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能够通过工具、记忆和结构化交互进行推理、规划与行动，正成为自动化复杂工作流程的一种有前景的范式。OpenClaw 和 Claude Code 等近期系统体现了从被动响应生成到面向行动的任务执行这一广泛转变。然而，随着智能体迈向开放式、真实世界的部署，对所有任务均依赖从头推理和底层工具调用，将变得愈发低效、易出错且难以维护。本综述从“智能体技能”的视角审视这一挑战，我们将智能体技能定义为在任务特定约束下协调工具、记忆和运行时上下文的可重用过程性构件。在此视角下，智能体与技能扮演着互补角色：智能体负责高层推理与规划，而技能则构成可操作层，实现可靠、可重用且可组合的执行。因此，技能是现代智能体系统可扩展性、鲁棒性和可维护性的核心。我们围绕智能体技能生命周期的四个阶段——表示、获取、检索与演化——组织相关文献，并评述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。所有相关资源，包括研究论文、开源数据和项目，均已为社区收集于 \textcolor{blue}{https://github.com/JayLZhou/Awesome-Agent-Skills}。