Agentic Skill Self-Creation via Multimodal Proactive Exploration

Agentic skills, which encode workflows, application, function specific conventions, and instructions, have become an indispensable component of LLM-based computer-use agents. However, whether written by humans or generated automatically, skills are often incomplete: their creators cannot easily anticipate which details an agent lacks, such as the location of a specific button or differences across application versions. Existing skill refinement pipelines also remain inefficient, as they typically require the AI agent to roll out a large number of task trajectories before invoking a separate refinement module to extract lessons from these trajectories. In this paper, we argue that the AI agent should take a more proactive role in creating and refining skills, because it is best positioned to identify what information is missing and can actively gather that information by interacting and exploring and searching resources, whereas prior work have much more limited access to such information. We therefore propose ProactSkill, task-specific agentic multimodal skill self-creation pipeline that, emulating human researchers, enables the AI agent to create skills for itself by searching application documentation and online resources, and to continuously refine these skills by exploring and interacting with the application during task execution. The created skills are multimodal, so they incorporate visual information throughout the pipeline to make the resulting skills more informative and actionable. We evaluate ProactSkill on agentic benchmarks and show that our proactive skill creation pipeline produces higher quality skills than existing skill creation baselines.