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var(--shiki-dark-text-decoration);}",{"title":339,"searchDepth":395,"depth":395,"links":501},[502,503,510,511],{"id":231,"depth":395,"text":231},{"id":292,"depth":395,"text":293,"children":504},[505,506,507,508,509],{"id":297,"depth":402,"text":298},{"id":344,"depth":402,"text":345},{"id":369,"depth":402,"text":370},{"id":414,"depth":402,"text":415},{"id":430,"depth":402,"text":430},{"id":436,"depth":395,"text":437},{"id":481,"depth":395,"text":481},"把 UDhold 开发文档接入 Cursor、TRAE、Claude 等 AI 工具，让 AI 帮你写插件。","md",{},{"icon":126},"---\ntitle: LLMs.txt\ndescription: 把 UDhold 开发文档接入 Cursor、TRAE、Claude 等 AI 工具，让 AI 帮你写插件。\nnavigation:\n  icon: i-lucide-file-text\n---\n\n[llms.txt](https:\u002F\u002Fllmstxt.org) 是一个专为大型语言模型设计的站点索引文件，思路类似 `robots.txt`，但目标不同：robots.txt 告诉搜索引擎爬虫「什么能爬」，llms.txt 则为 AI 工具提供网站内容的结构化摘要，帮助它们更快、更准地理解和引用文档。\n\nUDhold 开发文档已全量适配这一标准，并且**随文档更新自动同步**——你读到这篇指南时，AI 拿到的就是最新内容。\n\n## 可用资源\n\n| 地址 | 内容 | 适合场景 |\n|------|------|----------|\n| [https:\u002F\u002Fudhold.cn\u002Fllms.txt](https:\u002F\u002Fudhold.cn\u002Fllms.txt) | 全站结构化索引：每个文档页的标题、链接与一句话简介 | 加入工具的文档索引、让 AI 先知道「有什么」 |\n| [https:\u002F\u002Fudhold.cn\u002Fllms-full.txt](https:\u002F\u002Fudhold.cn\u002Fllms-full.txt) | 全部文档的 Markdown 全文（含 plugin.json 契约、API 用法、完整示例代码） | 一次性投喂给 AI，或接入上下文知识库 |\n\n本地开发调试时同样可访问：`http:\u002F\u002Flocalhost:7013\u002Fllms.txt`。\n\n## 在 AI 工具中使用\n\n### Cursor\n\n1. 打开 `Settings` → `Indexing & Docs` → `Docs` → `Add doc`\n2. 填入 `https:\u002F\u002Fudhold.cn\u002Fllms-full.txt`，保存\n3. 编码时用 `@Docs` 引用，例如：\n\n```text\n@Docs 按 UDhold 插件契约帮我生成一个「待办清单」插件的工程骨架，\n包含 plugin.json、Vue 3 入口和一个待办列表页面。\n```\n\n### TRAE\n\n1. 打开「设置」→「文档集」（或对话输入框的 `#Docs` 上下文）\n2. 添加文档集，URL 填 `https:\u002F\u002Fudhold.cn\u002Fllms-full.txt`\n3. 对话时 `#` 选中该文档集即可引用\n\n### Claude Code \u002F Codex 等 CLI 编码代理\n\n在项目（或全局）的 `AGENTS.md` \u002F 规则文件里加一行指引，代理会在开发 UDhold 插件前主动抓取文档：\n\n```markdown\n## UDhold 插件开发\n\n开发 UDhold 插件相关代码前，先读取 https:\u002F\u002Fudhold.cn\u002Fllms-full.txt 了解\nplugin.json 契约、权限模型与设计系统，再动手写代码。\n```\n\n### 网页版 AI 对话（Claude \u002F ChatGPT 等）\n\n- 支持联网的模型：直接把 `https:\u002F\u002Fudhold.cn\u002Fllms-full.txt` 链接发给它，让它基于文档回答\n- 不联网的模型：先打开 llms.txt 找到相关章节，把对应文档页内容粘贴进对话\n\n### 其他工具\n\n任何支持 llms.txt 标准或 URL 摄取的工具（知识库、RAG 管道、IDE 插件等）都可以直接使用上面两个地址，无需额外适配。\n\n## 让 AI 写出能跑的插件\n\n投喂文档只是第一步，提问方式决定产出质量：\n\n- **先文档后代码**：让 AI 先通读 llms-full.txt 再动手，避免它按 uTools 或浏览器扩展的旧经验瞎编 API\n- **点明两件事**：宿主方法统一返回 `IpcResult` 信封（失败不抛异常，要检查 `code`）；敏感能力必须在 `plugin.json` 的 `permissions` 里声明\n- **要完整骨架**：让 AI 按「目录结构 → plugin.json → package.json → 入口文件」的顺序输出，与[快速上手](\u002Fdocs\u002Fdevelopment\u002Fquick-start\u002Fgetting-started)的工程结构对齐，生成后可直接进宿主调试\n\n## 延伸阅读\n\n- [llmstxt.org](https:\u002F\u002Fllmstxt.org)：llms.txt 标准规范\n- [快速上手](\u002Fdocs\u002Fdevelopment\u002Fquick-start\u002Fgetting-started)：跟着文档走一遍完整开发流程\n",{"title":196,"description":512},"adoPtlKvyXq0arJpEvzRNSckOzjkTBq-479XEturpB8",[520,522],{"title":187,"path":188,"stem":189,"description":521,"icon":48,"children":-1},"LoadingState、SearchList、CodeBlock、SidebarNav 四个场景化组件，覆盖加载、搜索、代码展示与侧边导航。",null,1793010248947]