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create-viz

Automate publication-grade chart creation in Python with matplotlib, seaborn, and Plotly.

1|Updated Mar 30, 2026
Search Tags:#python#data#dashboard#visualization#plotting#matplotlib#seaborn
One-click install
npx skills add https://github.com/ilove323/comlan-skills --skill create-viz-ilove323
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: create-viz
Source: https://github.com/ilove323/comlan-skills/tree/main/data/skills/create-viz
Command: npx skills add https://github.com/ilove323/comlan-skills --skill create-viz-ilove323

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data visualization often requires translating raw data into clear, publication-quality figures, which is time-consuming and error-prone without consistent styling and automation.

Core Features & Use Cases

  • Generate publication-grade charts from various data sources (SQL results, CSV/Excel, or pandas DataFrames).
  • Recommend chart types based on data relationships and audience, and produce ready-to-share visuals for reports and dashboards.
  • Use Case: A data team needs weekly charts showing trends and comparisons for a board deck.

Quick Start

Load your data, choose a chart type, and generate a publication-ready figure with Python.

Frequently Asked Questions about create-viz

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

FAQPage Schema
How do I create publication-grade charts from a pandas DataFrame?▼

Create publication-grade charts from a pandas DataFrame by loading your data, choosing a chart type, and generating the figure with Python using matplotlib and seaborn for ready-to-share visuals.

Can I generate interactive dashboards using Python with this approach?▼

You can generate interactive dashboards using Python through optional Plotly support, while matplotlib and seaborn handle static plots for reports and board decks.

What is the best way to visualize SQL results for a weekly report?▼

Visualize SQL results for a weekly report by applying automated chart creation directly to SQL query outputs, producing publication-quality figures showing trends and comparisons.

Does this visualization method support data from CSV or Excel files?▼

This visualization method supports data from CSV or Excel files, alongside pandas DataFrames and SQL results, to automate chart creation and styling across various data sources.

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

Choose the right chart type for your data relationships by letting the automated system recommend visualizations based on data characteristics and audience, then generating the styled output.

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