Skip to content
TrustedSkillsbeta
Back to all skills

Agent Evaluation

Checked
neolabhq·vlatest·agents·GPL-3.0

Evaluates agent performance across diverse tasks using metrics like accuracy, efficiency, and robustness for actionable insights.

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 neolabhq/context-engineering-kit --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

neolabhq

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 neolabhq/context-engineering-kit at 23e2428e80.

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

Commit
23e2428e80
Manifest
skills/agent-evaluation/SKILL.md
Scanned
3 files, 4,833 lines
Copies in repo
3 — all of them scanned
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 neolabhq-agent-evaluation skill provides a structured framework for evaluating the performance of AI agents. It allows users to define evaluation criteria, run agent executions against specific tasks, and then systematically assess results based on those criteria. This facilitates objective comparison and improvement of different agent configurations or models.

When to use it

  • Comparing Agent Performance: Evaluate multiple agents performing the same task to determine which performs best according to defined metrics.
  • Debugging Agent Issues: Identify specific areas where an agent is struggling by analyzing its performance against detailed evaluation criteria.
  • Tracking Progress During Development: Monitor improvements in agent capabilities over time as changes are made and retrained.
  • Validating New Agent Versions: Ensure that updates or new versions of an agent maintain or improve upon existing performance levels.

Key capabilities

  • Define custom evaluation criteria.
  • Run agent executions against tasks.
  • Systematic assessment of results based on defined criteria.
  • Objective comparison of agents.

Example prompts

  • "Evaluate Agent A and Agent B on the 'summarization' task using these criteria: accuracy, conciseness, and clarity."
  • "Run Agent C through the 'question answering' benchmark and score its performance based on the provided rubric."
  • “Compare agent versions 1.0 and 2.0 against the ‘code generation’ test suite.”

Tips & gotchas

To get the most out of this skill, ensure you have clearly defined evaluation criteria before running any assessments. The quality of your evaluation depends directly on the specificity and relevance of those criteria.