seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill seaborn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Seaborn is presented as a Python visualization library for creating publication-quality statistical graphics. It targets dataset-oriented plotting, automatic statistical estimation, and the creation of complex multi-panel figures with minimal code. It emphasizes semantic mappings, built-in aggregation and confidence intervals, and compatibility with matplotlib. It supports both traditional axes-level plotting functions and a modern declarative objects interface for chaining methods to specify data mappings, marks, transformations, and scales.
How it works
The skill describes two plotting interfaces:
- Function Interface (Traditional): categories of plots with axes-level (single axes) and figure-level (facilitate faceting) functions. Examples include scatterplot, lineplot, relplot, histplot, heatmap, etc. It notes parameters like x, y, hue, size, style, col, row, and how figure-level functions manage subplots.
- Objects Interface (Modern): declarative API accessible via so.Plot to map data, add marks (e.g., Dot, Line, PolyFit), and compose visualizations. Example snippet shows chaining methods to build a plot with data=df, x='total_bill', y='tip', and adding marks.
It details many plotting categories and specific functions:
- Relational Plots: scatterplot, lineplot, relplot, with faceting via col/row.
- Distribution Plots: histplot, kdeplot, ecdfplot, rugplot, displot, jointplot, pairplot.
- Categorical Plots: stripplot, swarmplot, boxplot, violinplot, boxenplot, barplot, pointplot, countplot, catplot.
- Regression Plots: regplot, lmplot, residplot, with options for order, logistic, robust, ci, and custom kwargs.
- Matrix Plots: heatmap, clustermap, with annotations and color settings.
- Multi-Plot Grids: FacetGrid, PairGrid, JointGrid with examples.
- Figure-Level vs Axes-Level: explicit distinctions and examples for using ax= and returning Axes versus FacetGrid-like objects.
- Data Structure Requirements: long-form versus wide-form data with melt examples.
- Color Palettes: qualitative, sequential, diverging, and custom palettes with usage examples.
- Theming and Aesthetics: set_theme, set_style, set_context, and temporary contexts.
- Best Practices: data preparation, choosing plot types, faceting for quick exploration, semantic mappings, and statistical estimation controls with examples.
When to use it
The skill indicates when to use axes-level versus figure-level functions (e.g., for custom subplots vs quick faceted figures) and when to leverage semantic mappings or declarative interfaces for complex visuals. It also stresses data should be in long-form for maximum flexibility and highlights Pandas DataFrame integration.
What it can touch
The skill references interactions with Python code using seaborn and matplotlib. It shows usage of functions with dataframes and plotting calls, and notes the integration with matplotlib for fine-tuning. It does not specify external tools beyond the Seaborn API.
Caveats
The skill outlines standard limitations implicitly: reliance on Matplotlib for rendering, performance considerations for very large datasets (not explicitly stated), and the need to manage figure-level controls when composing complex plots. It does not state licensing beyond general context, but the project license is Apache-2.0 in the metadata.
# 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 wi
What does the seaborn skill do?
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
How do I install it?
Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill seaborn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.