skill-creator

Name: skill-creator
Availability: InStock
Author: huubar

Draft, validate, and iteratively improve AI skills with SKILL.md evaluations.

Updated Apr 21, 2026

Search Tags:#evaluation #iteration #benchmark #ai-agent #skill-creation #prompt-engineering

Ready to run

npx skills add https://github.com/HuuBar/skill-routing-experiment --skill skill-creator-huubar

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/HuuBar/skill-routing-experiment/tree/main/unified_skills/anthropics/skill-creator
Command: npx skills add https://github.com/HuuBar/skill-routing-experiment --skill skill-creator-huubar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) components.

What problem does it solve?

Creating and refining AI skills is a time-consuming, error-prone process. This Skill provides a structured workflow to design, validate, and iteratively improve skills, ensuring consistent quality across prompts, tests, and deployments.

Core Features & Use Cases

End-to-end Skill Lifecycle: draft, validate, and iterate on SKILL.md, scripts, references, and assets.
Evaluation and Benchmarking: run trigger evaluations and comparative experiments to measure triggering accuracy and performance.
Description Optimization: automatically propose stronger trigger descriptions based on eval results and holdout testing for better discovery.

Quick Start

Draft a new or improved skill, run the evaluation loop, and iterate until triggering accuracy is optimized.

Frequently Asked Questions about skill-creator

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

FAQPage Schema

How do I create and optimize AI skills for better triggering accuracy?▼

To create and optimize AI skills, draft a SKILL.md file, run trigger evaluations, and iteratively refine the prompts until accuracy improves. This structured loop ensures consistent quality across prompt engineering and deployments.

What is the best way to benchmark AI agent prompt performance?▼

Benchmarking AI agent prompt performance involves running comparative experiments and trigger evaluations to measure accuracy. Generate evaluation reports to identify weaknesses and propose stronger trigger descriptions for better discovery.

How does iterative prompt engineering improve skill deployment?▼

Iterative prompt engineering improves skill deployment by applying a structured create, validate, and refine loop. You modify the SKILL.md, run tests, and package the skill only after triggering accuracy meets your benchmark.

Do I need YAML to validate and package AI skills?▼

Yes, YAML is required as a dependency to validate and package AI skills. The skill lifecycle leverages YAML configurations for scripts and references, ensuring structured validation before deployment.

Can I automate trigger description optimization for my prompts?▼

Yes, you can automate trigger description optimization by leveraging built-in tooling that proposes stronger descriptions based on evaluation results and holdout testing, ensuring better discovery and performance.

Why are my AI agent skills not triggering correctly during evaluation?▼

AI agent skills fail to trigger correctly when prompt descriptions lack accuracy. Running evaluation loops and holdout testing identifies these gaps, allowing you to iteratively refine the SKILL.md until triggering improves.