Scientific Schematics and Diagrams Skill

Scientific Schematics and Diagrams

Overview

Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.6 Flash quality review.

How it works:

Describe your diagram in natural language
Nano Banana 2 generates publication-quality images automatically
Gemini 3.6 Flash reviews quality against document-type thresholds
Smart iteration: Only regenerates if quality is below threshold
Publication-ready output in minutes
No coding, templates, or manual drawing required

Quality Thresholds by Document Type: | Document Type | Threshold | Description | |---------------|-----------|-------------| | journal | 8.5/10 | Nature, Science, peer-reviewed journals | | conference | 8.0/10 | Conference papers | | thesis | 8.0/10 | Dissertations, theses | | grant | 8.0/10 | Grant proposals | | preprint | 7.5/10 | arXiv, bioRxiv, etc. | | report | 7.5/10 | Technical reports | | poster | 7.0/10 | Academic posters | | presentation | 6.5/10 | Slides, talks | | default | 7.5/10 | General purpose |

Simply describe what you want, and Nano Banana 2 creates it. All diagrams are stored in the figures/ subfolder and referenced in papers/posters.

Quick Start: Generate Any Diagram

Create any scientific diagram by simply describing it. Nano Banana 2 handles everything automatically with smart iteration:

# Generate for journal paper (highest quality threshold: 8.5/10)
python scripts/generate_schematic.py "CONSORT participant flow diagram with 500 screened, 150 excluded, 350 randomized" -o figures/consort.png --doc-type journal

# Generate for presentation (lower threshold: 6.5/10 - faster)
python scripts/generate_schematic.py "Transformer encoder-decoder architecture showing multi-head attention" -o figures/transformer.png --doc-type presentation

# Generate for poster (moderate threshold: 7.0/10)
python scripts/generate_schematic.py "MAPK signaling pathway from EGFR to gene transcription" -o figures/mapk_pathway.png --doc-type poster

# Custom max iterations (max 2)
python scripts/generate_schematic.py "Complex circuit diagram with op-amp, resistors, and capacitors" -o figures/circuit.png --iterations 2 --doc-type journal

What happens behind the scenes:

Generation 1: Nano Banana 2 creates initial image following scientific diagram best practices
Review 1: Gemini 3.6 Flash evaluates quality against document-type threshold
Decision: If quality >= threshold → DONE (no more iterations needed!)
If below threshold: Improved prompt based on critique, regenerate
Repeat: Until quality meets threshold OR max iterations reached

Smart Iteration Benefits:

✅ Saves API calls if first generation is good enough
✅ Higher quality standards for journal papers
✅ Faster turnaround for presentations/posters
✅ Appropriate quality for each use case

Output: Versioned images plus a detailed review log with quality scores, critiques, and early-stop information.

Configuration

Set your OpenRouter API key:

export OPENROUTER_API_KEY='your_api_key_here'

Get an API key at: https://openrouter.ai/keys

AI Generation Best Practices

Effective Prompts for Scientific Diagrams:

✓ Good prompts (specific, detailed):

"CONSORT flowchart showing participant flow from screening (n=500) through randomization to final analysis"
"Transformer neural network architecture with encoder stack on left, decoder stack on right, showing multi-head attention and cross-attention connections"
"Biological signaling cascade: EGFR receptor → RAS → RAF → MEK → ERK → nucleus, with phosphorylation steps labeled"
"Block diagram of IoT system: sensors → microcontroller → WiFi module → cloud server → mobile app"

✗ Avoid vague prompts:

"Make a flowchart" (too generic)
"Neural network" (which type? what components?)
"Pathway diagram" (which pathway? what molecules?)

Key elements to include:

Type: Flowchart, architecture diagram, pathway, circuit, etc.
Components: Specific elements to include
Flow/Direction: How elements connect (left-to-right, top-to-bottom)
Labels: Key annotations or text to include
Style: Any specific visual requirements

Scientific Quality Guidelines (automatically applied):

Clean white/light background
High contrast for readability
Clear, readable labels (minimum 10pt)
Professional typography (sans-serif fonts)
Colorblind-friendly colors (Okabe-Ito palette)
Proper spacing to prevent crowding
Scale bars, legends, axes where appropriate

When to Use This Skill

This skill should be used when:

