Token导航 LogoToken导航TokenDH.com
研究检索敏感数据github未标认证来源可访问clear审计通过

lark-docs云雀文档

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

648

周安装

27

GitHub Stars

2

下载量

216
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:lark-docs(云雀文档)
来源仓库:https://github.com/serendipityoneinc/srp-claude-code-marketplace
仓库路径:skills/lark-docs
安装命令:
npx skills add https://github.com/serendipityoneinc/srp-claude-code-marketplace --skill lark-docs
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/serendipityoneinc/srp-claude-code-marketplace --skill lark-docs

简介

lark-docs 用于辅助文档、README、Markdown 和内容稿件的整理与改写。

  • 适合提炼结构、补齐章节、统一术语或检查链接,将零散材料整理成可读文档。
  • 使用时应保留项目已有事实和路径,避免将未确认信息写成确定结论。
  • 涉及对外文案时需控制语气,避免过度营销或夸大能力。
  • 通过 npx skills add 命令从 serendipityoneinc/srp-claude-code-marketplace 仓库安装。

SKILL.md

Lark Docs Access (飞书云文档权限)

以用户身份访问和搜索飞书云文档,包括文档、表格、多维表格和知识库等。

Access and search Lark cloud documents as the authenticated user, including docs, sheets, bitables, and wiki spaces.

Quick Start

Search for Documents (搜索文档)

搜索包含'OKR'的文档
Search for documents containing 'product roadmap'

Access Document Content (访问文档内容)

获取这个文档的内容:https://example.feishu.cn/docx/abc123
Get the content of this document: https://example.feishu.cn/wiki/xyz789

Search Wiki Spaces (搜索知识库)

在知识库中搜索关于'架构设计'的内容
Search wiki for 'system architecture' documentation

Key Features

1. Document Search (文档搜索)

Search across all document types that the user has access to:

  • Documents (文档): docx format documents
  • Sheets (表格): spreadsheet documents
  • Bitables (多维表格): database/table documents
  • Wiki (知识库): wiki pages and spaces

Available MCP Tools:

  • mcp__lark__docx_builtin_search - Search cloud documents
  • mcp__lark__wiki_v1_node_search - Search wiki content

Search Parameters:

  • search_key (required): Keyword to search for
  • docs_types: Filter by document type (doc, sheet, bitable, mindnote, file)
  • owner_ids: Filter by document owner's open_id
  • chat_ids: Filter by group where document is located
  • count: Number of results (max 50, default 10)
  • offset: Pagination offset

2. Document Content Access (文档内容访问)

Retrieve the actual content of documents:

Available MCP Tools:

  • mcp__lark__docx_v1_document_rawContent - Get plain text content
  • mcp__lark__wiki_v2_space_getNode - Get wiki node information

Parameters:

  • document_id: The unique document identifier
  • lang: Language for @mentions (0=Chinese, 1=English)

3. Permission Management (权限管理)

Check and manage document permissions:

Available MCP Tools:

  • mcp__lark__drive_v1_permissionMember_create - Add permissions

Permission Roles:

  • view: Read-only access
  • edit: Can edit the document
  • full_access: Full management access

Common Workflows

Workflow 1: Search and Read Documents

1. User: "搜索包含'产品路线图'的文档"
2. Assistant uses: mcp__lark__docx_builtin_search
   - search_key: "产品路线图"
   - count: 10
3. Display results with titles and URLs
4. User: "打开第一个文档"
5. Assistant uses: mcp__lark__docx_v1_document_rawContent
   - document_id: extracted from URL
6. Display document content

Workflow 2: Wiki Navigation

1. User: "Show me the engineering wiki"
2. Assistant uses: mcp__lark__wiki_v1_node_search
   - query: "engineering"
3. Display wiki pages and their hierarchy
4. User: "Get the content of the API documentation page"
5. Assistant uses: mcp__lark__wiki_v2_space_getNode
   - token: wiki_page_token
6. Display wiki content

Workflow 3: Document Discovery by Owner

1. User: "张三创建的所有文档"
2. First get user ID: mcp__lark__contact_v3_user_batchGetId
   - emails: ["zhangsan@company.com"]
3. Then search: mcp__lark__docx_builtin_search
   - search_key: ""
   - owner_ids: [user_open_id]
4. Display documents created by that user

Examples (示例)

Example 1: Basic Search (基础搜索)

User (Chinese):

帮我搜索包含'季度OKR'的文档

Assistant Response:

I'll search for documents containing '季度OKR'.

