Guide creation and updating of AI skills with structure and best practices.
npx skills add https://github.com/Cripacx/agentic-dotnet --skill skill-creator-cripacx
Please help me install this Agent Skill. Skill: skill-creator Source: https://github.com/Cripacx/agentic-dotnet/tree/main/skills/skill-creator Command: npx skills add https://github.com/Cripacx/agentic-dotnet --skill skill-creator-cripacx
This Skill provides a comprehensive guide and process for creating new AI skills or updating existing ones, ensuring they are effective, efficient, and well-structured.
SKILL.md, what kind of scripts or references to include, and how to package it correctly.Use the skill-creator skill to learn how to create a new AI skill.
High-intent search queries and answers about installing and using this skill.
To create an AI skill, you follow a structured development workflow from planning to packaging. This process involves defining the SKILL.md file, organizing resources like scripts and references, and validating the final skill structure for optimal AI agent performance.
The correct structure for an AI copilot skill includes a root SKILL.md file alongside organized directories such as scripts/, references/, and assets/. This anatomy ensures the AI agent can efficiently locate and execute prompts, automation logic, and supporting documentation.
Best practices for prompt engineering in skill creation emphasize conciseness, progressive disclosure, and appropriate degrees of freedom. Structuring your SKILL.md with these core principles ensures the AI agent processes instructions effectively without being overwhelmed by excessive context.
Building AI skills for workflow automation requires an intermediate level of implementation depth, primarily utilizing scripts. While deep coding is not always mandatory, understanding resource organization and naming conventions is essential for packaging functional automation logic.
You should update an existing AI skill when modifying its core principles or adding new resource directories like references and assets. This approach maintains existing validation and structure while improving prompt engineering or expanding workflow automation capabilities.
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