CUA-Skill: Develop Skills for Computer Using Agent
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/.
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Tianyi Chen
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2026-02-05T10:57:11.849Z
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Tianyi Chen
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Yinheng Li
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Michael Solodko
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Sen Wang
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Nan Jiang
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2026-02-04T12:33:54.186Z
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Nan Jiang
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Tingyuan Cui
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2026-02-04T12:33:46.370Z
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TINGYUAN CUI
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Junheng Hao
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Jongwoo Ko
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Sara Abdali
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Suzhen Zheng
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Leon Xu
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Hao Fan
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Pashmina Cameron
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2026-02-10T09:08:09.288Z
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Pashmina Cameron
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Justin Wagle
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Kazuhito Koishida
CUA-Skill: Develop Skills for Computer Using Agent
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/.
CUA-Skill introduces a large-scale library of engineered computer-use skills that enhance agent performance and efficiency on Windows-based tasks.
computer-using agents skill base execution graphs composition graphs dynamic skill retrieval argument instantiation memory-aware failure recovery end-to-end agent WindowsAgentArena