skills.rest
TymorIbrahim
TymorIbrahimCOMMUNITY
Explore all skills by this author →
GitHub

eval-harness

Define and track AI development evaluations with multiple grader types and pass@k metrics.

Updated Jun 22, 2026
Search Tags:#AI evaluation#eval-driven development#evaluation framework#pass@k metrics#AI grading
One-click install
npx skills add https://github.com/TymorIbrahim/UniPilot --skill eval-harness-tymoribrahim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/TymorIbrahim/UniPilot/tree/main/.cursor/.agents/skills/eval-harness
Command: npx skills add https://github.com/TymorIbrahim/UniPilot --skill eval-harness-tymoribrahim

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps manage and streamline the process of evaluating AI code sessions, ensuring quality and reliability in development workflows.

Core Features & Use Cases

  • Eval-Driven Development: Supports eval-driven development by defining and tracking evaluations for AI-assisted workflows.
  • Multiple Grader Types: Integrates various grading methods including code-based, model-based, and human reviews.
  • Evaluation Workflow Management: Automates evaluation tasks with structured phases like definition, implementation, and reporting.
  • Performance Metrics: Monitors pass@k metrics to measure agent reliability.
  • Use Case: Ideal for maintaining a consistent level of quality in AI model development by setting up a robust evaluation framework.

Quick Start

Set up an evaluation for a new feature with the command: /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 and how does it improve AI code generation?▼

Eval-driven development evaluates AI code sessions through defined phases and grading methods to ensure quality. By tracking pass@k metrics, it measures agent reliability and maintains consistent performance across development workflows.

How do I set up an evaluation framework for AI-assisted workflows?▼

Set up an AI evaluation framework by defining evaluation phases like definition, implementation, and reporting. You can start by defining an evaluation for a new feature to formalize the tracking of AI code sessions.

Can I use model-based grading and human reviews together for AI evaluation?▼

Yes, AI evaluation frameworks can integrate multiple grader types simultaneously. You can combine code-based, model-based, and human reviews to comprehensively assess AI model outputs and ensure robust quality control.

What are pass@k metrics and why are they important for AI grading?▼

Pass@k metrics measure agent reliability by tracking how consistently an AI passes evaluations across multiple attempts. Monitoring these metrics during continuous evaluation helps maintain a consistent level of quality in AI development.

Does this AI evaluation workflow management require any external dependencies?▼

No, this AI evaluation workflow management operates without external dependencies. It provides standalone scripts, references, and assets to formalize evaluation phases and track performance metrics natively.

Related Skills

Explore More Software Engineering Skills→
yu13130122297yu13130122297

scout

Identify uncertainties in research framing, datasets, metrics, and baselines.

Community
Intermediate
kk20300113-pngkk20300113-png

eval-harness

Define pass/fail criteria and calculate pass@k metrics for Claude Code agent evals.

Community
Advanced
ThejanaJayalathThejanaJayalath

eval-harness

Define and run capability and regression evals for AI-assisted development sessions.

Community
Advanced
linnefromicelinnefromice

eval-harness

Evaluate Claude Code sessions with code-based, model-based, and human graders.

Community
Advanced