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eval-harness

Implement Eval-Driven Development frameworks for AI code generation sessions.

4|Updated Feb 14, 2026
Search Tags:#code quality#testing#devops#ai development#evaluation#regression testing
One-click install
npx skills add https://github.com/KuaaMU/omnihive --skill eval-harness-kuaamu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/KuaaMU/omnihive/tree/main/library/ecc-skills/eval-harness
Command: npx skills add https://github.com/KuaaMU/omnihive --skill eval-harness-kuaamu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework for evaluating AI-generated code and outputs, ensuring reliability and preventing regressions through Eval-Driven Development (EDD).

Core Features & Use Cases

  • Define Evals: Create clear pass/fail criteria before implementation.
  • Automated Testing: Run capability and regression evals using code-based or model-based graders.
  • Metrics Tracking: Measure performance with pass@k and pass^k metrics.
  • Use Case: When developing a new authentication feature, use this Skill to define tests for user registration, login, and session management, ensuring the new code doesn't break existing login flows.

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
How do I implement Eval-Driven Development for AI code generation?▼

Eval-Driven Development for AI code generation provides a formal evaluation framework to define clear pass/fail criteria before implementation, ensuring reliability and preventing regressions throughout the development lifecycle.

What are pass@k and pass^k metrics in AI evaluation?▼

Pass@k and pass^k metrics measure reliability in AI evaluation by tracking the success rates of capability and regression evals, providing quantitative performance tracking for generated code.

How do I set up automated regression testing for AI generated code?▼

Automated regression testing for AI generated code is established by defining capability and regression evals that utilize code-based, model-based, or human grading methods to prevent feature degradation.

Can I use model-based graders for capability evaluation?▼

Yes, capability evaluations support model-based graders alongside code-based and human grading methods to automatically assess whether AI-generated outputs meet the predefined pass/fail criteria.

What is the best way to prevent regressions in AI development?▼

The best way to prevent regressions in AI development is applying a formal evaluation harness that runs capability and regression evals, tracking pass@k and pass^k metrics to measure code reliability.

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