
Feishu Bitable
FreeManage and manipulate your Bitable data efficiently.
Free · Opens the source repo
What Feishu Bitable does
Feishu Bitable provides a comprehensive set of tools for creating, querying, editing, and managing multidimensional tables (Bitable) within the Feishu ecosystem. This skill supports 27 different field types, advanced filtering, batch operations, and view management, making it suitable for users who need to handle complex data structures effectively. Whether you are building a new app or managing existing data, Feishu Bitable streamlines the process with clear instructions and structured API calls.
When using this skill, users can create data tables, manage fields and views, and perform CRUD operations on records. It offers two modes for table creation: a predefined structure for clear requirements or a default table for exploratory scenarios. Users must be cautious of default empty rows that can lead to data pollution, and they are advised to delete these before inserting data. The skill also emphasizes the importance of correctly formatting field values according to their types, ensuring that operations execute smoothly without errors.
This skill is particularly useful for developers and data managers who work with Feishu's Bitable platform. It allows for efficient data manipulation, whether through individual record updates or bulk imports. The detailed documentation provides insights into field configurations, record value formats, and common error handling, making it easier for users to troubleshoot issues as they arise. With practical examples included, users can quickly learn how to implement various functionalities within their applications.
In summary, Feishu Bitable is an essential tool for anyone looking to harness the full potential of Feishu's multidimensional tables. Its structured approach to data management not only enhances productivity but also reduces the likelihood of errors during data operations, making it a valuable asset for developers and data analysts alike.
When to use it
Use this skill when you need to create, manage, or manipulate data in Feishu's Bitable, especially for batch operations or advanced filtering.
When not to use it
This skill is not suitable for users who do not require integration with Feishu's Bitable or those looking for a general-purpose database management tool.
What you can build with it
Creating a New Data Table
Utilize the skill to define a new data table in Bitable, specifying fields and their types according to your requirements.
Batch Importing Customer Data
Efficiently import customer records into Bitable using the batch_create function, ensuring correct field formatting.
Advanced Filtering of Records
Leverage the skill's filtering capabilities to query records based on specific conditions and sort them as needed.
How to install Feishu Bitable
View source1. Install with the skills CLI
