create-2d-composition
关于
This skill enables programmatic generation of 2D graphics like diagrams, charts, and visual assets using SVG creation and layout algorithms. It's ideal for automating reproducible figures, building custom visualizations, or batch-producing graphics with parameter variations. Developers can use it when standard charting libraries are insufficient or when needing scriptable graphic production workflows.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/create-2d-composition在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Create 2D Composition
Generate 2D graphics programmatically using SVG construction, diagram layout algorithms, image compositing, and batch processing workflows. Covers vector graphics generation, raster image manipulation, typography, and automated production of charts, diagrams, and infographics.
适用场景
- Generating diagrams, flowcharts, or infographics programmatically
- Creating reproducible scientific figures or publication graphics
- Automating production of badges, icons, or visual assets
- Compositing multiple images or data visualizations
- Building custom chart types not available in standard libraries
- Batch generating graphics with parameter variations
- Creating SVG templates for web or print applications
输入
| Input | Type | Description | Example |
|---|---|---|---|
| Layout specification | Configuration | Dimensions, margins, grid layout | Canvas 800x600px, 20px margins |
| Visual elements | Data/Assets | Shapes, text, images, data points | Rectangle coordinates, labels, icons |
| Style parameters | CSS/Attributes | Colors, fonts, stroke widths, opacity | fill="#3366cc", stroke-width="2" |
| Data sources | Files/Arrays | Values to visualize or annotate | CSV data, JSON configuration |
| Output format | String | SVG, PNG, PDF, composite formats | output.svg, 300 DPI PNG |
步骤
1. Set Up Python Environment
Install required libraries for 2D composition:
# Core libraries
pip install svgwrite pillow cairosvg
# Optional: advanced features
pip install drawsvg reportlab pycairo
# For data-driven graphics
pip install matplotlib numpy pandas
预期结果: Libraries installed successfully 失败处理: Check Python version (3.7+), use virtual environment to avoid conflicts
2. Create Basic SVG Graphics
Generate SVG using svgwrite:
import svgwrite
from svgwrite import cm, mm
def create_basic_svg(output_path):
"""Create a simple SVG graphic."""
# Initialize drawing (use mm for precise dimensions)
dwg = svgwrite.Drawing(output_path, size=('180mm', '120mm'), profile='full')
# Add background rectangle
dwg.add(dwg.rect(
insert=(0, 0),
size=('100%', '100%'),
fill='white'
))
# Add shapes
dwg.add(dwg.circle(
center=(90*mm, 60*mm),
r=30*mm,
fill='lightblue',
stroke='navy',
stroke_width=2
))
dwg.add(dwg.rect(
insert=(30*mm, 30*mm),
size=(60*mm, 40*mm),
fill='lightgreen',
stroke='darkgreen',
stroke_width=2,
rx=5, # Rounded corners
ry=5
))
# Add text
dwg.add(dwg.text(
'Example Graphic',
insert=(90*mm, 20*mm),
text_anchor='middle',
font_size='18pt',
font_family='Arial',
fill='black'
))
dwg.save()
print(f"Saved: {output_path}")
预期结果: SVG file generated with shapes and text 失败处理: Check svgwrite version, verify output directory writable
3. Build Diagrams with Layout Logic
Create structured diagrams with calculated positioning:
def create_flowchart(steps, output_path):
"""Generate a flowchart from list of steps."""
dwg = svgwrite.Drawing(output_path, size=('800px', '600px'))
# Layout parameters
box_width = 120
box_height = 60
spacing_y = 100
start_x = 340
start_y = 50
for i, step in enumerate(steps):
y_pos = start_y + i * spacing_y
# Draw box
box = dwg.add(dwg.g(id=f'step_{i}'))
box.add(dwg.rect(
insert=(start_x, y_pos),
size=(box_width, box_height),
fill='lightblue',
stroke='navy',
stroke_width=2,
rx=5,
ry=5
))
# Add text (wrapped if needed)
text_lines = wrap_text(step, max_width=16)
text_y = y_pos + box_height/2 - (len(text_lines)-1) * 7
for j, line in enumerate(text_lines):
box.add(dwg.text(
line,
insert=(start_x + box_width/2, text_y + j*14),
text_anchor='middle',
font_size='12pt',
font_family='Arial',
fill='black'
))
# Draw arrow to next step
if i < len(steps) - 1:
arrow_start_y = y_pos + box_height
arrow_end_y = y_pos + spacing_y
dwg.add(dwg.line(
start=(start_x + box_width/2, arrow_start_y),
end=(start_x + box_width/2, arrow_end_y),
stroke='black',
stroke_width=2,
marker_end=dwg.marker(
id='arrow',
viewBox='0 0 10 10',
refX=5,
refY=5,
markerWidth=6,
markerHeight=6,
orient='auto'
)
))
dwg.save()
def wrap_text(text, max_width=20):
"""Simple text wrapping."""
