Large language model (LLM)-based agents that reason, plan, and act through tools, memory, and structured interaction are emerging as a promising paradigm for automating complex workflows. Recent systems such as OpenClaw and Claude Code exemplify a broader shift from passive response generation to action-oriented task execution. Yet as agents move toward open-ended, real-world deployment, relying on from-scratch reasoning and low-level tool calls for every task become increasingly inefficient, error-prone, and hard to maintain. This survey examines this challenge through the lens of \emph{agent skills}, which we define as reusable procedural artifacts that coordinate tools, memory, and runtime context under task-specific constraints. Under this view, agents and skills play complementary roles: agents handle high-level reasoning and planning, while skills form the operational layer that enables reliable, reusable, and composable execution. Skills are therefore central to the scalability, robustness, and maintainability of modern agent systems. We organize the literature around four stages of the agent skill lifecycle -- representation, acquisition, retrieval, and evolution -- and review representative methods, ecosystem resources, and application settings across each stage. We conclude by discussing open challenges in quality control, interoperability, safe updating, and long-term capability management. All related resources, including research papers, open-source data, and projects, are collected for the community in \textcolor{blue}{https://github.com/JayLZhou/Awesome-Agent-Skills}.

翻译：基于大语言模型的智能体通过工具、记忆和结构化交互进行推理、规划与行动，正逐渐成为自动化复杂工作流程的重要范式。近期系统如OpenClaw和Claude Code展示了从被动响应生成到面向行动的任务执行的广泛转变。然而，当智能体转向开放式的现实世界部署时，依赖从头推理和底层工具调用的方式处理每项任务变得越来越低效、易出错且难以维护。本综述从"智能体技能"视角审视这一挑战——我们将智能体技能定义为在任务特定约束下协调工具、记忆和运行时环境的可复用程序性构件。基于此视角，智能体与技能形成互补关系：智能体负责高层推理与规划，而技能构成支撑可靠、可复用、可组合执行的操作层。因此，技能是决定现代智能体系统可扩展性、鲁棒性和可维护性的核心要素。我们围绕智能体技能生命周期的四个阶段——表示、获取、检索与进化——组织文献综述，并系统梳理各阶段的代表性方法、生态资源及应用场景。最后，我们探讨了质量控制、互操作性、安全更新及长期能力管理等开放挑战。本领域相关资源，包括研究论文、开源数据和项目，已收录于社区资源库：\textcolor{blue}{https://github.com/JayLZhou/Awesome-Agent-Skills}。