<uses mcp__lark__docx_builtin_search>
- search_key: "季度OKR"
- count: 10

Found 3 documents:

1. **2024 Q4 OKR Review** (docx)
   - Owner: 张三
   - Updated: 2024-01-10
   - Link: https://example.feishu.cn/docx/abc123

2. **Team OKR Template** (docx)
   - Owner: 李四
   - Updated: 2024-01-08
   - Link: https://example.feishu.cn/docx/def456

3. **Q1 2025 OKR Planning** (sheet)
   - Owner: 王五
   - Updated: 2024-01-12
   - Link: https://example.feishu.cn/sheets/ghi789

Would you like me to open any of these documents?

Example 2: Get Document Content (获取文档内容)

User (English):

Get the content of this document: https://example.feishu.cn/docx/abc123

Assistant Response:

I'll retrieve the content of that document.

<uses mcp__lark__docx_v1_document_rawContent>
- document_id: abc123

Here's the document content:

# 2024 Q4 OKR Review

## Engineering Team

### Objective 1: Improve System Reliability
- KR1: Achieve 99.9% uptime ✓ (Completed: 99.95%)
- KR2: Reduce MTTR to under 30 minutes ✓ (Completed: 25 min)
- KR3: Complete disaster recovery testing ⚠ (In Progress: 80%)

### Objective 2: Enhance Developer Productivity
- KR1: Reduce build time by 40% ✓ (Completed: 45% reduction)
- KR2: Implement automated code review ✓ (Completed)
- KR3: Launch internal developer portal ✗ (Delayed to Q1 2025)

[... more content ...]

Would you like me to summarize the key points or search for related documents?

Example 3: Wiki Search (知识库搜索)

User (Chinese):

在工程知识库中搜索关于'微服务架构'的文档

Assistant Response:

我会在工程知识库中搜索'微服务架构'相关内容。

<uses mcp__lark__wiki_v1_node_search>
- query: "微服务架构"

找到 5 个相关页面:

1. **微服务架构设计指南**
   - 路径: 工程文档 > 架构设计 > 微服务
   - 更新时间: 2024-01-10
   - [查看页面](https://example.feishu.cn/wiki/page1)

2. **服务拆分最佳实践**
   - 路径: 工程文档 > 架构设计 > 最佳实践
   - 更新时间: 2024-01-08
   - [查看页面](https://example.feishu.cn/wiki/page2)

3. **API Gateway 配置说明**
   - 路径: 工程文档 > 基础设施 > 网关
   - 更新时间: 2024-01-12
   - [查看页面](https://example.feishu.cn/wiki/page3)

需要我打开其中某个页面查看详细内容吗?

Important Notes

Permission Boundaries (权限边界)

  • All operations respect the user's actual Lark permissions
  • If a document is not accessible, you'll receive a permission error
  • Cannot access private documents or groups the user is not a member of

Document ID Extraction (文档ID提取)

Document URLs have this format:

  • Docx: https://*.feishu.cn/docx/{document_id}
  • Wiki: https://*.feishu.cn/wiki/{wiki_token}
  • Sheets: https://*.feishu.cn/sheets/{spreadsheet_id}
  • Bitable: https://*.feishu.cn/base/{app_token}

Extract the ID/token from the URL when accessing content.

Search Limitations (搜索限制)

  • Maximum 50 results per search (count parameter)
  • Use pagination (offset) for more results
  • Search is case-insensitive
  • Results are sorted by relevance

Content Format (内容格式)

  • docx_v1_document_rawContent returns plain text (no formatting)
  • @mentions are converted to display names based on lang parameter
  • For rich formatted content, use the API to get structured data

Error Handling

Common errors and solutions:

  1. "Permission denied" (权限被拒绝)

- The user doesn't have access to this document - Ask the document owner to grant permission

  1. "Document not found" (文档未找到)

- Document ID is invalid or document was deleted - Verify the document URL is correct

  1. "Invalid token" (无效令牌)

- MCP server authentication failed - Check LARK_APP_ID and LARK_APP_SECRET environment variables - Ensure OAuth token is valid

Tips for Effective Use

  1. Start with search: Use keyword search before asking for specific documents
  2. Be specific: Include relevant keywords to narrow down results
  3. Use filters: Filter by document type or owner to find documents faster
  4. Check permissions: Verify access before trying to read document content
  5. Bilingual support: Works with both Chinese and English queries

Related Skills

  • lark-messages: Access Lark messages and groups
  • Future: lark-approval, lark-calendar, lark-drive

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.97%
按下载量换算67

OpenCode

26.27%
按下载量换算57

Cursor

16.71%
按下载量换算36

Antigravity

12.65%
按下载量换算27

trae

7.15%
按下载量换算15

Gemini CLI

3.65%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

继续浏览同类 Skills