768 lines
24 KiB
Markdown
768 lines
24 KiB
Markdown
---
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tags:
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- Skill
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- Agent
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- 自动化
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create time: 2026-05-03 15:30
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---
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# 怎么创建一个自己的 CLAUDE CODE SKILL
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## 概述
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本文系统梳理 Claude Code（原 Claude Desktop / Anthropic SDK）的 **Agent Skill** 概念、定义语法、开发工作流和最佳实践，并通过多个实战场景案例帮助读者从零创建属于自己的可复用 AI 助手技能。
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> [!QUESTION] 💡 思考
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> 你是否有过这样的场景——反复向 AI 助手解释同样的上下文、规则或约束，才能让它按你的预期完成工作？Skill 机制正是为了解决这个问题而生的：将"重复教育"变成一键加载的「能力插件」。
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## 什么是 Agent Skill
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### 核心概念
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Agent Skill 是 Anthropic 提供的一种 **结构化指令注入机制**。它允许开发者/用户通过 YAML 格式的 skill definition，在不修改模型权重的前提下，向 Claude 注入特定的行为规则、知识框架和工具调用策略。
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```mermaid
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flowchart TD
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U["用户提示词\nFix the auth bug"] --> S{"Claude 模型"}
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subgraph Context ["Skill Context 注入层"]
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direction TB
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T["Trigger: trigger(auth)"]
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R["Rules: System Prompt"]
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C["Constraints: Tool Limits"]
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end
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S --> Context
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Context --> S
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S --> O["专精行为输出\nSecurity Review / Config Update / etc."]
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classDef normal fill:#e3f2fd,stroke:#1565c0
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classDef highlight fill:#fff3e0,stroke:#ef6c00
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class U,O normal
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class T,R,C highlight
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```
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### Skill vs 普通 Prompt vs Project Instructions
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| 维度 | 普通 Prompt | CLAUDE.md (Project Instructions) | Agent Skill |
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|------|------------|----------------------------------|-------------|
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| **粒度** | 单次对话级别 | 整个 workspace 级别 | 特定任务域级别 |
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| **触发方式** | 必须明确提及 | 始终生效 | 自动检测触发 / 手动 `@` 调用 |
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| **作用范围** | 不持久化 | 全局覆盖 | 按需挂载，不影响其他任务 |
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| **复杂度** | 简单 | 中等 | 高（含系统级指令注入） |
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| **典型用例** | 快速提问 | 编码规范、语言偏好 | 安全审计、配置管理、代码审查 |
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> [!NOTE] 🔑 关键区别
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> - **CLAUDE.md** 是你的"项目宪法"，适用于所有对话
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> - **Skill** 是你的"专业技能包"，在特定场景下挂载到对话上，提供更深层的行为控制
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> - 两者可以共存 —— Skill 中的指令仅在 skill 激活时生效，优先级高于 CLAUDE.md
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### 内置 Skill 一览
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Claude Code SDK 自带一组开箱即用的 Skill，理解它们是学习自定义 Skill 的最佳起点：
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| Skill | 功能 | 触发条件 |
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|-------|------|---------|
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| `update-config` | 修改 settings.json、管理权限和环境变量 | 涉及配置文件、hook、权限变更 |
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| `keybindings-help` | 定制键盘快捷键 | 用户询问快捷键相关 |
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| `simplify` | 审查并重用冗余代码 | 代码审查场景 |
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| `fewer-permission-prompts` | 扫描代码，生成 `.claude/settings.json` 的 allowlist | 请求减少权限提示时 |
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| `loop` | 运行定时/循环任务（如 `/loop 5m /foo`） | 用户要求周期性任务 |
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| `claude-api` | 构建和优化 Claude API 应用（含缓存策略） | 导入 anthropic SDK、问 API/模型相关问题 |
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| `init` | 初始化新的 CLAUDE.md | 新项目初始化 |
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| `review` | 审查 Pull Request | PR 审查请求 |
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| `security-review` | 安全审查当前分支变更 | 安全审查请求 |
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## Skill 定义语法详解
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### YAML Frontmatter 结构
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每个 Skill 通过 YAML frontmatter 定义其元数据和触发逻辑：
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```yaml
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---
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name: security-review
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description: Complete a security review of the pending changes on the current branch
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---
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```
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一个完整的核心字段表：
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| 字段 | 必填 | 说明 |
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|------|------|------|
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| `name` | ✅ | Skill 的唯一标识符（kebab-case） |
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| `description` | ✅ | 简短描述，用于匹配用户意图 |
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| `trigger` | ❌ | 何时自动激活（正则表达式） |
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| `skip` | ❌ | 什么情况下跳过该 Skill |
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| `tools` | ❌ | Skill 可用的工具白名单 |
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| `systemPrompt` | ❌ | 注入的系统级指令（核心内容区） |
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### Trigger 模式匹配
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Trigger 决定了 Skill 在什么情况下被自动激活。支持以下模式：
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```yaml
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trigger: "code imports .anthropic. or @anthropic-ai/sdk."
