arXiv:2606.29538v1 Announce Type: cross Abstract: Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving tutorial videos and other multimodal human resources largely underused. We present RESOURCE2SKILL, a framework that distills multimodal resources, including tutorial videos, repositories, articles, and reference artifacts, into executable skills for software agents. RESOURCE2SKILL organizes these skills as a hierarchic
The proliferation of multimodal data and advancements in large language models make it increasingly feasible to distill complex procedural knowledge from diverse human-created resources into actionable agent skills.
This framework significantly expands the potential for AI agents to acquire and operationalize knowledge, moving beyond hand-written or text-derived skills to leverage rich, multimodal human expertise.
The primary method for generating executable AI agent skills shifts from manual coding and text analysis towards automated extraction from diverse human-created multimodal resources, including videos and tutorials.
- · AI agent developers
- · Automation software providers
- · Knowledge management platforms
- · Organizations with extensive training resources
- · Companies relying solely on manual skill development
- · Outdated knowledge transfer methodologies
AI agents become significantly more capable and versatile by learning from a broader spectrum of human-created knowledge.
The cost and time associated with deploying AI agents for complex tasks decrease due to automated skill acquisition.
New industries emerge around the creation and curation of multimodal instructional content specifically designed for agent skill distillation.
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Read at arXiv cs.AI