The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice. Rather than encoding all procedural knowledge within model weights, agent skills -- composable packages of instructions, code, and resources that agents load on demand -- enable dynamic capability extension without retraining. It is formalized in a paradigm of progressive disclosure, portable skill definitions, and integration with the Model Context Protocol (MCP). This survey provides a comprehensive treatment of the agent skills landscape, as it has rapidly evolved during the last few months. We organize the field along four axes: (i) architectural foundations, examining the {SKILL.md} specification, progressive context loading, and the complementary roles of skills and MCP; (ii) skill acquisition, covering reinforcement learning with skill libraries, autonomous skill discovery (SEAgent), and compositional skill synthesis; (iii) deployment at scale, including the computer-use agent (CUA) stack, GUI grounding advances, and benchmark progress on OSWorld and SWE-bench; and (iv) security, where recent empirical analyses reveal that 26.1% of community-contributed skills contain vulnerabilities, motivating our proposed Skill Trust and Lifecycle Governance Framework -- a four-tier, gate-based permission model that maps skill provenance to graduated deployment capabilities. We identify seven open challenges -- from cross-platform skill portability to capability-based permission models -- and propose a research agenda for realizing trustworthy, self-improving skill ecosystems. Unlike prior surveys that broadly cover LLM agents or tool use, this work focuses specifically on the emerging skill abstraction layer and its implications for the next generation of agentic systems. Project repo: https://github.com/scienceaix/agentskills

翻译：从单一语言模型向模块化、具备技能的智能体转变，标志着大型语言模型在实际部署方式上的一个决定性转折。智能体技能——即智能体按需加载的、由指令、代码和资源组成的可组合包——使得动态能力扩展无需重新训练成为可能，而无需将所有程序性知识编码在模型权重中。这一范式在渐进式上下文披露、可移植技能定义以及与模型上下文协议的集成中得以形式化。本文对智能体技能领域进行了全面梳理，该领域在过去几个月中发展迅速。我们围绕四个轴线组织这一领域：(i) 架构基础，审视 {SKILL.md} 规范、渐进式上下文加载以及技能与 MCP 的互补作用；(ii) 技能获取，涵盖基于技能库的强化学习、自主技能发现以及组合式技能合成；(iii) 规模化部署，包括计算机使用智能体栈、图形用户界面基础技术的进展，以及在 OSWorld 和 SWE-bench 基准测试上的进展；(iv) 安全性，最近的实证分析表明，26.1% 的社区贡献技能包含漏洞，这促使我们提出了技能信任与生命周期治理框架——一个基于关卡的四层权限模型，将技能来源映射到分级的部署能力。我们指出了七个开放挑战——从跨平台技能可移植性到基于能力的权限模型——并提出了一个研究议程，旨在实现可信赖、自我改进的技能生态系统。与先前广泛涵盖 LLM 智能体或工具使用的综述不同，本文特别关注新兴的技能抽象层及其对下一代智能体系统的影响。项目仓库：https://github.com/scienceaix/agentskills