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Skill Evaluator

Checked
gotalab·vlatest·agents·MIT

This skill assesses and ranks skills based on defined criteria, providing valuable insights for prioritization and optimization.

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 gotalab/skillport --skill skill-evaluator -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

gotalab

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 gotalab/skillport at 51334ae949.

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

Commit
51334ae949
Manifest
.skills/experimental/skill-evaluator/SKILL.md
Scanned
7 files, 1,639 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 skill-evaluator enables AI agents to assess and validate other skills within the registry. It provides a mechanism for determining the suitability, reliability, or compatibility of specific skill implementations before they are adopted into an agent's workflow.

When to use it

  • Before integrating a new third-party capability into your autonomous agent stack.
  • To verify the trustworthiness and version compatibility of a newly discovered tool.
  • During the audit phase when configuring complex multi-agent systems.
  • When comparing multiple implementations of the same functional requirement.

Key capabilities

  • Evaluates skills for AI agents.
  • Assesses skill metadata and provenance.
  • Validates installation prerequisites.

Example prompts

  • "Evaluate the skill-evaluator to check if it is compatible with my current Python-based agent environment."
  • "Run an evaluation on a newly discovered data processing skill to determine its reliability score."
  • "Use the evaluator to compare the performance metrics of two different image analysis skills."

Tips & gotchas

Ensure your agent has read access to the registry metadata before attempting to evaluate external skills. The evaluator may return inconclusive results if the target skill lacks sufficient documentation or usage statistics in the source database.