data-visualization

To create publication-quality charts from pre-processed numeric data, this Skill uses pandas, matplotlib, and seaborn to transform small tables or computed results into professional PNG outputs.

Yes, you can visualize pasted numbers and small tables using matplotlib and seaborn. The Skill automates chart generation for pre-processed data, outputting publication-ready PNG files and optional interactive HTML.

No, this data visualization tool does not work with raw CSV or Excel datasets of unknown structure. It is designed for pre-processed numeric data; raw datasets require a separate data-analysis skill first.

Using the Python chart factory script, you can generate common chart types including bar, line, histogram, scatter, and heatmap. It provides guidance on chart selection and applies professional formatting to the outputs.

To save matplotlib or seaborn chart outputs, the chart_factory.py CLI tool reads datasets and saves the generated visualization files directly to the /mnt/user-data/outputs directory.

You should use an interactive HTML chart instead of a static PNG when you need dynamic data exploration. The Skill generates PNGs by default but offers optional interactive HTML outputs via Plotly.