SkillGrad introduces a novel framework for optimizing agent skills in large language models (LLMs) using a gradient descent approach, enhancing reliability and completeness of procedural knowledge. The method has shown significant performance improvements over existing training-based skill evolution techniques, indicating a potential shift in how AI agents can be adapted for specialized tasks.