dingtalk-ai-table

breath57/dingtalk-skills · updated Apr 8, 2026

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$npx skills add https://github.com/breath57/dingtalk-skills --skill dingtalk-ai-table
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summary

负责钉钉 AI 表格(.able 格式多维表格)的所有操作。本文件为策略指南,仅包含决策逻辑和工作流程。完整 API 请求格式见文末「references/api.md 查阅索引」。

skill.md

钉钉 AI 表格技能

负责钉钉 AI 表格(.able 格式多维表格)的所有操作。本文件为策略指南,仅包含决策逻辑和工作流程。完整 API 请求格式见文末「references/api.md 查阅索引」。

核心概念

  • AI 表格.able 文件):多维表格,使用 Notable API(/v1.0/notable),不是普通电子表格
  • base_id:AI 表格文件的 nodeId,从分享链接 https://alidocs.dingtalk.com/i/nodes/<base_id> 提取
  • 工作表(Sheet):表格内的单张表,包含字段和记录
  • 字段(Field):列定义,有名称和类型(textnumberdate
  • 记录(Record):数据行,fields 中用字段名称(非 ID)作键
  • operatorId:所有接口必须的 unionId 参数,通过 dt_helper.sh --to-unionid 自动转换

工作流程(每次执行前)

  1. 先识别本次任务类型 → 例如:列工作表、创建字段、查询记录、更新记录、删除记录
  2. 按本次任务校验所需配置 → 通过 bash scripts/dt_helper.sh --get KEY 读取;仅校验本任务必须项
  3. 仅收集缺失配置 → 若缺少某项,一次性询问用户所有缺失值,用 bash scripts/dt_helper.sh --set KEY=VALUE 写入
  4. 获取 Token / operatorId → 直接调用 bash scripts/dt_helper.sh,token 获取与缓存细节无需关心
  5. 执行操作 → 凡是包含变量替换、管道或多行逻辑的命令,写入 /tmp/<task>.shbash /tmp/<task>.sh 执行。不要把多行命令直接粘到终端里(终端工具会截断),也不要用 <<'EOF' 语法(heredoc 在工具中同样会被截断导致变量丢失)

按任务校验配置(必须先做)

  • 所有任务通用必需DINGTALK_APP_KEYDINGTALK_APP_SECRETDINGTALK_MY_USER_ID
  • 涉及任何 AI 表格 API 调用:必须有 DINGTALK_MY_OPERATOR_ID(若缺失,先用 bash scripts/dt_helper.sh --to-unionid 自动转换并写回)
  • 工作表/字段/记录相关操作:必须有 DINGTALK_AI_TABLE_BASE_ID(若缺失,要求用户提供 AI 表格链接并提取 /nodes/<base_id>

规则:未通过“本次任务配置校验”前,不得进入 API 调用步骤。

凭证禁止在输出中完整打印,确认时仅显示前 4 位 + ****

所需配置

配置键 必填 说明 如何获取
DINGTALK_APP_KEY 应用 AppKey 钉钉开放平台 → 应用管理 → 凭证信息
DINGTALK_APP_SECRET 应用 AppSecret 同上
DINGTALK_MY_USER_ID 当前用户的企业员工 ID(userId) 管理后台 → 通讯录 → 成员管理 → 点击姓名查看
DINGTALK_MY_OPERATOR_ID 当前用户的 unionId(operatorId) 首次由 bash scripts/dt_helper.sh --to-unionid 自动转换并写入
DINGTALK_AI_TABLE_BASE_ID AI 表格的 nodeId 从 AI 表格分享链接 /nodes/<id> 提取

身份标识说明

标识 说明
userId(= staffId 企业内部员工 ID,可通过管理后台 -> 通讯录 -> 成员管理 -> 点击姓名查看
unionId 跨企业/跨应用唯一标识,可通过 bash scripts/dt_helper.sh --to-unionid <userid> 获取

执行脚本模板

#!/bin/bash
set -e
HELPER="./scripts/dt_helper.sh"
NEW_TOKEN=$(bash "$HELPER" --token)
OPERATOR_ID=$(bash "$HELPER" --get DINGTALK_MY_OPERATOR_ID)
BASE_ID=$(bash "$HELPER" --get DINGTALK_AI_TABLE_BASE_ID)

# 在此追加具体 API 调用,例如列出工作表:
SHEETS=$(curl -s -X GET "https://api.dingtalk.com/v1.0/notable/bases/${BASE_ID}/sheets?operatorId=${OPERATOR_ID}" \
  -H "x-acs-dingtalk-access-token: $NEW_TOKEN")
echo "工作表列表: $SHEETS"

Token 失效处理:dt_helper 仅按时间缓存,无法感知 token 被提前吊销。若 API 返回 401(token 无效/过期),用 --nocache 跳过缓存强制重新获取:

NEW_TOKEN=$(bash "$HELPER" --token --nocache)

references/api.md 查阅索引

确定好要做什么之后,用以下命令从 references/api.md 中提取对应章节的完整 API 细节(请求格式、参数说明、返回值示例):

grep -A 20 "^## 1. 列出工作表" references/api.md
grep -A 15 "^## 2. 查询单个工作表" references/api.md
grep -A 30 "^## 3. 创建工作表" references/api.md
grep -A 15 "^## 4. 删除工作表" references/api.md
grep -A 25 "^## 5. 列出字段" references/api.md
grep -A 28 "^## 6. 创建字段" references/api.md
grep -A 15 "^## 7. 更新字段" references/api.md
grep -A 15 "^## 8. 删除字段" references/api.md
grep -A 25 "^## 9. 新增记录" references/api.md
grep -A 40 "^## 10. 查询记录列表" references/api.md
grep -A 18 "^## 11. 更新记录" references/api.md
grep -A 15 "^## 12. 删除记录" references/api.md
grep -A 10 "^## 错误码" references/api.md
grep -A 6  "^## 所需应用权限" references/api.md
how to use dingtalk-ai-table

How to use dingtalk-ai-table 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 dingtalk-ai-table
2

Execute installation command

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

$npx skills add https://github.com/breath57/dingtalk-skills --skill dingtalk-ai-table

The skills CLI fetches dingtalk-ai-table from GitHub repository breath57/dingtalk-skills 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/dingtalk-ai-table

Reload or restart Cursor to activate dingtalk-ai-table. Access the skill through slash commands (e.g., /dingtalk-ai-table) 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

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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

4.860 reviews
  • Soo Liu· Dec 28, 2024

    dingtalk-ai-table has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Lucas Menon· Dec 20, 2024

    dingtalk-ai-table fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Dec 8, 2024

    Registry listing for dingtalk-ai-table matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Jin Nasser· Dec 4, 2024

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

  • Oshnikdeep· Nov 27, 2024

    Keeps context tight: dingtalk-ai-table is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sofia Thompson· Nov 23, 2024

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

  • Rahul Santra· Nov 19, 2024

    dingtalk-ai-table reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Soo Lopez· Nov 19, 2024

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

  • Sofia Abebe· Nov 11, 2024

    dingtalk-ai-table is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ganesh Mohane· Oct 18, 2024

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

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