

Key capabilities

  • Customizable evaluation frameworks tailored to specific use cases.
  • Integration with test scenarios that simulate real-world agent interactions.
  • Automated feedback generation based on predefined success metrics.

Example prompts

  • "Evaluate the accuracy of this AI agent's code suggestions against a set of known solutions."
  • "Run a performance assessment for the agent using the provided benchmark dataset."
  • "Compare the response quality of two agents using the evaluation framework defined in the prompt."

Tips & gotchas

  • Ensure that evaluation criteria are clearly defined to avoid ambiguous results.
  • This skill may require additional configuration or integration with testing tools for advanced use cases.