Creating neural network architecture diagrams (Transformers, CNNs, RNNs, etc.)
Illustrating system architectures and data flow diagrams
Drawing methodology flowcharts for study design (CONSORT, PRISMA)
Visualizing algorithm workflows and processing pipelines
Creating circuit diagrams and electrical schematics
Depicting biological pathways and molecular interactions
Generating network topologies and hierarchical structures
Illustrating conceptual frameworks and theoretical models
Designing block diagrams for technical papers

How to Use This Skill

Simply describe your diagram in natural language. Nano Banana 2 generates it automatically:

python scripts/generate_schematic.py "your diagram description" -o output.png

That's it! The AI handles:

✓ Layout and composition
✓ Labels and annotations
✓ Colors and styling
✓ Quality review and refinement
✓ Publication-ready output

Works for all diagram types:

Flowcharts (CONSORT, PRISMA, etc.)
Neural network architectures
Biological pathways
Circuit diagrams
System architectures
Block diagrams
Any scientific visualization

No coding, no templates, no manual drawing required.

AI Generation Mode (Nano Banana 2 + Gemini 3.6 Flash Review)

Smart Iterative Refinement, Advanced Usage, and Examples

The generate-review-refine loop, the Python API and command-line options, prompt engineering guidance, and four worked examples (CONSORT flowchart, neural network architecture, biological pathway, system architecture) are in references/iterative_refinement.md.

The loop stops as soon as the review passes, so a simple diagram usually costs one iteration; only complex figures use the full budget.

Command-Line Usage

The main entry point for generating scientific schematics:

# Basic usage
python scripts/generate_schematic.py "diagram description" -o output.png

# Custom iterations (max 2)
python scripts/generate_schematic.py "complex diagram" -o diagram.png --iterations 2

# Verbose mode
python scripts/generate_schematic.py "diagram" -o out.png -v

Note: The Nano Banana 2 AI generation system includes automatic quality review in its iterative refinement process. Each iteration is evaluated for scientific accuracy, clarity, and accessibility.

Best Practices Summary

Design Principles

Clarity over complexity - Simplify, remove unnecessary elements
Consistent styling - Use templates and style files
Colorblind accessibility - Use Okabe-Ito palette, redundant encoding
Appropriate typography - Sans-serif fonts, minimum 7-8 pt
Vector format - Always use PDF/SVG for publication

Technical Requirements

Resolution - Vector preferred, or 300+ DPI for raster
File format - PDF for LaTeX, SVG for web, PNG as fallback
Color space - RGB for digital, CMYK for print (convert if needed)
Line weights - Minimum 0.5 pt, typical 1-2 pt
Text size - 7-8 pt minimum at final size

Integration Guidelines

Include in LaTeX - Use \includegraphics{} for generated images
Caption thoroughly - Describe all elements and abbreviations
Reference in text - Explain diagram in narrative flow
Maintain consistency - Same style across all figures in paper
Version control - Keep prompts and generated images in repository

Troubleshooting Common Issues

AI Generation Issues

Problem: Overlapping text or elements

Solution: AI generation automatically handles spacing
Solution: Increase iterations: --iterations 2 for better refinement

Problem: Elements not connecting properly

Solution: Make your prompt more specific about connections and layout
Solution: Increase iterations for better refinement

Image Quality Issues

Problem: Export quality poor

Solution: AI generation produces high-quality images automatically
Solution: Increase iterations for better results: --iterations 2

Problem: Elements overlap after generation

Solution: AI generation automatically handles spacing
Solution: Increase iterations: --iterations 2 for better refinement
Solution: Make your prompt more specific about layout and spacing requirements

Quality Check Issues

Problem: False positive overlap detection

Solution: Adjust threshold: detect_overlaps(image_path, threshold=0.98)
Solution: Manually review flagged regions in visual report

Problem: Generated image quality is low

Solution: AI generation produces high-quality images by default
Solution: Increase iterations for better results: --iterations 2

Problem: Colorblind simulation shows poor contrast

Solution: Switch to Okabe-Ito palette explicitly in code
Solution: Add redundant encoding (shapes, patterns, line styles)
Solution: Increase color saturation and lightness differences

Problem: High-severity overlaps detected

Solution: Review overlap_report.json for exact positions
Solution: Increase spacing in those specific regions
Solution: Re-run with adjusted parameters and verify again

