skill-maker

To create reusable AI workflows, start with an interview-driven design phase to capture purpose and triggers, then draft a structured SKILL.md file, and finish with iterative testing to reach production-ready quality.

The best way to formalize existing AI skills is through structured discovery interviews that capture workflow details, which are then translated into a SKILL.md file with clear rationale and scalable guidance.

You can audit and improve an existing AI skill by running structured discovery interviews to identify gaps, updating the SKILL.md documentation, and executing built-in test-case planning for iterative refinement.

Yes, you can use interview-driven design to capture your team's workflow, triggers, and skill type, which directly guides the structured drafting of the SKILL.md file for formalized reuse.

Iterative testing validates a new AI skill by applying built-in test-case planning and phased guidance, allowing you to refine the workflow and documentation until it reaches production-ready quality.

No prior documentation is needed to launch a new AI skill from a concept, as the process uses structured interviews to capture the initial design and automatically drafts the required SKILL.md file.