What does the Data Viz Plots AI skill do?

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

Why use Data Viz Plots on TypingMind?

Because you install it once and use it with any model. Data Viz Plots is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Viz Plots in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/data-viz-plots. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Viz Plots?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Viz Plots?

As many as you like. As long as a model supports skills, you can use Data Viz Plots with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Viz Plots AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Data Viz Plots by beita6969 — AI Skill for Any LLM

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))

# Plot multiple lines
ax.plot(time_points, group1_values, marker='o', label='Group 1', color='#E74C3C', linewidth=2)
ax.plot(time_points, group2_values, marker='s', label='Group 2', color='#3498DB', linewidth=2)
ax.plot(time_points, group3_values, marker='^', label='Group 3', color='#2ECC71', linewidth=2)

# Styling
ax.set_xlabel('Time Point', fontsize=12)
ax.set_ylabel('Expression Level', fontsize=12)
ax.set_title('Gene Expression Over Time', fontsize=14, fontweight='bold')
ax.legend(frameon=True, loc='best', fontsize=10)
ax.grid(alpha=0.3, linestyle='--')

plt.tight_layout()
plt.savefig('line_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Step 4: Box Plot and Violin Plot

python
# Prepare data (long-form DataFrame)
# df should have columns: 'cluster', 'expression', 'gene', etc.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Box plot
sns.boxplot(data=df, x='cluster', y='expression', palette='Set2', ax=ax1)
ax1.set_title('Box Plot: Expression by Cluster', fontsize=12, fontweight='bold')
ax1.set_xlabel('Cluster', fontsize=11)
ax1.set_ylabel('Expression Level', fontsize=11)
ax1.tick_params(axis='x', rotation=45)

# Violin plot
sns.violinplot(data=df, x='cluster', y='expression', palette='muted', ax=ax2, inner='quartile')
ax2.set_title('Violin Plot: Expression Distribution', fontsize=12, fontweight='bold')
ax2.set_xlabel('Cluster', fontsize=11)
ax2.set_ylabel('Expression Level', fontsize=11)
ax2.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.savefig('box_violin_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Step 5: Heatmap

python
# Prepare data matrix (rows=genes, columns=samples or clusters)
# gene_expression_matrix: pandas DataFrame or numpy array

fig, ax = plt.subplots(figsize=(8, 6))

# Create heatmap
sns.heatmap(
    gene_expression_matrix,
    cmap='viridis',
    cbar_kws={'label': 'Expression'},
    xticklabels=True,
    yticklabels=True,
    linewidths=0.5,
    linecolor='gray',
    ax=ax
)

ax.set_title('Gene Expression Heatmap', fontsize=14, fontweight='bold')
ax.set_xlabel('Samples', fontsize=12)
ax.set_ylabel('Genes', fontsize=12)

plt.tight_layout()
plt.savefig('heatmap.png', dpi=300, bbox_inches='tight')
plt.show()

Step 6: Bar Plot with Error Bars

python
fig, ax = plt.subplots(figsize=(7, 5))

# Data
categories = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3']
means = [120, 85, 200, 150]
errors = [15, 10, 25, 20]

# Bar plot
bars = ax.bar(categories, means, yerr=errors, capsize=5,
               color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12'],
               edgecolor='black', linewidth=1.2, alpha=0.8)

# Labels
ax.set_ylabel('Cell Count', fontsize=12)
ax.set_title('Cell Counts by Cluster', fontsize=14, fontweight='bold')
ax.set_ylim(0, max(means) * 1.3)

# Add value labels on bars
for bar, mean in zip(bars, means):
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width()/2., height + 5,
            f'{mean}', ha='center', va='bottom', fontsize=10)

plt.tight_layout()
plt.savefig('bar_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Advanced Features

Multi-Panel Figure

python
# Create complex layout
fig = plt.figure(figsize=(12, 8))
gs = gridspec.GridSpec(2, 3, figure=fig, hspace=0.3, wspace=0.3)

