eval-harness

Define and run capability and regression evals for Claude Code sessions.

1|Updated Jan 30, 2021
One-click install
npx skills add https://github.com/fideguch/my_dotfiles --skill eval-harness-fideguch
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/fideguch/my_dotfiles/tree/main/claude/skills/eval-harness
Command: npx skills add https://github.com/fideguch/my_dotfiles --skill eval-harness-fideguch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a formal evaluation framework to design, run, and interpret evaluations for Claude Code sessions, enabling reliable validation of AI behavior and progress.

Core Features & Use Cases

  • Capability and Regression Evals to test new features and guard against regressions.
  • Grader Types (Code-Based, Model-Based, Human) to produce robust assessments.
  • Metrics and Storage: pass@k, pass^k, and structured eval storage to track changes over time.
  • Workflow Guidance: define, implement, evaluate, and report evals with a repeatable process.

Quick Start

Define your evals, run them with the harness, and review the resulting reports to guide development.

Frequently Asked Questions about eval-harness

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

FAQPage Schema
How do I set up regression evals for Claude Code sessions?

Pass@k and pass^k are standardized evaluation metrics used to track AI performance changes over time. They provide objective scoring for capability and regression evals, allowing you to benchmark progress and validate behavior across different model versions within your evaluation framework.

What grader types can I use for AI evaluation frameworks?

Capability evals test new features and validate AI behavior, while regression evals guard against performance degradation in existing workflows. Both use the formal evaluation framework's standardized metrics and structured storage to track changes and ensure objective pass/fail criteria are met.

What is eval-driven development for AI-assisted workflows?

Eval-driven development for AI-assisted workflows is a formal process to define, implement, evaluate, and report tests with objective pass/fail criteria. It enables reliable validation of AI behavior and progress using standardized metrics and structured storage to track changes over time.

How do I evaluate AI behavior changes across model versions?

You evaluate AI behavior changes across model versions by running capability and regression evals through the harness, applying standardized metrics like pass@k, and comparing results stored in the structured evaluation storage to track performance benchmarks over time.