Graphical Abstract Wizard

---

name: graphical-abstract-wizard

description: Generate graphical abstract layout recommendations based on paper abstracts

version: 1.0.0

category: Visual

tags:

author: AIPOCH

license: MIT

status: Draft

risk_level: Medium

skill_type: Tool/Script

owner: AIPOCH

reviewer: ''

last_updated: '2026-02-06'

---

# Graphical Abstract Wizard

This Skill analyzes academic paper abstracts and generates graphical abstract layout recommendations, including element suggestions, visual arrangements, and AI art prompts for Midjourney and DALL-E.

Usage

python scripts/main.py --abstract "Your paper abstract text here"

Or from stdin:

cat abstract.txt | python scripts/main.py

Parameters

| Parameter | Type | Required | Description |

|-----------|------|----------|-------------|

| `--abstract` / `-a` | string | Yes* | The paper abstract text to analyze |

| `--style` / `-s` | string | No | Visual style preference (scientific/minimal/colorful/sketch) |

| `--format` / `-f` | string | No | Output format (json/markdown/text), default: markdown |

| `--output` / `-o` | string | No | Output file path (default: stdout) |

*Required if not providing input via stdin

Examples

Example 1: Basic Usage

python scripts/main.py -a "We propose a novel deep learning approach for protein structure prediction that combines transformer architectures with geometric constraints. Our method achieves state-of-the-art accuracy on CASP14 benchmarks."

Example 2: With Style Preference

python scripts/main.py -a "abstract.txt" -s scientific -o layout.md

Example 3: JSON Output for Integration

python scripts/main.py -a "$(cat abstract.txt)" -f json > result.json

Output Format

The Skill produces a structured analysis including:

1. Key Concepts Extracted

2. Visual Element Recommendations

3. AI Art Prompts

4. Layout Blueprint

Example Output

# Graphical Abstract Recommendation

## Abstract Summary
**Topic**: Deep learning protein structure prediction
**Method**: Transformer + Geometric constraints
**Result**: State-of-the-art CASP14 accuracy

## Key Concepts
- 🧬 Protein structures
- 🤖 Neural networks
- 📊 Accuracy metrics

## Visual Elements
| Element | Symbol | Position | Color |
|---------|--------|----------|-------|
| Core Concept | Brain + DNA | Center | Blue |
| Method | Neural Network | Left | Purple |
| Result | Trophy/Chart | Right | Gold |

## Layout Suggestion

┌─────────────────────────────────┐

│ [Title/Concept] │

│ 🧬🤖 │

├──────────┬──────────┬───────────┤

│ Input │ Process │ Output │

│ 📥 │ ⚙️ │ 📈 │

└──────────┴──────────┴───────────┘

## AI Art Prompts

### Midjourney

Scientific graphical abstract, protein structure prediction with neural networks, 3D molecular structures connected by glowing neural network nodes, blue and purple gradient background, clean minimalist style, academic journal style, high quality --ar 16:9 --v 6

### DALL-E

A clean scientific illustration for a research paper about protein structure prediction using deep learning. Show a 3D protein structure in the center surrounded by abstract neural network connections. Use a professional blue and white color scheme with subtle gradients. Include geometric shapes representing data flow. Modern, minimalist academic style suitable for a Nature or Science journal cover.

Technical Details

The Skill uses NLP techniques to:

1. Extract named entities (methods, materials, concepts)

2. Identify research actions and outcomes

3. Map concepts to visual representations

4. Generate style-appropriate prompts

Dependencies

License

MIT License - Part of OpenClaw Skills Collection

Risk Assessment

| Risk Indicator | Assessment | Level |

|----------------|------------|-------|

| Code Execution | Python/R scripts executed locally | Medium |

| Network Access | No external API calls | Low |

| File System Access | Read input files, write output files | Medium |

| Instruction Tampering | Standard prompt guidelines | Low |

| Data Exposure | Output files saved to workspace | Low |

Security Checklist

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

Test Cases

1. **Basic Functionality**: Standard input → Expected output

2. **Edge Case**: Invalid input → Graceful error handling

3. **Performance**: Large dataset → Acceptable processing time

Lifecycle Status

- Performance optimization

- Additional feature support