visual-design（视觉设计）

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. Write code with plt.savefig(filename, dpi=300, bbox_inches='tight')
4. Review the generated chart

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. Complete at least one fix-verify cycle before finishing

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