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eval-harness

Define capability and regression evaluations with code-based, model-based, and human graders.

1|1|Updated Feb 21, 2026
Search Tags:#testing#ai development#metrics#evaluation#regression#edd
One-click install
npx skills add https://github.com/VenTheZone/favorite-opencode-setup --skill eval-harness-venthezone
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/VenTheZone/favorite-opencode-setup/tree/main/skills/eval-harness
Command: npx skills add https://github.com/VenTheZone/favorite-opencode-setup --skill eval-harness-venthezone

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill establishes a structured framework for evaluating AI-generated code and outputs, ensuring reliability and preventing regressions through formal testing and metrics.

Core Features & Use Cases

  • Eval-Driven Development (EDD): Integrates evaluation as a core part of the development lifecycle.
  • Capability & Regression Evals: Define and run tests to ensure new features work and existing ones don't break.
  • Metrics Tracking: Utilizes pass@k and pass^k metrics for quantifiable reliability assessment.
  • Use Case: When developing a new authentication module, you can define capability evals to test registration, login, and logout flows, and regression evals to ensure existing user flows remain unaffected, all before writing the actual code.

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 does it prevent AI code regressions?▼

Eval-driven development (EDD) integrates formal evaluation into the AI development lifecycle to prevent regressions. It defines capability and regression evaluations using code-based, model-based, and human graders to assess AI-generated code reliability and quantify outputs with pass@k metrics.

How do I set up capability and regression evaluations for AI-assisted development features?▼

Define capability and regression evaluations to test new AI-assisted features and existing user flows before writing code. Specify tests using code-based, model-based, or human graders to ensure new features function correctly and existing flows remain unaffected by AI-generated code.

Can I use different types of graders to evaluate AI-generated code outputs?▼

Yes, you can evaluate AI-generated code using code-based, model-based, and human graders. This multi-grader approach allows you to flexibly assess reliability and run regression evaluations across different capability sets within your formal evaluation framework.

What metrics should I track to quantify the reliability of AI-generated code?▼

Track pass@k and pass^k metrics to quantify the reliability of AI-generated code. These metrics provide a formal assessment of AI reliability, ensuring that new capability evaluations pass and regression evaluations prevent degradations in your development lifecycle.

Does this eval framework require external testing dependencies to run capability evaluations?▼

No external dependencies are required to run capability and regression evaluations. The framework implements a standalone formal evaluation harness for AI-assisted development, allowing you to define tests, apply graders, and track metrics independently.

Why should I formalize AI evaluation instead of relying on manual testing for generated code?▼

Formalizing AI evaluation integrates eval-driven development directly into your workflow, preventing regressions through structured capability and regression tests. It replaces ad-hoc manual testing with quantifiable pass@k metrics and automated graders to ensure AI reliability.

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