eval-harness

Implement evaluation-driven development workflows for Claude Code sessions.

Updated Feb 11, 2026
One-click install
npx skills add https://github.com/shygoly/sapbase --skill eval-harness-shygoly
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/shygoly/sapbase/tree/main/docs/zh-CN/skills/eval-harness
Command: npx skills add https://github.com/shygoly/sapbase --skill eval-harness-shygoly

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a formal evaluation framework for Claude Code sessions, implementing evaluation-driven development (EDD) principles to treat evaluation as the unit test for AI development, ensuring early success criteria, continual checks, and traceable changes.

Core Features & Use Cases

  • Capability Evaluation: define and validate new capabilities before coding.
  • Regression Evaluation: guard existing functionality against changes.
  • Multi-Grader support: code-based, model-based, and human scoring with pass@k tracking.
  • Workflow Integration: define, implement, evaluate, and report within a structured repo.

Quick Start

Define your evaluation plan, implement the evaluators, run tests, and review results.

Frequently Asked Questions about eval-harness

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

FAQPage Schema
What is evaluation-driven development for AI coding sessions?▼

Evaluation-driven development (EDD) treats evaluation as the unit test for AI development, ensuring early success criteria, continual checks, and traceable changes within Claude Code sessions.

How do I set up capability and regression evaluations for Claude Code?▼

Define an evaluation plan to validate new capabilities or guard existing functionality, implement code-based, model-based, or human graders, and store them in a structured repo under .claude/evals.

Does this evaluation framework support pass@k metrics?▼

Yes, the framework supports pass@k metrics alongside multiple grading methods including code-based, model-based, and human scoring to track evaluation success rates accurately.

What's the best way to structure an AI evaluation repository?▼

Use a structured repo layout under .claude/evals with clear guidelines for planning, running, and reviewing assessments to maintain evaluation baselines and track capability regressions.

Can I use multiple graders for AI regression testing?▼

Yes, you can apply code-based, model-based, and human scoring graders simultaneously within the evaluation workflow to comprehensively assess regression and capability test results.

When do I need formal capability evaluation in my development workflow?▼

You need formal capability evaluation when defining and validating new AI capabilities before coding, ensuring early success criteria are met and existing functionality is guarded against regressions.