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Nixdorfer
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eval-harness

Implement Eval-Driven Development frameworks with code, model, and human graders.

3|Updated Feb 8, 2026
Search Tags:#testing#devops#ai development#metrics#evaluation#regression testing
One-click install
npx skills add https://github.com/Nixdorfer/ClaudeCodeTool --skill eval-harness-nixdorfer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/Nixdorfer/ClaudeCodeTool/tree/main/plugins/cache/everything-claude-code/everything-claude-code/1.4.1/.cursor/skills/eval-harness
Command: npx skills add https://github.com/Nixdorfer/ClaudeCodeTool --skill eval-harness-nixdorfer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework for evaluating AI model performance, ensuring reliability and tracking regressions through formal evaluation and metrics.

Core Features & Use Cases

  • Eval-Driven Development (EDD): Implements principles where evaluations define expected behavior before implementation.
  • Capability & Regression Evals: Supports testing new features and ensuring existing functionality isn't broken.
  • Grading Mechanisms: Includes code-based, model-based, and human grading for comprehensive assessment.
  • Metrics Tracking: Utilizes pass@k and pass^k metrics to quantify reliability.
  • Use Case: A team developing an AI coding assistant can use this harness to define tests for new code generation capabilities and ensure that updates don't degrade existing code completion accuracy.

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 and how does it evaluate AI model performance?▼

Eval-driven development (EDD) defines expected AI behavior through formal evaluations before implementation. This framework structures AI model evaluation by supporting capability and regression testing to ensure reliability and track performance regressions.

How do I set up capability and regression evaluations for an AI coding assistant?▼

You can define capability evaluations to test new features and regression evaluations to ensure existing functionality remains intact. The framework integrates with your project structure to store evaluation definitions and logs for tracking AI assistant updates.

Can I use model-based and human graders alongside code-based grading for AI testing?▼

Yes, the evaluation framework supports code-based, model-based, and human grading mechanisms. This allows for comprehensive assessment of AI model performance across different evaluation contexts and testing requirements.

What metrics are used for measuring AI reliability in an evaluation harness?▼

The evaluation harness utilizes pass@k and pass^k metrics to quantify AI reliability. These metrics help measure the consistency and accuracy of AI model outputs during capability and regression evaluations.

Does this evaluation framework require external dependencies to run AI evaluations?▼

No, the evaluation framework operates without external dependencies. It integrates directly with your project structure to store evaluation definitions and logs, allowing you to implement formal AI evaluations independently.

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