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rhixecompany
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skill-creator

Create, test, and optimize AI skills with Python and subagents.

Updated Jul 25, 2025
Search Tags:#skill creation#benchmarking#AI development#evaluation#skill optimization
One-click install
npx skills add https://github.com/rhixecompany/banking --skill skill-creator-rhixecompany
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/rhixecompany/banking/tree/main/.claude/skills/skill-creator
Command: npx skills add https://github.com/rhixecompany/banking --skill skill-creator-rhixecompany

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill unit provides a comprehensive toolset for creating, modifying, and optimizing AI skills, enabling users to design and refine skills efficiently.

Core Features & Use Cases

  • Skill Creation: Guided process for drafting, testing, and iterating on new skills.
  • Skill Optimization: Benchmarking and optimizing skill descriptions for improved triggering accuracy.
  • Use Case: A user looking to create a new skill for automated data analysis can use this unit to define the skill's purpose, create test cases, run evaluations, and iteratively improve the skill based on feedback.

Quick Start

Start by providing a brief description of the skill you want to create, and let me assist you through the process.

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 from scratch?▼

You can optimize AI skills through benchmarking by creating test cases, running structured evaluations, and iteratively refining skill descriptions. This process measures triggering accuracy and identifies performance improvements for automated tasks.

What is the process for benchmarking AI skill performance?▼

Benchmarking AI skill performance involves defining test cases, running evaluations against those cases, and measuring skill triggering accuracy. This structured evaluation mechanism highlights areas for description optimization and overall performance enhancement.

Do I need Python to design and test new AI skills?▼

Yes, Python is required to design and test new AI skills with this toolset. The framework relies on Python and subagents to execute the iterative development workflow, run evaluations, and optimize skill descriptions.

Can I modify existing AI skills to improve triggering accuracy?▼

Yes, you can modify existing AI skills to improve triggering accuracy. The toolset supports modifying skill descriptions and running benchmark evaluations to iteratively optimize performance for existing automated workflows.

What are the limitations of using subagents for skill evaluation?▼

The primary limitation is that skill evaluation requires a structured approach using Python and subagents, meaning unstructured or manual testing falls outside its scope. It is designed for automated, iterative benchmarking rather than ad-hoc skill validation.

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