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、配备技能的智能体转变，标志着大语言模型在实际部署中发生了决定性变革。智能体技能——智能体按需加载的可组合指令、代码和资源包——无需重新训练即可实现动态能力扩展，而非将所有过程性知识编码于模型权重之中。这一范式被形式化为渐进式披露、可移植技能定义以及与模型上下文协议的集成。本综述对智能体技能领域进行了全面探讨，该领域在过去数月内快速演进。我们沿四个维度组织该领域：(i)架构基础，考察SKILL$.$md规范、渐进式上下文加载以及技能与MCP的互补角色；(ii)技能获取，涵盖基于技能库的强化学习、自主技能发现（SEAgent）与组合技能合成；(iii)大规模部署，包括计算机使用智能体栈、图形用户界面基座进展以及OSWorld和SWE-bench上的基准测试进展；(iv)安全，近期实证分析表明26.1%的社区贡献技能存在漏洞，这促使我们提出技能信任与生命周期治理框架——一个基于门控的四级权限模型，将技能来源映射到渐进式部署能力。我们识别出七个开放挑战——从跨平台技能可移植性到基于能力的权限模型——并提出了实现可信、自我改进技能生态系统的研究议程。与先前广泛涵盖大语言模型智能体或工具使用的综述不同，本工作聚焦于新兴的技能抽象层及其对下一代智能体系统的影响。项目地址：https://github.com/scienceaix/agentskills