skill-creator

To create and validate AI skill frontmatter, use a standardized SKILL.md template that enforces naming conventions, structural integrity, and resource organization. This ensures your domain-specific knowledge packs maintain consistent frontmatter and pass validation workflows before packaging.

The recommended structures for modular skill units include Workflow-Based, Task-Based, Reference/Guidelines, and Capabilities-Based patterns. Choosing the right structure keeps context lean through progressive disclosure while enabling on-demand access to scripts, references, and assets.

You can package skills by applying validation and packaging workflows that enforce naming, frontmatter structure, and security constraints during packaging. This process tidies modular skill units and ensures consistent resource organization across your repository.

Yes, the skill validation workflow requires a YAML dependency to parse and audit frontmatter structures correctly. This dependency supports the enforcement of frontmatter structure and naming conventions during the initialization, editing, and validation of skill units.

The best way to audit and tidy modular skill units is to apply standardized frontmatter templates and run validation tooling like quick_validate.py. This enforces consistent structure, naming, and resource organization across all skill units in the repository.

Use workflow-based skill structures for multi-step processes and task-based structures for discrete, independent actions. Documenting these recommended patterns helps teams choose the right structure to keep context lean while enabling on-demand resource access.