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 3.1 Pro Preview quality review.

How it works:

• Describe your diagram in natural language
• Nano Banana 2 generates publication-quality images automatically
• Gemini 3.1 Pro Preview 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:

1. Generation 1: Nano Banana 2 creates initial image following scientific diagram best practices
2. Review 1: Gemini 3.1 Pro Preview evaluates quality against document-type threshold
3. Decision: If quality >= threshold → DONE (no more iterations needed!)
4. If below threshold: Improved prompt based on critique, regenerate
5. 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.

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AI Generation Mode (Nano Banana 2 + Gemini 3.1 Pro Preview Review)

Smart Iterative Refinement Workflow

The AI generation system uses smart iteration - it only regenerates if quality is below the threshold for your document type:

How Smart Iteration Works

┌─────────────────────────────────────────────────────┐
│  1. Generate image with Nano Banana 2             │
│                    ↓                                │
│  2. Review quality with Gemini 3.1 Pro Preview                │
│                    ↓                                │
│  3. Score >= threshold?                             │
│       YES → DONE! (early stop)                      │
│       NO  → Improve prompt, go to step 1            │
│                    ↓                                │
│  4. Repeat until quality met OR max iterations      │
└─────────────────────────────────────────────────────┘

Iteration 1: Initial Generation

Prompt Construction:

Scientific diagram guidelines + User request

Output: diagram_v1.png

Quality Review by Gemini 3.1 Pro Preview

Gemini 3.1 Pro Preview evaluates the diagram on:

1. Scientific Accuracy (0-2 points) - Correct concepts, notation, relationships
2. Clarity and Readability (0-2 points) - Easy to understand, clear hierarchy
3. Label Quality (0-2 points) - Complete, readable, consistent labels
4. Layout and Composition (0-2 points) - Logical flow, balanced, no overlaps
5. Professional Appearance (0-2 points) - Publication-ready quality

Example Review Output:

SCORE: 8.0

STRENGTHS:
- Clear flow from top to bottom
- All phases properly labeled
- Professional typography

ISSUES:
- Participant counts slightly small
- Minor overlap on exclusion box

VERDICT: ACCEPTABLE (for poster, threshold 7.0)

Decision Point: Continue or Stop?

If Score...	Action
>= threshold	STOP - Quality is good enough for this document type
< threshold	Continue to next iteration with improved prompt

Example:

• For a poster (threshold 7.0): Score of 7.5 → DONE after 1 iteration!
• For a journal (threshold 8.5): Score of 7.5 → Continue improving

Subsequent Iterations (Only If Needed)

If quality is below threshold, the system:

1. Extracts specific issues from Gemini 3.1 Pro Preview's review
2. Enhances the prompt with improvement instructions
3. Regenerates with Nano Banana 2
4. Reviews again with Gemini 3.1 Pro Preview
5. Repeats until threshold met or max iterations reached

Review Log

All iterations are saved with a JSON review log that includes early-stop information:

{
  "user_prompt": "CONSORT participant flow diagram...",
  "doc_type": "poster",
  "quality_threshold": 7.0,
  "iterations": [
    {
      "iteration": 1,
      "image_path": "figures/consort_v1.png",
      "score": 7.5,
      "needs_improvement": false,
      "critique": "SCORE: 7.5\nSTRENGTHS:..."
    }
  ],
  "final_score": 7.5,
  "early_stop": true,
  "early_stop_reason": "Quality score 7.5 meets threshold 7.0 for poster"
}

Note: With smart iteration, you may see only 1 iteration instead of the full 2 if quality is achieved early!

Advanced AI Generation Usage

Python API

from scripts.generate_schematic_ai import ScientificSchematicGenerator

# Initialize generator
generator = ScientificSchematicGenerator(
    api_key="your_openrouter_key",
    verbose=True
)

# Generate with iterative refinement (max 2 iterations)
results = generator.generate_iterative(
    user_prompt="Transformer architecture diagram",
    output_path="figures/transformer.png",
    iterations=2
)

# Access results
print(f"Final score: {results['final_score']}/10")
print(f"Final image: {results['final_image']}")

# Review individual iterations
for iteration in results['iterations']:
    print(f"Iteration {iteration['iteration']}: {iteration['score']}/10")
    print(f"Critique: {iteration['critique']}")

Command-Line Options

# Basic usage (default threshold 7.5/10)
python scripts/generate_schematic.py "diagram description" -o output.png

# Specify document type for appropriate quality threshold
python scripts/generate_schematic.py "diagram" -o out.png --doc-type journal      # 8.5/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type conference   # 8.0/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type poster       # 7.0/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type presentation # 6.5/10

