scientific-schematics

name	scientific-schematics
description	Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
allowed-tools	Read Write Edit Bash
license	MIT license
metadata	{"version":"1.4","skill-author":"K-Dense Inc.","openclaw":{"primaryEnv":"OPENROUTER_API_KEY","envVars":[{"name":"OPENROUTER_API_KEY","required":false,"description":"OpenRouter API key for the skill's LLM-powered steps."}]}}

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:

1. Generation 1: Nano Banana 2 creates initial image following scientific diagram best practices
2. Review 1: Gemini 3.6 Flash 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 3.6 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

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.