Visual Design

Quick Reference

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

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

Typography

Prefer thin/light fonts. Minimize text in designs.

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