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chart-generator

Generates bar, line, pie, scatter, heatmap, and 3D charts using Matplotlib, Seaborn, Chart.js, and Plotly.

27|4|Updated Oct 20, 2025
Search Tags:#data visualization#reporting#data analysis#chart generation#plotly#matplotlib#seaborn
One-click install
npx skills add https://github.com/CuriousLearner/devkit --skill chart-generator-curiouslearner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chart-generator
Source: https://github.com/CuriousLearner/devkit/tree/main/skills/chart-generator
Command: npx skills add https://github.com/CuriousLearner/devkit --skill chart-generator-curiouslearner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, chartjs-node-canvas, plotly, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill transforms raw data into clear, insightful visual representations, making complex information easy to understand and communicate.

Core Features & Use Cases

  • Diverse Chart Types: Generates various charts like bar, line, pie, scatter, heatmaps, and more.
  • Multiple Libraries: Supports popular charting libraries such as Matplotlib, Seaborn, Chart.js, and Plotly.
  • Customization & Styling: Allows for extensive customization of appearance, themes, and color palettes.
  • Interactive Outputs: Can create interactive HTML charts for web embedding.
  • Use Case: You have a CSV file with sales data over the last year. Use this Skill to generate a line chart showing monthly sales trends and a bar chart comparing sales by product category.

Quick Start

Use the chart-generator skill to create a bar chart from the data in 'sales_data.csv', with 'product' on the x-axis and 'revenue' on the y-axis.

Frequently Asked Questions about chart-generator

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

FAQPage Schema
How do I generate interactive charts from a CSV file for data visualization?▼

To generate interactive charts for data visualization, you can use this Skill to process CSV data and output interactive HTML charts using Plotly or Chart.js, supporting line, bar, pie, scatter, and heatmap types.

What is the best way to create a styled heatmap using Seaborn and Matplotlib?▼

The best way to create a styled heatmap for data visualization is using this Skill, which leverages Seaborn and Matplotlib to apply custom themes, color palettes, and export the result as PNG, SVG, PDF, or JPG.

Can I use Plotly to create 3D plots and export them as static image files?▼

Yes, you can use Plotly through this Skill to generate 3D plots for data visualization and export the resulting charts as static image files in formats including PNG, SVG, PDF, and JPG.

Does this chart generation approach support both static Matplotlib graphs and interactive HTML outputs?▼

Yes, this chart generation approach supports static graphs via Matplotlib and Seaborn alongside interactive HTML outputs via Plotly and Chart.js, allowing you to switch between presentation styles for data analysis.

Why use multiple data visualization libraries like Seaborn and Chart.js instead of just one?▼

Using multiple data visualization libraries like Seaborn and Chart.js provides specialized strengths: Seaborn handles statistical heatmaps, while Chart.js and Plotly enable interactive HTML outputs for web embedding and reporting.

What are the limitations when creating interactive HTML charts with Chart.js for reporting?▼

When creating interactive HTML charts with Chart.js for reporting, outputs are constrained to web formats and require an HTML viewer, unlike static Matplotlib or Seaborn charts that export directly to PDF or PNG files.

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