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Evaluation

Checked
guanyang·vlatest·other·MIT

This "Evaluation" skill assesses text for clarity, coherence, and potential issues, ensuring high-quality outputs and informed decisions.

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 guanyang/open-agent-hub --skill 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

guanyang

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 guanyang/open-agent-hub at 0d6f1e7d3f.

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

Commit
0d6f1e7d3f
Manifest
skills/evaluation/SKILL.md
Scanned
3 files, 1,227 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 enables AI agents to perform evaluations, though specific functional details regarding the evaluation methodology or target systems are not provided in the available documentation. It is part of the guanyang/antigravity-skills collection designed for agent capabilities.

When to use it

  • When you need an agent to assess performance metrics within a workflow.
  • For scenarios requiring structured feedback generation on completed tasks.
  • To validate outputs against specific criteria defined in your project scope.
  • When integrating custom evaluation logic into broader automation pipelines.

Key capabilities

  • Evaluation functionality provided by the guanyang/antigravity-skills suite.
  • Integration support for AI agent workflows (specifics undefined).
  • Installation capability via the TrustedSkills registry.

Example prompts

  • "Run an evaluation on the current task output using the guanyang-evaluation skill."
  • "What capabilities does the antigravity-skills evaluation module offer?"
  • "Install and configure the evaluation agent for my project."

Tips & gotchas

  • The source content lacks specific details on input formats, required parameters, or the exact nature of the evaluations performed. Users should consult the full registry documentation or author notes for implementation specifics.
  • With only 29 installations, this is a niche skill; ensure it aligns with your specific agent architecture before deployment.