A local-first visualization engine that transforms numbers into clear, professional charts without ever leaving your workspace or hitting a cloud API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chart-data-viz
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 chart-data-viz 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 Chart Generation Engine?
The Chart skill is a specialized tool within the Openclaw Skills library designed for developers and analysts who prioritize data privacy and speed. It serves as a local-first engine for creating trends, comparisons, and distributions directly from your datasets. By leveraging Python's robust visualization libraries, it enables users to convert raw CSV or JSON data into high-quality images stored directly in the local workspace.
Built with a philosophy of clarity over complexity, this skill focuses on providing the most impactful visual representations for your data. It operates entirely offline, ensuring that sensitive information remains within your infrastructure while providing the necessary visual context for reports, presentations, and data-driven decision-making.
Chart Generation Engine Use Cases
- Converting CSV or JSON data files into visual trend reports.
- Comparing categorical data using bar charts for quick analysis.
- Visualizing part-to-whole relationships in datasets using pie charts.
- Identifying correlations between variables with scatter plots.
- Automating the generation of chart assets for local documentation and slides.
How Chart Generation Engine Works
- The user provides data via CLI or the AI agent interface using labels and values.
- The suggest_chart utility analyzes the data structure to recommend the most effective visualization type.
- The make_chart engine processes the data locally using Python and matplotlib.
- A high-resolution image is generated and saved to the local output directory.
- Metadata regarding the chart is logged in a local JSON database for future reference and history tracking.
Chart Generation Engine Setup
To use this contribution to Openclaw Skills, ensure you have Python 3 and the necessary libraries installed:
# Install required dependencies
pip install matplotlib
# Initialize the local storage directories
python3 init_storage.py
Chart Generation Engine Data Schema & Taxonomy
The skill maintains a local-first data structure to track all generated assets and history:
| Path | Description |
|---|---|
~/.openclaw/workspace/memory/chart/charts.json |
Registry of all generated charts and their metadata |
~/.openclaw/workspace/memory/chart/output/ |
Directory containing the actual image files (PNG/JPG) |
Supported Types:
bar: Best for category comparisonline: Ideal for trends over timepie: Simple part-to-whole visualizationscatter: Relationship between two variables
Chart Generation Engine Advanced Features
- Intelligent chart type suggestion based on data distribution patterns.
- Local history tracking via list_charts for rapid retrieval of previous visualizations.
- Zero-network dependency for maximum security and performance.
- Seamless integration into automated reporting workflows using the command-line interface.
SKILL.md
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