A sophisticated AI-driven analysis tool for DingTalk AI Tables that performs cross-sheet insights and risk identification.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dingtalk-ai-table-insights
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dingtalk-ai-table-insights using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is DingTalk AI Table Insights?
DingTalk AI Table Insights is a powerful extension within the Openclaw Skills ecosystem designed to liberate users from manual data cross-referencing. By leveraging advanced Large Language Models (LLMs), it analyzes multiple DingTalk AI Tables simultaneously to identify data inconsistencies, business trends, and potential anomalies. It is built to prioritize specific business lines or projects through keyword filtering, ensuring that users receive relevant, high-impact intelligence without being overwhelmed by raw data.
The skill operates as a high-level analytical layer atop the base dingtalk-ai-table integration. It features a robust multi-dimensional analysis framework covering data consistency, trend insights, risk warnings, and actionable suggestions. Whether you are tracking a complex sales pipeline or monitoring cross-departmental budgets, this tool provides a clear, natural language report to guide decision-making.
DingTalk AI Table Insights Use Cases
- Business Line Analysis: Focus on specific sectors like sales, marketing, or customer service to evaluate overall health.
- Project Tracking: Aggregate data from multiple sheets related to a single project (e.g., Project A) to monitor holistic progress.
- Functional Auditing: Perform cross-sheet deep dives into recruitment, procurement, or budget management.
- Global Risk Scanning: Scan all accessible tables without keywords to find hidden anomalies or high-priority risks.
How DingTalk AI Table Insights Works
- Keyword Filtering: The skill scans the DingTalk environment to find AI Tables matching your specified keyword or project name.
- Data Sampling & Pagination: It retrieves data from identified tables, utilizing pagination (50 records per page) and sampling (up to 100 records) to ensure efficiency and minimize token consumption.
- LLM Deep Analysis: The processed data is sent to an AI agent (defaulting to Openclaw agent) for contextual analysis, risk identification, and pattern recognition.
- Fallback Mechanism: If the LLM is unavailable or times out, the skill automatically falls back to a local template-based analysis to ensure a report is still generated.
- Report Generation: A comprehensive Markdown or JSON report is produced, highlighting analytical scope, risk warnings, and specific action items.
DingTalk AI Table Insights Setup
First, ensure you have the prerequisite Openclaw Skills installed:
# Install the base dependency
clawhub install dingtalk-ai-table
Configure your MCP credentials in <workspace>/config/mcporter.json:
{
"mcpServers": {
"dingtalk-ai-table": {
"baseUrl": "https://mcp-gw.dingtalk.com/server/YOUR_ID?key=YOUR_KEY"
}
}
}
Then run the analysis script:
# Analyze tables containing the keyword 'Sales'
python3 scripts/analyze_tables.py --keyword "Sales"
DingTalk AI Table Insights Data Schema & Taxonomy
The skill organizes and processes data using a structured taxonomy to ensure cross-table compatibility:
| Data Component | Description | Format |
|---|---|---|
| Table Metadata | Names, IDs, and sheet distributions | JSON/Cached |
| Data Samples | Sampled records (default 100) per table | List of Objects |
| Insight Report | Categorized findings (Consistency, Trends, Risks) | Markdown |
| Action Plan | Specific 'Who/What/When' suggestions | Bulleted List |
| Cache | Temporary storage for table lists (5-min TTL) | Local File |
DingTalk AI Table Insights Advanced Features
- Multi-Agent Support: Specifically optimized to use the Openclaw agent main session for complex reasoning.
- Smart Pagination & Cursor Support: Seamlessly handles large datasets by merging paginated data using cursor-based retrieval.
- Configurable LLM Usage: Power users can use the
--no-llmflag for faster, local template-based processing when depth is less critical. - Automated Fallback Path: Gracefully degrades from LLM analysis to Local analysis to ensure reliability in restricted environments.
SKILL.md
Loading
Related Openclaw Skills
Developer Skillset Management Agent
A comprehensive framework for AI agents to track, manage, and optimize a developer's technical and professional skill evolution.
JDxB Node Baby Link Management
A comprehensive automation skill for managing the JDxB (Node Baby Link) remote access service on Linux systems.
Mock RPM Secure Build Environment
A professional toolset for managing clean, isolated chroot environments to build secure and reproducible RPM packages.
SOX Compliance Checker
An enterprise-grade assessment platform designed to automate and scale Sarbanes-Oxley compliance evaluations across financial and IT control domains.
Nearby Cinemas
A location-based skill that enables AI agents to find and filter movie theaters within a specified radius using precise coordinates.
WeChat Share
A specialized tool for exporting and importing selected Openclaw workspace files between users with optional burn-after-read security.
Featured*
