evaluation

Automate AI agent evaluation with multi-dimensional rubrics and LLM-as-judge.

Updated Mar 26, 2025

Search Tags:#quality assurance #llm-as-judge #evaluation #performance metrics #rubrics #agent testing

Ready to run

npx skills add https://github.com/GSU-FrankJ/tournament_experiment --skill evaluation-gsu-frankj

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluation
Source: https://github.com/GSU-FrankJ/tournament_experiment/tree/main/.cursor/skills/evaluation
Command: npx skills add https://github.com/GSU-FrankJ/tournament_experiment --skill evaluation-gsu-frankj

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of systematically measuring and improving the performance of AI agents, ensuring they meet quality standards and perform reliably.

Core Features & Use Cases

Systematic Testing: Design and execute test cases to validate agent behavior.
Multi-dimensional Rubrics: Define and apply comprehensive quality metrics beyond simple accuracy.
LLM-as-Judge & Human Eval: Leverage scalable automated evaluation and crucial human oversight.
Use Case: Before deploying a new customer service agent, use this Skill to run it against a suite of test queries, ensuring it meets accuracy, completeness, and efficiency benchmarks.

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 AI agent performance systematically before deployment?▼

To evaluate AI agent performance systematically, you can design and execute targeted test cases using multi-dimensional rubrics to assess factual accuracy, completeness, and tool efficiency before deployment.

What is LLM-as-judge and how does it work for automated testing?▼

LLM-as-judge is an automated evaluation mechanism where a language model scores agent outputs against multi-dimensional rubrics, providing scalable assessment of factual accuracy and completeness without human intervention.

Can I measure citation accuracy and source quality with automated rubrics?▼

You can measure citation accuracy and source quality by applying multi-dimensional rubrics that systematically test agent outputs against predefined quality metrics during the evaluation process.

How to monitor production AI agents to identify quality regressions?▼

To monitor production AI agents and identify regressions, implement continuous testing frameworks that run agent outputs against established multi-dimensional rubrics and performance metrics over time.

Does agent testing support human evaluation alongside automated metrics?▼

Agent testing supports human evaluation alongside automated metrics by integrating LLM-as-judge methodologies with human oversight to assess factual accuracy, completeness, and tool efficiency.

When do I need complexity stratification for agent test sets?▼

You need complexity stratification for agent test sets when evaluating performance across varied query difficulties, ensuring the rubrics accurately measure factual accuracy and tool efficiency across all levels.