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MrNeo01
MrNeo01COMMUNITY
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data-visualization

Create publication-quality charts and multi-panel summaries from data outputs.

Updated Mar 18, 2026
Search Tags:#dashboards#charts#plotting#matplotlib#seaborn#data-visualization#headless-rendering
One-click install
npx skills add https://github.com/MrNeo01/deepagent --skill data-visualization-mrneo01
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/MrNeo01/deepagent/tree/main/examples/nvidia_deep_agent/skills/data-visualization
Command: npx skills add https://github.com/MrNeo01/deepagent --skill data-visualization-mrneo01

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates the creation of publication-quality charts and multi-panel analysis summaries from data outputs, enabling fast, repeatable visual storytelling.

Core Features & Use Cases

  • Visualize results with publication-ready charts using matplotlib and seaborn in headless environments.
  • Produce multi-panel analysis summaries and figures that can be embedded in reports or dashboards.
  • Ensure inline display by exporting charts to /workspace and rendering them for quick review with the agent.

Quick Start

Create a publication-quality chart from the current dataset and save it to /workspace/chart.png.

Frequently Asked Questions about data-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-quality charts in a headless environment?▼

To generate publication-quality charts in a headless environment, use matplotlib with the Agg backend to render figures without a display. The skill automates this process, creating bar, line, and heatmap charts, and outputs PNG files directly to your workspace.

What is the best way to automate multi-panel analysis summaries from data outputs?▼

Automating multi-panel analysis summaries from data outputs is best handled by using matplotlib and seaborn to construct multi-panel figures. This approach produces publication-ready visual summaries that can be embedded directly into reports or dashboards.

Does this data-visualization skill support seaborn for statistical plotting?▼

Yes, this data-visualization skill supports seaborn for statistical plotting alongside matplotlib. It leverages both libraries to automate the creation of complex visual summaries from analytical results in headless rendering setups.

Can I use matplotlib to output charts to a specific directory and display them inline?▼

Yes, you can use matplotlib to output charts to a specific directory and display them inline. The skill exports PNG files to the workspace directory and enforces inline display by reading the file back after each chart is generated.

Why does plotting fail in headless environments and how do I fix it?▼

Plotting fails in headless environments because matplotlib defaults to a GUI backend that requires a display. To fix this, you must configure headless rendering by setting matplotlib to use the Agg backend before generating any charts.

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