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trigger: 'filename like \*-openai.py/.*-generic\.py'
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trigger: "/loop"
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```
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常见的触发方式：
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| 方式 | 示例 | 适用场景 |
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| ----------- | -------------------------------- | ---------------- |
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| **关键词匹配** | `trigger: "send email"` | 功能明确的任务 |
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| **文件路径匹配** | `trigger: "\.secrets\.""` | 特定文件或目录变更时 |
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| **命令前缀** | `trigger: "/deploy"` | Slash Command 触发 |
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| **正则表达式** | `trigger: "imports .anthropic."` | 复杂模式匹配 |
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| **手动 @ 引用** | `@my-skill` | 用户主动调用 |
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> [!TIP] ⚡ 最佳实践
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> Trigger 要尽可能精确。过于宽泛会导致错误的 Skill 被激活，干扰正常对话。遵循 **"最小充分原则"**：用最少的匹配条件覆盖目标场景。
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## Skill 开发工作流
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### Step 1: 确定需求边界
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> [!QUESTION] 🧭 先问自己
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>
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> 1. 这个 Skill 解决的是 **一次性问题** 还是 **反复出现的需求**？
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> - 一次性 → 直接写 prompt
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> - 反复出现 → 考虑做成 Skill
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>
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> 2. 这个 Skill 的规则能否用 **明确的 trigger + system prompt** 表达？
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> - 能 → 适合做成 Skill
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> - 不能 → 可能需要更复杂的 Agent 类型
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>
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> 3. 这个 Skill 会和其他 Skill **冲突**吗？
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> - 检查已有 Skill 的 trigger，避免重叠
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### Step 2: 编写 Skill Definition
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一个典型的 Skill 定义放在项目的 `.claude/skills/<skill-name>/SKILL.md` 文件中：
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```markdown
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---
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name: my-cool-skill
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description: Help with X when Y happens
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trigger: "X scenario appears"
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---
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# My Cool Skill
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When this skill is active, follow these rules:
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## Rules
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1. Rule one...
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2. Rule two...
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## Workflow
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Step-by-step instructions for Claude to follow.
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## Examples
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Concrete examples showing expected behavior.