# Panel A: Scatter
ax1 = fig.add_subplot(gs[0, :2])
ax1.scatter(x_data, y_data, c=cluster_labels, cmap='tab10', s=10, alpha=0.6)
ax1.set_title('A. UMAP Projection', fontsize=12, fontweight='bold', loc='left')
ax1.set_xlabel('UMAP1')
ax1.set_ylabel('UMAP2')

# Panel B: Violin
ax2 = fig.add_subplot(gs[0, 2])
sns.violinplot(data=df, y='expression', palette='Set2', ax=ax2)
ax2.set_title('B. Expression', fontsize=12, fontweight='bold', loc='left')

# Panel C: Heatmap
ax3 = fig.add_subplot(gs[1, :])
sns.heatmap(matrix, cmap='coolwarm', center=0, ax=ax3, cbar_kws={'label': 'Z-score'})
ax3.set_title('C. Gene Expression Heatmap', fontsize=12, fontweight='bold', loc='left')

plt.savefig('multi_panel_figure.png', dpi=300, bbox_inches='tight')
plt.show()

Custom Color Palette

python
# Define custom colors
custom_palette = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6']

# Use in seaborn
sns.set_palette(custom_palette)

# Or create color dict for specific mapping
color_dict = {
    'T cells': '#E74C3C',
    'B cells': '#3498DB',
    'Monocytes': '#2ECC71',
    'NK cells': '#F39C12'
}

# Use in scatter plot
for cell_type, color in color_dict.items():
    mask = df['celltype'] == cell_type
    ax.scatter(df.loc[mask, 'x'], df.loc[mask, 'y'],
               c=color, label=cell_type, s=20, alpha=0.7)
ax.legend()

Density Plot

python
from scipy.stats import gaussian_kde

fig, ax = plt.subplots(figsize=(8, 6))

# Calculate density
xy = np.vstack([x_data, y_data])
z = gaussian_kde(xy)(xy)

# Sort points by density for better visualization
idx = z.argsort()
x, y, z = x_data[idx], y_data[idx], z[idx]

# Scatter with density colors
scatter = ax.scatter(x, y, c=z, s=20, cmap='viridis', alpha=0.6, edgecolors='none')
plt.colorbar(scatter, ax=ax, label='Density')

ax.set_xlabel('UMAP1', fontsize=12)
ax.set_ylabel('UMAP2', fontsize=12)
ax.set_title('Density Scatter Plot', fontsize=14, fontweight='bold')

plt.tight_layout()
plt.savefig('density_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Common Use Cases

QC Metrics Visualization

python
# Assuming adata.obs has QC columns: n_genes, n_counts, percent_mito

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

# Plot 1: Histogram of genes per cell
axes[0].hist(adata.obs['n_genes'], bins=50, color='steelblue', edgecolor='black', alpha=0.7)
axes[0].axvline(adata.obs['n_genes'].median(), color='red', linestyle='--', label='Median')
axes[0].set_xlabel('Genes per Cell', fontsize=11)
axes[0].set_ylabel('Frequency', fontsize=11)
axes[0].set_title('Genes per Cell Distribution', fontsize=12, fontweight='bold')
axes[0].legend()

# Plot 2: Scatter UMI vs Genes
axes[1].scatter(adata.obs['n_counts'], adata.obs['n_genes'],
                s=5, alpha=0.5, c='coral')
axes[1].set_xlabel('UMI Counts', fontsize=11)
axes[1].set_ylabel('Genes Detected', fontsize=11)
axes[1].set_title('UMIs vs Genes', fontsize=12, fontweight='bold')

# Plot 3: Violin plot of mitochondrial percentage
sns.violinplot(y=adata.obs['percent_mito'], ax=axes[2], color='lightgreen')
axes[2].axhline(y=20, color='red', linestyle='--', label='20% threshold')
axes[2].set_ylabel('Mitochondrial %', fontsize=11)
axes[2].set_title('Mitochondrial Content', fontsize=12, fontweight='bold')
axes[2].legend()

plt.tight_layout()
plt.savefig('qc_metrics.png', dpi=300, bbox_inches='tight')
plt.show()

UMAP/tSNE Visualization

python
# Assuming adata.obsm['X_umap'] exists and adata.obs['clusters'] exists

fig, ax = plt.subplots(figsize=(8, 7))

