eval-harness

Evaluate Claude Code sessions for regressions and quality through continuous testing.

Updated Nov 19, 2025

Search Tags:#Claude Code #reliability #AI evaluation #eval-driven development #performance measurement

Ready to run

npx skills add https://github.com/Sake-Team/SmartSake --skill eval-harness-sake-team

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/Sake-Team/SmartSake/tree/main/backup/skills/eval-harness
Command: npx skills add https://github.com/Sake-Team/SmartSake --skill eval-harness-sake-team

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The eval-harness Skill addresses the challenge of evaluating AI development through a structured framework that emphasizes continuous testing, tracking, and reporting.

Core Features & Use Cases

Formal Evaluation Framework: Implement eval-driven development principles.
Continuous Testing: Run evaluations throughout the development process.
Tracking Regressions: Monitor changes for stability and reliability.
Use Case: Suitable for developing Claude Code sessions and ensuring the reliability of AI agents across different model versions.

Quick Start

Initialize the eval-harness by running /eval define feature-name.

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 workflows?▼

Eval-driven development is a structured framework that evaluates AI development through continuous testing, tracking, and reporting to ensure quality and reliability. It measures performance, validates changes, and detects regressions in AI workflows.

How do I run evaluations on Claude Code sessions?▼

Run evaluations on Claude Code sessions by initializing the eval-harness with the `/eval define feature-name` command. This sets up continuous testing to measure performance, validate changes, and monitor stability across different model versions.

Can I track regressions in AI agents across different model versions?▼

Yes, you can track regressions in AI agents across different model versions by running continuous evaluations throughout the development process. This monitors changes for stability and ensures reliability over time.

What's the best way to measure performance changes in AI workflows?▼

The best way to measure performance changes in AI workflows is using a formal evaluation framework that emphasizes continuous testing, tracking, and reporting. This approach validates changes and detects regressions systematically.

When should I not use eval-driven development principles?▼

Eval-driven development principles are not ideal for quick prototyping or exploratory phases where continuous testing and tracking overhead outweigh the need for formal regression detection and performance measurement.