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RyanCallahan312
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skill-creator

Automates creation, evaluation, and iteration of AI trigger-response skills.

Updated Mar 1, 2026
Search Tags:#automation#workflow#evaluation#packaging#iteration#skill#prompt-engineering
One-click install
npx skills add https://github.com/RyanCallahan312/crayon --skill skill-creator-ryancallahan312
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/RyanCallahan312/crayon/tree/main/.agents/skills/skill-creator
Command: npx skills add https://github.com/RyanCallahan312/crayon --skill skill-creator-ryancallahan312

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, pyyaml.

What problem does it solve?

This capability reduces the time and effort needed to design, test, and improve AI skills by providing a structured, repeatable loop that combines skill authoring, trigger evaluation, and iterative refinement.

Core Features & Use Cases

  • End-to-end skill creation: define SKILL.md, run trigger evaluations, and iterate based on results.
  • Automated evaluation toolkit: leverage the built-in evaluators (run_eval, improve_description, and benchmark) to measure and compare skill triggering performance.
  • Deployment-ready packaging: validate, package, and document skills for distribution and reuse.

Quick Start

Write SKILL.md to describe the skill, then start the trigger-evaluation loop against your eval set to begin iterating.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I automate AI skill creation and prompt engineering iteration?▼

You can automate AI skill creation by defining a SKILL.md file and running a trigger-evaluation loop to iteratively refine prompt engineering and boost triggering reliability based on result analysis.

What is trigger evaluation in AI skill authoring?▼

Trigger evaluation in AI skill authoring is the process of testing a skill's SKILL.md description against an evaluation set to measure and benchmark how reliably the skill triggers for intended prompts.

Do I need Python and pyyaml to package AI skills for deployment?▼

Yes, you need a Python environment with anthropic and pyyaml dependencies to validate, package, and document your AI skills for distribution and reuse.

What's the best way to improve AI skill triggering reliability?▼

The best way to improve triggering reliability is to run automated benchmarking and evaluation loops, using the improve_description evaluator to guide description optimization and track progress iteratively.

Can I benchmark and compare different prompt engineering iterations?▼

Yes, you can use the built-in benchmark evaluator to measure and compare skill triggering performance across different prompt engineering iterations and evaluation sets.

Are there limitations when using automated workflows for skill evaluation?▼

Automated skill evaluation workflows require structured prompt formats and valid SKILL.md inputs; results depend heavily on the quality of your evaluation set and may need manual iterative improvement for complex triggers.

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