# Data Visualization

A skill that steers your agent toward the right chart type and gives working matplotlib and seaborn code for analysis and stakeholder decks.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 568 installs · ★ 371
- Author: aj-geddes
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/data-visualization/SKILL.md
- Updated: 2026-03-04
- HTML: https://affpapa.org/en/nevibecode/skills/aj-geddes-data-visualization

## What it does

The skill maps chart types to questions: histograms and KDE for distributions, scatter and hexbin for relationships, bar and box plots for comparisons, heatmaps for correlation. Each comes with runnable Python built on pandas, numpy, matplotlib and seaborn, including style setup such as `whitegrid` and figure sizing.

A design section sits alongside the code. Trim ink that carries no data, use color on purpose, label axes and legends fully, keep scales consistent, think about accessibility. That part matters more than the snippets, because an agent left alone tends to emit default plots with clipped labels.

Everything assumes a Python stack with pandas, numpy, matplotlib and seaborn, and one example reaches for scipy. If your charts come from R or a JS library, you will be rewriting the examples. Install works through `npx skills add` for Claude, Codex, Cursor, Gemini CLI and OpenCode.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill data-visualization -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/data-visualization/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/data-visualization/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill data-visualization -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/data-visualization/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill data-visualization -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/data-visualization/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install aj-geddes/useful-ai-prompts data-visualization
```

2. **Manually: put the files in place**
- File: `.github/skills/data-visualization/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill data-visualization -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/aj-geddes/useful-ai-prompts --path skills/data-visualization
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/data-visualization/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill data-visualization -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/data-visualization/SKILL.md

## When to use it
- pick a chart type that fits the data
- build a correlation matrix with annotated coefficients
- show a metric's spread across categories
- spot outliers on a scatter plot
- format charts for a report or dashboard

## Example prompts
- plot age against income split by customer category
- build a correlation heatmap from the numeric columns
- compare income by region on a box plot with labeled axes

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: b017fb0eec6368a9.

BEGIN THIRD-PARTY FILE b017fb0eec6368a9
````text
---
name: Data Visualization
description: Create effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholders
---

# Data Visualization

## Overview

Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making.

## When to Use

- Exploratory data analysis and pattern discovery
- Communicating insights to stakeholders
- Comparing distributions and relationships
- Presenting findings in reports and dashboards
- Identifying outliers and anomalies visually
- Creating publication-ready charts and graphs

## Visualization Types

- **Distributions**: Histograms, KDE, violin plots
- **Relationships**: Scatter plots, line plots, heatmaps
- **Comparisons**: Bar charts, box plots, ridge plots
- **Compositions**: Pie charts, stacked bars, treemaps
- **Temporal**: Line plots, area charts, time series
- **Multivariate**: Pair plots, correlation heatmaps

## Design Principles

- Choose appropriate chart type for data
- Minimize ink-to-data ratio
- Use color purposefully
- Label clearly and completely
- Maintain consistent scales
- Consider accessibility

## Implementation with Python

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.gridspec import GridSpec

# Set style
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 6)

# Generate sample data
np.random.seed(42)
n = 500
data = pd.DataFrame({
    'age': np.random.uniform(20, 70, n),
    'income': np.random.exponential(50000, n),
    'education_years': np.random.uniform(12, 20, n),
    'category': np.random.choice(['A', 'B', 'C'], n),
    'region': np.random.choice(['North', 'South', 'East', 'West'], n),
    'satisfaction': np.random.uniform(1, 5, n),
    'purchased': np.random.choice([0, 1], n),
})

print(data.head())

# 1. Distribution Plots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Histogram
axes[0, 0].hist(data['age'], bins=30, color='skyblue', edgecolor='black')
axes[0, 0].set_title('Age Distribution (Histogram)')
axes[0, 0].set_xlabel('Age')
axes[0, 0].set_ylabel('Frequency')

# KDE plot
data['income'].plot(kind='kde', ax=axes[0, 1], color='green', linewidth=2)
axes[0, 1].set_title('Income Distribution (KDE)')
axes[0, 1].set_xlabel('Income')

# Box plot
sns.boxplot(data=data, y='satisfaction', x='category', ax=axes[1, 0], palette='Set2')
axes[1, 0].set_title('Satisfaction by Category (Box Plot)')

# Violin plot
sns.violinplot(data=data, y='age', x='category', ax=axes[1, 1], palette='Set2')
axes[1, 1].set_title('Age by Category (Violin Plot)')

plt.tight_layout()
plt.show()

# 2. Relationship Plots
````
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