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leesk212
leesk212COMMUNITY
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data-visualization

Generate publication-quality PNG charts from analysis results using matplotlib's Agg backend.

1|Updated Apr 8, 2026
Search Tags:#charts#visualization#data-analysis#matplotlib#headless#seaborn#publication-quality
One-click install
npx skills add https://github.com/leesk212/dannys-coding-ai-agent-final --skill data-visualization-leesk212
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/leesk212/dannys-coding-ai-agent-final/tree/main/ETC/deepagents_sourcecode/examples/nvidia_deep_agent/skills/data-visualization
Command: npx skills add https://github.com/leesk212/dannys-coding-ai-agent-final --skill data-visualization-leesk212

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables production-grade charts and multi-panel reports directly from analysis results, reducing manual plotting time and improving consistency across visuals.

Core Features & Use Cases

  • Headless rendering with matplotlib's Agg backend for server/CI environments.
  • Save charts as PNG files to /workspace/ for easy retrieval.
  • Use publication-quality defaults and a colorblind-safe palette to ensure accessible visuals.
  • Create multi-panel analyses (2x2 layouts) to present relations among multiple charts in a single figure.
  • Ensure inline display by calling read_file on saved charts to render images in-context.

Quick Start

Create a publication-quality chart by plotting your data with matplotlib, save it to /workspace/chart.png, and display it with read_file('/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 server environment?▼

Generate publication-quality charts in a headless environment by using matplotlib's Agg backend for rendering. This Skill saves outputs as PNG files to a designated /workspace/ directory, ensuring consistent visual generation without a display server in CI or server workflows.

Can I create multi-panel reports for data analysis results with matplotlib?▼

Yes, you can create multi-panel reports using 2x2 layouts to present relationships among multiple charts in a single figure. This Skill handles multi-panel visualization directly from data analysis results with publication-quality defaults.

How do I display matplotlib charts inline after saving them?▼

Display matplotlib charts inline by calling read_file on the saved PNG files in the /workspace/ directory. This Skill ensures explicit inline display of generated visuals by reading the saved chart files back into the execution context.

Does this visualization approach use a colorblind-safe palette?▼

Yes, this visualization approach applies a colorblind-safe palette to ensure accessible visuals. It uses publication-quality defaults to maintain accessibility and visual consistency across all generated charts and multi-panel reports.

What is the best way to automate statistical analysis visualization for reports?▼

Automate statistical analysis visualization by generating charts directly from data processing results and saving them as PNG files. This Skill reduces manual plotting time and applies consistent color palettes across all visual outputs for multi-panel reports.

Can I use seaborn for publication-quality data visualization with this setup?▼

Yes, seaborn is supported for publication-quality data visualization. This Skill works with seaborn to generate charts from statistical analyses, saving outputs to /workspace/ while maintaining headless rendering compatibility through matplotlib's Agg backend.

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