RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources
About this episode
The episode discusses RESOURCE2SKILL, a framework for distilling executable skills for software agents from multimodal resources.
🤗 Upvotes: 119 | cs.SE, cs.AI Authors: Yijia Fan, Zonglin Di, Zimo Wen, Yifan Yang, Mingxi Cheng, Qi Dai, Bei Liu, Kai Qiu, Yue Dong, Ji Li, Chong Luo Title: RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources Arxiv: http://arxiv.org/abs/2606.29538v4 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 hierarchical multimodal Skill Wiki, where each entry combines structured text, code, visual examples, metadata, and provenance. This design preserves complementary signals from different resources: videos capture temporal operations and visual effects, code captures executable tool patterns, and articles or artifacts provide conceptual and stylistic…
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