skills/43-wentorai-research-plugins/skills/tools/diagram/scientific-illustration-guide/SKILL.md
Create graphical abstracts, schematic diagrams, and scientific illustrations
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research scientific-illustration-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A skill for creating clear, professional scientific illustrations including graphical abstracts, schematic diagrams, workflow visualizations, and architecture diagrams. Covers both programmatic and design tool approaches.
Layout Guidelines for Graphical Abstracts:
- Dimensions: typically 500x300px to 1200x800px (check journal spec)
- Flow direction: left-to-right or top-to-bottom
- Maximum 5-7 visual elements
- Use arrows to show process flow
- Include 1-2 key data points or results
- Minimal text (30-50 words maximum)
- Consistent color scheme (3-4 colors)
Structure:
[Input/Problem] --> [Method/Process] --> [Output/Finding]
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
def create_workflow_diagram(steps: list[dict], output: str = 'workflow.pdf'):
"""
Create a horizontal workflow diagram.
Args:
steps: List of dicts with 'label', 'color', and optional 'sublabel'
output: Output file path
"""
fig, ax = plt.subplots(figsize=(12, 3))
n = len(steps)
box_width = 0.12
gap = (1 - n * box_width) / (n + 1)
for i, step in enumerate(steps):
x = gap + i * (box_width + gap)
y = 0.3
# Draw box
box = FancyBboxPatch(
(x, y), box_width, 0.4,
boxstyle="round,pad=0.01",
facecolor=step.get('color', '#3B82F6'),
edgecolor='#1E293B',
linewidth=1.5,
alpha=0.9
)
ax.add_patch(box)
# Add label
ax.text(x + box_width/2, y + 0.2, step['label'],
ha='center', va='center', fontsize=9,
fontweight='bold', color='white')
if 'sublabel' in step:
ax.text(x + box_width/2, y - 0.08, step['sublabel'],
ha='center', va='center', fontsize=7, color='#475569')
# Draw arrow to next step
if i < n - 1:
ax.annotate('', xy=(x + box_width + gap*0.2, 0.5),
xytext=(x + box_width + gap*0.8, 0.5),
arrowprops=dict(arrowstyle='->', color='#64748B',
lw=1.5))
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.axis('off')
fig.savefig(output, dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
plt.close()
return output
# Example: research pipeline
steps = [
{'label': 'Data\nCollection', 'color': '#3B82F6', 'sublabel': 'N=1,200'},
{'label': 'Preprocessing', 'color': '#6366F1', 'sublabel': 'QC + Filtering'},
{'label': 'Analysis', 'color': '#8B5CF6', 'sublabel': 'ML Pipeline'},
{'label': 'Validation', 'color': '#A855F7', 'sublabel': 'Cross-validation'},
{'label': 'Results', 'color': '#EC4899', 'sublabel': 'AUC = 0.92'}
]
create_workflow_diagram(steps, 'research_pipeline.pdf')
import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
def create_architecture_diagram(components: list[dict],
connections: list[tuple],
output: str = 'architecture.pdf'):
"""
Create a system architecture diagram.
Args:
components: List of {'name', 'x', 'y', 'width', 'height', 'color', 'type'}
connections: List of (source_name, target_name, label) tuples
"""
fig, ax = plt.subplots(figsize=(10, 7))
# Draw components
comp_positions = {}
for comp in components:
x, y = comp['x'], comp['y']
w, h = comp.get('width', 1.5), comp.get('height', 0.8)
color = comp.get('color', '#3B82F6')
if comp.get('type') == 'database':
# Cylinder shape for databases
ellipse_h = 0.15
ax.add_patch(patches.Rectangle((x, y), w, h-ellipse_h,
facecolor=color, edgecolor='#1E293B', linewidth=1.2))
ax.add_patch(patches.Ellipse((x+w/2, y+h-ellipse_h), w, ellipse_h*2,
facecolor=color, edgecolor='#1E293B', linewidth=1.2))
ax.add_patch(patches.Ellipse((x+w/2, y), w, ellipse_h*2,
facecolor=color, edgecolor='#1E293B', linewidth=1.2))
else:
box = FancyBboxPatch((x, y), w, h,
boxstyle="round,pad=0.05",
facecolor=color, edgecolor='#1E293B',
linewidth=1.2, alpha=0.9)
ax.add_patch(box)
ax.text(x + w/2, y + h/2, comp['name'],
ha='center', va='center', fontsize=10,
fontweight='bold', color='white')
comp_positions[comp['name']] = (x + w/2, y + h/2, w, h)
# Draw connections
for src, tgt, label in connections:
sx, sy, sw, sh = comp_positions[src]
tx, ty, tw, th = comp_positions[tgt]
ax.annotate('', xy=(tx, ty + th/2 if sy > ty else ty - th/2),
xytext=(sx, sy - sh/2 if sy > ty else sy + sh/2),
arrowprops=dict(arrowstyle='->', color='#64748B',
lw=1.5, connectionstyle='arc3,rad=0.1'))
mid_x = (sx + tx) / 2
mid_y = (sy + ty) / 2
if label:
ax.text(mid_x + 0.1, mid_y, label, fontsize=7, color='#475569')
ax.set_xlim(-0.5, 10)
ax.set_ylim(-0.5, 8)
ax.set_aspect('equal')
ax.axis('off')
fig.savefig(output, dpi=300, bbox_inches='tight',
facecolor='white')
plt.close()
For complex diagrams, use draw.io (diagrams.net) which exports to SVG, PDF, and PNG:
<!-- Example draw.io XML structure -->
<mxGraphModel>
<root>
<mxCell id="0"/>
<mxCell id="1" parent="0"/>
<mxCell id="2" value="Data Source" style="rounded=1;fillColor=#3B82F6;
fontColor=#FFFFFF;strokeColor=#1E40AF;" vertex="1" parent="1">
<mxGeometry x="80" y="80" width="120" height="60" as="geometry"/>
</mxCell>
</root>
</mxGraphModel>
| Diagram Type | Best Tool | Output Format | Learning Curve | |-------------|-----------|---------------|----------------| | Flowcharts | draw.io / Mermaid | SVG, PDF | Low | | Molecular structures | ChemDraw / RDKit | SVG, PNG | Medium | | Biological pathways | BioRender / KEGG | PNG, SVG | Low | | Network graphs | Cytoscape / NetworkX | SVG, PDF | Medium | | 3D protein structures | PyMOL / ChimeraX | PNG, TIFF | High | | Circuit diagrams | KiCad / CircuiTikZ | PDF, SVG | Medium | | Math diagrams | TikZ/PGF | PDF | High | | UML diagrams | PlantUML / Mermaid | SVG, PNG | Low |
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