A streamlined CLI-based production alert and status board for managing industrial tasks and monitoring real-time operational health.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install andon
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 andon 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 Andon?
The Andon skill is a specialized tool designed to bring the principles of lean manufacturing and visual management to the command line. It functions as a digital production status board, allowing developers and floor managers to track manufacturing issues, signal alerts, and manage production tasks through a simple yet powerful interface. By integrating this tool into your workflow via Openclaw Skills, you can maintain high visibility over operational bottlenecks and ensure that production status is always transparent and actionable.
Built for speed and reliability, Andon handles data management through structured formats, making it ideal for environments that require quick updates and clear historical logging. Whether you are managing software build pipelines or physical production lines, this skill provides the necessary framework to implement a functional Andon system without the need for heavy, proprietary software.
Andon Use Cases
- Tracking real-time production issues and alerts in a centralized dashboard.
- Managing industrial tasks using JSON or CSV data formats for interoperability.
- Generating production statistics to identify recurring bottlenecks and downtime trends.
- Exporting historical task data for compliance, reporting, or further data analysis.
- Facilitating team communication by providing a shared source of truth for production status.
How Andon Works
- The user initializes an entry or alert using the add command to signal a production event.
- The system logs the entry into the local data directory, typically managed as a structured file for persistence.
- Team members use the status and list commands to view the current state of the production board in real-time.
- Users can search through historical entries or filter specific tasks to address urgent production needs.
- Upon completion or resolution, entries can be managed or removed, and the entire dataset can be exported using Openclaw Skills for external auditing.
Andon Setup
To install and configure the Andon skill, clone the repository and ensure your environment is ready to execute the control scripts. Use the following commands to get started:
# Navigate to the skill directory
cd ai-skills/andon
# Make the script executable
chmod +x scripts/script.sh
# Configure the data directory (Optional)
./scripts/script.sh config ANDON_DIR ~/.andon/
# Verify the installation
./scripts/script.sh version
Andon Data Schema & Taxonomy
The skill organizes its data within a configurable directory, defaulting to ~/.andon/. It supports various formats for task ingestion and reporting:
| Feature | Description |
|---|---|
| Storage Format | Supports JSON and CSV for task storage and manipulation. |
| Metadata | Tracks entry IDs, timestamps, and production status indicators. |
| Config Taxonomy | Managed via a key-value pair system using the ANDON_DIR environment variable. |
| Exports | Generates flat-file exports for external tool integration. |
Andon Advanced Features
- Multi-format support for processing both JSON and CSV andon tasks.
- Customizable data directory via the config command to support shared network drives or specific project folders.
- Comprehensive statistical engine to generate insights from production history.
- Seamless integration with other Openclaw Skills for automated alert triggering and status reporting.
- Robust search functionality to quickly isolate specific production events within large datasets.
SKILL.md
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