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data-viz

Select charting methods and improve figure quality for matplotlib, seaborn, and plotly.

Updated May 5, 2026
Search Tags:#data visualization#charts#dashboard#plotly#matplotlib#seaborn#eda
One-click install
npx skills add https://github.com/yanochka11/harness_bro --skill data-viz-yanochka11
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-viz
Source: https://github.com/yanochka11/harness_bro/tree/main/.claude/skills/curated/data-viz
Command: npx skills add https://github.com/yanochka11/harness_bro --skill data-viz-yanochka11

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users turn raw data into effective visualizations by choosing appropriate charting methods, applying design principles, and avoiding misleading or cluttered figures.

Core Features & Use Cases

  • Library Selection Guidance: Recommends matplotlib, seaborn, plotly, and other visualization tools based on goals such as papers, dashboards, EDA, or interactive analysis.
  • Visualization Best Practices: Applies accessible color palettes, data-ink principles, uncertainty visualization, and chart selection rules for high-quality outputs.
  • Use Case: Prepare polished machine learning experiment figures, exploratory data analysis charts, or interactive dashboards suitable for research and reporting.

Quick Start

Ask the data-viz skill to create a publication-quality loss curve visualization from my experiment results.

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 data visualizations for a research paper?▼

To create publication-ready data visualizations, you need to select appropriate charting methods and apply design principles like accessible color palettes and data-ink ratios to ensure high-quality, clear outputs suitable for academic papers.

What is the best way to choose between matplotlib, seaborn, and plotly for my dashboard?▼

Choosing between matplotlib, seaborn, and plotly depends on your goal: use matplotlib or seaborn for static, publication-ready figures and EDA, and plotly for building interactive dashboards and exploratory visualizations.

How do I apply accessible color palettes and data-ink principles to my charts?▼

Applying accessible color palettes and data-ink principles involves selecting visualization best practices that reduce clutter and avoid misleading figures, ensuring your charts remain clear and readable for all audiences.

Can I use this approach for exploratory data analysis and machine learning experiments?▼

Yes, you can use this data visualization approach for exploratory data analysis and machine learning experiments to turn raw data into polished charts, such as training loss curves, that effectively communicate your findings.

Why do my data visualizations look cluttered and how can I fix them?▼

Your data visualizations may look cluttered due to ignoring data-ink principles and poor chart selection; you can fix them by applying visualization best practices that eliminate unnecessary elements and improve figure quality.

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