skill-creator

Creating Claude skills involves defining a standardized SKILL.md frontmatter, actionable body instructions, and optional bundles for scripts and assets. You refine them through a repeatable evaluation loop that measures triggering accuracy and records iteration history.

Progressive disclosure architecture organizes SKILL.md files by separating metadata, detailed instructions, and optional bundled resources like scripts, references, and assets. This structure keeps skills maintainable and extensible while ensuring reliable triggering and predictable behavior.

You evaluate triggering accuracy by running a repeatable evaluation loop that tests the skill against expected prompts, measures how accurately it triggers, and captures the history of these iterations to support continuous refinement and deployment readiness.

Yes, the workflow includes built-in scripts to validate SKILL.md packaging quality and produce distributable skill artifacts. This validation ensures your skills are deployment-ready and can be reliably distributed across teams.

Yes, the skill creation and validation workflow relies on the Anthropic and PyYAML dependencies to parse SKILL.md frontmatter, run the evaluation loop, and package the final distributable skill artifacts correctly.

Yes, the workflow is designed for teams to collaboratively craft, test, and improve skills. It provides clear versioning, validation, and an evaluation loop that records history, enabling multiple contributors to refine skills systematically.