Computer-Using Agents (CUAs) aim to autononomously operate computer systems to complete real-world desktop tasks. However, existing agentic systems remain difficult to scale and continue to lag behind human performance. A key limitation is the absence of reusable and structured skill abstractions that capture how humans interact with graphical user interfaces. We introduce CUA-Skill, a computer-using agentic skill base that encodes human computer-use knowledge as skills coupled with parameterized execution graphs. CUA-Skill is a large-scale library of carefully engineered skills spanning common Windows applications, serving as a practical infrastructure and tool substrate for scalable, reliable agent development. Built upon this skill base, we construct CUA-Skill Agent, an end-to-end computer-using agent that supports dynamic skill retrieval, argument instantiation, and memory-aware failure recovery. Our results demonstrate that CUA-Skill substantially improves execution success rates and robustness on challenging end-to-end agent benchmarks, establishing a strong foundation for future computer-using agent development. On WindowsAgentArena, CUA-Skill Agent achieves state-of-the-art 57.5% (best of three) successful rate while being significantly more efficient than prior and concurrent approaches.
@article{chen2025cuaskill,
title={CUA-Skill: Develop Skills for Computer Using Agent},
author={Chen, Tianyi and Li, Yinheng and Solodko, Michael and Wang, Sen and Jiang, Nan and Hao, Junheng and Cui, Tingyuan and Ko, Jongwoo and Abdali, Sara and Zheng, Suzhen and Fan, Hao and Cameron, Pashmina and Wagle, Justin and Koishida, Kazuhito}
journal={arXiv preprint arXiv:----},
year={2026}
}