# Custom max iterations (1-2)
python scripts/generate_schematic.py "complex diagram" -o diagram.png --iterations 2

# Verbose output (see all API calls and reviews)
python scripts/generate_schematic.py "flowchart" -o flow.png -v

# Provide API key via flag
python scripts/generate_schematic.py "diagram" -o out.png --api-key "sk-or-v1-..."

# Combine options
python scripts/generate_schematic.py "neural network" -o nn.png --doc-type journal --iterations 2 -v

Prompt Engineering Tips

1. Be Specific About Layout:

✓ "Flowchart with vertical flow, top to bottom"
✓ "Architecture diagram with encoder on left, decoder on right"
✓ "Circular pathway diagram with clockwise flow"

2. Include Quantitative Details:

✓ "Neural network with input layer (784 nodes), hidden layer (128 nodes), output (10 nodes)"
✓ "Flowchart showing n=500 screened, n=150 excluded, n=350 randomized"
✓ "Circuit with 1kΩ resistor, 10µF capacitor, 5V source"

3. Specify Visual Style:

✓ "Minimalist block diagram with clean lines"
✓ "Detailed biological pathway with protein structures"
✓ "Technical schematic with engineering notation"

4. Request Specific Labels:

✓ "Label all arrows with activation/inhibition"
✓ "Include layer dimensions in each box"
✓ "Show time progression with timestamps"

5. Mention Color Requirements:

✓ "Use colorblind-friendly colors"
✓ "Grayscale-compatible design"
✓ "Color-code by function: blue for input, green for processing, red for output"

AI Generation Examples

Example 1: CONSORT Flowchart

python scripts/generate_schematic.py \
  "CONSORT participant flow diagram for randomized controlled trial. \
   Start with 'Assessed for eligibility (n=500)' at top. \
   Show 'Excluded (n=150)' with reasons: age<18 (n=80), declined (n=50), other (n=20). \
   Then 'Randomized (n=350)' splits into two arms: \
   'Treatment group (n=175)' and 'Control group (n=175)'. \
   Each arm shows 'Lost to follow-up' (n=15 and n=10). \
   End with 'Analyzed' (n=160 and n=165). \
   Use blue boxes for process steps, orange for exclusion, green for final analysis." \
  -o figures/consort.png

Example 2: Neural Network Architecture

python scripts/generate_schematic.py \
  "Transformer encoder-decoder architecture diagram. \
   Left side: Encoder stack with input embedding, positional encoding, \
   multi-head self-attention, add & norm, feed-forward, add & norm. \
   Right side: Decoder stack with output embedding, positional encoding, \
   masked self-attention, add & norm, cross-attention (receiving from encoder), \
   add & norm, feed-forward, add & norm, linear & softmax. \
   Show cross-attention connection from encoder to decoder with dashed line. \
   Use light blue for encoder, light red for decoder. \
   Label all components clearly." \
  -o figures/transformer.png --iterations 2

Example 3: Biological Pathway

python scripts/generate_schematic.py \
  "MAPK signaling pathway diagram. \
   Start with EGFR receptor at cell membrane (top). \
   Arrow down to RAS (with GTP label). \
   Arrow to RAF kinase. \
   Arrow to MEK kinase. \
   Arrow to ERK kinase. \
   Final arrow to nucleus showing gene transcription. \
   Label each arrow with 'phosphorylation' or 'activation'. \
   Use rounded rectangles for proteins, different colors for each. \
   Include membrane boundary line at top." \
  -o figures/mapk_pathway.png

Example 4: System Architecture

python scripts/generate_schematic.py \
  "IoT system architecture block diagram. \
   Bottom layer: Sensors (temperature, humidity, motion) in green boxes. \
   Middle layer: Microcontroller (ESP32) in blue box. \
   Connections to WiFi module (orange box) and Display (purple box). \
   Top layer: Cloud server (gray box) connected to mobile app (light blue box). \
   Show data flow arrows between all components. \
   Label connections with protocols: I2C, UART, WiFi, HTTPS." \
  -o figures/iot_architecture.png

---

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

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

Technical Requirements

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

Integration Guidelines

1. Include in LaTeX - Use \includegraphics{} for generated images
2. Caption thoroughly - Describe all elements and abbreviations
3. Reference in text - Explain diagram in narrative flow
4. Maintain consistency - Same style across all figures in paper
5. 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

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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.