Lessons Learned in Skill Engineering for AI Assistants

The article discusses three key lessons the author learned while developing AI skills: the importance of optimizing skill entry points, the need for comprehensive evaluation, and the benefits of using system guarantees instead of relying on AI discipline.

Details

The author initially created an email-processing skill with 8 detailed rules, but found that the output was correct but useless. Deleting the rules and replacing them with two simple questions led to a much better result, as the AI started organizing information more effectively. This experience taught the author that writing instructions is not the same as skill engineering. The article then covers three key lessons: 1) The skill's entry point (description) is often broken and should be optimized as a classifier for the AI, not just documentation. 2) Evaluating skills requires comparing performance with and without the skill, not just the raw skill score. 3) Enforcing rules through system guarantees (e.g. pre/post-tool hooks) is more effective than relying on the AI to remember and follow instructions.