words = text.split()
lines = []
current_line = []
for word in words:
test_line = ' '.join(current_line + [word])
if len(test_line) <= max_width:
current_line.append(word)
else:
if current_line:
lines.append(' '.join(current_line))
current_line = [word]
if current_line:
lines.append(' '.join(current_line))
return lines
预期结果: Flowchart with connected boxes and arrows 失败处理: Adjust layout calculations, verify arrow marker definitions
4. Composite Raster Images
Combine multiple images using Pillow:
from PIL import Image, ImageDraw, ImageFont, ImageFilter
import os
def composite_images(image_paths, output_path, layout='grid'):
"""Composite multiple images into single output."""
# Load images
images = [Image.open(path) for path in image_paths]
if layout == 'grid':
# Calculate grid dimensions
n = len(images)
cols = int(n ** 0.5)
rows = (n + cols - 1) // cols
# Get max dimensions
max_width = max(img.width for img in images)
max_height = max(img.height for img in images)
# Create composite canvas
canvas_width = cols * max_width
canvas_height = rows * max_height
composite = Image.new('RGB', (canvas_width, canvas_height), 'white')
# Paste images
for i, img in enumerate(images):
row = i // cols
col = i % cols
x = col * max_width
y = row * max_height
composite.paste(img, (x, y))
elif layout == 'horizontal':
# Horizontal concatenation
total_width = sum(img.width for img in images)
max_height = max(img.height for img in images)
composite = Image.new('RGB', (total_width, max_height), 'white')
x_offset = 0
for img in images:
composite.paste(img, (x_offset, 0))
x_offset += img.width
elif layout == 'vertical':
# Vertical concatenation
max_width = max(img.width for img in images)
total_height = sum(img.height for img in images)
composite = Image.new('RGB', (max_width, total_height), 'white')
y_offset = 0
for img in images:
composite.paste(img, (0, y_offset))
y_offset += img.height
composite.save(output_path)
print(f"Saved composite: {output_path}")
def add_annotations(image_path, annotations, output_path):
"""Add text annotations to image."""
img = Image.open(image_path)
draw = ImageDraw.Draw(img)
# Load font
try:
font = ImageFont.truetype("Arial.ttf", 24)
except:
font = ImageFont.load_default()
for annotation in annotations:
text = annotation['text']
position = annotation['position']
color = annotation.get('color', 'black')
# Add text shadow for readability
shadow_offset = 2
draw.text(
(position[0] + shadow_offset, position[1] + shadow_offset),
text,
font=font,
fill='white'
)
draw.text(position, text, font=font, fill=color)
img.save(output_path)
预期结果: Composite image created with proper layout 失败处理: Check all input images exist, verify image modes compatible
5. Generate Data-Driven Graphics
Create visualizations from data:
import numpy as np
def create_bar_chart_svg(data, labels, output_path):
"""Generate SVG bar chart from data."""
