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jvsandhu
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data_visualization

Generate charts and plots from structured data using matplotlib, seaborn, D3.js, or Recharts.

Updated Jan 14, 2026
Search Tags:#data visualization#charts#dashboard#data analysis#d3.js#plotting#matplotlib
One-click install
npx skills add https://github.com/jvsandhu/agentic-skills --skill data-visualization-jvsandhu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data_visualization
Source: https://github.com/jvsandhu/agentic-skills/tree/main/skills/data_visualization
Command: npx skills add https://github.com/jvsandhu/agentic-skills --skill data-visualization-jvsandhu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Choosing the right chart type and producing clear, accurate visualizations from raw data is time-consuming and error-prone. This Skill analyzes your data structure and automatically selects and generates the most appropriate visualization. ## Core Features & Use Cases - Automatic Chart Selection: Analyzes data type, volume, and goal (comparison, distribution, relationship, composition) to pick the right chart, following the Financial Times Visual Vocabulary. - Multi-Stack Generation: Produces visualizations in Python with matplotlib/seaborn or on the web with D3.js/Recharts. - Design Refinement: Applies data-ink ratio principles, colorblind-friendly palettes, and non-manipulative axis checks. - Use Case: Ask for a bar chart of sales by region or a line graph of AAPL stock prices over the past year, and receive a properly labeled, clutter-free chart. ## Quick Start Create a bar chart showing sales by region from my CSV data.

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?▼

Match the chart to your goal: bar charts for comparisons, histograms for distributions, scatter plots for relationships, and line graphs for time-series. The skill profiles your data type and volume first, then selects the appropriate visualization automatically.

How to create a bar chart from CSV data in Python?▼

Load the CSV with pandas, identify the categorical and numerical columns, then plot with matplotlib or seaborn. Label both axes clearly, start the y-axis at zero, and remove unnecessary gridlines for a clean result.

Matplotlib vs D3.js for data visualization, which should I use?▼

Use matplotlib or seaborn for Python-based analysis and static report charts. Use D3.js or Recharts when building interactive web dashboards. The choice depends on whether the output target is a Python environment or a browser.

Why is a truncated y-axis misleading in bar charts?▼

A truncated y-axis exaggerates differences between values, making small gaps look dramatic. Bar charts should start at zero; line charts have more flexibility but truncations should still be flagged and justified.

What are the limitations of pie charts for data composition?▼

Pie charts become hard to read with more than a few categories or when slices are similar in size. For composition with many categories, stacked bar charts are usually clearer and easier to compare.

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