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```
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### Step 3: 测试与迭代
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| 阶段 | 操作 | 目的 |
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|------|------|-----|
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| **单元测试** | 针对单个触发场景验证 | 确认 trigger 正确匹配 |
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| **集成测试** | 模拟真实用户输入 | 观察 Skill 是否正确激活 |
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| **负样本测试** | 用不应触发的输入测试 | 防止误触发 |
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| **冲突检查** | 同时存在多 Skill 时测试 | 确保无歧义匹配 |
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## 最佳实践
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### ✅ Do's
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> [!SUCCESS] 推荐做法
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**1. 职责单一原则（Single Responsibility）**
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每个 Skill 只负责一个明确的领域。不要试图在一个 Skill 里塞入所有邮件发送逻辑。拆分为独立的 Skill：
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```mermaid
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graph LR
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Root["skills/"] --> A["send-email-report/\n发送邮件报告"]
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Root --> B["generate-changelog/\n生成 Changelog"]
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Root --> C["notify-team/\n团队通知"]
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classDef folder fill:#e8f5e9,stroke:#2e7d32
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classDef item fill:#fff3e0,stroke:#ef6c00
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class Root folder
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class A,B,C item
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```
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**2. System Prompt 要明确且封闭**
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```yaml
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# good — 明确的正面指令 + 负面约束
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## Rules
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- ALWAYS use the smtp.send() helper from utils/email.py
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- NEVER hardcode credentials; always use os.getenv()
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- After sending, log the message ID to the database
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- If SMTP fails, queue the email and retry in 5 minutes
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# bad — 模糊、开放式的描述
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## Notes
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You can send emails however you think is best, just make sure it works.
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```
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**3. Trigger 要精确且有排他性**
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```yaml
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# good — 具体的文件名 + 动作组合
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trigger: "user asks to review .migrations/ or changes to schema files"
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# bad — 太宽泛
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trigger: "database"
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```
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**4. 包含错误处理路径**
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优秀的 Skill 不是只在理想情况下工作，还要定义失败时的行为：
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```markdown
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## Error Handling
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- If the required config file is missing: create a template and ask the user to fill it in
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- If the API returns 429: implement exponential backoff (initial: 1s, max: 30s)
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- If the recipient list is empty: abort with an informative error message
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```
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### ❌ Don'ts
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> [!WARNING] ⚠️ 常见陷阱
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**1. 过度依赖 Skill 解决简单问题**
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如果只是一个简单的 `git commit` 封装，不需要写成 Skill。Skill 适合 **复杂、多步骤、有严格规则** 的场景。
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**2. Trigger 相互冲突**
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两个 Skill 的 trigger 匹配同一个输入时，行为不可预测：
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```mermaid
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flowchart LR
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Input["用户说: fix auth bug"] --> SkillA["Skill A\ntrigger: fix the bug in auth"]
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Input --> SkillB["Skill B\ntrigger: fix authentication bug"]
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classDef input fill:#e3f2fd,stroke:#1565c0
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classDef conflict fill:#f9d0c4,stroke:#d84315
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class Input input
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class SkillA,SkillB conflict
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```
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**3. 在 Skill 中硬编码敏感信息**
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绝对不要在 system prompt 中写入密码、API Key、URL 等。使用环境变量或配置文件引用：
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```markdown
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❌ BAD:
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Always use password = "mySuperSecret123!" at host smtp.example.com
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✅ GOOD:
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Use credentials from ENV vars EMAIL_USER and EMAIL_PASS
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```
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**4. 忽略性能影响**
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过长的 system prompt 会增加 token 消耗和响应延迟。保持精简，只注入必要的上下文。
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## 实战场景案例集
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下面通过四个不同难度级别的实战场景，展示如何从需求到完整的 Skill 定义。
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### 场景一：中间件集成的最佳编码实践（入门级）
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**需求**: 当检测到项目中引入 Redis/MQ 等中间件时，自动注入最佳实践指导。
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> [!QUESTION] 🤔 为什么需要这个 Skill？
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> 每次引入新中间件，你都需要告诉 AI："记得连接池、超时设置、重试策略..." —— 这正是 Skill 的用武之地。
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#### Skill 定义
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```markdown
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---
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name: redis-best-practices
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description: Guide proper Redis integration patterns when Redis-related code is detected
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trigger: "redis OR redigo OR go-redis OR ioredis OR Lettuce"
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---
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# Redis Integration Best Practices
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## Overview
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This skill activates when Redis-related imports or configuration appear in the codebase.
