基础原理：概念定义、架构设计、生命周期、加载机制与协作编排。
一、什么是 Agent Skills
Agent Skills 将领域专业知识和执行能力封装为可复用模块。每个 Skill 是一段预定义指令集合，告诉 AI Agent 面对特定任务时应该：
可以将 Skill 理解为给 AI Agent 的"操作手册"。
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用户请求
AI Agent
Skill 指令集
底层工具
二、Skills 的核心架构
三层架构：描述层（名称、触发条件）→ 逻辑层（流程定义、判断规则）→ 执行层（工具绑定、API 调用）。小型 Skill 中三层常混合，复杂度上升后才值得严格分离。
内部组成：名称标识、描述文本、可用工具列表、执行流程、输入/输出规范。其中工具列表和输入规范是可选的，过度设计反而增加维护成本。
三、Skill 的生命周期
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匹配成功
无匹配
是
否
是
否
用户发起请求
意图识别
加载 Skill 指令
通用能力处理
解析任务参数
需要调用工具?
执行工具调用
直接生成响应
继续调用?
汇总结果
返回结果
- 意图识别：遍历已注册 Skill，语义匹配描述文本，综合考虑上下文、偏好、优先级
- 指令加载：分全量加载和按需加载，后者节省上下文空间
- 任务执行：按流程逐步执行，根据中间结果动态调整策略
- 结果返回：按规定格式整理并返回
四、Skill 的加载与执行机制
注册机制：Skill 在系统启动时注册到注册表。少量时用线性遍历匹配，大量时用倒排索引或向量检索加速。
上下文注入：Skill 指令以系统级提示注入到 Agent 上下文，注入位置在用户消息之前，确保 Agent 始终能看到操作规范。
动态加载：预加载元数据 → 触发加载完整指令 → 执行期有效 → 完成后卸载释放上下文。连续同类任务会保留指令避免重复加载。
五、Skill 间的协作与编排
- 串行协作：按依赖依次调用，输出作为下一个输入。流程清晰但总耗时为各 Skill 之和
- 并行协作：无依赖时同时调用，缩短 IO 密集型任务耗时。需注意资源竞争
- 条件路由：根据任务特征动态选择 Skill，通常先规则过滤再模型处理模糊情况
下一篇（中篇）：工具调用原理、错误处理、描述规范、权限与安全
