# Scientific Schematics

> Crea diagramas científicos de calidad de publicación con la IA Nano Banana 2 y refinamiento iterativo inteligente. Gemini 3.6 Flash revisa la calidad y solo regenera si está por debajo del umbral de tu tipo de documento.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-schematics
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-schematics.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT license
Actualizado: hace 15 días
Coste de contexto: 87 tok instalada, 4.1k tok al activarse, 23.4k tok con todos los archivos del bundle
Bundle: 6 archivos, 92 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-schematics --agent cline
```

## Qué hace

- Genera diagramas científicos a partir de una descripción en lenguaje natural con Nano Banana 2
- Revisa la calidad de cada imagen con Gemini 3.6 Flash contra un umbral según el tipo de documento (journal, poster, presentación, etc.)
- Regenera automáticamente solo si la calidad queda por debajo del umbral, hasta un máximo de 2 iteraciones
- Guarda versiones (`name_v1.png`, `name_v2.png`) y un log de revisión con puntuación y crítica por iteración
- Produce únicamente PNG raster, sin salida vectorial ni control de DPI

## Cuándo usarla

- Se crean diagramas de arquitecturas de redes neuronales, sistemas, flujogramas o vías biológicas
- Se necesitan figuras de calidad de publicación para un paper, póster o presentación
- Se dibujan diagramas de metodología de estudio (CONSORT, PRISMA) u otras visualizaciones científicas complejas

## Qué la activa

- "Genera un diagrama de flujo CONSORT con 500 evaluados y 350 aleatorizados"
- "Crea la arquitectura de este transformer con atención multi-cabeza"
- "Dibuja la vía de señalización MAPK de EGFR a la transcripción génica"
- "Genera un diagrama de bloques de este sistema IoT para mi paper"

## Antes de instalar

- Requiere OPENROUTER_API_KEY; el prompt y la imagen generada se envían a OpenRouter, así que no describas datos no publicados o bajo embargo.
- Necesita en el PATH: python
- Variables de entorno: OPENROUTER_API_KEY
- makes network requests
- needs API credentials

## Archivos

- SKILL.md — 16 KB
- references/best_practices.md — 16 KB
- references/iterative_refinement.md — 11 KB
- scripts/example_usage.sh — 3 KB
- scripts/generate_schematic.py — 7 KB
- scripts/generate_schematic_ai.py — 38 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT license. Esta sección es el documento original y está en inglés.

# 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**:

```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 3.6 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:
```bash
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.
- **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 3.6 Flash Review)

## Smart Iterative Refinement, Advanced Usage, and Examples

The generate-review-refine loop, the Python API and command-line options, prompt
engineering guidance, and four worked examples (CONSORT flowchart, neural network
architecture, biological pathway, system architecture) are in
[references/iterative_refinement.md](references/iterative_refinement.md).

The loop stops as soon as the review passes, so a simple diagram usually costs one
iteration; only complex figures use the full budget.

## Command-Line Usage

The main entry point for generating scientific schematics:

```bash
# 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 2 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 — ask for these in the prompt

1. **Clarity over complexity** - Simplify, remove unnecessary elements
2. **Consistent styling** - Describe the same visual conventions across a paper's figures
3. **Colorblind accessibility** - Ask for the Okabe-Ito palette and redundant encoding
4. **Appropriate typography** - Sans-serif fonts, generously sized labels
5. **Logical flow** - State the direction (left-to-right, top-to-bottom) explicitly

The generator applies all of these by default, but naming them in your own words for the specific
diagram works better than relying on the built-in guidelines alone.

### What the pipeline cannot do

1. **Vector output** - PNG only; no PDF, SVG, or EPS is produced
2. **Resolution control** - the image model chooses; there is no DPI flag
3. **Color space** - RGB only; convert for CMYK print workflows downstream
4. **Exact line weights or text sizes** - describe them in the prompt, then verify by eye

For a journal that requires vector art or 300+ dpi TIFF, convert the PNG after generation and check
the result at the size it will actually be printed.

