I've watched smart people build AI skills and make the same mistake: they write the skill for the model.
They optimise for making the AI behave correctly today and ship it. Six months later, nobody knows why the rules exist, whether the model still needs them, or how to change the skill without breaking something else. This is not an AI problem. It is a knowledge problem.
Four layers of a durable skill
The system prompt defines role, reasoning, output schema, constraints, and examples. The input structure gives the model consistent fields in a consistent order. The workflow places the skill inside real work. Evaluation tells you whether it still works.
Keep one home for every piece of information. Duplication is maintenance debt. Write for intelligence, not compliance: define the goal and what done means, attach a reason to every rule, and remove instructions that add noise rather than value.
Any skill touching an external system should look around before acting. The information a task depends on often sits somewhere the original request did not mention.
Finally, add a human-readable layer. A plain-language header should say what the skill does, when to use it, and when not to use it. A skill only a machine can interpret is a liability wearing the costume of a solution.
The model will follow instructions it does not understand. Humans cannot maintain rules they cannot interpret. Write for both.
This is part of the Parenting Your AI series, a practitioner's guide to building AI skills that are safe, effective, and worth trusting. Written from inside enterprise AI systems by someone who has spent years diagnosing what goes wrong when AI meets real work at scale.
Read the full series at KnowledgeManagement.ie