Guide developers through creating and structuring AI skills with SKILL.md.
npx skills add https://github.com/snyk/agent-scan --skill skill-creator-snyk
Please help me install this Agent Skill. Skill: skill-creator Source: https://github.com/snyk/agent-scan/tree/main/tests/skills/skill-creator Command: npx skills add https://github.com/snyk/agent-scan --skill skill-creator-snyk
This Skill provides guidance for creating effective skills, helping users understand how to extend Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Use the skill-creator skill to learn how to create a new skill.
High-intent search queries and answers about installing and using this skill.
To create an AI skill, structure your project with a SKILL.md file, scripts, and references. Follow a step-by-step process from understanding requirements to iteration, ensuring modular and self-contained extensions for your AI agent.
Progressive disclosure in skill development is a design principle that structures documentation and resources to reveal information sequentially. It helps maintain conciseness while providing comprehensive guidance only when needed by the AI agent.
Building a new AI skill requires a specific anatomy: a SKILL.md file for documentation, alongside scripts/, references/, and assets/ directories. This structure ensures your skill remains modular and self-contained.
Document best practices for developer guides by focusing on conciseness, managing degrees of freedom, and applying progressive disclosure. This ensures the AI skill documentation provides effective, specialized knowledge without overwhelming the user.
When structuring modular extensions for AI capabilities, limitations arise if you ignore the core design principles of conciseness and progressive disclosure. Overcomplicating the skill anatomy or bypassing the iteration lifecycle reduces effectiveness.
The best way to bundle resources for an AI agent skill is to organize them into dedicated directories like scripts/, references/, and assets/. This modular approach ensures the skill is self-contained and easily extensible.
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