Create high-impact, professional data visualizations using expert design principles and automated chart generation tools.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-visualization-2
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install data-visualization-2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
This skill provides a comprehensive framework for creating effective data visualizations that go beyond simple graphing. By integrating with Openclaw Skills, developers and analysts can leverage proven design rules for axes, color theory, and chart selection to transform raw data into compelling visual stories. It emphasizes clarity, accessibility, and professional aesthetics, ensuring that every chart conveys a clear insight rather than just displaying numbers.
The skill covers the entire lifecycle of data presentation, from choosing between bar and line charts to implementing advanced Python-based rendering. By following these best practices, you can avoid common pitfalls like misleading axes or cluttered designs, making your data more actionable and persuasive.
To get started with this data visualization skill within the ecosystem of Openclaw Skills, install the inference.sh CLI using the following command:
curl -fsSL https://cli.inference.sh | sh && infsh login
Once logged in, you can run the Python executor to generate your first chart:
infsh app run infsh/python-executor --input '{"code": "import matplotlib.pyplot as plt\n..."}'
The skill organizes visualization data and metadata through code-defined parameters and structured outputs.
| Component | Description | Format |
|---|---|---|
| Source Data | Numerical arrays or structured time-series data | Python Lists / NumPy |
| Styling | Hex-based color palettes and Matplotlib parameters | Python Dict |
| Output | Rendered image files or HTML snippets | PNG, JPG, HTML |
| Annotations | Text-based callouts, reference lines, and arrows | String Metadata |
| Context | Storytelling arc and insight-driven titles | String |
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