将教程视频等多模态资源转化为可执行的智能体技能，提升任务完成能力。
RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources
- 从视频、代码库等多源资源中提炼可执行技能，构建分层多模态技能知识库。
- 在7个创作领域中，平均性能比无技能智能体提升11.9个百分点。
- 适合需要高效复用人类经验的智能体开发人员和自动化工具研究者。
技能是软件智能体的重要抽象，能将人类与智能体的经验转化为可复用的程序化知识。然而现有技能库多为手工编写、以文本为主或源自智能体日志，导致教程视频等多模态人类资源未被充分利用。本文提出RESOURCE2SKILL框架，将教程视频、代码仓库、文章及参考材料等多模态资源转化为智能体可执行技能。该框架构建层次化多模态技能维基（Skill Wiki），每条记录融合结构化文本、代码、视觉示例、元数据与来源信息，保留不同资源的互补信号：视频捕捉时间序列操作与视觉效果，代码呈现可执行工具模式，文章与资料提供概念与风格支撑。推理时，智能体从维基中检索并组合相关技能；当覆盖不足时，可在线动态获取新技能。在七个实际创作领域中，RESOURCE2SKILL相比无技能智能体平均得分提升11.9个百分点，在28个主要模型-领域组合中，有26个优于强基线。消融实验验证了多模态格式、层级组织、来源多样性、选择策略及在线获取机制的有效性。
原文摘要 · Abstract (English)
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 grounding. At inference time, agents retrieve and compose relevant skills from the wiki; when coverage is insufficient, the same construction operator can acquire new skills online. Across seven practical authoring domains, RESOURCE2SKILL improves average overall score by +11.9 percentage points over no-skill agents and outperforms strong harness baselines in 26 of 28 main-aggregate model-domain cells. Ablations confirm the value of multimodal skill format, hierarchical organization, source diversity, selection strategy, and online acquisition.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。