### 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

Generation is stochastic and iteration is capped at 2, so the levers that actually change the
outcome are the prompt, the document type, and re-running. There is no post-processing step and no
quality-checking library in this skill: everything you can inspect lives in the generated PNG and
in `<name>_review_log.json`.

### The diagram is wrong

**Overlapping text, crowded elements, or arrows that miss their targets**
- Name the layout in the prompt: "vertical flow, one box per row, generous spacing between stages"
- Name the connections: "arrow from RAF to MEK labelled phosphorylation", not "show the cascade"
- Re-run. Two runs of the same prompt differ, and a bad layout is often just an unlucky draw

**Content is scientifically wrong or a component is missing**
- List the components explicitly, with counts and labels — the model will not infer them
- Read the `critique` field in the review log: the reviewer usually names what it saw missing

**Wrong text in labels, or figure numbering baked into the image**
- The prompt already forbids "Figure 1:" captions; if one appears anyway, re-run
- Misspelled labels are the most common failure of image models. Read every label before using it

### The score seems wrong

**Score is lower than the diagram deserves**
- Read the critique before re-running; the reviewer's complaint is often legitimate and specific
- The threshold, not the score, decides whether it iterates — `--doc-type journal` demands 8.5

**A run stops at a score below the threshold**
- That is the iteration cap. `--iterations 2` is the maximum; the last image is kept and reported
  with its real score

**`"score": null` and `"reviewed": false` in the log**
- The review call failed or answered in an unusable shape. The image is fine and was kept; only its
  quality was never measured. Check `"review_error"`, look at the image yourself, and re-run

### Setup

**`Error: OPENROUTER_API_KEY not found`**
- `export OPENROUTER_API_KEY='sk-or-v1-...'`, or add it to a `.env` file, or pass `--api-key`

**`Error: requests library not found`**
- `uv pip install requests`

**Any API error** — run with `-v` to see the request, the model slug, and the full error body

## Resources and References

### Detailed References

Load these files for comprehensive information on specific topics:

- **`references/iterative_refinement.md`** - The generate-review-refine loop, the Python API, every
  command-line option, prompt engineering guidance, and four worked examples
- **`references/best_practices.md`** - Publication standards and accessibility guidelines to draw
  on when writing prompts and when judging the result

### External Resources

**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:

### Read the review log (this is the only automated check there is)
- [ ] `<name>_review_log.json` exists and `"reviewed"` is `true` on the final iteration
- [ ] `"final_score"` is a real number, not `null`, and meets the threshold for your document type
- [ ] Read the `"critique"` — the reviewer's remaining issues are listed even on a passing score
- [ ] If more than one version was generated, compare `_v1` and `_v2` and keep the better one

### Look at the image yourself
- [ ] Every label is spelled correctly — image models misspell text, and no automated check here
      catches it
- [ ] No overlapping or clipped text
- [ ] All arrows connect the elements they are meant to connect
- [ ] The science is right: correct components, correct direction, nothing invented
- [ ] Units and counts match what you asked for

### Accessibility (by eye, or in an external checker)
- [ ] Colorblind-safe palette, and the encoding is not colour alone
- [ ] Still readable converted to grayscale
- [ ] Adequate contrast between adjacent elements

### Publication fit
- [ ] Consistent styling with the other figures in the manuscript
- [ ] Legible at the column width it will actually be printed at
- [ ] Converted to the journal's required format if PNG is not accepted
- [ ] Caption written, with every abbreviation defined
- [ ] Referenced in the manuscript text

### Version control
- [ ] The prompt is recorded (it is stored verbatim in the review log)
- [ ] Review log committed alongside the image, so the score is auditable
- [ ] The command that regenerates the figure is written down

### 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

```bash
# Required
export OPENROUTER_API_KEY='your_api_key_here'

# Get key at: https://openrouter.ai/keys
```

## Getting Started

**Simplest possible usage:**
```bash
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.

## Dónde encaja

- Categoría: [Diseño y UI](https://skillsagentes.com/categorias/diseno-ui.md) — Sistemas de diseño, trabajo con componentes y acabado visual.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

---

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