Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining
Summary
The paper "Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining" explores whether explicit skill libraries, derived from interaction data, can enhance downstream policies for computer-using agents. It introduces a three-stage pipeline that segments GUI trajectories, clusters these segments into candidate skills, and then trains a skill-aware policy using the resulting annotations. While the mined clusters demonstrate high readability, with five of eight clusters achieving at least 0.95 purity against InteraSkill Workflows labels, this readability does not translate to significant policy transfer. The GRPO method only marginally improves IW skill-step accuracy from 18.5% to 20.5%, leaves BrowseComp+ largely unaffected, and performs worse than simple frequency priors on source-domain metrics. The authors present this as a diagnostic study, highlighting that current limitations in the boundary detector, orderless segment representation, and offline reward model hinder reliable cross-domain policy improvement.
Key takeaway
For AI Scientists developing computer-using agents, if you are considering automated skill generation from interaction data, understand that current trajectory mining techniques primarily offer inspectable skill structures rather than direct policy improvement. While these methods can reveal skill purity, like 0.95 against InteraSkill Workflows, they do not reliably transfer to better agent performance. You should prioritize research into more robust boundary detectors, richer segment representations, and effective offline reward models to achieve meaningful cross-domain policy enhancements.
Key insights
Trajectory mining can expose inspectable skill structure, but current methods fall short for reliable cross-domain policy improvement.
Principles
- Skill library readability does not guarantee policy transfer.
- Explicit skill libraries enhance agent inspectability.
Method
A three-stage pipeline segments GUI trajectories, clusters segments into candidate skills, and trains a skill-aware policy from resulting annotations.
In practice
- Apply trajectory mining to reveal inspectable skill structures.
- Evaluate skill purity against established benchmarks like InteraSkill Workflows.
Topics
- Computer-Using Agents
- Skill Learning
- Trajectory Mining
- GUI Automation
- Reinforcement Learning
- Policy Improvement
Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.