# Get unique clusters
clusters = adata.obs['clusters'].unique()
n_clusters = len(clusters)

# Generate colors
colors = plt.cm.tab20(np.linspace(0, 1, n_clusters))

# Plot each cluster
for i, cluster in enumerate(clusters):
    mask = adata.obs['clusters'] == cluster
    ax.scatter(
        adata.obsm['X_umap'][mask, 0],
        adata.obsm['X_umap'][mask, 1],
        c=[colors[i]],
        label=f'Cluster {cluster}',
        s=10,
        alpha=0.7,
        edgecolors='none'
    )

ax.set_xlabel('UMAP1', fontsize=12)
ax.set_ylabel('UMAP2', fontsize=12)
ax.set_title('UMAP Projection by Cluster', fontsize=14, fontweight='bold')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=9)

plt.tight_layout()
plt.savefig('umap_clusters.png', dpi=300, bbox_inches='tight')
plt.show()

Gene Expression Dot Plot

python
# genes: list of gene names
# clusters: list of cluster IDs
# Create matrix: rows=genes, columns=clusters with mean expression and % expressing

fig, ax = plt.subplots(figsize=(10, 6))

# Prepare data
from matplotlib.colors import Normalize

# dot_size_matrix: % cells expressing (0-100)
# color_matrix: mean expression level

for i, gene in enumerate(genes):
    for j, cluster in enumerate(clusters):
        # Size proportional to % expressing
        size = dot_size_matrix[i, j] * 5  # Scale factor
        # Color by expression level
        color_val = color_matrix[i, j]

        ax.scatter(j, i, s=size, c=[color_val], cmap='Reds',
                   vmin=0, vmax=color_matrix.max(),
                   edgecolors='black', linewidths=0.5)

# Labels
ax.set_xticks(range(len(clusters)))
ax.set_xticklabels(clusters, rotation=45, ha='right')
ax.set_yticks(range(len(genes)))
ax.set_yticklabels(genes)
ax.set_xlabel('Cluster', fontsize=12)
ax.set_ylabel('Gene', fontsize=12)
ax.set_title('Marker Gene Expression', fontsize=14, fontweight='bold')

# Colorbar
norm = Normalize(vmin=0, vmax=color_matrix.max())
sm = plt.cm.ScalarMappable(cmap='Reds', norm=norm)
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, pad=0.02)
cbar.set_label('Mean Expression', rotation=270, labelpad=15)

plt.tight_layout()
plt.savefig('gene_dotplot.png', dpi=300, bbox_inches='tight')
plt.show()

Volcano Plot (DEG Analysis)

python
# Assuming deg_df has columns: gene, log2FC, pvalue

fig, ax = plt.subplots(figsize=(8, 7))

# Calculate -log10(pvalue)
deg_df['-log10_pvalue'] = -np.log10(deg_df['pvalue'])

# Classify genes
deg_df['significant'] = 'Not Significant'
deg_df.loc[(deg_df['log2FC'] > 1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Up-regulated'
deg_df.loc[(deg_df['log2FC'] < -1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Down-regulated'

# Plot
for category, color in zip(['Not Significant', 'Up-regulated', 'Down-regulated'],
                            ['gray', 'red', 'blue']):
    mask = deg_df['significant'] == category
    ax.scatter(deg_df.loc[mask, 'log2FC'],
               deg_df.loc[mask, '-log10_pvalue'],
               c=color, label=category, s=20, alpha=0.6, edgecolors='none')

# Threshold lines
ax.axvline(x=1, color='black', linestyle='--', linewidth=1, alpha=0.5)
ax.axvline(x=-1, color='black', linestyle='--', linewidth=1, alpha=0.5)
ax.axhline(y=-np.log10(0.05), color='black', linestyle='--', linewidth=1, alpha=0.5)

# Labels
ax.set_xlabel('log2 Fold Change', fontsize=12)
ax.set_ylabel('-log10(p-value)', fontsize=12)
ax.set_title('Volcano Plot: Differential Expression', fontsize=14, fontweight='bold')
ax.legend(frameon=True, loc='upper right')

plt.tight_layout()
plt.savefig('volcano_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Best Practices

