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

CODEBLOCK0

Or from stdin:

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Parameters

Parameter	Type	Required	Description
INLINECODE0 / INLINECODE1	string	Yes*	The paper abstract text to analyze
INLINECODE2 / INLINECODE3

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

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Example 2: With Style Preference

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Example 3: JSON Output for Integration

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Output Format

The Skill produces a structured analysis including:

1. Key Concepts Extracted

• - Core research topic
• Methods/techniques used
• Key findings/results
• Implications

2. Visual Element Recommendations

• - Recommended icons/symbols
• Color palette suggestions
• Layout structure

3. AI Art Prompts

• - Midjourney Prompt: Optimized for Midjourney v6
• DALL-E Prompt: Optimized for DALL-E 3

4. Layout Blueprint

• - Grid-based layout suggestion
• Element positioning
• Flow direction

Example Output

CODEBLOCK5
┌─────────────────────────────────┐
│ [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.
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Technical Details

The Skill uses NLP techniques to:

1. 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

• - Python 3.8+
• OpenAI API (optional, for enhanced analysis)
• Standard library: re, json, argparse, sys

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

• - [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

CODEBLOCK9

Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• - Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:

- Performance optimization - Additional feature support