skill-creator

Develop, evaluate, and refine custom AI skills with iterative testing and benchmarking.

5|1|Updated Apr 9, 2026
One-click install
npx skills add https://github.com/teamniteo/hakuto --skill skill-creator-teamniteo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/teamniteo/hakuto/tree/main/skills/skill-creator
Command: npx skills add https://github.com/teamniteo/hakuto --skill skill-creator-teamniteo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of creating high-quality, reliable AI skills by providing a structured, iterative framework for drafting, testing, and refining instructions.

Core Features & Use Cases

  • Iterative Development: Guides you through the full lifecycle of skill creation, from intent capture to final deployment.
  • Quantitative Benchmarking: Automates the generation of test cases, baseline comparisons, and performance metrics to ensure your skill is actually effective.
  • Trigger Optimization: Includes a specialized loop to refine your skill's description, ensuring it triggers accurately when needed and stays silent when it doesn't.

Quick Start

Use the skill-creator skill to help me draft a new skill for automating my weekly project status reports.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I benchmark and test custom AI skills during development?

You can optimize AI skill triggering descriptions through a specialized refinement loop that ensures instructions activate accurately only when needed. This automated optimization leverages quantitative metrics to maintain silent state when triggers are unnecessary.

What's the best way to automate AI skill creation and optimization?

Python is required as a dependency to run the scripts and leverage the assets included for skill creation. You need to ensure your environment supports Python execution before attempting to generate test suites or execute comparative baseline runs.

How does iterative testing improve AI prompt engineering outcomes?

Iterative testing improves prompt engineering by providing structured evaluation cycles that combine quantitative metrics with qualitative user feedback. This process ensures high-accuracy performance by refining instructions based on actual benchmarking data rather than assumptions.

Can I refine AI skill trigger descriptions to prevent false activations?

You can optimize AI skill triggering descriptions through a specialized refinement loop that ensures instructions activate accurately only when needed. This automated optimization leverages quantitative metrics to maintain silent state when triggers are unnecessary.

Do I need Python to run automated skill benchmarking and evaluation?

Python is required as a dependency to run the scripts and leverage the assets included for skill creation. You need to ensure your environment supports Python execution before attempting to generate test suites or execute comparative baseline runs.