发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何编写智能体技能，遵循软件工程原则，以Claude Code为参考实现阐述技能结构等，与其他塑造智能体行为机制比较并给出选择规则，还介绍评估驱动编写过程等，为智能体技能编写提供方法。
AI 中文摘要
智能体技能是一种通过可重复使用的过程性知识来扩展大语言模型智能体的新兴方式，智能体可按需加载这些知识。Anthropic引入了智能体技能并将其格式作为一种跨多种智能体工具支持的开放规范发布。本文认为技能是一种软件工件，其构建应遵循软件工程原则，包括单一职责、接口与实现分离、低耦合、共享令牌预算下的经济性，以及用行为评估代替确定性测试。以Claude Code作为参考实现，描述了技能的结构、内容如何分阶段加载以及如何编写选择所依赖的描述。将技能与开发者可用于塑造智能体行为的其他机制（如项目内存文件、斜杠命令、子智能体、外部工具连接和钩子）进行比较，并给出基于由谁决定机制运行以及它提供何种保证来在它们之间进行选择的规则。接着阐述了评估驱动的编写过程、编写中常见的一组模式和故障，以及使用第三方技能引发的信任问题。我们用UML类风格说明了比较、加载模型、技能剖析、每种机制的相对位置以及技能和钩子在会话期间的作用点。
英文摘要
Agent Skills are an emerging way to extend large language model agents with reusable procedural knowledge that the agent loads on demand. Anthropic introduced Agent Skills and published the format as an open specification supported across several agent tools. This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of deterministic testing. Using Claude Code as the reference implementation, it describes how a skill is structured, how its contents are loaded in stages, and how to write the description on which selection depends. It places skills against the other mechanisms a developer can use to shape agent behaviour, like project memory files, slash commands, subagents, external tool connections, and hooks, and gives a rule for choosing between them based on who decides that a mechanism runs and what guarantee it provides. It then sets out an evaluation-driven authoring process, a set of patterns and faults commonly encountered in authoring, and the trust question raised by using skills from third parties. We illustrate the comparison drawn in UML class style, the loading model, the anatomy of a skill, the relative position of each mechanism, and the points at which skills and hooks act during a session.
