Agent skills extend large language model (LLM) agents with reusable, program-like modules that define triggering conditions, procedural logic, and tool interactions. As these skills proliferate in public marketplaces, it is unclear what types are available, how users adopt them, and what risks they pose. To answer these questions, we conduct a large-scale, data-driven analysis of 40,285 publicly listed skills from a major marketplace. Our results show that skill publication tends to occur in short bursts that track shifts in community attention. We also find that skill content is highly concentrated in software engineering workflows, while information retrieval and content creation account for a substantial share of adoption. Beyond content trends, we uncover a pronounced supply-demand imbalance across categories, and we show that most skills remain within typical prompt budgets despite a heavy-tailed length distribution. Finally, we observe strong ecosystem homogeneity, with widespread intent-level redundancy, and we identify non-trivial safety risks, including skills that enable state-changing or system-level actions. Overall, our findings provide a quantitative snapshot of agent skills as an emerging infrastructure layer for agents and inform future work on skill reuse, standardization, and safety-aware design.

翻译：智能体技能通过可复用的类程序模块扩展大语言模型（LLM）智能体，这些模块定义了触发条件、过程逻辑与工具交互机制。随着此类技能在公共市场的快速扩散，其可用类型、用户采用模式及潜在风险尚不明确。为探究这些问题，我们对某主流市场的40,285个公开上架技能进行了大规模数据驱动分析。研究发现：技能发布活动呈现与社区关注度变化同步的短期爆发模式；技能内容高度集中于软件开发工作流，而信息检索与内容创作类技能在实际采用中占据显著份额。除内容趋势外，我们揭示了跨技能类别的显著供需失衡现象，并证明尽管技能描述长度呈现重尾分布，绝大多数技能仍保持在典型提示词预算范围内。最后，我们观察到生态系统存在高度同质化特征，表现为广泛存在的意图级冗余，同时识别出包括支持状态变更或系统级操作在内的非平凡安全风险。本研究通过量化视角呈现了作为新兴智能体基础设施层的技能生态现状，为未来技能复用、标准化及安全感知设计的研究提供实证依据。