skill-creator

To structure an AI skill for token efficiency, organize components like SKILL.md, scripts, and references using progressive disclosure. This approach keeps core instructions concise while bundling detailed resources separately, minimizing token consumption during model processing.

Best practices for AI skill development emphasize conciseness, progressive disclosure, and setting appropriate degrees of freedom. Properly document the skill anatomy and bundle resources effectively to ensure robust and efficient model utilization.

Progressive disclosure in prompt engineering is a design principle that structures AI skills by revealing information iteratively. It keeps core prompts concise while providing detailed references and scripts only when needed, optimizing token usage.

To integrate an external API when creating a new AI skill, use the skill development lifecycle to plan structure and bundle necessary scripts. Follow best practices for documentation to ensure the AI model can effectively utilize the API endpoints and resources.

The development lifecycle for building AI skills guides you from initial planning and understanding the skill's purpose to structuring its anatomy, bundling resources, and iterating. This process ensures robust, token-efficient tool integration and comprehensive documentation.

You should set strict degrees of freedom in AI skill design when the task requires predictable, deterministic outputs. This design principle constrains the model's autonomy, ensuring it adheres closely to provided scripts and documentation for precise tool integration.