Figure Size: Use appropriate dimensions for target medium (papers: 6-8 inches wide, posters: larger)
DPI: Save at 300 DPI for publications, 150 DPI for presentations
Colors: Use colorblind-friendly palettes (e.g., viridis, Set2, tab10)
Fonts: Keep font sizes readable (titles: 12-14pt, labels: 10-12pt, ticks: 8-10pt)
Transparency: Use alpha for overlapping points to show density
Layout: Always call plt.tight_layout() before saving to prevent label clipping
File Format: PNG for general use, SVG for vector graphics (editable in Illustrator)
Close Figures: Call plt.close() after saving to free memory when generating many plots

Troubleshooting

Issue: "Figure too cluttered with many points"

Solution: Use transparency and smaller point sizes

python
ax.scatter(x, y, s=5, alpha=0.3, edgecolors='none')

Issue: "Legend overlaps with data"

Solution: Place legend outside the plot area

python
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

Issue: "Labels are cut off in saved figure"

Solution: Use bbox_inches='tight'

python
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Issue: "Colors don't match between plots"

Solution: Define color palette once and reuse

python
PALETTE = {'Group A': '#E74C3C', 'Group B': '#3498DB'}
# Use PALETTE in all plots

Issue: "Heatmap text too small"

Solution: Adjust figure size or font size

python
fig, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(data, ax=ax, annot_kws={'fontsize': 8})

Technical Notes

Libraries: Uses matplotlib and seaborn (widely supported, stable)
Execution: Runs locally in the agent's sandbox
Compatibility: Works with ALL LLM providers (GPT, Gemini, Claude, DeepSeek, Qwen, etc.)
File Formats: Supports PNG, PDF, SVG, JPEG
Performance: Typical plot generation takes <1 second for standard plots, 2-5 seconds for complex multi-panel figures
Memory: Keep figure count reasonable; close figures after saving if generating many plots

References

Matplotlib documentation: https://matplotlib.org/stable/contents.html
Seaborn documentation: https://seaborn.pydata.org/
Matplotlib gallery: https://matplotlib.org/stable/gallery/index.html
Seaborn gallery: https://seaborn.pydata.org/examples/index.html

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`data-viz-plots`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Data Viz Plots

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Visualization (Universal)

Overview

When to Use This Skill

How to Use

Step 1: Import Required Libraries

Step 2: Basic Scatter Plot

Step 3: Line Plot with Multiple Series

Step 4: Box Plot and Violin Plot

Step 5: Heatmap

Step 6: Bar Plot with Error Bars

Advanced Features

Multi-Panel Figure

Custom Color Palette

Density Plot

Common Use Cases

QC Metrics Visualization

UMAP/tSNE Visualization

Gene Expression Dot Plot

Volcano Plot (DEG Analysis)

Best Practices

Troubleshooting

Issue: "Figure too cluttered with many points"

Issue: "Legend overlaps with data"

Issue: "Labels are cut off in saved figure"

Issue: "Colors don't match between plots"

Issue: "Heatmap text too small"

Technical Notes

References

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Visualization (Universal)

Overview

When to Use This Skill

How to Use

Step 1: Import Required Libraries

Step 2: Basic Scatter Plot

Step 3: Line Plot with Multiple Series

Step 4: Box Plot and Violin Plot

Step 5: Heatmap

Step 6: Bar Plot with Error Bars

Advanced Features

Multi-Panel Figure

Custom Color Palette

Density Plot

Common Use Cases

QC Metrics Visualization

UMAP/tSNE Visualization

Gene Expression Dot Plot

Volcano Plot (DEG Analysis)

Best Practices

Troubleshooting

Issue: "Figure too cluttered with many points"

Issue: "Legend overlaps with data"

Issue: "Labels are cut off in saved figure"

Issue: "Colors don't match between plots"

Issue: "Heatmap text too small"

Technical Notes

References

Frequently asked questions

Related AI Skills

Set up your own AI workspace now