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It ensures the assistant guides the developer through production-ready patterns.
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## Core Principles
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> [!INFO] 三大基石
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> 1. **资源管理**: 连接必须复用，禁止每次请求新建连接
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> 2. **容错设计**: 网络异常必须有降级方案，Redis 不可用时服务不能完全崩溃
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> 3. **数据安全**: 敏感数据不能明文存储，大 value 要考虑内存水位
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## Checklist
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When reviewing or writing Redis code, verify:
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- [ ] Connection pool configured with MinIdle/MaxActive ratios
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- [ ] Read/write timeouts explicitly set (> 0)
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- [ ] Retry logic implemented with bounded attempts (max 3)
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- [ ] Hot key detection strategy in place
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- [ ] Cache penetration/breakdown/avalanche handled
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- [ ] Distributed lock has auto-expiry + reentrant design
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- [ ] Pipeline used for batch operations (> 3 commands)
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- [ ] Keys have TTL set (except explicit no-expiry cases)
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## Common Patterns
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### Pattern 1: Safe Cache-Aside
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```go
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func GetProductCacheable(ctx context.Context, id string) (*Product, error) {
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// 1. Try cache first
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data, err := rdb.Get(ctx, productKey(id)).Result()
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if err == nil {
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// deserialized...
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return parse(data)
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}
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// 2. Cache miss -> query DB with double-check pattern
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mu.Lock()
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defer mu.Unlock()
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// Double-check after acquiring lock
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data, err = rdb.Get(ctx, productKey(id)).Result()
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if err == nil {
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return parse(data)
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}
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result, err := db.QueryProduct(ctx, id)
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if err != nil {
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// Write cache with short TTL to prevent penetration
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rdb.Set(ctx, productKey(id), "", 30*time.Second)
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return nil, fmt.Errorf("product not found: %w", err)
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}
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rdb.Set(ctx, productKey(id), marshal(result), 15*time.Minute)
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return result, nil
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}
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```
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> [!SUMMARY] 这段代码的关键点
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> - 使用了 **Cache-Aside** 模式（缓存旁路），而非 Write-Through
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> - 实现了 **Double-Check** 加锁机制，防止缓存击穿下的并发穿透
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> - 穿透保护用了 30 秒短 TTL（空值缓存），常规数据用 15 分钟
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> - 注意这里用 `marshal/unmarshal` 抽象了序列化，实际应统一使用 msgpack 或 protobuf
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### Pattern 2: Distributed Lock (Redisson-style)
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```go
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func WithDistributedLock(ctx context.Context, key string, ttl time.Duration, fn func() error) error {
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lockKey := "lock:" + key
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locked, err := rdb.SetNX(ctx, lockKey, uuid.New().String(), ttl).Result()
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if err != nil {
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return fmt.Errorf("acquire lock failed: %w", err)
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}
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if !locked {
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return ErrLockAcquireFailed
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}
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// Auto-release on exit
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defer rdb.Del(ctx, lockKey)
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return fn()
