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 |
Use colorblind-safe palettes consistently. The key rules:
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".