A new paradigm treats agent skills not as brittle prompts or expensive fine-tuning targets, but as an optimizable external text state — exactly like model parameters in deep learning.

SkillOpt introduces a systematic optimizer for natural-language skills: bounded edits, validation gates, meta-updates, and a clean separation between skill storage and execution. The result is compact, transferable, interpretable procedural rules that improve agent performance with zero additional inference cost.