| 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:
python scripts/generate_schematic.py "CONSORT participant flow diagram with 500 screened, 150 excluded, 350 randomized" -o figures/consort.png --doc-type journal
python scripts/generate_schematic.py "Transformer encoder-decoder architecture showing multi-head attention" -o figures/transformer.png --doc-type presentation
python scripts/generate_schematic.py "MAPK signaling pathway from EGFR to gene transcription" -o figures/mapk_pathway.png --doc-type poster
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:
python scripts/generate_schematic.py "diagram description" -o output.png
python scripts/generate_schematic.py "complex diagram" -o diagram.png --iterations 2
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
Publication Standards
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
Accessibility
Typography and Readability
Publication Standards
Quality Verification (Required)
Documentation and Version Control
Final Integration Check
Environment Setup
export OPENROUTER_API_KEY='your_api_key_here'
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.
