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data-viz

Standardize scientific data visualizations with matplotlib, seaborn, and plotly.

Updated May 5, 2026
Search Tags:#visualization#plotly#matplotlib#seaborn#eda#data-science
One-click install
npx skills add https://github.com/iani-kuli/harness_bro --skill data-viz-iani-kuli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-viz
Source: https://github.com/iani-kuli/harness_bro/tree/main/.claude/skills/curated/data-viz
Command: npx skills add https://github.com/iani-kuli/harness_bro --skill data-viz-iani-kuli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of creating high-quality, publication-ready, and accessible data visualizations by providing standardized workflows for matplotlib, seaborn, and plotly.

Core Features & Use Cases

  • Library Selection: Provides clear guidance on choosing the right tool for EDA, paper figures, or interactive dashboards.
  • Design Standards: Enforces data-ink ratio principles, colorblind-safe palettes, and proper type hierarchy for academic and professional reports.
  • Workflow Automation: Includes templates for multi-panel figures, error bars, and vector-based PDF exports for LaTeX integration.

Quick Start

Use the data-viz skill to generate a publication-quality line plot from the current dataframe using seaborn and save it as a PDF.

Frequently Asked Questions about data-viz

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

FAQPage Schema
How do I create publication-ready figures for academic papers using Python?▼

You can create publication-ready figures by using matplotlib and seaborn to generate vector-based PDF outputs. This process enforces data-ink optimization, colorblind-safe palettes, and proper type hierarchy for academic papers.

What's the best way to choose between matplotlib, seaborn, and plotly for data visualization?▼

Choosing between matplotlib, seaborn, and plotly depends on your goal: use seaborn for exploratory data analysis, matplotlib for static paper figures, and plotly for interactive dashboards. Selecting the right tool ensures optimal data visualization output.

How do I ensure my data visualizations are accessible and colorblind-safe?▼

To ensure data visualizations are accessible, apply design standards that enforce colorblind-safe palettes and data-ink ratio principles. This standardized workflow guarantees accessibility for professional and academic reports.

Can I automate multi-panel figures and error bars for exploratory data analysis?▼

Yes, you can automate multi-panel figures and error bars using standardized visualization templates. This workflow automation facilitates efficient exploratory data analysis and consistent chart generation across your projects.

Does this data visualization workflow support vector-based PDF exports for LaTeX integration?▼

Yes, the data visualization workflow supports vector-based PDF exports specifically for LaTeX integration. This ensures that generated scientific figures maintain high quality and scalability within academic documents.

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