Agent Evaluation

Name: Agent Evaluation
Author: dokhacgiakhoa

Listed

dokhacgiakhoa·vlatest·agents

Evaluates agent performance based on provided metrics, offering 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.

Run this in your project

terminal

$ npx skills add dokhacgiakhoa/agent-skills-4-vibe-coding-cli --skill agent-evaluation -a claude-code

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

dokhacgiakhoa

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 1 Oct 2026

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.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
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 dokhacgiakhoa/antigravity-ide at c4a059bbf7.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: c4a059bbf7
Manifest: .agent/skills/agent-evaluation/SKILL.md
Scanned: 0 files
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 agent-evaluation skill allows you to assess and provide feedback on the performance of AI agents. It can analyze agent responses, identify areas for improvement, and generate reports summarizing its findings. This helps refine agent behavior and ensure they meet desired objectives.

When to use it

Evaluating a newly trained agent before deployment to production.
Identifying weaknesses in an existing agent's performance on specific tasks.
Comparing the effectiveness of different agent configurations or training datasets.
Generating reports for stakeholders demonstrating agent progress and areas needing attention.

Key capabilities

Response analysis
Performance reporting
Identification of improvement areas
Agent feedback generation

Example prompts

"Evaluate this agent's response to the prompt: 'Write a short story about a cat.'"
"Generate a report on the agent’s performance in summarizing news articles."
"Identify any biases present in the agent's responses regarding [topic]."

Tips & gotchas

The quality of the evaluation depends heavily on the clarity and specificity of your prompts. Providing detailed instructions or example outputs can significantly improve the accuracy and usefulness of the feedback generated by this skill.