CUA-Skill: Develop Skills for Computer Using Agent
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arXiv
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| Format: | Preprint |
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2026
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| author | Chen, Tianyi Li, Yinheng Solodko, Michael Wang, Sen Jiang, Nan Cui, Tingyuan Hao, Junheng Ko, Jongwoo Abdali, Sara Xu, Leon Zheng, Suzhen Fan, Hao Cameron, Pashmina Wagle, Justin Koishida, Kazuhito |
| author_facet | Chen, Tianyi Li, Yinheng Solodko, Michael Wang, Sen Jiang, Nan Cui, Tingyuan Hao, Junheng Ko, Jongwoo Abdali, Sara Xu, Leon Zheng, Suzhen Fan, Hao Cameron, Pashmina Wagle, Justin Koishida, Kazuhito |
| contents | Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and 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 and how to leverage these skills. We introduce CUA-Skill, a computer-using agentic skill base that encodes human computer-use knowledge as skills coupled with parameterized execution and composition 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. The project page is available at https://microsoft.github.io/cua_skill/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21123 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CUA-Skill: Develop Skills for Computer Using Agent Chen, Tianyi Li, Yinheng Solodko, Michael Wang, Sen Jiang, Nan Cui, Tingyuan Hao, Junheng Ko, Jongwoo Abdali, Sara Xu, Leon Zheng, Suzhen Fan, Hao Cameron, Pashmina Wagle, Justin Koishida, Kazuhito Artificial Intelligence Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and 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 and how to leverage these skills. We introduce CUA-Skill, a computer-using agentic skill base that encodes human computer-use knowledge as skills coupled with parameterized execution and composition 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. The project page is available at https://microsoft.github.io/cua_skill/. |
| title | CUA-Skill: Develop Skills for Computer Using Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.21123 |