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}
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```
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## Anti-patterns to Avoid
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> [!FAILURE] ❌ 反模式
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- **全量导出**: `KEYS *` 在生产环境禁用，改用 `SCAN`
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- **阻塞操作**: `BLPOP` 无超时时间可能导致 goroutine 泄漏
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- **主从切换丢失**: 同步写后立即读可能命中 slave 导致不一致，需用 `READONLY` 或 `WAIT`
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- **Large Key**: Value > 10KB 的 hash/string 需特别注意内存和网络开销
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```mermaid
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graph LR
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A["客户端请求"] --> B{"缓存命中?"}
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B -- "Yes" --> C["返回缓存值"]
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B -- "No" --> D["获取分布式锁"]
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D --> E{"获取成功?"}
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E -- "No" --> F["等待后重试"]
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F --> D
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E -- "Yes" --> G["查询数据库"]
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G --> H["写入缓存 + 设置 TTL"]
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H --> C2["返回数据"]
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```
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```mermaid
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flowchart TD
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A["客户端"] --> B["Sentinel / Cluster"]
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B --> C[Master Node]
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C -- "异步复制" --> D[Slave 1]
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C -- "异步复制" --> E[Slave N]
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subgraph Problem ["主从切换期间的风险"]
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W1["写入 Master"] -.->|"可能未复制到 Slave"| W2["Master 故障"]
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W2 --> W3["Slave 晋升为新 Master"]
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W3 --> W4["新写入丢失"]
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end
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||
|
||
classDef warn fill:#f9d0c4,stroke:#d84315
|
||
class W1,W2,W3,W4 warn
|
||
```
|
||
|
||
## 关联笔记
|
||
- [[AI/Claude Code 生态/Agent 架构基础]]
|
||
- [[后端/中间件集成/Redis 实战指南]]
|
||
```
|
||
|
||
---
|
||
|
||
### 场景二：自动化邮件报告（进阶级）
|
||
|
||
**需求**: 创建 Skill 让 AI 能够自动生成周报、月报并通过邮件发送。
|
||
|
||
> [!SUCCESS] 这个 Skill 的优势在于 —— 它不仅是一个模板，还包含了完整的错误处理和幂等保证。
|
||
|
||
#### Skill 定义
|
||
|
||
```markdown
|
||
---
|
||
name: email-report
|
||
description: Generate and send automated reports (weekly/monthly) via email
|
||
trigger: "generate report AND email OR send weekly OR monthly report OR 生成报告"
|
||
---
|
||
|
||
# Automated Email Report Skill
|
||
|
||
## Overview
|
||
Guides the creation of structured reports and their delivery via email,
|
||
handling template rendering, attachment generation, and notification delivery.
|
||
|
||
## Pre-flight Checks
|
||
|
||
Before sending any report, verify these environment variables are set:
|
||
|
||
| Variable | Required | Description |
|
||
|----------|----------|-------------|
|
||
| `SMTP_HOST` | Yes | SMTP server address |
|
||
| `SMTP_PORT` | Yes | SMTP port (587 for TLS) |
|
||
| `EMAIL_USER` | Yes | Sender address |
|
||
| `EMAIL_PASS` | Yes | App-specific password or API token |
|
||
| `REPORT_RECIPIENTS` | No | Comma-separated recipient list |
|
||
|
||
> [!IMPORTANT] 安全提醒
|
||
> All credentials must come from ENV or secrets manager. NEVER hardcode.
|
||
|
||
## Workflow
|
||
|
||
```mermaid
|
||
sequenceDiagram
|
||
participant U as User
|
||
participant S as Email Report Skill
|
||
participant T as Template Engine
|
||
participant A as Attachment Generator
|
||
participant M as SMTP Client
|
||
participant R as Recipients
|
||
|
||
U->>S: "Generate weekly report"
|
||
S->>S: Parse scope & date range
|
||
S->>T: Render HTML template
|
||
T-->>S: Generated HTML
|
||
S->>A: Generate PDF attachment
|
||
A-->>S: PDF bytes
|
||
S->>M: Send email with attachments
|
||
M-->>R: Deliver to all recipients
|
||
S->>S: Log delivery status
|
||
S-->>U: Report sent successfully
|
||
```
|
||
|
||
## Step 1: Report Template Structure
|
||
|
||
All reports follow this standard structure:
|
||
|
||
```html
|
||
<h1>{{report_type}} Report — {{period}}</h1>
|
||
|
||
<section>
|
||
<h2>Summary</h2>
|
||
<p>{{summary}}</p>
|
||
</section>
|
||
|
||
<section>
|
||
<h2>Key Metrics</h2>
|
||
<table>
|
||
<!-- rendered metrics table -->
|
||
</table>
|
||
</section>
|
||
|
||
<section>
|
||
<h2>Action Items</h2>
|
||
<ul>
|
||
<!-- rendered action items -->
|
||
</ul>
|
||
</section>
|
||
|
||
<footer>Sent by automated reporting system</footer>
|
||
```
|
||
|
||
## Step 2: Sending Implementation
|
||
|
||
```python
|
||
import smtplib
|
||
from email.mime.multipart import MIMEMultipart
|
||
from email.mime.text import MIMEText
|
||
from email.mime.base import MIMEBase
|
||
from email import encoders
|
||
import os
|
||
|
||
def send_report(subject: str, html_body: str,
|
||
attachments: list[str] | None = None,
|
||
recipients: list[str] | None = None):
|
||
"""Send report email with optional attachments."""
