lark-doc

larksuite/cli · updated Apr 28, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/larksuite/cli --skill lark-doc
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summary

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理

skill.md

docs (v1)

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理

核心概念

文档类型与 Token

飞书开放平台中,不同类型的文档有不同的 URL 格式和 Token 处理方式。在进行文档操作(如添加评论、下载文件等)时,必须先获取正确的 file_token

文档 URL 格式与 Token 处理

URL 格式 示例 Token 类型 处理方式
/docx/ https://example.larksuite.com/docx/doxcnxxxxxxxxx file_token URL 路径中的 token 直接作为 file_token 使用
/doc/ https://example.larksuite.com/doc/doccnxxxxxxxxx file_token URL 路径中的 token 直接作为 file_token 使用
/wiki/ https://example.larksuite.com/wiki/wikcnxxxxxxxxx wiki_token ⚠️ 不能直接使用,需要先查询获取真实的 obj_token
/sheets/ https://example.larksuite.com/sheets/shtcnxxxxxxxxx file_token URL 路径中的 token 直接作为 file_token 使用
/drive/folder/ https://example.larksuite.com/drive/folder/fldcnxxxx folder_token URL 路径中的 token 作为文件夹 token 使用

Wiki 链接特殊处理(关键!)

知识库链接(/wiki/TOKEN)背后可能是云文档、电子表格、多维表格等不同类型的文档。不能直接假设 URL 中的 token 就是 file_token,必须先查询实际类型和真实 token。

处理流程

  1. 使用 wiki.spaces.get_node 查询节点信息

    lark-cli wiki spaces get_node --params '{"token":"wiki_token"}'
    
  2. 从返回结果中提取关键信息

    • node.obj_type:文档类型(docx/doc/sheet/bitable/slides/file/mindnote)
    • node.obj_token真实的文档 token(用于后续操作)
    • node.title:文档标题
  3. 根据 obj_type 使用对应的 API

    obj_type 说明 使用的 API
    docx 新版云文档 drive file.comments.*docx.*
    doc 旧版云文档 drive file.comments.*
    sheet 电子表格 sheets.*
    bitable 多维表格 bitable.*
    slides 幻灯片 drive.*
    file 文件 drive.*
    mindnote 思维导图 drive.*

查询示例

# 查询 wiki 节点
lark-cli wiki spaces get_node --params '{"token":"wiki_token"}'

返回结果示例:

{
   "node": {
      "obj_type": "docx",
      "obj_token": "xxxx",
      "title": "标题",
      "node_type": "origin",
      "space_id": "12345678910"
   }
}

资源关系

Wiki Space (知识空间)
└── Wiki Node (知识库节点)
    ├── obj_type: docx (新版文档)
    │   └── obj_token (真实文档 token)
    ├── obj_type: doc (旧版文档)
    │   └── obj_token (真实文档 token)
    ├── obj_type: sheet (电子表格)
    │   └── obj_token (真实文档 token)
    ├── obj_type: bitable (多维表格)
    │   └── obj_token (真实文档 token)
    └── obj_type: file/slides/mindnote
        └── obj_token (真实文档 token)

Drive Folder (云空间文件夹)
└── File (文件/文档)
    └── file_token (直接使用)

重要说明:画板编辑

⚠️ lark-doc skill 不能直接编辑已有画板内容,但 docs +update 可以新建空白画板

场景 1:已通过 docs +fetch 获取到文档内容和画板 token

如果用户已经通过 docs +fetch 拉取了文档内容,并且文档中已有画板(返回的 markdown 中包含 <whiteboard token="xxx"/> 标签),请引导用户:

  1. 记录画板的 token
  2. 查看 ../lark-whiteboard/SKILL.md 了解如何编辑画板内容

场景 2:刚创建画板,需要编辑

如果用户刚通过 docs +update 创建了空白画板,需要编辑时: 步骤 1:按空白画板语法创建

  • --markdown 中直接传 <whiteboard type="blank"></whiteboard>
  • 需要多个空白画板时,在同一个 --markdown 里重复多个 whiteboard 标签 步骤 2:从响应中记录 token
  • docs +update 成功后,读取响应字段 data.board_tokens
  • data.board_tokens 是新建画板的 token 列表,后续编辑直接使用这里的 token 步骤 3:引导编辑
  • 记录需要编辑的画板 token
  • 查看 ../lark-whiteboard/SKILL.md 了解如何编辑画板内容

注意事项

快速决策

  • 用户说“找一个表格”“按名称搜电子表格”“找报表”“最近打开的表格”,先用 lark-cli docs +search 做资源发现。
  • docs +search 不是只搜文档 / Wiki;结果里会直接返回 SHEET 等云空间对象。
  • 拿到 spreadsheet URL / token 后,再切到 lark-sheets 做对象内部读取、筛选、写入等操作。

补充说明

docs +search 除了搜索文档 / Wiki,也承担“先定位云空间对象,再切回对应业务 skill 操作”的资源发现入口角色;当用户口头说“表格 / 报表”时,也优先从这里开始。

Shortcuts(推荐优先使用)

Shortcut 是对常用操作的高级封装(lark-cli docs +<verb> [flags])。有 Shortcut 的操作优先使用。

Shortcut 说明
+search Search Lark docs, Wiki, and spreadsheet files (Search v2: doc_wiki/search)
+create Create a Lark document
+fetch Fetch Lark document content
+update Update a Lark document
+media-insert Insert a local image or file at the end of a Lark document (4-step orchestration + auto-rollback)
+media-download Download document media or whiteboard thumbnail (auto-detects extension)
+whiteboard-update Update an existing whiteboard in lark document with whiteboard dsl. Such DSL input from stdin. refer to lark-whiteboard skill for more details.
how to use lark-doc

How to use lark-doc on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add lark-doc
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/larksuite/cli --skill lark-doc

The skills CLI fetches lark-doc from GitHub repository larksuite/cli and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lark-doc

Reload or restart Cursor to activate lark-doc. Access the skill through slash commands (e.g., /lark-doc) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.636 reviews
  • Ganesh Mohane· Dec 12, 2024

    lark-doc reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kofi Sharma· Dec 12, 2024

    lark-doc is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sophia Martin· Dec 4, 2024

    lark-doc reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kofi Shah· Nov 23, 2024

    I recommend lark-doc for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nia Thompson· Nov 15, 2024

    Solid pick for teams standardizing on skills: lark-doc is focused, and the summary matches what you get after install.

  • Rahul Santra· Nov 3, 2024

    I recommend lark-doc for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Pratham Ware· Oct 22, 2024

    Useful defaults in lark-doc — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kofi Chen· Oct 14, 2024

    Useful defaults in lark-doc — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kaira Park· Oct 6, 2024

    lark-doc has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Nia Jackson· Sep 25, 2024

    lark-doc fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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