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

Architecture / flow diagram create_visual_design

Read diagram-design.md

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 )

• Identify data structure and choose appropriate chart type

• Select color palette (see Design Ideas below)

• Write code with plt.savefig(filename, dpi=300, bbox_inches='tight')

• Review the generated chart

Visual Design (create_visual_design )

• Establish design concept/philosophy (internally)

• Follow the process in canvas-design.md

• Select appropriate library and write code

• Save: reportlab canvas.save() , Pillow image.save() , matplotlib plt.savefig()

• 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.

• Review the generated image/PDF

• Check for overlapping elements, clipped text, insufficient margins

• Verify sufficient color contrast

• If issues found, fix the code and regenerate

• Complete at least one fix-verify cycle before finishing