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Agent Evaluation

Checked
xfstudio·vlatest·agents

Evaluates agent performance across defined metrics, providing actionable insights to improve xfstudio agent capabilities.

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 xfstudio/skills --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

xfstudio

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 xfstudio/skills at 1bf6044db2.

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

Commit
1bf6044db2
Manifest
agent-evaluation/SKILL.md
Scanned
1 file, 65 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 xfstudio-agent-evaluation skill provides a structured way to assess and benchmark AI agents. It allows users to define evaluation criteria, run agents against predefined tasks or prompts, and generate reports summarizing agent performance. This facilitates objective comparison of different agents and identification areas for improvement.

When to use it

  • Agent Selection: Compare multiple AI agents to determine which best suits a specific task or application.
  • Performance Monitoring: Track the performance of an existing agent over time, identifying regressions or improvements after updates.
  • Benchmarking: Establish baseline scores for agents against standardized tasks and datasets.
  • Development & Tuning: Evaluate changes made to an agent’s configuration or training data.

Key capabilities

  • Define evaluation criteria (e.g., accuracy, speed, cost).
  • Run agents against predefined prompts/tasks.
  • Generate performance reports.
  • Agent comparison functionality.

Example prompts

  • "Evaluate Agent A and Agent B on the sentiment analysis task."
  • "Run a benchmark test of Agent C using the provided dataset and report accuracy, latency, and cost."
  • "Compare Agent D's performance against its previous version on the customer service chatbot scenario."

Tips & gotchas

The skill requires clear definition of evaluation criteria for meaningful results. Ensure that tasks or prompts used for evaluation are representative of the intended use case to get accurate comparisons.