From file to useful result
How to ask with this skill installed
Installing the skill gives your AI a method. Your request still has to provide the case-specific facts, constraints and expected output.
Describe the decision or deliverable, not just the topic.
Add source material, audience, limits and known facts.
Set format, quality criteria and checks.
When to use it
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
Mistakes to avoid
AI Generation Issues
Problem: Overlapping text or elements
- Solution: AI generation automatically handles spacing
- Solution: Increase iterations:
--iterations 2for better refinement
Problem: Elements not connecting properly
- Solution: Make your prompt more specific about connections and layout
- Solution: Increase iterations for better refinement
Image Quality Issues
Problem: Export quality poor
- Solution: AI generation produces high-quality images automatically
- Solution: Increase iterations for better results:
--iterations 2
Problem: Elements overlap after generation
- Solution: AI generation automatically handles spacing
- Solution: Increase iterations:
--iterations 2for better refinement - Solution: Make your prompt more specific about layout and spacing requirements
Quality Check Issues
Problem: False positive overlap detection
- Solution: Adjust threshold:
detect_overlaps(image_path, threshold=0.98) - Solution: Manually review flagged regions in visual report
Problem: Generated image quality is low
- Solution: AI generation produces high-quality images by default
- Solution: Increase iterations for better results:
--iterations 2
Problem: Colorblind simulation shows poor contrast
- Solution: Switch to Okabe-Ito palette explicitly in code
- Solution: Add redundant encoding (shapes, patterns, line styles)
- Solution: Increase color saturation and lightness differences
Problem: High-severity overlaps detected
- Solution: Review overlap_report.json for exact positions
- Solution: Increase spacing in those specific regions
- Solution: Re-run with adjusted parameters and verify again
Problem: Visual report generation fails
- Solution: Check Pillow and matplotlib installations
- Solution: Ensure image file is readable:
Image.open(path).verify() - Solution: Check sufficient disk space for report generation
Accessibility Problems
Problem: Colors indistinguishable in grayscale
- Solution: Run accessibility checker:
verify_accessibility(image_path) - Solution: Add patterns, shapes, or line styles for redundancy
- Solution: Increase contrast between adjacent elements
Problem: Text too small when printed
- Solution: Run resolution validator:
validate_resolution(image_path) - Solution: Design at final size, use minimum 7-8 pt fonts
- Solution: Check physical dimensions in resolution report
Problem: Accessibility checks consistently fail
- Solution: Review accessibility_report.json for specific failures
- Solution: Increase color contrast by at least 20%
- Solution: Test with actual grayscale conversion before finalizing