Agent skill
bio-reporting-figure-export
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-reporting-figure-export
SKILL.md
name: bio-reporting-figure-export description: Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses. tool_type: mixed primary_tool: matplotlib measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Publication-Ready Figure Export
Python (matplotlib)
import matplotlib.pyplot as plt
# Set publication defaults
plt.rcParams.update({
'font.size': 8,
'font.family': 'Arial',
'axes.linewidth': 0.5,
'lines.linewidth': 1,
'figure.dpi': 300
})
fig, ax = plt.subplots(figsize=(3.5, 3)) # Single column width
# ... create plot ...
# Save in multiple formats
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)
fig.savefig('figure1.svg', bbox_inches='tight')
R (ggplot2)
library(ggplot2)
p <- ggplot(data, aes(x, y)) + geom_point() +
theme_classic(base_size = 8) +
theme(text = element_text(family = 'Arial'))
# PDF for vector graphics
ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')
# High-res PNG
ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)
# TIFF (some journals require)
ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in',
dpi = 300, compression = 'lzw')
Journal Requirements
| Journal Type | Format | Resolution | Width |
|---|---|---|---|
| Most journals | PDF/EPS | Vector | 3.5" (1-col), 7" (2-col) |
| Online-only | PNG | 300 DPI | Variable |
| TIFF | 300-600 DPI | Column width |
Multi-panel Figures
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(7, 5)) # Two-column width
gs = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, :])
# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
ax.text(-0.1, 1.1, label, transform=ax.transAxes,
fontsize=10, fontweight='bold')
fig.savefig('figure_multipanel.pdf', bbox_inches='tight')
Color Considerations
- Use colorblind-friendly palettes (viridis, cividis)
- Ensure sufficient contrast for grayscale printing
- Maintain consistency across all figures
Related Skills
- data-visualization/ggplot2-fundamentals - Creating plots in R
- data-visualization/heatmaps-clustering - Complex visualizations
- data-visualization/multipanel-figures - Figure composition
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