evaluation

Define rubrics, manage test sets, and analyze agent performance.

Updated Feb 4, 2026
One-click install
npx skills add https://github.com/jaydubya818/Dental_Agent --skill evaluation-jaydubya818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluation
Source: https://github.com/jaydubya818/Dental_Agent/tree/main/.claude/skills/evaluation
Command: npx skills add https://github.com/jaydubya818/Dental_Agent --skill evaluation-jaydubya818

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a structured approach to evaluating the performance and quality of AI agent systems, ensuring reliability and identifying areas for improvement.

Core Features & Use Cases

  • Systematic Testing: Define and execute test cases to measure agent performance across various dimensions.
  • Quality Measurement: Utilize multi-dimensional rubrics (accuracy, completeness, efficiency) for comprehensive scoring.
  • Use Case: Before deploying a new agent feature, use this Skill to run it against a suite of predefined tests, comparing its performance metrics against a baseline to catch regressions.

Quick Start

Use the evaluation skill to run the standard test set and report on 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 before deploying a new feature?▼

Evaluate AI agent performance by defining rubrics and running predefined test sets to measure factual accuracy, completeness, and tool efficiency, ensuring you catch regressions against a baseline before deployment.

What dimensions should I include in agent testing rubrics?▼

Agent testing rubrics should include multi-dimensional scoring for factual accuracy, completeness, citation accuracy, source quality, and tool efficiency to validate context engineering and agent configurations comprehensively.

How do I measure agent quality and catch regressions systematically?▼

Measure agent quality systematically by defining test cases, executing them against your agent system, and comparing performance metrics against a baseline to identify regressions using multi-dimensional rubrics.

Can I use this evaluation framework to validate context engineering configurations?▼

Yes, you can validate context engineering configurations by running comprehensive performance analyses that apply multi-dimensional scoring rubrics to test sets, ensuring your agent system meets factual accuracy and efficiency standards.

What's the best way to run test cases for agent performance analysis?▼

Run test cases for agent performance analysis by utilizing a structured evaluation framework that defines test sets, applies multi-dimensional rubrics, and reports on metrics like citation accuracy and tool efficiency.

When do I need a structured evaluation framework for quality assurance?▼

You need a structured evaluation framework for quality assurance when deploying new agent features, validating context engineering, or identifying performance improvements through systematic testing and multi-dimensional rubric scoring.