eval-skills

Evaluate AI agent skills atomically with deterministic scoring and regression diffing.

4|Updated Feb 28, 2026
One-click install
npx skills add https://github.com/isLinXu/eval-skills --skill eval-skills-islinxu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eval-skills
Source: https://github.com/isLinXu/eval-skills/tree/main
Command: npx skills add https://github.com/isLinXu/eval-skills --skill eval-skills-islinxu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill provides a framework to evaluate AI agent skills at the atomic unit level (L1), enabling reliable quality gates before integration into agents and pipelines.

Core Features & Use Cases

  • Framework-agnostic discovery, scaffolding, evaluation, and reporting for AI Skill units.
  • Regression diffing, multi-dimensional scoring, sandboxed execution, and CI/CD integration.
  • Use cases include comparing candidate skills on the same benchmark, enforcing quality gates in CI, and producing human-readable reports for teams.

Quick Start

Install the skill and run eval-skills to generate a quality report for a sample skill.

Frequently Asked Questions about eval-skills

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

FAQPage Schema
How do I evaluate AI agent skills for quality gates in CI/CD pipelines?

You evaluate AI agent skills by applying atomic evaluation units with runtime validation and deterministic scoring. This framework enforces quality gates in CI/CD pipelines through regression diffing and structured outputs.

What is atomic skill evaluation and how does it work?

Atomic skill evaluation assesses AI agent skills at the L1 unit level to ensure reliable quality gates. It applies adapter-agnostic evaluation across the full skill lifecycle, from discovery and scaffolding to multi-dimensional scoring and reporting.

Do I need specific dependencies to run adapter-agnostic skill evaluation?

No specific dependencies are required to run adapter-agnostic skill evaluation. The framework operates independently using scripts to provide sandboxed execution, deterministic scoring, and structured outputs for candidate skills.

Can I compare candidate AI skills on the same benchmark?

Yes, you can compare candidate AI skills on the same benchmark. The framework applies regression diffing and multi-dimensional scoring to evaluate multiple skills and produce human-readable comparison reports.

How do I generate multi-format reports for AI skill benchmarks?

You generate multi-format reports for AI skill benchmarks by executing the evaluation scripts across the skill lifecycle. The framework produces human-readable reports with structured outputs and deterministic scoring for team review.

What's the best way to enforce runtime validation when evaluating AI skills?

The best way to enforce runtime validation when evaluating AI skills is using adapter-agnostic evaluation with deterministic scoring. This framework ensures structured outputs and sandboxed execution across the full skill lifecycle.