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Agent Evaluation

Listed
b-step62·vlatest·agents·Apache-2.0

Evaluates agent performance across defined metrics, providing actionable insights for improvement based on b-step62's methodology.

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 b-step62/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

b-step62

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: mlflow.org.

    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.

    Hosts contacted

    127.0.0.1localmlflow.orgundeclared

    What it found

    • agent-evaluation/references/tracing-integration.md:35contacts mlflow.org
      curl https://mlflow.org/docs/latest/llms.txt | grep -A 20 "tracing"

  • 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 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 b-step62/skills at fdd9839ce2.

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

Commit
fdd9839ce2
Manifest
agent-evaluation/SKILL.md
Scanned
20 files, 5,130 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 structured approach to evaluating AI agents. It assesses agents based on predefined criteria, generating reports that highlight strengths and weaknesses. The evaluation process includes scoring agent responses against expected outcomes and identifying areas for improvement in agent performance.

When to use it

  • Performance Monitoring: Regularly evaluate an agent's effectiveness after updates or changes to its configuration.
  • Comparative Analysis: Compare the performance of different agents tackling similar tasks.
  • Training Data Validation: Assess whether training data is leading to desired agent behaviors and outcomes.
  • Debugging Agent Issues: Identify specific areas where an agent struggles, aiding in troubleshooting and refinement.

Key capabilities

  • Predefined evaluation criteria
  • Scoring of agent responses
  • Generation of performance reports
  • Identification of areas for improvement

Example prompts

  • "Evaluate the agent's response to 'Summarize this article: [article text]'."
  • "Compare the performance of Agent A and Agent B on task 'Write a marketing email'."
  • "Generate a report detailing the agent’s accuracy in answering questions about historical events."

Tips & gotchas

The quality of the evaluation depends heavily on well-defined evaluation criteria. Ensure these are clear, measurable, and aligned with the desired agent behavior for accurate results.