Imported from agenciaraca/Pco (.agents/skills/scientific-schematics/SKILL.md). Install upstream with npx skills add agenciaraca/Pco --skill scientific-schematics. Copyright stays with the author.

Scientific Schematics and Diagrams

Overview

Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. This skill uses Nano Banana Pro AI for diagram generation with Gemini 3 Pro quality review.

How it works:

Describe your diagram in natural language
Nano Banana Pro generates publication-quality images automatically
Gemini 3 Pro 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 Pro 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 Pro 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 Pro creates initial image following scientific diagram best practices
Review 1: Gemini 3 Pro 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 Pro 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 Pro + Gemini 3 Pro 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 Pro             │
│                    ↓                                │
│  2. Review quality with Gemini 3 Pro                │
│                    ↓                                │
│  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 Pro

Gemini 3 Pro evaluates the diagram on:

Scientific Accuracy (0-2 points) - Correct concepts, notation, relationships
Clarity and Readability (0-2 points) - Easy to understand, clear hierarchy
Label Quality (0-2 points) - Complete, readable, consistent labels
Layout and Composition (0-2 points) - Logical flow, balanced, no overlaps
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:

Extracts specific issues from Gemini 3 Pro's review
Enhances the prompt with improvement instructions
Regenerates with Nano Banana Pro
Reviews again with Gemini 3 Pro
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 Pro 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/diagram_types.md - Catalog of scientific diagram types with examples
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.

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