write-data-viz

Name: write-data-viz
Availability: InStock
Author: Sir-chawakorn

Generate static, interactive, and dashboard visualizations from structured datasets.

9|3|Updated Jun 13, 2026

Search Tags:#data #dashboard #visualization #plotly #plotting #matplotlib

Ready to run

npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill write-data-viz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: write-data-viz
Source: https://github.com/Sir-chawakorn/sanook-cli/tree/main/skills/write-data-viz
Command: npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill write-data-viz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the manual effort of creating charts and dashboards by generating high-quality, reproducible data visualizations directly from your datasets.

Core Features & Use Cases

Multi-Engine Support: Create static reports with Matplotlib/Seaborn, interactive charts with Plotly, or custom web dashboards with D3.js.
Analytical Mapping: Automatically selects the optimal chart type based on your data structure and analytical goals.
Use Case: Quickly transform a raw CSV file of sales data into an interactive Plotly dashboard that allows stakeholders to filter trends and compare regional performance.

Quick Start

Use the write-data-viz skill to analyze the provided sales.csv file and generate an interactive Plotly dashboard showing the monthly revenue trends.

Frequently Asked Questions about write-data-viz

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

FAQPage Schema

How do I generate interactive data visualizations from a CSV file?▼

This skill generates interactive data visualizations from CSV files by mapping analytical goals to appropriate chart types and producing reproducible Plotly code directly from your structured datasets.

What is the best way to build a multi-panel dashboard from structured datasets?▼

The best way to build a multi-panel dashboard from structured datasets is to use this skill, which assembles multi-panel dashboards and generates web-standard visualization framework code with accurate, annotated visual artifacts.

Can I use Matplotlib and Plotly together for static and interactive charts?▼

Yes, you can use Matplotlib for static image reports and Plotly for interactive web-based charts, as this skill supports multiple engines to produce static images, interactive charts, and custom web dashboards.

Do I need Python data science libraries to create dashboards with this approach?▼

Yes, you need Python data science libraries and web-standard visualization frameworks, because the generated charts and dashboards are output as code requiring these dependencies to render accurate and accessible visual artifacts.

How does analytical mapping select the right chart type for my data?▼

Analytical mapping selects the right chart type by evaluating your data structure and analytical goals, automatically determining the optimal visualization format to ensure your charts accurately represent the underlying dataset.