Five Pitfalls in AI Skill Design and How to Avoid Them
This article outlines five common pitfalls in AI skill development — missing acceptance criteria, skipping domain knowledge, misusing LLMs for deterministic tasks, over-automating workflows, and neglecting output examples — with practical fixes like validation checklists, reference materials, script delegation, human checkpoints, and sample-driven formatting.
