skill-creator

Creates and iteratively improves AI skills with quantitative benchmarking.

2.0k|267|Updated Jan 30, 2026
One-click install
npx skills add https://github.com/openakita/openakita --skill skill-creator-openakita
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/openakita/openakita/tree/main/skills/skill-creator
Command: npx skills add https://github.com/openakita/openakita --skill skill-creator-openakita

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 process of creating new AI skills, from initial concept to iterative improvement and performance optimization, making AI development more accessible and efficient.

Core Features & Use Cases

  • Skill Creation: Guides users through defining intent, writing SKILL.md, and structuring skill resources.
  • Iterative Improvement: Facilitates testing, evaluation, and refinement of skills based on user feedback and performance metrics.
  • Description Optimization: Enhances skill discoverability and triggering accuracy through automated prompt-based evaluation.
  • Use Case: A user wants to build a new skill to summarize meeting transcripts. This Skill will help them define the skill's purpose, write the initial SKILL.md, set up test cases, run evaluations against a baseline, analyze the results, and iterate on the skill's logic and description until it performs optimally.

Quick Start

Use the skill creator to help me build a new skill that can automatically generate commit messages from code changes.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I build and optimize an AI agent skill from scratch?

To build an AI agent skill, define the skill intent, draft the SKILL.md content, structure bundled resources, and execute rigorous evaluation loops with quantitative benchmarking for iterative refinement and performance optimization.

What is the best way to evaluate LLM prompt performance during skill development?

Evaluating LLM prompt performance involves executing rigorous evaluation loops that combine quantitative benchmarking against a baseline with qualitative feedback mechanisms to analyze results and iterate on skill logic.

How can I improve AI triggering accuracy for my custom skills?

Improve AI triggering accuracy through description optimization, which enhances skill discoverability by executing automated prompt-based testing and analysis on the skill descriptions.

What is the process for iterative improvement of an existing LLM skill?

Iterative improvement of an LLM skill requires setting up test cases, running evaluations against a baseline, analyzing performance metrics, and refining the skill's logic and description until it performs optimally.

Do I need external dependencies to structure bundled resources for AI skill creation?

No external dependencies are required to structure bundled resources for AI skill creation; the process relies on internal components like scripts, references, and assets to define and bundle the skill.