Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological p

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 : What happens behind the scenes: 1. Generation 1 : Nano Banana Pro creates initial image following scientific diagram best practices 2. Review 1 : Gemini 3 Pro 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: 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: 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 Iteration 1: Initial Generation Prompt Construction: Output: diagram v1.png Quality Review by Gemini 3 Pro Gemini 3 Pro 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: 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 Pro's review 2. Enhances the prompt with improvement instructions 3. Regenerates with Nano Banana Pro 4. Reviews again with Gemini 3 Pro 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: 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 Command Line Options Prompt Engineering Tips 1. Be Specific About Layout: 2. Include Quantitative Details: 3. Specify Visual Style: 4. Request Specific Labels: 5. Mention Color Requirements: AI Generation Examples Example 1: CONSORT Flowchart Example 2: Neural Network Architecture Example 3: Biological Pathway Example 4: System Architecture Command Line Usage The main entry point for generating scientific schematics: 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 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/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 Diagra

scientific-schematics