|
||
|
||
msg = MIMEMultipart("mixed")
|
||
msg["From"] = os.getenv("EMAIL_USER")
|
||
msg["To"] = ", ".join(recipients or [])
|
||
msg["Subject"] = subject
|
||
|
||
# HTML body
|
||
msg.attach(MIMEText(html_body, "html"))
|
||
|
||
# Attachments (PDFs, charts, etc.)
|
||
for filepath in attachments or []:
|
||
with open(filepath, "rb") as f:
|
||
part = MIMEBase("application", "octet-stream")
|
||
part.set_payload(f.read())
|
||
encoders.encode_base64(part)
|
||
part.add_header("Content-Disposition", "attachment", filename=os.path.basename(filepath))
|
||
msg.attach(part)
|
||
|
||
# Send with TLS and timeout protection
|
||
with smtplib.SMTP(os.getenv("SMTP_HOST"), int(os.getenv("SMTP_PORT"))) as server:
|
||
server.starttls()
|
||
server.login(msg["From"], os.getenv("EMAIL_PASS"))
|
||
server.send_message(msg, to_addrs=recipients)
|
||
```
|
||
|
||
## Step 3: Retry Strategy
|
||
|
||
```python
|
||
import tenacity
|
||
|
||
@tenacity.retry(
|
||
stop=tenacity.stop_after_attempt(3),
|
||
wait=tenacity.wait_exponential(multiplier=1, min=2, max=30),
|
||
retry=tenacity.retry_if_exception_type(smtplib.SMTPException),
|
||
before_sleep=lambda retry_state: print(f"Retrying email delivery... ({retry_state.attempt_number}/3)")
|
||
)
|
||
def send_with_retry(email_fn, *args, **kwargs):
|
||
"""Email sending with exponential backoff retry."""
|
||
return email_fn(*args, **kwargs)
|
||
```
|
||
|
||
## Error Handling Matrix
|
||
|
||
| Scenario | Action | Logging Level |
|
||
|----------|--------|---------------|
|
||
| Missing env vars | Abort early, output help text | ERROR |
|
||
| SMTP connection refused | Queue job for retry | WARN |
|
||
| Authentication failed | Notify admin via web hook | CRITICAL |
|
||
| Attachment too large (> 25MB) | Split into multiple emails | WARN |
|
||
| Empty recipient list | Skip sending, log info | INFO |
|
||
|
||
## Cron Integration (Optional)
|
||
|
||
If scheduling is needed, recommend platform-native schedulers over custom cron scripts:
|
||
|
||
| Platform | Recommendation | Example |
|
||
|----------|---------------|---------|
|
||
| Linux | systemd timer or crontab | `0 9 * * 1 /usr/bin/python report.py` |
|
||
| Docker | entrypoint wrapper script | Health-checked container |
|
||
| Cloud | EventBridge / Cloud Scheduler | Configured via IaC |
|
||
|
||
## 关联笔记
|
||
- [[AI/Automated Reporting/报告生成架构]]
|
||
- [[后端/基础设施/SMTP 邮件服务]]
|
||
```
|
||
|
||
---
|
||
|
||
### 场景三：PR Review Checklist（实用级）
|
||
|
||
**需求**: 每当用户请求审查某个 PR 或 diff 时，自动以预设的检查清单来审视代码变更。
|
||
|
||
#### 快速定义
|
||
|
||
```markdown
|
||
---
|
||
name: pr-review-checklist
|
||
description: Run a structured code review checklist on PR diffs
|
||
trigger: "review PR OR review diff OR review pull request"
|
||
---
|
||
|
||
# PR Review Checklist
|
||
|
||
## Quick Reference
|
||
|
||
Review each change against these categories:
|
||
|
||
### 1. Correctness
|
||
- [ ] Logic matches requirements?
