eval-harness

Define, run, and assess AI code sessions with structured evals.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/mthang1801/go-domain-driven-design --skill eval-harness-mthang1801
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/mthang1801/go-domain-driven-design/tree/main/.claude/skills/eval-harness
Command: npx skills add https://github.com/mthang1801/go-domain-driven-design --skill eval-harness-mthang1801

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automate and standardize the evaluation of AI code sessions by providing a deterministic framework for defining tests, running them, and recording results.

Core Features & Use Cases

  • Capability Evals: predefine functional goals and verify AI outputs against criteria.
  • Regression Evals: safeguard against regressions across changes with structured checks.
  • Metrics & Reports: generate pass@k and pass^k style reports to quantify reliability.

Quick Start

Install and configure the eval harness in your project, then define your first eval and run checks to produce a report.

Frequently Asked Questions about eval-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate AI code sessions and generate quantitative reliability metrics?▼

You can evaluate AI code sessions by using a structured harness to define, run, and assess tests. This framework generates pass@k and pass^k style reports to quantify AI reliability across code changes.

What is a regression eval framework for AI-assisted software engineering?▼

A regression eval framework safeguards against regressions across code changes by applying structured checks. It verifies AI outputs against predefined functional goals using deterministic capability and regression evaluations.

How do I set up a deterministic eval lifecycle for AI code generation?▼

You set up an eval lifecycle by organizing tests under a .claude/evals directory. This provides formal guidance for implementation, evaluation, and reporting to standardize AI behavior assessments.

Can I use both code and model graders to assess AI capability evaluations?▼

Yes, the framework supports both code and model graders. These graders assess AI-driven code sessions against predefined criteria to verify deterministic capability and regression evaluations.

What is the best way to standardize AI code reviews using automated testing?▼

The best way to standardize reviews is implementing an eval-driven development harness. It automates defining tests, running them, and recording results to guide AI behavior quantitatively across code changes.