/scientific-schematics

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: "1.7"
  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.

**What the output is:** a raster PNG at whatever resolution the image model returns. This skill has no vector path and no DPI control — if a journal demands PDF, EPS, or 300 dpi TIFF, convert the PNG downstream and check the result at final print size.

Quick Start: Generate Any Diagram

Create any scientific diagram by simply describing it. Nano Banana 2 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:** 1. **Generation 1**: Nano Banana 2 creates initial image following scientific diagram best practices 2. **Review 1**: **Gemini 3.6 Flash** 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 (`name_v1.png`, `name_v2.png`), a copy of the winner at the path you asked for, and `name_review_log.json` with the score, critique, and early-stop reason per iteration.

**When the review cannot run** — a rate limit, a content filter, a reviewer that answers in some unexpected shape — the image is still generated and saved, but no score is invented for it. The log records `"score": null` and `"reviewed": false` with the reason in `"review_error"`, and the run prints `Review unavailable — image kept, quality not verified`. Treat that image as unchecked and look at it yourself; re-running is worth a try, since the failure is usually transient.

Configuration

Set your OpenRouter API key:

export OPENROUTER_API_KEY='your_api_key_here'

Get an API key at: https://openrouter.ai/keys

**Data leaves the machine.** Your prompt is sent to OpenRouter to generate the image, and the generated image is sent back to OpenRouter for the quality review. Both are subject to OpenRouter's data policies and those of the underlying model providers. Do not describe unpublished data, patient information, or anything under embargo in the prompt.

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.

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