Problem: Visual report generation fails

Solution: Check Pillow and matplotlib installations
Solution: Ensure image file is readable: Image.open(path).verify()
Solution: Check sufficient disk space for report generation

Accessibility Problems

Problem: Colors indistinguishable in grayscale

Solution: Run accessibility checker: verify_accessibility(image_path)
Solution: Add patterns, shapes, or line styles for redundancy
Solution: Increase contrast between adjacent elements

Problem: Text too small when printed

Solution: Run resolution validator: validate_resolution(image_path)
Solution: Design at final size, use minimum 7-8 pt fonts
Solution: Check physical dimensions in resolution report

Problem: Accessibility checks consistently fail

Solution: Review accessibility_report.json for specific failures
Solution: Increase color contrast by at least 20%
Solution: Test with actual grayscale conversion before finalizing

Resources and References

Detailed References

Load these files for comprehensive information on specific topics:

references/best_practices.md - Publication standards and accessibility guidelines

External Resources

Python Libraries

Schemdraw Documentation: https://schemdraw.readthedocs.io/
NetworkX Documentation: https://networkx.org/documentation/
Matplotlib Documentation: https://matplotlib.org/

Publication Standards

Nature Figure Guidelines: https://www.nature.com/nature/for-authors/final-submission
Science Figure Guidelines: https://www.science.org/content/page/instructions-preparing-initial-manuscript
CONSORT Diagram: http://www.consort-statement.org/consort-statement/flow-diagram

Integration with Other Skills

This skill works synergistically with:

Scientific Writing - Diagrams follow figure best practices
Scientific Visualization - Shares color palettes and styling
LaTeX Posters - Generate diagrams for poster presentations
Research Grants - Methodology diagrams for proposals
Peer Review - Evaluate diagram clarity and accessibility

Quick Reference Checklist

Before submitting diagrams, verify:

Visual Quality

[ ] High-quality image format (PNG from AI generation)
[ ] No overlapping elements (AI handles automatically)
[ ] Adequate spacing between all components (AI optimizes)
[ ] Clean, professional alignment
[ ] All arrows connect properly to intended targets

Accessibility

[ ] Colorblind-safe palette (Okabe-Ito) used
[ ] Works in grayscale (tested with accessibility checker)
[ ] Sufficient contrast between elements (verified)
[ ] Redundant encoding where appropriate (shapes + colors)
[ ] Colorblind simulation passes all checks

Typography and Readability

[ ] Text minimum 7-8 pt at final size
[ ] All elements labeled clearly and completely
[ ] Consistent font family and sizing
[ ] No text overlaps or cutoffs
[ ] Units included where applicable

Publication Standards

[ ] Consistent styling with other figures in manuscript
[ ] Comprehensive caption written with all abbreviations defined
[ ] Referenced appropriately in manuscript text
[ ] Meets journal-specific dimension requirements
[ ] Exported in required format for journal (PDF/EPS/TIFF)

Quality Verification (Required)

[ ] Ran run_quality_checks() and achieved PASS status
[ ] Reviewed overlap detection report (zero high-severity overlaps)
[ ] Passed accessibility verification (grayscale and colorblind)
[ ] Resolution validated at target DPI (300+ for print)
[ ] Visual quality report generated and reviewed
[ ] All quality reports saved with figure files

Documentation and Version Control

[ ] Source files (.tex, .py) saved for future revision
[ ] Quality reports archived in quality_reports/ directory
[ ] Configuration parameters documented (colors, spacing, sizes)
[ ] Git commit includes source, output, and quality reports
[ ] README or comments explain how to regenerate figure

Final Integration Check

[ ] Figure displays correctly in compiled manuscript
[ ] Cross-references work (\ref{} points to correct figure)
[ ] Figure number matches text citations
[ ] Caption appears on correct page relative to figure
[ ] No compilation warnings or errors related to figure

Environment Setup

# Required
export OPENROUTER_API_KEY='your_api_key_here'

# Get key at: https://openrouter.ai/keys

Getting Started

Simplest possible usage:

python scripts/generate_schematic.py "your diagram description" -o output.png

Use this skill to create clear, accessible, publication-quality diagrams that effectively communicate complex scientific concepts. The AI-powered workflow with iterative refinement ensures diagrams meet professional standards.

Agent Skills: Scientific Schematics and Diagrams

Install this agent skill to your local

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