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eval-harness

Implement eval-driven development with code-based, model-based, and human grading.

Updated Jan 26, 2026
Search Tags:#testing#devops#ai development#metrics#evaluation#regression
One-click install
npx skills add https://github.com/vinayakg/claude-dotfiles --skill eval-harness-vinayakg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/vinayakg/claude-dotfiles/tree/main/skills/eval-harness
Command: npx skills add https://github.com/vinayakg/claude-dotfiles --skill eval-harness-vinayakg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured approach to evaluating AI model performance, ensuring reliability and preventing regressions through formal testing and metrics.

Core Features & Use Cases

  • Eval-Driven Development (EDD): Implement AI development practices similar to traditional software engineering, using evals as unit tests.
  • Capability & Regression Evals: Define and run tests to ensure new features work and existing functionality remains intact.
  • Grading Mechanisms: Supports code-based, model-based, and human grading for comprehensive evaluation.
  • Use Case: Before deploying a new AI feature, define its expected behavior and success criteria using this skill. Then, run automated tests to confirm it meets those criteria and doesn't break existing functionality.

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 for AI and how does it prevent regressions?▼

Eval-driven development formalizes AI reliability by using capability and regression evaluations like unit tests to track pass@k and pass^k metrics, preventing model regressions before deployment.

How do I set up a capability evaluation for a new AI feature?▼

Define expected behavior and success criteria, then run automated capability evaluations using code-based, model-based, or human grading mechanisms to confirm new features work without breaking existing functionality.

Can I use model-based grading for AI testing alongside code-based checks?▼

Yes, AI evaluation supports code-based, model-based, and human grading mechanisms simultaneously, providing comprehensive evaluation coverage for capability and regression testing within Claude Code sessions.

What metrics does an AI evaluation framework track for reliability?▼

An AI evaluation framework tracks pass@k and pass^k metrics to measure AI reliability, ensuring new capabilities function correctly while preventing existing functionality from regressing during development.

Does this AI testing framework work without external dependencies?▼

Yes, the formal AI evaluation framework operates with no external dependencies, implementing eval-driven development principles directly within Claude Code sessions for immediate capability and regression testing.

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