Skills can be trained like model parameters: use a separate optimizer to iteratively edit skill text based on validation feedback, not just generate them once. This approach is reproducible, stable, and transfers across models.
SkillOpt treats agent skills like neural network weights—optimizing them systematically through an external optimizer model that suggests bounded edits to skill documents based on scored rollouts.