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data-visualization

Create data visualizations from datasets with chart selection and clear labeling.

53|1|Updated Dec 18, 2025
Search Tags:#dashboard#plotly#matplotlib#d3#data-visualization
One-click install
npx skills add https://github.com/cosmix/claude-code-setup --skill data-visualization-cosmix
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/cosmix/claude-code-setup/tree/main/skills/data-visualization
Command: npx skills add https://github.com/cosmix/claude-code-setup --skill data-visualization-cosmix

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill focuses on selecting appropriate charts, designing accessible visuals, and building dashboards to convey insights clearly.

Core Features & Use Cases

  • Chart Selection: Matching chart type to data relationships.
  • Design & Accessibility: Color schemes, labels, and readability.
  • Interactive Dashboards: Basic interactivity and dashboard composition.

Quick Start

Build a simple Python plot that compares revenue vs. target over time.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I choose the right chart type for my data?▼

Chart selection depends on your data relationships: use line charts for trends over time, bar charts for comparisons, scatter plots for correlations, and histograms for distributions. Match the chart type to how you want to reveal patterns in your dataset.

How do I create an accessible data visualization?▼

Design accessible visuals by using colorblind-friendly palettes, adding clear axis labels and titles, ensuring sufficient contrast, and including data tables alongside charts. Accessibility makes insights readable for all audiences.

Can I build interactive dashboards with Python visualization libraries?▼

Yes, libraries like Plotly enable interactive dashboards with filtering, zooming, and hover details. Interactive dashboards let users explore data dynamically rather than viewing static reports.

What's the best way to visualize time-series data across multiple business metrics?▼

Use line charts with clear legends for multiple metrics, or create small multiples—separate subplots for each metric—to avoid clutter. This approach reveals trends and comparisons simultaneously across time-series datasets.

How do I ensure my visualizations communicate insights clearly?▼

Label axes and data points explicitly, use consistent styling across related charts, remove unnecessary elements, and validate designs with real data. Clear labeling and iterative validation transform raw visuals into effective data stories.

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