skill-building

Modular Claude Code skills use progressive disclosure by loading metadata always, keeping a core SKILL.md, and bundling references as resources to optimize context usage and maintainability. This tiered content strategy separates deterministic scripts from documentation.

AI plugin skill authoring leverages five skill building patterns and six behavioral dimensions to guide structured design. These patterns focus on modularity, explicit ambiguity policy enforcement, and toxicity checks for complex plugin workflows.

Claude Code plugins require YAML frontmatter with name and description fields, while supporting optional resources like references, scripts, and assets. This structure enables progressive disclosure by bundling tiered content as modular resources.

Use reference materials when documenting API clients and providing context-optimized guidance, and deterministic scripts for complex workflow execution. The asset utilization pattern demonstrates how to bundle both as optional resources to maintain modular skill architecture.

Start with minimal skill design and refine based on real usage data and observed failure modes. This validation workflow ensures the progressive disclosure structure and tiered content strategy remain maintainable as the plugin evolves.

Building modular Claude Code skills does not require external dependencies, as the guide focuses on structuring YAML frontmatter, references, and assets internally. Skills enforce explicit ambiguity policy and toxicity checks without relying on outside tooling.