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data-visualization

Automate publication-quality charts from analysis results using matplotlib and seaborn.

Updated Apr 23, 2026
Search Tags:#charts#plotting#matplotlib#headless#seaborn#data-visualization
One-click install
npx skills add https://github.com/ladinglogichq/lading-logic-hackathon --skill data-visualization-ladinglogichq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/ladinglogichq/lading-logic-hackathon/tree/main/.agents/skills/data-visualization
Command: npx skills add https://github.com/ladinglogichq/lading-logic-hackathon --skill data-visualization-ladinglogichq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Publication-quality charts and visual summaries are time-consuming to create from analysis outputs; this skill automates that process, enabling quick, publication-ready visuals.

Core Features & Use Cases

  • Automated chart creation using matplotlib and seaborn in a headless environment with an Agg backend
  • Save charts to /workspace/ as PNG files for retrieval and inline display
  • Supports common chart types (bar, line, scatter, heatmap, histogram, box plot) and multi-panel analysis summaries
  • Clear, presentation-grade defaults and styling to maintain consistency across reports

Quick Start

Run the visualization pipeline on your analysis results to generate and save a PNG chart to /workspace.

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 from analysis results in a headless environment?▼

To generate publication-quality charts in a headless environment, this skill automates rendering using matplotlib and seaborn with an Agg backend. It processes your analysis results and saves the output as PNG files.

Can matplotlib and seaborn save PNG charts to a specific workspace directory?▼

Yes, matplotlib and seaborn can save PNG charts directly to the `/workspace/` directory. This skill automates the saving process, allowing retrieval and inline display of the generated visual summaries via `read_file`.

What chart types are supported for automated data visualization summaries?▼

Supported chart types for data visualization include bar, line, scatter, heatmap, histogram, and box plot. The skill also supports creating multi-panel analysis summaries for comprehensive documentation.

Do I need a display server to render seaborn and matplotlib charts?▼

No display server is needed because the skill uses a headless Agg backend to render matplotlib and seaborn charts. This allows you to generate presentation-grade visual summaries in server environments without GUI support.

What is the best way to create multi-panel reports from data analysis outputs?▼

The best way to create multi-panel reports is by automating chart creation with presentation-grade defaults. This skill applies consistent styling across multi-panel analysis summaries, saving them as PNG files for immediate documentation use.

Why are my matplotlib charts not displaying inline in my analysis documentation?▼

Charts may not display inline if they are not properly saved and retrieved. This skill saves PNG outputs to `/workspace/` and uses `read_file` for inline display, ensuring visual summaries integrate seamlessly into documentation.

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