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eval-harness

Formalizes evaluation frameworks for AI-assisted development using eval-driven development principles.

19|4|Updated Mar 1, 2026
Search Tags:#testing#AI development#metrics#evaluation#framework#regression testing
One-click install
npx skills add https://github.com/j7-dev/everything-github-copilot --skill eval-harness-j7-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/j7-dev/everything-github-copilot/tree/main/.agents/skills/eval-harness
Command: npx skills add https://github.com/j7-dev/everything-github-copilot --skill eval-harness-j7-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework to formally evaluate AI-assisted development workflows, ensuring reliability and tracking regressions.

Core Features & Use Cases

  • Eval-Driven Development (EDD): Implement a rigorous process where evaluations define success before coding begins.
  • Metric Tracking: Measure agent reliability using pass@k and pass^k metrics.
  • Use Case: When developing a new AI feature, use this Skill to define clear pass/fail criteria for user registration, then run these evaluations continuously to ensure the AI implementation meets the requirements and doesn't break existing functionality.

Quick Start

Use the eval-harness skill to define a capability evaluation for a new 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 for AI-assisted workflows?▼

Capability and regression evaluations support code-based, model-based, and human grading methods to measure AI agent reliability. This framework formalizes evaluation processes to track pass@k and pass^k metrics.

How do I measure AI agent reliability using pass@k and pass^k metrics?▼

You can measure AI agent reliability by tracking pass@k and pass^k metrics using this evaluation framework. These metrics quantify consistency and capability across continuous regression and capability evaluations.

How do I set up regression testing for AI development features?▼

Define clear pass/fail criteria for your AI feature, such as user registration, then run continuous evaluations using this framework. This ensures the AI implementation meets requirements without breaking existing functionality.

Does this AI evaluation framework support human and model-based grading?▼

Yes, the evaluation framework supports code-based, model-based, and human grading for capability and regression evals. This allows flexible evaluation strategies tailored to your AI-assisted development workflow.

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

Use a formal evaluation harness when developing new AI features to define clear pass/fail criteria and run continuous evaluations. This ensures AI implementations meet requirements and prevents regressions in existing functionality.

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