skill-creator

To create reusable AI agent skills, define the skill's purpose, principles, workflow, structure, and verifiable definition of done. This approach transforms domain expertise into production-grade skills with explicit trigger conditions and realistic validation cases.

Trigger optimization refines activation descriptions so an AI skill activates for relevant requests while avoiding adjacent tasks. It ensures explicit trigger conditions are met, preventing false activations and improving instruction design reliability.

Test and evaluate AI skill outputs by creating realistic test cases, reviewing outputs, and identifying failure patterns. This quality testing process iteratively improves instructions against common and edge-case prompts before deployment.

Yes, you can refactor existing SKILL.md files to improve instruction design. This involves applying principled workflows, explicit trigger conditions, and a verifiable definition of done to upgrade them into reliable, production-grade AI agent skills.

The best way to define a verifiable definition of done for agent workflows is establishing explicit trigger conditions, principled workflows, and realistic validation cases. This ensures the skill produces reliable, measurable outputs before deployment.

You should not use generic instruction files for specialized workflows when they fail to provide explicit trigger conditions, principled workflows, or realistic validation cases. Production-grade AI skills require a verifiable definition of done to ensure reliability.