# Data Visualization

Creates effective data visualizations using various libraries and tools, with focus on...

- Repository: https://github.com/ynulihao/AgentSkillOS (618 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-03-07
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ynulihao/AgentSkillOS --skill data-visualization -g -a claude-code -y`
Codex: `npx skills add ynulihao/AgentSkillOS --skill data-visualization -g -a codex -y`
Cursor: `npx skills add ynulihao/AgentSkillOS --skill data-visualization -g -a cursor -y`
Gemini CLI: `npx skills add ynulihao/AgentSkillOS --skill data-visualization -g -a gemini-cli -y`

## Description

Creates effective data visualizations using various libraries and tools, with focus on clarity and insight communication. Trigger keywords: chart, graph, plot, visualization, dashboard, matplotlib, d3, plotly, visualization.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Data Visualization

## Overview

This skill focuses on creating effective data visualizations that communicate insights clearly. It covers various visualization libraries, chart selection, and design principles for impactful data presentation.

## Instructions

### 1. Understand the Data

- Analyze data structure and types
- Identify key metrics and dimensions
- Determine the story to tell
- Consider the target audience

### 2. Select Appropriate Visualization

- Match chart type to data relationship
- Consider data volume and complexity
- Plan for interactivity needs
- Account for accessibility

### 3. Design for Clarity

- Choose effective color schemes
- Label axes and data clearly
- Remove chart junk
- Highlight key insights

### 4. Implement and Iterate

- Build visualization with chosen tool
- Test with real data
- Gather feedback
- Refine based on usage

## Best Practices

1. **Right Chart for Data**: Match visualization to data type
2. **Less is More**: Remove unnecessary elements
3. **Consistent Styling**: Use coherent color schemes
4. **Accessible Design**: Consider colorblind users
5. **Clear Labels**: Descriptive titles and axis labels
6. **Context Matters**: Include reference points
7. **Interactive When Helpful**: Add tooltips and filters

## Examples

### Example 1: Python with Matplotlib/Seaborn

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

# Set style for professional look
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Create figure with subplots
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Example 1: Line chart for time series
df_sales = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=12, freq='M'),
    'revenue': [100, 120, 115, 140, 155, 170, 165, 180, 195, 210, 225, 250],
    'target': [110, 115, 120, 130, 145, 160, 175, 185, 200, 215, 230, 245]
})

The rest: https://github.com/ynulihao/AgentSkillOS/blob/HEAD/data/skill_seeds/data-visualization/SKILL.md