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Agent Evaluation

Checked
oimiragieo·vlatest·agents

Evaluates agent performance based on defined metrics, providing actionable insights for improvement 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 oimiragieo/agent-studio --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

oimiragieo

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 oimiragieo/agent-studio at 64b580eab2.

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

Commit
64b580eab2
Manifest
.claude/skills/agent-evaluation/SKILL.md
Scanned
10 files, 473 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 provides a framework for evaluating AI agents based on predefined criteria. It allows users to assess an agent's performance across various dimensions, providing structured feedback and identifying areas for improvement. The evaluation process is designed to be repeatable and objective, facilitating consistent comparisons between different agents or versions of the same agent.

When to use it

  • Comparing Agent Performance: Evaluate multiple AI agents tackling the same task to determine which performs best.
  • Iterative Development: Track an agent's progress over time by repeatedly evaluating its performance after updates and modifications.
  • Identifying Weaknesses: Pinpoint specific areas where an agent struggles, enabling targeted improvements in training or design.
  • Benchmarking New Agents: Establish a baseline for new agents entering your workflow through standardized evaluation metrics.

Key capabilities

  • Predefined evaluation criteria
  • Repeatable assessment process
  • Objective performance measurement
  • Comparative analysis of agents

Example prompts

  • "Evaluate agent 'TaskMaster' on the summarization task using the standard criteria."
  • "Run a full evaluation cycle for agent 'CodeGenius' and report the scores."
  • "Compare the results of the last two evaluation runs for agent 'DataMiner'."

Tips & gotchas

The effectiveness of this skill depends on clearly defined and relevant evaluation criteria. Ensure these are aligned with your specific goals and use cases to obtain meaningful insights into agent performance.