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arXiv · 2606.18448

VISUALSKILL: Multimodal Skills for Computer-Use Agents

Ziyan Jiang·Li An·Yujian Liu·Jiabao Ji·Qiucheng Wu·Jacob Andreas·Yang Zhang·Shiyu Chang
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Abstract

Computer-use agents (CUAs) approach human-level performance on standardised benchmarks but still struggle on long-horizon tasks and unseen software. Existing skill libraries address this with reusable skills, but represent the skill artifact as text only, despite the visual nature of GUI interaction. We propose VISUALSKILL: a hierarchical multimodal skill, tailored to each target application and organised as a central index over per-topic files, which the agent consumes through a load_topic MCP tool that fetches the relevant topic's text and figures on demand. We construct each skill with a two-stage pipeline that combines authored documentation with live-application UI exploration. On two CUA benchmarks, CUA-World and OSExpert-Eval, a Claude Code CLI agent backed by Claude Opus 4.6 reaches an average score of 0.456 with VISUALSKILL, a +15.3 point absolute lift over the no-skill baseline (0.303). Against a matched text-only skill that is generated from the same source content and differs from VISUALSKILL only in modality, VISUALSKILL yields a further +8.3 point absolute gain over the matched text-only skill (0.373 vs. 0.456), providing direct evidence that retaining visual figures in the skill artifact, rather than verbalizing them away, helps the agent both identify UI elements and verify workflow state after each action. Our code is available at https://github.com/XMHZZ2018/VisualSkills.

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Ziyan Jiang, Li An, Yujian Liu, Jiabao Ji, Qiucheng Wu, Jacob Andreas, Yang Zhang, Shiyu Chang. 2026-06-16. VISUALSKILL: Multimodal Skills for Computer-Use Agents. https://arxiv.org/abs/2606.18448

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