elvis@omarsar0 · X微软研究院提出了SkillOpt方法，将AI智能体的技能文档视为可训练的外部状态，而非由工程师手动编写。该方法利用一个优化器模型对技能文件进行验证门控编辑，通过添加、删除或替换指令来优化文档，并引入文本学习率控制每轮重写力度，而智能体本身保持不变。实验显示，在全部52个测试单元（涵盖不同模型、基准测试和工具链）中，SkillOpt均达到最佳或并列最佳。具体在GPT-5.5上，相比无技能文档，SkillOpt在直接聊天、Codex和Claude Code下分别取得23.5、24.8和19.1分的提升，超越人类手写技能及其他自动化方法，且不增加推理时开销，学到的技能还能跨模型和工具链迁移。
New research from Microsoft Research
I see a lot of AI engineers handwriting agent skill docs and hope they generalize.
Probably not optimal. This works show why.
It treats the skill doc as a trainable external state of a frozen agent instead.
It introduces SkillOpt， where an optimizer model makes validation-gated edits to the skill file. It adds， deletes， or replaces instructions， with a textual learning rate that controls how aggressively each round rewrites the doc. The agent itself never changes.