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dataviz-enhanced

Transforms CSV, JSON and Excel tabular data into publication-ready Python visualizations.

3|Updated Jan 19, 2026
Search Tags:#python#data#visualization#pandas#matplotlib#chart#seaborn
One-click install
npx skills add https://github.com/cyborgoat/skills-enhanced --skill dataviz-enhanced
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataviz-enhanced
Source: https://github.com/cyborgoat/skills-enhanced/tree/main/dataviz-enhanced
Command: npx skills add https://github.com/cyborgoat/skills-enhanced --skill dataviz-enhanced

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, pandas, seaborn, scipy, yaml, beautifulsoup4, and includes scripts (resource) components.

What problem does it solve?

Automates turning raw datasets into polished, publication-ready visualizations, saving time and reducing manual plotting effort.

Core Features & Use Cases

  • Generates multiple chart types (line, bar, hbar, scatter, histogram, heatmap, box, pie, donut, area, bubble, timeseries, small_multiples) from CSV, JSON, or Excel data.
  • Applies Tufte-inspired styling with configurable palettes, typography, and layout defaults to deliver publication-ready figures.
  • Supports anomaly highlighting and a review-grid workflow to compare visuals across datasets.

Quick Start

Load a dataset (CSV/Excel) and run the chart generator to produce your first visualization.

Frequently Asked Questions about dataviz-enhanced

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

FAQPage Schema
How do I create publication-quality charts from CSV or Excel data?▼

Create publication-quality charts from CSV or Excel data by running a Python chart generator that parses tabular inputs and applies Tufte-inspired styling with configurable palettes and typography.

What chart types can I generate using Python for data visualization?▼

Data visualization in Python supports generating line, bar, scatter, histogram, heatmap, box, pie, donut, area, bubble, timeseries, and small multiples chart types from your datasets.

Do I need pandas and matplotlib installed to automate plotting from raw datasets?▼

Yes, automating plots from raw datasets requires pandas, matplotlib, seaborn, numpy, and scipy installed, as these Python dependencies handle the data parsing, statistical computations, and chart rendering.

Can I highlight anomalies in a timeseries visualization?▼

Yes, you can highlight anomalies in a timeseries visualization using optional anomaly highlighting features built into the chart rendering workflow to draw attention to outlier data points.

Is there a way to compare visualizations across multiple datasets?▼

Compare visualizations across multiple datasets using the review-grid workflow, which renders output figures in a grid layout to contrast different charts and review them side-by-side.

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