|
||
- [ ] Edge cases handled (empty input, null, zero)?
|
||
- [ ] Error propagation correct (not swallowed)?
|
||
|
||
### 2. Performance
|
||
- [ ] No O(n²) loops where O(n) suffices?
|
||
- [ ] Database queries not N+1?
|
||
- [ ] Large data streaming instead of loading entirely into memory?
|
||
|
||
### 3. Security
|
||
- [ ] Input sanitization present?
|
||
- [ ] AuthN/AuthZ verified before sensitive operations?
|
||
- [ ] Secrets never logged or committed?
|
||
|
||
### 4. Maintainability
|
||
- [ ] Function size reasonable (< 50 lines)?
|
||
- [ ] Meaningful variable/function names?
|
||
- [ ] Comments explain WHY not WHAT?
|
||
|
||
### 5. Testing
|
||
- [ ] Unit tests cover happy path + edge cases?
|
||
- [ ] Test isolation maintained (no shared mutable state)?
|
||
- [ ] Integration tests if external dependency involved?
|
||
```
|
||
|
||
---
|
||
|
||
### 场景四：数据库迁移脚本生成器（高级）
|
||
|
||
**需求**: 基于当前的数据库 schema 和用户提出的变更需求，自动生成带版本控制和回滚能力的迁移脚本。
|
||
|
||
> [!FAILURE] ⚠️ 这个场景比较危险 —— 涉及数据库变更的操作，必须在 Skill 中加入强制确认步骤。
|
||
|
||
```markdown
|
||
---
|
||
name: db-migration-generator
|
||
description: Generate safe database migration scripts with rollback support
|
||
trigger: "migration OR alter table OR add column AND database"
|
||
---
|
||
|
||
# Database Migration Generator
|
||
|
||
> [!CRITICAL] 安全协议
|
||
> This skill operates on real databases. Every generated migration MUST include:
|
||
> 1. A rollback script
|
||
> 2. Data safety analysis
|
||
> 3. Explicit confirmation requirement before execution
|
||
|
||
## Safe Migration Rules
|
||
|
||
### Zero-Downtime Pattern for Schema Changes
|
||
|
||
```mermaid
|
||
flowchart LR
|
||
A["Step 1: Add nullable column"] --> B["Step 2: Backfill existing data"]
|
||
B --> C["Step 3: Deploy app with dual-write"]
|
||
C --> D["Step 4: Validate new data correctness"]
|
||
D --> E["Step 5: Switch reads to new column"]
|
||
E --> F["Step 6: Remove old column (next release)"]
|
||
classDef step fill:#e8f5e9,stroke:#2e7d32
|
||
class A,B,C,D,E,F step
|
||
```
|
||
|
||
### Never Do This on Large Tables
|
||
|
||
> [!FAILURE] 高危操作 —— 以下操作在 > 100K 行的表上执行会导致锁表:
|
||
|
||
| 操作 | 风险等级 | 推荐替代方案 |
|
||
|------|---------|------------|
|
||
| `ALTER TABLE ADD NOT NULL` | 🔴 致命 | 分步添加 + backfill |
|
||
| `ALTER TABLE DROP COLUMN` | 🔴 致命 | 标记废弃 + 下周期清理 |
|
||
| `ALTER TABLE MODIFY TYPE` | 🟠 危险 | 新建列 + 数据迁移 + 切换 |
|
||
