--- id: sm-scientific-schematics name: "scientific-schematics" url: https://skills.yangsir.net/skill/sm-scientific-schematics author: davila7 domain: science tags: ["scientific-illustration", "diagramming-tools", "technical-drawing", "data-visualization", "research-communication"] install_count: 701 rating: 4.20 (22 reviews) github: https://github.com/davila7/claude-code-templates --- # scientific-schematics > 将复杂的科学概念转化为清晰的视觉图表和示意图，用于出版和交流，提升表达效果，简化理解。 **Stats**: 701 installs · 4.2/5 (22 reviews) ## Before / After 对比 ### 科学示意图与图表自动化生成 | Metric | Before | After | Change | |---|---|---|---| | - | - | - | - | | - | - | - | - | | - | - | - | - | ## Readme # 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](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/](https://schemdraw.readthedocs.io/) - NetworkX Documentation: [https://networkx.org/documentation/](https://networkx.org/documentation/) - Matplotlib Documentation: [https://matplotlib.org/](https://matplotlib.org/) **Publication Standards** - Nature Figure Guidelines: [https://www.nature.com/nature/for-authors/final-submission](https://www.nature.com/nature/for-authors/final-submission) - Science Figure Guidelines: [https://www.science.org/content/page/instructions-preparing-initial-manuscript](https://www.science.org/content/page/instructions-preparing-initial-manuscript) - CONSORT Diagram: [http://www.consort-statement.org/consort-statement/flow-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 Installs352Repository[davila7/claude-…emplates](https://github.com/davila7/claude-code-templates)GitHub Stars23.1KFirst SeenJan 21, 2026Security Audits[Gen Agent Trust HubPass](/davila7/claude-code-templates/scientific-schematics/security/agent-trust-hub)[SocketPass](/davila7/claude-code-templates/scientific-schematics/security/socket)[SnykFail](/davila7/claude-code-templates/scientific-schematics/security/snyk)Installed onopencode295gemini-cli282codex263claude-code257cursor253github-copilot242 --- *Source: https://skills.yangsir.net/skill/sm-scientific-schematics* *Markdown mirror: https://skills.yangsir.net/api/skill/sm-scientific-schematics/markdown*