Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining

Yuexing Hao; Xiaomin Li

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arXiv · 2606.20363

Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining

Yuexing Hao·Xiaomin Li

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Abstract

Explicit skill libraries make computer-using agents easier to inspect, but it remains unclear whether such libraries can be mined from interaction data in a way that improves downstream policies. We study this question through a three-stage pipeline that segments GUI trajectories, clusters segments into candidate skills, and trains a skill-aware policy from the resulting annotations. The mined clusters are readable on the source benchmark: five of eight clusters have at least 0.95 purity against InteraSkill Workflows labels. However, readability does not imply transfer. GRPO improves IW skill-step accuracy only from 18.5\% to 20.5\%, leaves BrowseComp+ essentially unchanged, and underperforms trivial frequency priors on key source-domain metrics. We therefore present the method as a diagnostic study: trajectory mining can expose inspectable skill structure, but the current boundary detector, orderless segment representation, and offline reward model are insufficient for reliable cross-domain policy improvement.

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Yuexing Hao, Xiaomin Li. 2026-06-18. Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining. https://arxiv.org/abs/2606.20363

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