skill-creator

Create, refine, and benchmark custom AI skills with Python-based test cases.

8|3|Updated Apr 18, 2026
One-click install
npx skills add https://github.com/KunCheng-He/kk-ai --skill skill-creator-kuncheng-he
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/KunCheng-He/kk-ai/tree/main/skills/common/skill-creator
Command: npx skills add https://github.com/KunCheng-He/kk-ai --skill skill-creator-kuncheng-he

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill streamlines the entire lifecycle of AI skill development, from initial intent capture and drafting to rigorous quantitative evaluation and triggering optimization.

Core Features & Use Cases

  • Iterative Development: Provides a structured loop for drafting, testing, and refining skill instructions based on real-world performance.
  • Quantitative Benchmarking: Automates the creation of test cases, baseline comparisons, and performance metrics to ensure skill reliability.
  • Trigger Optimization: Uses automated loops to refine skill descriptions, ensuring the AI invokes the skill accurately when needed.
  • Use Case: If you need to create a specialized coding assistant, use this skill to draft the instructions, run it against a set of 20 challenging coding prompts, and automatically optimize its description so it triggers only for relevant tasks.

Quick Start

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

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I optimize AI agent triggering descriptions automatically?

AI agent triggering descriptions are optimized automatically through iterative loops that refine skill instructions based on quantitative benchmarking against defined test cases, ensuring accurate skill invocation for relevant tasks.

What is the best way to benchmark custom AI skills quantitatively?

Quantitative benchmarking of custom AI skills is achieved by automating test case creation, executing baseline comparisons, and aggregating performance metrics to ensure reliability and evaluate real-world skill performance.

How do I set up an iterative development loop for prompt engineering?

An iterative development loop for prompt engineering is set up by defining test cases, drafting skill instructions, running comparative evaluations, and refining triggers within a Python-based execution environment managing subagent workflows.

Do I need a Python environment to run AI skill benchmarking scripts?

A Python execution environment is required to manage subagent workflows, execute grading scripts, and aggregate benchmark data for the end-to-end creation and automated optimization of custom AI skills.

Can I test custom coding assistants against challenging prompts before deployment?

Custom coding assistants can be tested against challenging prompts by defining specific test cases, running comparative evaluations, and using automated grading scripts to measure performance and refine skill instructions.

Why does my custom AI skill trigger for irrelevant tasks?

Custom AI skills trigger for irrelevant tasks when triggering descriptions lack optimization, an issue resolved by running automated optimization loops that refine descriptions based on quantitative benchmarking data.