data-visualization

Generate publication-quality charts with matplotlib and seaborn as PNG files.

Updated Mar 18, 2026

Search Tags:#charts #visualization #data-analysis #matplotlib #seaborn #publication

Ready to run

npx skills add https://github.com/coinvest518/deepagents-LANGCLAW --skill data-visualization-coinvest518

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/coinvest518/deepagents-LANGCLAW/tree/main/examples/nvidia_deep_agent/skills/data-visualization
Command: npx skills add https://github.com/coinvest518/deepagents-LANGCLAW --skill data-visualization-coinvest518

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data analysts and researchers often need publication-quality charts produced from analysis results, but doing so manually is time-consuming and error-prone. This skill provides a repeatable, headless workflow for generating professional charts using matplotlib and seaborn, with sensible defaults and a consistent style.

Core Features & Use Cases

Chart types: supports bar, line, scatter, heatmap, histogram, and box plots.
Headless rendering: uses a headless backend (Agg) to render charts without a display.
Publication-ready styling: default fonts, colors, and DPI settings ensure outputs are ready for publication.
Multi-panel summaries: can create multi-panel figures to present analyses compactly.
Output management: saves charts as PNGs to /workspace for retrieval.

Quick Start

Create a publication-quality line chart from the latest dataset and save it as a PNG to the /workspace directory.

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 data in a headless environment?▼

You generate publication-quality charts by running matplotlib and seaborn with a headless Agg backend, which renders bar, line, scatter, heatmap, histogram, and box plots without a display and saves them as PNGs.

What's the best way to automate matplotlib chart creation for reproducible data analysis pipelines?▼

Automating matplotlib chart creation uses a headless workflow with sensible default fonts, colors, and DPI settings to ensure consistent, reproducible outputs that integrate directly into data-analysis pipelines.

Can I create multi-panel figures with seaborn for compact analysis summaries?▼

Yes, you can create multi-panel figures with seaborn to present analyses compactly, leveraging publication-ready styling and consistent defaults before saving the combined output as a PNG.

Does this headless visualization workflow support saving charts as PNG files to a specific directory?▼

Yes, the headless visualization workflow renders charts using the Agg backend and saves the resulting PNG outputs directly to the /workspace directory for easy retrieval.

Why use a headless matplotlib backend instead of standard rendering for data visualization?▼

A headless matplotlib backend like Agg renders charts without requiring a display, making it essential for automated, reproducible workflows in server environments where graphical interfaces are unavailable.

What chart types are available for data visualization in this publication workflow?▼

The publication workflow supports common chart types including bar, line, scatter, heatmap, histogram, and box plots, all styled with consistent publication-ready defaults.