How do I install the Seaborn skill?

Run npx skillmds@latest add newmindsgroup/seaborn in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Seaborn safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Seaborn?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Seaborn free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Seaborn?

newmindsgroup (@newmindsgroup) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Seaborn by newmindsgroup — Agent Skill

Name: Seaborn
Author: newmindsgroup

Seaborn

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.

newmindsgroup 788a1f8 2 files · 22.2 KB Updated Sep 17, 2026

File contents

Seaborn Statistical Visualization

When to Use

You need publication-quality statistical graphics directly from tabular datasets.
You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.

Core Workflow

Confirm the request matches this skill's trigger, scope, and risk profile.
Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
Load references/full-guidance.md when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
Apply only the relevant guidance instead of loading or repeating the entire reference by default.
Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

Topic Map

Overview
Design Philosophy
Quick Start
Core Plotting Interfaces
Function Interface (Traditional)
Objects Interface (Modern)
Plotting Functions by Category
Relational Plots (Relationships Between Variables)
Distribution Plots (Single and Bivariate Distributions)
Categorical Plots (Comparisons Across Categories)
Regression Plots (Linear Relationships)
Matrix Plots (Rectangular Data)
Multi-Plot Grids
FacetGrid
PairGrid
JointGrid
Figure-Level vs Axes-Level Functions
Axes-Level Functions

Reference Map

references/full-guidance.md preserves the complete original guidance, including examples and detailed edge cases.

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Progressive Loading

Keep this SKILL.md as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.

---name: seaborndescription: 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.license: MIT---# Seaborn Statistical VisualizationSeaborn 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.## When to Use- You need publication-quality statistical graphics directly from tabular datasets.- You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.- You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.## Core Workflow1. Confirm the request matches this skill's trigger, scope, and risk profile.2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.3. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.## Topic Map- Overview- Design Philosophy- Quick Start- Core Plotting Interfaces- Function Interface (Traditional)- Objects Interface (Modern)- Plotting Functions by Category- Relational Plots (Relationships Between Variables)- Distribution Plots (Single and Bivariate Distributions)- Categorical Plots (Comparisons Across Categories)- Regression Plots (Linear Relationships)- Matrix Plots (Rectangular Data)- Multi-Plot Grids- FacetGrid- PairGrid- JointGrid- Figure-Level vs Axes-Level Functions- Axes-Level Functions## Reference Map- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.## Limitations- Use this skill only when the task clearly matches the scope described above.- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.## Progressive LoadingKeep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.

Frequently asked questions