Agent skill · data analytics · aiskillstore
seaborn
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Why this skill is useful
Provides domain-specific knowledge on statistical visualization techniques and integration with pandas that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, pandas, seaborn installed locally. About 10k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Seaborn Statistical Visualization Overview Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code. Design Philosophy Seaborn follows these core principles: 1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates 2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles) 3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals 4. Aesthetic defaults: Publication-ready themes and color palettes out of the box 5. Matplotlib integration: Full compatibility with matplotlib customization when needed Quick Start Core Plotting Interfaces Function Interface (Traditional) The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting). When to use: Quick exploratory analysis Single-purpose visualizations When you need a specific plot type Objects Interface (Modern) The seaborn.objects interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/seaborn--31b9b5