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eval-harness

Implement formal evaluation frameworks for AI agents with pass@k metrics.

Updated Mar 4, 2026
Search Tags:#testing#devops#ai#metrics#evaluation#regression
One-click install
npx skills add https://github.com/Fabio29T/Everything-Claude --skill eval-harness-fabio29t
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/Fabio29T/Everything-Claude/tree/main/.agents/skills/eval-harness
Command: npx skills add https://github.com/Fabio29T/Everything-Claude --skill eval-harness-fabio29t

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework for implementing Eval-Driven Development (EDD) principles, ensuring AI agent reliability and predictable behavior through formal evaluation.

Core Features & Use Cases

  • Formal Evaluation Framework: Define and execute capability and regression evals to measure AI performance.
  • Metrics & Reporting: Track key metrics like pass@k and generate comprehensive eval reports.
  • Use Case: When developing a new feature for an AI agent, use this Skill to define success criteria upfront, implement the feature, and then run automated evals to confirm it meets the requirements and hasn't introduced regressions.

Quick Start

Use the eval-harness skill to define a new capability evaluation for user authentication.

Frequently Asked Questions about eval-harness

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

FAQPage Schema
What is eval-driven development for AI agents?▼

You can track AI performance using pass@k metrics and model-based grading. This evaluation framework computes these metrics automatically during capability and regression testing to quantify agent reliability.

How do I run regression tests for an AI agent feature?▼

To run regression tests, define success criteria for the new feature, implement the changes, and execute automated evals. This framework integrates directly into AI workflows to confirm requirements are met without introducing regressions.

Can I use model-based grading for AI evaluation instead of manual checks?▼

Yes, model-based grading is supported for assessing AI performance and reliability. It automates the evaluation process within the formal framework, enabling continuous evaluation of agent capabilities without manual intervention.

When do I need a formal evaluation framework for AI development?▼

You need a formal evaluation framework when developing new AI agent features that require predictable behavior. It allows you to establish success criteria upfront and run continuous regression tests to maintain reliability.

How do I generate evaluation reports for AI capability tests?▼

This framework generates comprehensive evaluation reports after executing capability and regression tests. It tracks key metrics like pass@k to provide actionable insights into AI agent performance and reliability.

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