visual-design

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	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
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