eval-harness

Automate Claude Code session evaluations with code, model, and human graders.

Updated Mar 26, 2026
One-click install
npx skills add https://github.com/cescrafli/compyrasion --skill eval-harness-cescrafli
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/cescrafli/compyrasion/tree/main/skills/eval-harness
Command: npx skills add https://github.com/cescrafli/compyrasion --skill eval-harness-cescrafli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Formal evaluation is essential for AI-assisted development to prevent regressions and misbehavior. It provides a structured way to define, run, and track evaluations for Claude Code sessions, ensuring consistent quality and measurable progress.

Core Features & Use Cases

  • Capability Evals to verify new features or model capabilities before integration.
  • Regression Evals to ensure existing behavior remains stable after changes.
  • Grader options (Code-Based, Model-Based, Human) to accommodate deterministic checks, open-ended judgments, and manual reviews.
  • Pass@k and baseline tracking to quantify reliability over time and across model versions.
  • Configurable evaluation workflows that integrate with CI and prompts guidelines.

Quick Start

Define a new eval as feature and run the evaluation suite 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 run regression evals for AI-assisted coding sessions?

You can automate regression evals for AI-assisted coding sessions by defining an evaluation suite that applies deterministic code-based graders and model-based graders. This tracks behavior stability after changes to prevent regressions.

What is eval-driven development and when do I need formal evaluation?

Eval-driven development is a formal evaluation process needed when integrating AI-assisted development to prevent misbehavior and regressions. It provides a structured way to define, run, and track evaluations for measurable progress.

Can I use model-based graders for open-ended AI evaluations?

Yes, model-based graders accommodate open-ended judgments for AI evaluations. The framework supports code-based grading for deterministic checks, model-based grading for flexible evaluations, and human grading for manual reviews.

How do I track baseline performance and pass@k metrics for AI models?

You can track baseline performance and pass@k metrics by running configurable evaluation workflows that quantify reliability over time. This enables measurable progress tracking across different model versions and capability evaluations.

How do I set up capability evals to verify new AI features before integration?

To set up capability evals, define a new eval as a feature and run the evaluation suite to produce a report. This verifies new features or model capabilities before integration into your AI-driven development workflow.