npx skills add larksuite/openclaw-lark/feishu-bitable --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by larksuiteFeishu Bitable (多维表格) SKILL
🚨 执行前必读
- ✅ 创建数据表:支持两种模式 — ① 明确需求时,在
create时通过table.fields一次性定义字段(减少 API 调用);② 探索式场景时,使用默认表 + 逐步修改字段(更稳定,易调整) - ⚠️ 默认表的空行坑:
app.create自带的默认表中会有空记录(空行)!插入数据前建议先调用feishu_bitable_app_table_record.list+batch_delete删除空行,避免数据污染 - ✅ 写记录前:先调用
feishu_bitable_app_table_field.list获取字段 type/ui_type - ✅ 人员字段:默认 open_id(ou_...),值必须是
[{id:"ou_xxx"}](数组对象) - ✅ 日期字段:毫秒时间戳(例如
1674206443000),不是秒 - ✅ 单选字段:字符串(例如
"选项1"),不是数组 - ✅ 多选字段:字符串数组(例如
["选项1", "选项2"]) - ✅ 附件字段:必须先上传到当前多维表格,使用返回的 file_token
- ✅ 批量上限:单次 ≤ 500 条,超过需分批(批量操作是原子性的)
- ✅ 并发限制:同一数据表不支持并发写,需串行调用 + 延迟 0.5-1 秒
📋 快速索引:意图 → 工具 → 必填参数
| 用户意图 | 工具 | action | 必填参数 | 常用可选 |
|---|---|---|---|---|
| 查表有哪些字段 | feishu_bitable_app_table_field | list | app_token, table_id | - |
| 查记录 | feishu_bitable_app_table_record | list | app_token, table_id | filter, sort, field_names |
| 新增一行 | feishu_bitable_app_table_record | create | app_token, table_id, fields | - |
| 批量导入 | feishu_bitable_app_table_record | batch_create | app_token, table_id, records (≤500) | - |
| 更新一行 | feishu_bitable_app_table_record | update | app_token, table_id, record_id, fields | - |
| 批量更新 | feishu_bitable_app_table_record | batch_update | app_token, table_id, records (≤500) | - |
| 创建多维表格 | feishu_bitable_app | create | name | folder_token |
| 创建数据表 | feishu_bitable_app_table | create | app_token, name | fields |
| 创建字段 | feishu_bitable_app_table_field | create | app_token, table_id, field_name, type | property |
| 创建视图 | feishu_bitable_app_table_view | create | app_token, table_id, view_name, view_type | - |
🎯 核心约束(Schema 未透露的知识)
📚 详细参考文档
当遇到字段配置、记录值格式问题或需要完整示例时,查阅以下文档:
- 字段 Property 配置详解 - 每种字段类型创建/更新时需要的
property参数结构(单选的 options、进度的 min/max、关联的 table_id 等) - 记录值数据结构详解 - 每种字段类型在记录中对应的
fields值格式(人员字段只传 id、日期是毫秒时间戳、附件需先上传等) - 使用场景完整示例 - 8 个完整场景示例(创建表模式对比、批量导入、筛选查询、附件处理、关联字段等)
何时查阅:
- 创建/更新字段时收到
125408X错误码(property 结构错误)→ 查 field-properties.md - 写入记录时收到
125406X错误码(字段值转换失败)→ 查 record-values.md - 需要完整的操作流程和参数示例 → 查 examples.md
1. 字段类型与值格式必须严格匹配
Bitable 最大的坑:不同字段类型对 value 的数据结构要求完全不同。
最易错的字段类型(完整列表见 record-values.md)
| type | ui_type | 字段类型 | 正确格式 | ❌ 常见错误 |
|---|---|---|---|---|
| 11 | User | 人员 | [{id: "ou_xxx"}] | 传字符串 "ou_xxx" 或 [{name: "张三"}] |
| 5 | DateTime | 日期 | 1674206443000(毫秒) | 传秒时间戳或字符串 |
| 3 | SingleSelect | 单选 | "选项名" | 传数组 ["选项名"] |
| 4 | MultiSelect | 多选 | ["选项1", "选项2"] | 传字符串 "选项1" |
| 15 | Url | 超链接 | {link: "...", text: "..."} | 只传字符串 URL |
| 17 | Attachment | 附件 | [{file_token: "..."}] | 传外部 URL 或本地路径 |
强制流程:
- 先调用
feishu_bitable_app_table_field.list获取字段的type和ui_type - 根据上表或 record-values.md 构造正确格式
- 错误码
125406X或1254015→ 检查字段值格式
人员字段特别注意:
- 默认使用 open_id(ou_...),与 calendar/task 一致
- 格式:
[{id: "ou_xxx"}](数组对象) - 只能传 id 字段,不能传 name/email 等
📌 核心使用场景
完整示例: 查阅 examples.md 了解更多场景(创建表模式对比、空行处理、附件上传、关联字段等)
场景 1: 查字段类型(必做第一步)
{
"action": "list",
"app_token": "S404b...",
"table_id": "tbl..."
