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（LLM）的智能体能够通过工具、记忆和结构化交互实现推理、规划与行动，正成为自动化复杂工作流程的重要范式。近期如OpenClaw和Claude Code等系统的涌现，标志着从被动响应生成向面向行动的任务执行的根本性转变。然而，当智能体走向开放式真实世界部署时，每次任务都依赖从零开始的推理和底层工具调用将日益低效、易错且难以维护。本综述从"智能体技能"视角审视这一挑战——我们将技能定义为在特定任务约束下协调工具、记忆与运行时环境的可复用程序化构件。在此框架下，智能体与技能形成互补：智能体负责高层推理与规划，技能则构建可执行层，实现可靠、可复用且可组合的操作。因此，技能成为现代智能体系统可扩展性、鲁棒性与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、检索与演化——组织文献综述，系统梳理各阶段的代表性方法、生态资源及应用场景。最后，我们探讨质量管控、互操作性、安全更新及长期能力管理等开放挑战。相关资源（包括研究论文、开源数据和项目）已汇总至社区仓库：\textcolor{blue}{https://github.com/JayLZhou/Awesome-Agent-Skills}。