From the source
SkillOpt is a method that treats agent skill files as trainable parameters outside a frozen model, using a training-style optimization loop to improve agent performance without changing model weights.
It achieves consistent gains across 52 evaluation cells spanning six benchmarks, seven target models, and three execution modes, outperforming human-written skills and other baseline methods.
The optimized skills remain compact, auditable, and transferable across model scales and tasks.
