---
title: "Scientific Schematics and Diagrams"
description: "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 3.1 Pro Preview quality review.**"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/skill-328
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:44:32.822Z
license: CC-BY-4.0
attribution: "Scientific Schematics and Diagrams — Claudary (https://claudary.paisolsolutions.com/skills/skill-328)"
---

# Scientific Schematics and Diagrams
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 3.1 Pro Preview quality review.**

## Overview

---
name: scientific-schematics
description: Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview 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:
    skill-author: K-Dense Inc.
---

# 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 3.1 Pro Preview quality review.**

**How it works:**
- Describe your diagram in natural language
- Nano Banana 2 generates publication-quality images automatically
- **Gemini 3.1 Pro Preview 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**:

```bash
# 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 3.1 Pro Preview** 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:
```bash
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:

```bash
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 3.1 Pro Preview 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 2             │
│                    ↓                                │
│  2. Review quality with Gemini 3.1 Pro Preview                │
│                    ↓                                │
│  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 3.1 Pro Preview

Gemini 3.1 Pro Preview 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:**
```
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:
1. Extracts specific issues from Gemini 3.1 Pro Preview's review
2. Enhances the prompt with improvement instructions
3. Regenerates with Nano Banana 2
4. Reviews again with Gemini 3.1 Pro Preview
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:
```json
{
  "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

```python
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

```bash
# 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
```bash
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
```bash
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
```bash
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
```bash
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:

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/skill-328) · https://claudary.paisolsolutions.com