SkillOpt

SkillOpt, from Microsoft, is a framework for improving AI agent performance by treating skill documents as trainable text artifacts rather than fine-tuning the underlying model. It runs a loop of rollouts, reflection, bounded edits, and validation to evolve a skill's natural-language instructions, exporting reusable skill files that transfer across different models and execution environments. This 'text-space optimization' approach demonstrated gains ranging from 9-50% across 6 benchmarks and 7 target models, making it useful for developers and researchers who want to systematically improve agent behavior without retraining.