SkillOpt presents a novel approach to agent skill maintenance by treating skill definitions (like SKILL.md files) as parameters in a verifiable training loop. The process involves sampling, reflection, constrained editing, and gated filtering to iteratively improve agent behavior without retraining the underlying model. This addresses a key bottleneck in agent projects: maintaining effective textual instructions that guide agent actions. The method is particularly relevant for developers building complex agent systems where skill quality directly impacts performance. By framing skill optimization as a training problem, SkillOpt offers a systematic way to enhance agent reliability and adaptability.
SkillOpt introduces a method where agent skills are treated as parameters in a verifiable training loop, improving agent behavior without retraining the model.