evals

Name: evals
Availability: InStock
Author: gratefuljinx77

Evaluate AI agents using code, model, and human graders.

1|Updated Mar 15, 2026

Search Tags:#benchmarking #regression testing #ai evaluation #agent testing #performance measurement

Ready to run

npx skills add https://github.com/GratefulJinx77/tai --skill evals-gratefuljinx77

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evals
Source: https://github.com/GratefulJinx77/tai/tree/main/.tai/skills/utilities/Evals
Command: npx skills add https://github.com/GratefulJinx77/tai --skill evals-gratefuljinx77

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, json, sqlite, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for evaluating AI agents across various domains, ensuring they meet specific quality and performance standards.

Core Features & Use Cases

Multi-Grader System: Utilizes code-based, model-based, and human graders for thorough evaluation.
Scalable Evaluation: Supports large-scale evaluation of agents, with customizable test cases and scoring criteria.
Use Case: For a software development team, evaluate the performance of an AI agent in coding tasks by creating a test suite with specific requirements and using the Skill to run the evaluations.

Quick Start

Run the evals skill with the specific use case and test cases to evaluate the performance of an AI agent.

Frequently Asked Questions about evals

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

FAQPage Schema

How do I run regression testing on an AI agent?▼

AI agent regression testing requires configuring specific test cases, prompts, and graders. This framework uses code-based, model-based, and human graders to evaluate whether agents maintain performance standards across updates.

What is the best way to benchmark AI agent performance objectively?▼

Objective AI agent benchmarking applies code, model, and human graders to measure performance against customizable test cases. This approach ensures agents meet specific quality standards by scoring outputs across various domains.

Can I use code-based and model-based graders together for agent evaluation?▼

Yes, agent evaluation supports a multi-grader system combining code-based, model-based, and human graders. This allows thorough evaluation by applying different scoring criteria to the same test cases and prompts.

How do I set up test cases for AI capability testing?▼

AI capability testing setup requires defining test cases, prompts, and grading criteria in formats like yaml or json. The framework processes these configurations to run scalable evaluations and measure agent capabilities.

Do I need sqlite to measure AI agent performance at scale?▼

Sqlite is used as a dependency for performance measurement of AI agents. It supports the scalable evaluation framework by storing test configurations and scoring results for large-scale agent benchmarking.

What are the limitations of using a multi-grader system for agent benchmarking?▼

The multi-grader system for agent benchmarking requires careful configuration of test cases and scoring criteria. While it supports code, model, and human graders, human grading can limit scalability compared to automated evaluation methods.