arXiv:2606.20363v1 Announce Type: new 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
The proliferation of AI agents necessitates more robust and transparent methods for their operation, making automated skill generation a timely development for managing their complexity.
Automating skill generation for computer-using agents directly addresses a critical bottleneck in agentic systems, enhancing their autonomy, efficiency, and inspectability.
The ability to automatically mine and curate skill libraries from agent interactions suggests a future where agents can learn and adapt more independently, moving beyond manually programmed routines.
- · AI Agent developers
- · Automation software providers
- · Companies adopting AI agents
- · Manual workflow programmers
- · Inefficient task automation platforms
AI agents become more capable of autonomously learning and performing complex tasks by extracting skills from their own usage data.
The development and deployment of agentic systems will accelerate as the overhead of defining granular agent skills is significantly reduced.
This could lead to a 'skill economy' where pre-mined and validated agent skills are traded or shared, further democratizing advanced automation.
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Read at arXiv cs.AI