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wakeeys
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skill-creator

Create, modify, and optimize AI skills through a structured workflow.

Updated May 31, 2026
Search Tags:#skill creation#benchmarking#AI development#skill optimization#performance evaluation
One-click install
npx skills add https://github.com/wakeeys/awesome-cc-skills --skill skill-creator-wakeeys
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/wakeeys/awesome-cc-skills/tree/main/skill-creator
Command: npx skills add https://github.com/wakeeys/awesome-cc-skills --skill skill-creator-wakeeys

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, json, numpy, pandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the process of creating, modifying, and optimizing AI skills. It provides a structured workflow to iterate on skills and improve their performance.

Core Features & Use Cases

  • Skill Creation: Guide users through drafting, testing, and improving new skills.
  • Skill Modification: Allow users to optimize existing skills based on feedback and benchmarking.
  • Skill Benchmarking: Evaluate skill performance using quantitative metrics and user feedback.
  • Description Optimization: Optimize skill descriptions for better triggering accuracy.
  • Use Case: Imagine a user wants to create a skill to automatically generate code. Use the skill creator to guide them through the process, from defining the skill's functionality to refining its description and testing its performance.

Quick Start

Use the skill-creator to create a new skill for generating code from user prompts.

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 using a structured workflow?▼

To create and optimize AI skills, you need a structured workflow that automates draft creation, testing, and iteration. This process involves defining functionality, refining descriptions for better triggering, and testing performance to improve skill accuracy.

What is the best way to evaluate AI skill performance using quantitative metrics?▼

Evaluating AI skill performance requires quantitative benchmarking and user feedback analysis. By using data analysis libraries like numpy and pandas, you can measure skill effectiveness, identify performance gaps, and iterate on modifications systematically.

Do I need Python libraries to automate skill development and benchmarking?▼

Yes, automating skill development requires Python libraries such as requests, json, numpy, and pandas. These dependencies handle task execution, process user feedback, and analyze performance data to support the iteration and optimization workflow.

Can I modify existing AI skills based on user feedback and benchmarking data?▼

You can modify existing AI skills by analyzing benchmarking data and user feedback. The optimization workflow allows you to iterate on skill drafts, adjust descriptions for triggering accuracy, and refine functionality based on quantitative evaluation results.

How does description optimization improve AI skill triggering accuracy?▼

Description optimization improves AI skill triggering accuracy by refining the text that defines when a skill should activate. Iterating on descriptions during the testing phase ensures the skill executes correctly for targeted user prompts and use cases.

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