| `ADD UNIQUE INDEX CONCURRENTLY` | 🟢 安全 | PostgreSQL 原生支持 |
|
||
|
||
### Generated Script Template
|
||
|
||
```sql
|
||
-- Migration: 0042_add_user_email_verified
|
||
-- Date: YYYY-MM-DD
|
||
-- Risk: LOW (additive change only)
|
||
|
||
BEGIN;
|
||
|
||
-- Step 1: Add new nullable column
|
||
ALTER TABLE users
|
||
ADD COLUMN email_verified_at TIMESTAMP WITH TIME ZONE DEFAULT NULL;
|
||
|
||
-- Step 2: Create index (non-blocking where possible)
|
||
CREATE INDEX CONCURRENTLY idx_users_email_verified
|
||
ON users(email_verified_at) WHERE email_verified_at IS NOT NULL;
|
||
|
||
COMMIT;
|
||
|
||
-- ============================
|
||
-- ROLLBACK SCRIPT
|
||
-- ============================
|
||
-- BEGIN;
|
||
-- DROP INDEX CONCURRENTLY IF EXISTS idx_users_email_verified;
|
||
-- ALTER TABLE users DROP COLUMN IF EXISTS email_verified_at;
|
||
-- COMMIT;
|
||
```
|
||
|
||
## Confirmation Steps
|
||
|
||
Before generating any migration, ask the user:
|
||
|
||
1. What is the target database and version? (PostgreSQL 15, MySQL 8.x, etc.)
|
||
2. Is the table large (> 1M rows)? If yes, enforce zero-downtime pattern.
|
||
3. Can rolling deploy be supported? If no, plan full downtime window.
|
||
4. Has the migration been reviewed by a second person?
|
||
|
||
## 关联笔记
|
||
- [[后端/数据库/迁移最佳实践]]
|
||
- [[DevOps/零停机部署策略]]
|
||
```
|
||
|
||
## 总结与进阶方向
|
||
|
||
### Skill 成熟度模型
|
||
|
||
```mermaid
|
||
graph BT
|
||
P1["Level 1: Prompt 库"]
|
||
P2["Level 2: CLAUDE.md snippets"]
|
||
P3["Level 3: Custom Skill"]
|
||
P4["Level 4: Custom Agent Type"]
|
||
P5["Level 5: MCP Server Integration"]
|
||
|
||
P1 --> P2 --> P3 --> P4 --> P5
|
||
|
||
classDef low fill:#fff3e0,stroke:#ef6c00
|
||
classDef mid fill:#e3f2fd,stroke:#1565c0
|
||
classDef high fill:#e8f5e9,stroke:#2e7d32
|
||
|
||
class P1,P2 low
|
||
class P3,P4 mid
|
||
class P5 high
|
||
```
|
||
|
||
### 下一步学习路线
|
||
|
||
| 阶段 | 重点 | 推荐资源 |
|
||
|------|------|---------|
|
||
| **Phase 1** | 理解现有内置 Skill 的触发逻辑 | 阅读本 Skill 的定义源码 |
|
||
| **Phase 2** | 创建第一个极简 Skill（3 条 rule） | 参考 PR Review Checklist 案例 |
|
||
| **Phase 3** | 加入 workflow 图表 + 错误处理 | 参考 Email Report 案例 |
|
||
| **Phase 4** | 探索自定义 Agent Type（超越 Skill 的能力边界） | Study Explore / Plan agent 的实现 |
|
||
| **Phase 5** | 构建 MCP Server 实现跨工具链联动 | Anthropic MCP Protocol 文档 |
|
||
|
||
### 一句话总结
|
||
|
||
> **好的 Skill 不是"教 AI 怎么做"，而是"给 AI 一套它做这件事时一定会遵守的思维框架"。** 就像给高级工程师一份技术规范——细节他可以自己决定，但红线和标准你必须写好。
|