Skip to the rows

agentsclimarketplace

Scientific figures

Skill rhowardstone/Claude-Code-Scientist/.claude/skills/scientific-figures

Transform Claude Code into a semi-autonomous, self-improving scientific researcher. Literature review, data acquisition, experimentation, synthesis, peer review

Install

npx -y skills add rhowardstone/Claude-Code-Scientist --skill scientific-figures

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Generate publication-quality scientific figures with consistent styling. Use when experiments need visualization, benchmarking results need plots, or papers need figures.

SKILL.md

4.7 KB, as published. Nobody here has run it

Scientific Figures Skill

Generate publication-quality visualizations for experimental results.

When to Use

After running experiments that produce numerical results
When synthesizing papers that need figures
When benchmarking tools and need ROC/PR curves, bar plots, etc.
When visualizing data distributions (violin plots, histograms)
When creating graphical abstracts

Output Requirements

ALWAYS generate both formats:

PNG at 300 DPI (for web/preview)
PDF vector (for publication)

ALWAYS include:

Clear axis labels with units
Legends when multiple series
Panel labels (A, B, C, D) for multi-panel figures
Figure titles
Consistent color scheme

Standard Figure Types

1. Performance Metrics (Benchmarking)

import matplotlib.pyplot as plt
import seaborn as sns

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

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

# Panel A: ROC curves
axes[0].set_title('A. ROC Curves', fontsize=12, fontweight='bold')
axes[0].set_xlabel('False Positive Rate')
axes[0].set_ylabel('True Positive Rate')
axes[0].plot([0, 1], [0, 1], 'k--', label='Random')

# Panel B: Bar comparison
axes[1].set_title('B. Performance Comparison', fontsize=12, fontweight='bold')

plt.tight_layout()
plt.savefig('figure.png', dpi=300, bbox_inches='tight')
plt.savefig('figure.pdf', bbox_inches='tight')

2. Distribution Plots (QC, Score Distributions)

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

for ax, (data, title) in zip(axes, [(d1, 'Metric A'), (d2, 'Metric B'), (d3, 'Metric C')]):
    sns.violinplot(data=data, ax=ax)
    ax.set_title(title)

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

3. Confusion Matrices

from sklearn.metrics import confusion_matrix
import numpy as np

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

for ax, (cm, title) in zip(axes, [(cm1, 'A. Method 1'), (cm2, 'B. Method 2')]):
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,
                xticklabels=['Pred Neg', 'Pred Pos'],
                yticklabels=['True Neg', 'True Pos'])
    ax.set_title(title, fontsize=12, fontweight='bold')

plt.tight_layout()

4. UMAP/Scatter Plots

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

scatter1 = axes[0].scatter(umap[:, 0], umap[:, 1], c=labels, cmap='tab20', s=5, alpha=0.7)
axes[0].set_title('A. Cell Types')
axes[0].legend(*scatter1.legend_elements(), loc='best', markerscale=2)

scatter2 = axes[1].scatter(umap[:, 0], umap[:, 1], c=scores, cmap='coolwarm', s=5, alpha=0.7)
axes[1].set_title('B. Doublet Scores')
plt.colorbar(scatter2, ax=axes[1])

5. Graphical Abstract

For synthesis papers, create a workflow diagram:

# Use matplotlib patches and arrows for simple workflow diagrams
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch

fig, ax = plt.subplots(figsize=(12, 6))
ax.set_xlim(0, 12)
ax.set_ylim(0, 6)
ax.axis('off')

# Add boxes for each step
steps = [
    (1, 3, 'Input\nData'),
    (4, 3, 'Method A'),
    (7, 3, 'Method B'),
    (10, 3, 'Results'),
]

for x, y, text in steps:
    box = FancyBboxPatch((x-0.8, y-0.5), 1.6, 1,
                         boxstyle="round,pad=0.1", facecolor='lightblue',
                         edgecolor='black', linewidth=2)
    ax.add_patch(box)
    ax.text(x, y, text, ha='center', va='center', fontsize=10, fontweight='bold')

Color Schemes

Default categorical: sns.color_palette("husl", n_colors) Sequential: "Blues", "Greens", "Oranges" Diverging: "coolwarm", "RdBu_r"

Figure Naming Convention

figures/
├── 01_qc_metrics.png
├── 01_qc_metrics.pdf
├── 02_method_comparison.png
├── 02_method_comparison.pdf
├── 03_roc_pr_curves.png
├── 03_roc_pr_curves.pdf
├── graphical_abstract.png
└── graphical_abstract.pdf

Checklist Before Saving

Integration with Papers

When writing LaTeX papers, reference figures:

\begin{figure}[htbp]
\centering
\includegraphics[width=\textwidth]{figures/01_qc_metrics.pdf}
\caption{\textbf{Quality Control Metrics.} (A) UMI counts per cell...}
\label{fig:qc}
\end{figure}

K-Dense produced 8 beautiful figures. We must match or exceed that quality.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.