}
返回:包含每个字段的 field_id、field_name、type、ui_type、property
场景 2: 批量导入客户数据
{
"action": "batch_create",
"app_token": "S404b...",
"table_id": "tbl...",
"records": [
{
"fields": {
"客户名称": "Bytedance",
"负责人": [{"id": "ou_xxx"}],
"签约日期": 1674206443000,
"状态": "进行中"
}
},
{
"fields": {
"客户名称": "飞书",
"负责人": [{"id": "ou_yyy"}],
"签约日期": 1675416243000,
"状态": "已完成"
}
}
]
}
字段值格式:
- 人员:
[{id: "ou_xxx"}](数组对象) - 日期:毫秒时间戳
- 单选:字符串
- 多选:字符串数组
限制: 最多 500 条记录
场景 3: 筛选查询(高级筛选)
{
"action": "list",
"app_token": "S404b...",
"table_id": "tbl...",
"filter": {
"conjunction": "and",
"conditions": [
{
"field_name": "状态",
"operator": "is",
"value": ["进行中"]
},
{
"field_name": "截止日期",
"operator": "isLess",
"value": ["ExactDate", "1740441600000"]
}
]
},
"sort": [
{
"field_name": "截止日期",
"desc": false
}
]
}
filter 说明:
- 支持 10 种 operator(is/isNot/contains/isEmpty 等,见附录 C)
- ⚠️ isEmpty/isNotEmpty 必须传
value: [](虽然逻辑上不需要值,但 API 要求必须传空数组) - 日期筛选可使用
["Today"]、["ExactDate", "时间戳"]等 sort可指定多个排序字段
🔍 常见错误与排查
| 错误码 | 错误现象 | 根本原因 | 解决方案 |
|---|---|---|---|
| 1254064 | DatetimeFieldConvFail | 日期字段格式错误 | 必须用毫秒时间戳(如 1772121600000),不能用字符串("2026-02-27"、RFC3339)或秒级时间戳 |
| 1254068 | URLFieldConvFail | 超链接字段格式错误 | 必须用对象 {text: "显示文本", link: "URL"},不能直接传字符串 URL |
| 1254066 | UserFieldConvFail | 人员字段格式错误或 ID 类型不匹配 | 必须传 [{id: "ou_xxx"}],确认 user_id_type |
| 1254015 | Field types do not match | 字段值格式与类型不匹配 | 先 list 字段,按类型构造正确格式 |
| 1254104 | RecordAddOnceExceedLimit | 批量创建超过 500 条 | 分批调用,每批 ≤ 500 |
| 1254291 | Write conflict | 并发写冲突 | 串行调用 + 延迟 0.5-1 秒 |
| 1254303 | AttachPermNotAllow | 附件未上传到当前表格 | 先调用上传素材接口 |
| 1254045 | FieldNameNotFound | 字段名不存在 | 检查字段名(包括空格、大小写) |
📚 附录:背景知识
A. 资源层级关系
App (多维表格应用)
├── Table (数据表) ×100
│ ├── Record (记录/行) ×20,000
│ ├── Field (字段/列) ×300
│ └── View (视图) ×200
└── Dashboard (仪表盘)
B. 筛选条件 operator 列表
| operator | 含义 | 支持字段 | value 要求 |
|---|---|---|---|
is | 等于 | 所有 | 单个值 |
isNot | 不等于 | 除日期外 | 单个值 |
contains | 包含 | 除日期外 | 可多个值 |
doesNotContain | 不包含 | 除日期外 | 可多个值 |
isEmpty | 为空 | 所有 | 必须为 [] |
isNotEmpty | 不为空 | 所有 | 必须为 [] |
isGreater | 大于 | 数字、日期 | 单个值 |
isGreaterEqual | 大于等于 | 数字(不支持日期) | 单个值 |
isLess | 小于 | 数字、日期 | 单个值 |
isLessEqual | 小于等于 | 数字(不支持日期) | 单个值 |
日期字段特殊值: ["Today"], ["Tomorrow"], ["ExactDate", "时间戳"] 等(完整列表见 examples.md)
C. 使用限制
| 限制项 | 上限 |
|---|---|
| 数据表 + 仪表盘 | 100(单个 App) |
| 记录数 | 20,000(单个数据表) |
| 字段数 | 300(单个数据表) |
| 视图数 | 200(单个数据表) |
| 批量创建/更新/删除 | 500(单次 API 调用) |
| 单元格文本 | 10 万字符 |
| 单选/多选选项 | 20,000(单个字段) |
| 单元格附件 | 100 |
| 单元格人员 | 1,000 |
D. 其他约束
- 从其他数据源同步的数据表,不支持增删改记录
- 公式字段、查看引用字段是只读的
- 删除操作无法恢复
- 视图筛选条件使用
field_id,需先调用 field.list 获取
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