aliramw

aliramw / dingtalk-ai-table

Public

钉钉 AI 表格（多维表）操作技能 - OpenClaw Skill

15
1
89% credibility
Found Mar 03, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Python scripts for bulk adding fields to and importing records from CSV or JSON files into DingTalk AI Tables.

How It Works

1
🔍 Discover helpful tools

You hear about simple scripts that make adding columns and loading data into your DingTalk AI Table super easy and fast.

2
📝 Gather your table details

Jot down your table's special ID and the name of the sheet you want to work with.

3
➕ List new columns

Create a short list in a file describing the new columns you want, like text or numbers, so your table has the right setup.

4
✨ Add columns in bulk

Use the first tool with your table ID, sheet name, and list – it quickly adds all your new columns at once, saving hours of clicking.

5
📊 Prepare your data

Get your information ready in a simple spreadsheet or list file, matching your new columns.

6
📥 Load data in batches

Run the second tool with your details and data file – it smartly loads everything row by row without mistakes.

🎉 Table ready to shine

Your DingTalk table is now perfectly organized with columns and full of data, ready for you to explore and share.

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AI-Generated Review

What is dingtalk-ai-table?

This OpenClaw skill on GitHub automates DingTalk AI table operations using Python scripts run via CLI. It lets you bulk add fields to sheets from a JSON config—like text, number, or singleSelect types—and import records from CSV or JSON files in configurable batches up to 100 rows. Solves the pain of manually populating multi-dimensional tables in DingTalk, especially for large datasets.

Why is it gaining traction?

Stands out in the OpenClaw skills hub for its tight focus on DingTalk AI tables, with built-in safety like path sandboxing, file size limits, and UUID validation to prevent mishaps. Developers dig the simple CLI flow: pipe in your dentryUuid, sheet name, and data file, no complex setup beyond installing mcporter. As an OpenClaw skills GitHub Copilot-style tool, it hooks teams already in the ecosystem needing quick bulk ops without custom API wrappers.

Who should use this?

DingTalk admins bulk-migrating data to AI tables from spreadsheets. Python scripters automating table setup for enterprise workflows, like adding 50+ fields or importing thousands of product records. Teams in Chinese orgs heavy on DingTalk who want OpenClaw skills for repeatable table management.

Verdict

Grab it if you're deep in DingTalk and OpenClaw—solid for niche bulk tasks despite 15 stars signaling early maturity and thin docs. Low 0.9% credibility score flags risks like untested edge cases; test small before production.

(178 words)

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