capability-architect

To structure AI agent skills for reusable workflow automation, organize contents into modular directories like scripts, references, and assets, then document requirements in a concise SKILL.md file to ensure efficient context window management.

Progressive disclosure in AI skill development is a design pattern that manages the context window efficiently by loading detailed instructions and reference documents only when needed, preventing token overload during agent interactions.

Document code generation components by writing concise instructions in a SKILL.md file, detailing resource management for scripts and references, and iterating on the documentation based on actual usage and feedback.

Building AI agent skills requires understanding your workflow requirements and structuring files like scripts and references, but the methodology focuses on systematic architecture and documentation rather than complex manual coding.

The best way to plan reusable AI components is to systematically understand skill requirements first, then map out modular contents like validation scripts and schema references before initializing the skill structure.

AI agents lose context during large workflow automation tasks when skill structures lack progressive disclosure, causing context window overload; organizing data into references and assets mitigates this limitation.