evaluation

Name: evaluation
Availability: InStock
Author: guanyang

Evaluates agent performance using multidimensional rubrics and systematic testing.

947|163|Updated Jan 13, 2026

Search Tags:#testing #quality assurance #metrics #evaluation #rubrics #agent performance

Ready to run

npx skills add https://github.com/guanyang/antigravity-skills --skill evaluation-guanyang

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluation
Source: https://github.com/guanyang/antigravity-skills/tree/main/skills/evaluation
Command: npx skills add https://github.com/guanyang/antigravity-skills --skill evaluation-guanyang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of systematically evaluating agent performance, ensuring quality, and identifying areas for improvement in complex, non-deterministic AI systems.

Core Features & Use Cases

Systematic Testing: Build robust test frameworks to measure agent quality.
Quality Gates: Implement checks to ensure agent performance meets standards before deployment.
Use Case: You've made changes to your agent's prompt. Use this Skill to run a suite of tests against the new version and compare its performance metrics (accuracy, completeness, efficiency) against the previous version to ensure no regressions occurred.

Quick Start

Use the evaluation skill to build a test framework for agent performance.

Frequently Asked Questions about evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I evaluate agent performance after changing a prompt?▼

You can evaluate agent performance by running a systematic test suite against the new version to compare metrics like factual accuracy, completeness, and tool efficiency, ensuring no regression occurred from prompt changes.

What is LLM-as-judge evaluation for AI agents?▼

LLM-as-judge is an evaluation methodology where a language model assesses agent performance using multi-dimensional rubrics to measure factual accuracy, citation accuracy, and source quality systematically.

Can I use multi-dimensional rubrics for quality gating in agent pipelines?▼

Yes, you can implement quality gates using multi-dimensional rubrics to ensure agent performance meets defined standards for factual accuracy and tool efficiency before pipeline deployment.

What's the best way to measure factual accuracy and citation accuracy in non-deterministic AI systems?▼

The best way to measure accuracy in non-deterministic AI systems is applying multi-dimensional rubrics through systematic testing frameworks that support both LLM-as-judge and human evaluation methodologies.

How do I set up continuous monitoring for agent pipelines?▼

You can set up continuous monitoring by integrating systematic test frameworks and quality gates into your agent pipelines, enabling ongoing assessment of factual accuracy, completeness, and tool efficiency.