Feishu Bitable Creator

Creates clean, ready-to-use Feishu Bitable tables with automatic cleanup and data population.

Authentication & Permissions

This skill requires a pre-configured Feishu (Lark) integration via OpenClaw's channel system. It does not accept API keys directly from the user.

How Authentication Works

1. 1. Feishu Channel Configuration: Your OpenClaw instance must have the Feishu channel enabled and configured with a valid app ID and app secret in ~/.openclaw/openclaw.json:

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1. 2. Token Management: OpenClaw's Feishu extension automatically handles token acquisition and refresh using the configured app credentials. No manual token management is required.

1. 3. User Consent: When creating a table, the tool uses the agent's own identity (the Feishu app/bot). The addBitableAdmin() function requires the owner's explicit user_id obtained from the conversation context, ensuring consent.

Required Feishu App Permissions

Your Feishu app must have these permissions granted:

Permission	Purpose
INLINECODE3	Create and manage Bitable apps
INLINECODE4

Add users as admins to documents |
| drive:drive:read | Read drive contents |

Security Notes

• - ⚠️ No User-Supplied Credentials: This skill does NOT accept API keys, tokens, or secrets from user prompts. All authentication is handled through OpenClaw's secure channel configuration.
• ⚠️ Scope Limitation: The skill only performs actions on Bitable tables it creates. It cannot access existing tables without explicit sharing.
• ⚠️ Admin Addition Requires Explicit User ID: Adding an admin requires the specific user_id from conversation context — the agent cannot arbitrarily add unknown users.

Setup Instructions

Before using this skill, ensure your OpenClaw host admin has:

1. 1. Created a Feishu app at https://open.feishu.cn/app
2. Enabled the required permissions in the Feishu app console
3. Configured the app ID and secret in INLINECODE7
4. Restarted the OpenClaw gateway to apply changes

For detailed setup, see: https://docs.openclaw.ai/channels/feishu

Problem with default Bitable creation:

• - Feishu creates 10 empty placeholder rows by default
• Creates 4 default columns (文本, 单选, 日期, 附件) that are often unused
• Primary field is always named "文本" which is not descriptive
• Creator has full control, but human owner has no permissions

This skill solves these issues:

• - ✅ Automatically deletes empty placeholder rows and default columns
• ✅ Intelligently renames primary field based on table name
• ✅ Adds owner as admin with full permissions
• ✅ Provides clean slate for your actual data

Quick Start

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Designing Rich Fields (发挥多维表格价值)

💡 核心建议: 为了让多维表格真正发挥作用，建议设计丰富的标签/分类字段，但根据实际场景灵活选择。

推荐的字段设计思路

根据数据类型，考虑添加以下维度字段：

数据场景	建议字段	用途
项目管理	状态、优先级、负责人、截止日期	追踪进展，分配任务
研究/调研

分类、标签、来源、评分 | 归类信息，评估价值 |
| 产品/功能 | 模块、优先级、状态、负责人 | 产品规划，迭代管理 |
| 客户/用户 | 类型、阶段、标签、负责人 | 客户分层，跟进管理 |
| 竞品分析 | 分类、评分、标签、来源 | 对比分析，筛选查看 |

字段类型选择指南

字段名	类型	适用场景
标签	MultiSelect	一个项目可属于多个类别，用于交叉筛选
分类

SingleSelect | 一级分类，用于分组统计 | | 状态 | SingleSelect | 有明确流转阶段，如待办→进行中→完成 | | 优先级 | SingleSelect | 需要排序或区分重要性 | | 负责人 | User | 需要分配到人 | | 日期 | DateTime | 有时间节点要求 | | 评分 | Number | 需要量化评估 | | 来源 | SingleSelect | 需要追踪信息出处 |

实际设计示例

示例1：开源大模型研究
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示例2：工作任务清单
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设计原则

1. 1. 先想怎么用：先思考用户会如何筛选/分组/排序数据
2. 适度原则：字段不是越多越好，选择真正能区分数据的维度
3. 数据可得：确保收集数据时能填上这些字段，不要设计无法获取的维度
4. 灵活调整：如果某个字段使用率很低，后续可以删除或修改

检验标准

设计完字段后，问自己：

• - ❓ 用户能按哪些维度筛选数据？
• ❓ 能按哪些维度分组查看？
• ❓ 能按哪些维度排序？
• ❓ 数据收集时这些字段是否容易获取？

如果以上问题都有明确答案，说明字段设计合理！

实际示例：开源大模型表格

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Add Owner as Admin

IMPORTANT: Tool creates table but does NOT add permissions. You must add the owner as admin manually.

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Required Permissions

• - bitable:app - Manage Bitable apps
• INLINECODE9 - Manage document permissions

Primary Field Naming

Table name contains	Primary field becomes
"项目"	"项目名称"
"研究"

"研究名称" | | "测试" | "测试项" | | "数据" | "数据项" | | "任务" | "任务名称" | | "记录" | "记录项" | | Other (≤6 chars) | Use table name as-is | | Other (>6 chars) | First 4 chars + "..." |

Common Field Types

Type ID	Name	Use For
1	Text	Names, descriptions
2

Number | Counts, amounts | | 3 | SingleSelect | Status, priority | | 4 | MultiSelect | Tags, features | | 5 | DateTime | Dates, deadlines | | 7 | Checkbox | Done/Incomplete | | 11 | User | Assignees |

Complete Example: Research Data Table with Rich Tags

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{ name: "GitHub星标", type: 2 },
{ name: "使用场景", type: 4 }
],
records: [{
"AI框架对比": "AutoGPT",
"框架名称": "AutoGPT",
"GitHub星标": 157000,
"使用场景": ["自动化", "研究"]
}],
bossUserId: "ouxxxxxxxxxxxxxxxx" // Replace with actual user's openid
});

## Tips

1. **设计丰富的标签字段**: 为了让多维表格发挥作用，建议根据数据特点添加标签、分类、状态等维度字段（参考 "Designing Rich Fields" 章节）

2. **根据场景选择字段**: 不同数据类型需要不同的维度字段，如项目管理需要"状态"，研究调研需要"来源"，灵活设计

3. **先想怎么用**: 设计字段前先思考用户会如何筛选/分组/排序数据

4. **Multi-select values**: Pass as array: `["标签A", "标签B"]`

5. **User ID**: Get from conversation context (e.g., `ou_xxxxxxxxxxxxxxxx`)

6. **Cleanup verified**: Tool returns `cleaned_placeholder_rows` and `cleaned_default_fields`

7. **Field order**: Create fields first, then add records

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| FieldNameNotFound | Wrong primary field name | Check `primary_field_name` in result |
| 400 Bad Request | Invalid field type/value | Verify field_type ID |
| Permission Denied | Missing `drive:permission:manage` | Check app permissions |
| User Not Found | Wrong user_id format | Use `openid` type |

## Output Format

✅ Table "项目名称" created successfully!

🔗 URL: https://my.feishu.cn/base/xxxxxxxxxxxxx
🧹 Cleanup: 10 placeholder rows, 4 default columns deleted
👤 Owner: Added as admin (full_access)
📊 Records: X records added
📝 Fields: 名称, 标签, 分类, 状态, 优先级, 负责人, 日期, 描述
💡 Tip: 建议添加标签/分类/状态等维度字段，让表格支持多维筛选和分组
```

设计检查建议:

• - 是否可以根据标签交叉筛选数据？
• 是否可以按分类分组查看？
• 是否可以按状态/优先级排序？
• 数据收集时这些字段是否容易获取？

如果以上问题都有答案，说明字段设计合理！

• - ✅ 优先级 (SingleSelect) - for sorting