agent-skill-author

To write agent skills that fix repeatable AI failures, use a test-first workflow that probes the agent with representative prompts, classifies real failures, and converts genuine knowledge gaps into concrete SKILL.md instruction rules.

The best way to design YAML frontmatter triggers is to group root causes of observed agent failures and translate them into trigger-aware frontmatter that activates prioritized instruction rules for specific domain or API behaviors.

Debug prompt failures by probing the agent with 5 to 10 real prompts where it currently fails, classifying the subtle failures, and identifying knowledge gaps that cannot be consistently resolved from prompting alone.

You need a test-first design approach for authoring agent skills when an AI agent requires reliable guidance for specific domain or API behaviors that it cannot consistently get right from prompting alone.

Maintain agent skills over time by iterating against runnable examples, validating improvements as models and APIs evolve, and updating instruction rules to address newly observed agent failures.

Model validation improves agent instruction rules by testing designed rules against runnable examples, ensuring the SKILL.md guidance reliably addresses the identified knowledge gaps and subtle agent failures.