scientific-schematics

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.Weekly Installs340Repositorydavila7/claude-…emplatesGitHub Stars23.0KFirst SeenJan 21, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykFailInstalled onopencode284gemini-cli271claude-code253codex252cursor242github-copilot231