DisCo代理把GitHub仓库变成可重用技能，在多个AI研究基准上大幅提升性能，比无技能版本表现更好。
研究人员提出DisCo技能驱动型研究代理，能将广泛使用的ML仓库提炼为可重用技能。该代理创建了AREX-Skill Library，包含5000多个从1000个常用ML仓库中提炼的验证技能，组织成20个领域和178个能力家族。使用GPT-5.5作为骨干模型，在MLE-bench基准上得分提升134.3%，在PaperBench上提升34.4%，在FrontierCS上提升9.2%，在PassNet上提升14.0%。
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for human readers and too large to load during a task. Once distilled into compact, verified skills, this knowledge can be reused across tasks rather than rediscovered during each run. We present DisCo, a skill-powered research agent that creates skills and uses them during research. Its distillation runs in two complementary forms: task-agnostic, condensing the field's widely used repositories into reusable skills, and task-oriented, producing the skills a concrete task calls for. The former, applied across the open ecosystem, yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families. With the GPT-5.5 backbone, research harness, and downstream execution budget held fixed, the skill-equipped research agent scores 134.3% higher on MLE-bench, 34.4% higher on PaperBench, 9.2% higher on FrontierCS, and 14.0% higher on PassNet than the same agent without skills. These gains come from adding distilled operating context under that fixed setup.