eval-harness

Implement eval-driven development frameworks with capability and regression evals.

Updated Jan 27, 2026

Search Tags:#testing #ai development #metrics #evaluation #regression #edd

Ready to run

npx skills add https://github.com/AtlasRoX/super-fiesta --skill eval-harness-atlasrox

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/AtlasRoX/super-fiesta/tree/main/skills/eval-harness
Command: npx skills add https://github.com/AtlasRoX/super-fiesta --skill eval-harness-atlasrox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework to define, implement, and evaluate AI development tasks, ensuring predictable outcomes and preventing regressions.

Core Features & Use Cases

Eval-Driven Development (EDD): Treat AI evaluations as unit tests for AI development.
Capability & Regression Evals: Define what the AI should do and ensure it doesn't break existing functionality.
Metrics: Track reliability with pass@k and pass^k metrics.
Use Case: Before implementing a new AI feature, define its expected behavior and success criteria using this Skill, then run evaluations to confirm it meets requirements and doesn't negatively impact other parts of the system.

Quick Start

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

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 and how do AI evaluations prevent regressions?▼

Eval-driven development (EDD) treats AI evaluations as unit tests for AI development, defining expected behavior pre-implementation to ensure predictable outcomes and prevent functionality regressions.

How do I set up capability and regression evals for an AI feature?▼

Define a capability evaluation to specify what the AI should do, then run regression evaluations to confirm it meets requirements without breaking existing functionality during the development workflow.

What metrics are used to measure AI model reliability during evaluation?▼

Reliability is tracked using pass@k and pass^k metrics, which are integrated into the formal evaluation framework to measure the consistency and success of AI development tasks.

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

Yes, the evaluation framework supports various grader types including code-based, model-based, and human graders to assess capability and regression evals across different AI tasks.

When should I use a formal evaluation framework for AI development?▼

You need a formal evaluation framework when implementing new AI features that require structured development workflows, pre-implementation success criteria definition, and post-implementation reporting to prevent regressions.