# Data Viz Plots

Create publication-quality plots and visualizations using matplotlib and seaborn.

- Repository: https://github.com/beita6969/ScienceClaw (1024 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add beita6969/ScienceClaw --skill data-viz-plots -g -a claude-code -y`
Codex: `npx skills add beita6969/ScienceClaw --skill data-viz-plots -g -a codex -y`
Cursor: `npx skills add beita6969/ScienceClaw --skill data-viz-plots -g -a cursor -y`
Gemini CLI: `npx skills add beita6969/ScienceClaw --skill data-viz-plots -g -a gemini-cli -y`

## Description

Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

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

# Data Visualization (Universal)

## Overview
This skill enables you to create professional scientific visualizations including scatter plots, line charts, heatmaps, violin plots, and more. Unlike cloud-hosted solutions, this skill uses the **matplotlib** and **seaborn** Python libraries and executes **locally** in your environment, making it compatible with **ALL LLM providers** including GPT, Gemini, Claude, DeepSeek, and Qwen.

## When to Use This Skill
- Create publication-quality figures for papers and presentations
- Generate exploratory data analysis (EDA) plots
- Visualize gene expression, QC metrics, or clustering results
- Create multi-panel figures combining different plot types
- Export high-resolution images for reports
- Customize plot aesthetics (colors, fonts, styles)

## How to Use

### Step 1: Import Required Libraries
```python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib import gridspec
import matplotlib.patches as mpatches

# Set style for publication-quality plots
sns.set_style("whitegrid")
plt.rcParams['figure.dpi'] = 150
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['font.size'] = 10
```

### Step 2: Basic Scatter Plot
```python
# Create figure and axis
fig, ax = plt.subplots(figsize=(6, 5))

# Scatter plot
ax.scatter(x_data, y_data, s=20, alpha=0.6, c='steelblue', edgecolors='k', linewidths=0.5)

# Labels and title
ax.set_xlabel('Gene Expression (log2)', fontsize=12)
ax.set_ylabel('Cell Count', fontsize=12)
ax.set_title('Expression vs. Cell Count', fontsize=14, fontweight='bold')

# Grid and styling
ax.grid(alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Save figure
plt.tight_layout()
plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight')
plt.show()
print("✅ Scatter plot saved to: scatter_plot.png")
```

### Step 3: Line Plot with Multiple Series
```python
fig, ax = plt.subplots(figsize=(8, 5))

The rest: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/data-viz-plots/SKILL.md