dwg = svgwrite.Drawing(output_path, size=('600px', '400px'))
# Chart area
margin = 50
chart_width = 500
chart_height = 300
bar_spacing = 10
# Calculate bar dimensions
n_bars = len(data)
bar_width = (chart_width - (n_bars - 1) * bar_spacing) / n_bars
# Scale data to fit chart
max_value = max(data)
scale = chart_height / max_value
# Draw axes
dwg.add(dwg.line(
start=(margin, margin),
end=(margin, margin + chart_height),
stroke='black',
stroke_width=2
))
dwg.add(dwg.line(
start=(margin, margin + chart_height),
end=(margin + chart_width, margin + chart_height),
stroke='black',
stroke_width=2
))
# Draw bars
for i, (value, label) in enumerate(zip(data, labels)):
x = margin + i * (bar_width + bar_spacing)
bar_height = value * scale
y = margin + chart_height - bar_height
# Bar
dwg.add(dwg.rect(
insert=(x, y),
size=(bar_width, bar_height),
fill='steelblue',
stroke='navy',
stroke_width=1
))
# Value label
dwg.add(dwg.text(
f'{value:.1f}',
insert=(x + bar_width/2, y - 5),
text_anchor='middle',
font_size='10pt',
fill='black'
))
# X-axis label
dwg.add(dwg.text(
label,
insert=(x + bar_width/2, margin + chart_height + 20),
text_anchor='middle',
font_size='10pt',
fill='black'
))
dwg.save()
预期结果: SVG bar chart with scaled data 失败处理: Handle edge cases (empty data, negative values), add validation
6. Batch Generate Graphics
Automate creation of multiple graphics:
def batch_generate_badges(users, template_path, output_dir):
"""Generate badge for each user."""
os.makedirs(output_dir, exist_ok=True)
for user in users:
output_path = os.path.join(output_dir, f"{user['id']}_badge.svg")
dwg = svgwrite.Drawing(output_path, size=('300px', '100px'))
# Background
dwg.add(dwg.rect(
insert=(0, 0),
size=('100%', '100%'),
fill='#3366cc',
rx=10,
ry=10
))
# User name
dwg.add(dwg.text(
user['name'],
insert=(150, 40),
text_anchor='middle',
font_size='20pt',
font_weight='bold',
fill='white'
))
# User role
dwg.add(dwg.text(
user['role'],
insert=(150, 70),
text_anchor='middle',
font_size='14pt',
fill='lightblue'
))
dwg.save()
print(f"Generated badge: {output_path}")
预期结果: Individual graphic generated for each data item 失败处理: Check data structure, handle missing fields with defaults
7. Convert SVG to Raster
Export SVG to PNG/PDF for various uses:
import cairosvg
def svg_to_png(svg_path, png_path, dpi=300):
"""Convert SVG to PNG with specified DPI."""
# Calculate pixel dimensions from DPI
# Assuming A4 size as example
width_inches = 8.27
height_inches = 11.69
width_px = int(width_inches * dpi)
height_px = int(height_inches * dpi)
cairosvg.svg2png(
url=svg_path,
write_to=png_path,
output_width=width_px,
output_height=height_px
)
print(f"Converted to PNG: {png_path}")
def svg_to_pdf(svg_path, pdf_path):
"""Convert SVG to PDF."""
cairosvg.svg2pdf(url=svg_path, write_to=pdf_path)
print(f"Converted to PDF: {pdf_path}")
预期结果: Raster output generated at specified resolution 失败处理: Install cairo system library if missing, check SVG validity
Validation Checklist
- Graphics render correctly in target applications
- Text is readable and properly positioned
- Colors match specifications (check hex codes)
- Dimensions appropriate for use case
- SVG validates against standard (if required)
- Raster exports have correct DPI
- Layout adapts to data variations
- Batch processing completes without errors
- Output files organized logically
- Code includes error handling
常见问题
- Unit confusion: SVG units (px, mm, cm) vs screen pixels vs print DPI
- Text overflow: Text exceeding shape boundaries, implement wrapping
- Font availability: System fonts may differ, embed or use web-safe fonts
- Coordinate calculations: Off-by-one errors in grid layouts
- Color format: SVG uses hex strings (
#rrggbb), not tuples - SVG validity: Check XML structure, close all tags
- File paths: Handle special characters and spaces in filenames
- Memory/aspect ratio: Chunk large batches; maintain proportions when resizing; PNG supports alpha, JPEG does not
相关技能
- render-publication-graphic: Publication-specific output requirements
- create-3d-scene: Similar programmatic approach for 3D
- generate-quarto-report: Integrating graphics into reports
GitHub 仓库
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