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data-visualization

Create static and interactive data visualizations with matplotlib, seaborn, and Plotly.

1|Updated May 5, 2026
Search Tags:#data visualization#data storytelling#data analysis#plotly#matplotlib#seaborn#chart creation
One-click install
npx skills add https://github.com/kollaborai/kollab --skill data-visualization-kollaborai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/kollaborai/kollab/tree/main/bundles/skills/data-visualization
Command: npx skills add https://github.com/kollaborai/kollab --skill data-visualization-kollaborai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the guesswork and inconsistency in creating data visualizations that fail to clearly communicate insights to their intended audience, whether for business reporting, research, or exploratory analysis.

Core Features & Use Cases

  • Optimal Chart Type Selection: Guides users to pick the right visualization for comparison, distribution, relationship, composition, or geospatial data based on audience and analysis goal.
  • Full Environment Setup Support: Verifies and configures matplotlib, seaborn, and related dependencies, with adjustments for Jupyter notebooks, static script outputs, and different display environments.
  • Advanced Visualization Techniques: Includes implementations for small multiples, correlation heatmaps, violin plots, time series decomposition, and interactive Plotly charts for complex data storytelling.
  • Quality and Accessibility Guardrails: Provides pre-visualization, design, accuracy, and accessibility checklists, plus guidance to avoid common mistakes like misleading axes or poor color choices. Use case example: If you need to present quarterly sales performance to executive stakeholders, use this Skill to generate a clear, annotated line chart with a 7-day moving average that highlights key growth trends and anomalies.

Quick Start

Use the data-visualization skill to create a high-resolution, color-blind friendly bar chart of monthly sales by product category from the provided sales dataset, saved as a PNG file for your team meeting presentation.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I create interactive data visualizations for reporting and presentations?▼

To create interactive data visualizations, generate Plotly HTML outputs that support complex data storytelling for reporting and presentations. The workflow covers chart type selection, styling, annotation, and accessibility compliance for all common data types.

What is the best chart type for visualizing time series and distribution data?▼

The best chart type for visualizing time series and distribution data depends on your analysis goal, with optimal selection guiding you toward comparison, distribution, relationship, or composition visualizations. Advanced techniques include time series decomposition, violin plots, and correlation heatmaps.

Can I use matplotlib and seaborn in Jupyter notebooks for data storytelling?▼

Yes, you can use matplotlib and seaborn in Jupyter notebooks for data storytelling, as the environment setup verifies and configures dependencies with specific adjustments for Jupyter notebook displays and static script outputs.

How do I avoid misleading axes and poor color choices when creating charts?▼

To avoid misleading axes and poor color choices when creating charts, apply built-in quality and accessibility guardrails including pre-visualization, design, accuracy, and accessibility checklists to ensure visualizations communicate insights clearly to target audiences.

Does Plotly work with matplotlib for generating high-resolution static outputs?▼

Plotly generates interactive HTML outputs, while matplotlib and seaborn generate high-resolution static outputs like PNG, PDF, and SVG. The workflow supports both static and interactive chart creation across all common data types for analysis and reporting.

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