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
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlark-docExecute the skills CLI command in your project's root directory to begin installation:
Fetches lark-doc from larksuite/cli and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate lark-doc. Access via /lark-doc in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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
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。
| 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 |
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。
使用 wiki.spaces.get_node 查询节点信息
lark-cli wiki spaces get_node --params '{"token":"wiki_token"}'
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 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"
}
}
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)
⚠️ 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
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:
../lark-whiteboard/SKILL.md 了解如何编辑画板内容如果用户刚通过 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>--markdown 里重复多个 whiteboard 标签
步骤 2:从响应中记录 tokendocs +update 成功后,读取响应字段 data.board_tokensdata.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../lark-whiteboard/SKILL.md 了解如何编辑画板内容docs +update 直接编辑../lark-whiteboard/SKILL.mdlark-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。lark-sheets 做对象内部读取、筛选、写入等操作。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。
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. |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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lark-doc reduced setup friction for our internal harness; good balance of opinion and flexibility.
lark-doc is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
lark-doc reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend lark-doc for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: lark-doc is focused, and the summary matches what you get after install.
I recommend lark-doc for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in lark-doc — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in lark-doc — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
lark-doc has been reliable in day-to-day use. Documentation quality is above average for community skills.
lark-doc fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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