---
name: visual-design
description: "Use this skill any time the user needs a visual output as an image
  or PDF — charts, diagrams, posters, infographics, abstract artwork, or any
  visual design. Trigger for: data visualization requests, poster/flyer creation,
  infographic design, abstract or artistic visuals, architecture/flow diagrams,
  or any request mentioning 'chart', 'graph', 'poster', 'infographic', 'design',
  'visual', or referencing .png/.pdf image output."
---

# Visual Design

## Quick Reference

| Task                        | Tool                   | Guide                                      |
|-----------------------------|------------------------|--------------------------------------------|
| Data chart or graph         | `generate_chart`       | Read SKILL.md Design Ideas                 |
| Poster / infographic / art  | `create_visual_design` | Read [canvas-design.md](canvas-design.md)  |
| Architecture / flow diagram | Excalidraw skill | Use `create_excalidraw_diagram` for an editable in-app result. Use image generation only when a raster or print deliverable is requested. |

## Available Tools

### generate_chart
Data visualization. Executes matplotlib/plotly code to produce chart PNGs.
- `python_code` (str, required): Chart generation Python code
- `output_filename` (str, required): `.png` filename

### create_visual_design
Visual design creation: posters, infographics, artwork, diagrams.
Uses reportlab, Pillow, svgwrite, or any available library.
- `python_code` (str, required): Design generation Python code
- `output_filename` (str, required): `.png` or `.pdf` filename

## Available Libraries

| Purpose | Libraries | Output | Notes |
|---------|-----------|--------|-------|
| Data charts | matplotlib, plotly, bokeh | PNG | Best for charts |
| PDF design | reportlab, fpdf | PDF | Full control |
| Image design | Pillow + fonttools | PNG | Best for PNG designs |
| Vector graphics | svgwrite → svglib + renderPDF | SVG → PDF | SVG→PNG NOT supported (no renderPM) |
| Image processing | Wand (ImageMagick), opencv-python | PNG | Check availability first |

**IMPORTANT**: For PNG output, use Pillow or matplotlib. Do NOT use svgwrite→renderPM (rlPyCairo is unavailable).

## Design Workflow

### Data Charts (`generate_chart`)
1. Identify data structure and choose appropriate chart type
2. Select color palette (see Design Ideas below)
3. Preserve requested labels, units, colors, dimensions, and explicit axis limits in the code. Verify these against the generated output.
4. Write code with `plt.savefig(filename, dpi=300, bbox_inches='tight')`
5. Review the generated chart and confirm the saved file matches the requested format and filename

### Visual Design (`create_visual_design`)
1. Establish design concept/philosophy (internally)
2. Follow the process in [canvas-design.md](canvas-design.md)
3. Select appropriate library and write code
4. Save: reportlab `canvas.save()`, Pillow `image.save()`, matplotlib `plt.savefig()`
5. Review output and refine

## Design Ideas

### Color Palettes
| Theme | Primary | Accent | Background |
|-------|---------|--------|------------|
| Midnight Executive | `1E2761` | `408EC6` | `0D1B2A` |
| Forest & Moss | `2C5F2D` | `97BC62` | `1A1A1A` |
| Coral Energy | `F96167` | `F9E795` | `2F3C7E` |
| Ocean Gradient | `065A82` | `1B9AAA` | `021B29` |
| Charcoal Minimal | `36454F` | `E8E8E8` | `1C1C1E` |
| Cherry Bold | `990011` | `FCF6F5` | `150E11` |
| Sage Calm | `84B59F` | `69A297` | `2D3A2D` |
| Warm Terracotta | `B85042` | `E7E8D1` | `2A1F1C` |

### Typography
Prefer thin/light fonts. Minimize text in designs.
| Element | Size | Style |
|---------|------|-------|
| Main title | 48-72pt | Bold or Thin |
| Subtext | 14-18pt | Light |
| Labels/captions | 8-12pt | Regular, muted |

**Text-to-Canvas Balance (IMPORTANT):**
- Text size must be proportional to the overall canvas and surrounding design elements
- Common mistake: text that is too small relative to the canvas, making it unreadable at normal viewing distance
- Rule of thumb: if you need to zoom in to read it, it's too small
- Titles should command attention — when in doubt, go larger
- Labels/captions should be clearly legible, not decorative afterthoughts
- Test: mentally shrink the output to 50% — all text should still be readable

### Spacing & Composition
- Generous margins (minimum 10% of canvas)
- Consistent spacing between elements
- No overlapping; all elements within canvas bounds
- Visual hierarchy: convey importance via size, color, position

### Avoid
- Elements flush to canvas edges (insufficient margins)
- Overlapping elements
- Too many colors (stick to 3-4)
- Excessive text — visual elements are the focus
- Default matplotlib styles without customization

## Code Requirements
- Code must save a file to disk
- Use the exact `output_filename` provided
- PNG: `dpi=300` or higher recommended
- PDF: A4 or Letter size recommended
- For Korean text: configure appropriate fonts

## QA

**Assume there are problems and look for them.**

1. Review the generated image/PDF
2. Check for overlapping elements, clipped text, insufficient margins
3. Verify sufficient color contrast
4. If issues found, fix the code and regenerate
5. If a check fails, correct and verify it. Do not regenerate an already valid output solely to perform a fix cycle.

## UI Guidance (from tools-config)

**Tool Selection:**
- generate_chart: Data charts/graphs (matplotlib, plotly, bokeh) → PNG
- create_visual_design: Posters, infographics, artwork, flow diagrams (reportlab, Pillow, svgwrite) → PNG or PDF

**Code Requirements:**
- Charts: plt.savefig(filename, dpi=300, bbox_inches='tight')
- PDF designs: canvas.save() (reportlab) or equivalent
- Image designs: image.save(filename) (Pillow)
- PNG: dpi=300+ recommended
- PDF: A4 or Letter size recommended