From rasterize
Generate raster images (PNG, JPEG, WebP) from natural language prompts using Python rendering engines. Covers graphics, charts, layouts, and procedural art. Not for SVG output.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rasterize:renderThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate professional raster images by routing each request to the best
Generate professional raster images by routing each request to the best rendering engine. You write Python code; the engine does the pixel work.
./references/pdm run python <script.py> (to use the plugin's venv), verify the output, deliver the imageEnvironment: This plugin uses a pdm-managed virtual environment. Always run generation scripts with
pdm run pythonfrom the plugin root. If pdm is not set up yet, runpython scripts/setup.pyfirst.
| Request type | Engine | Reference file |
|---|---|---|
| Photo manipulation, compositing, filters, pixel ops, simple graphics | Pillow | ./references/pillow.md |
| Clean vector-style graphics, logos, icons, anti-aliased shapes, typography-heavy designs | Cairo | ./references/cairo.md |
| Statistical charts, scientific plots, data visualization (static) | Matplotlib + Seaborn | ./references/matplotlib.md |
| Complex interactive-looking dashboards, rich data viz with annotations | Plotly (static export) | ./references/plotly.md |
| Complex layouts, HTML/CSS designs, UI mockups, anything with web fonts or CSS effects | Playwright (HTML→PNG) | ./references/playwright.md |
Some requests need two engines. Common combos:
When combining engines, save intermediate outputs as temporary PNGs and load them in the second engine.
These files contain cross-engine patterns — read them before the engine docs:
./shared/setup.md — Installation, pdm setup, running scripts./shared/fonts.md — Font directory resolution, per-engine loading, font selection guide./shared/canvas.md — Dimensions, size presets, retina scaling, output saving (PNG/JPEG/WebP)./shared/themes.md — Dark/light themes, color tokens, categorical/sequential/diverging palettesAll generated files go into the generated/ directory at the plugin root:
generated/
<slug>_render.py # generation script
<slug>.png # output image
generated/<slug>_render.py where <slug> is a
short kebab-case name derived from the request (e.g., social-card_render.py)generated/<slug>.png (or .jpg/.webp)generated/ too — prefix with _tmp_generated/ directory is gitignored. Do NOT place scripts in scripts/
or output in output/ — those are not for generated artifacts._tmp_* files.Every generation script should follow this pattern:
#!/usr/bin/env python3
"""Rasterize: [brief description of what this generates]"""
import os
# === CONFIGURATION ===
WIDTH, HEIGHT = 1200, 630 # Logical dimensions
SCALE = 2 # Retina multiplier
# Paths — all generated files go in generated/
PLUGIN_ROOT = os.path.dirname(os.path.abspath(__file__))
GENERATED_DIR = os.path.join(PLUGIN_ROOT, "generated")
os.makedirs(GENERATED_DIR, exist_ok=True)
OUTPUT_PATH = os.path.join(GENERATED_DIR, "<slug>.png")
FONT_DIR = os.path.join(PLUGIN_ROOT, "assets", "fonts")
# Engine-specific imports here
# === HELPERS ===
# Reusable drawing functions here
# === COMPOSITION ===
def render():
# Main rendering logic
# ...
pass
# === EXECUTE ===
if __name__ == "__main__":
render()
print(f"Saved to {OUTPUT_PATH}")
When generating images, aim for professional quality:
./shared/setup.md../shared/fonts.md). Never use system fonts
without checking availability.Before writing a generation script, ALWAYS read the relevant engine
reference doc from ./references/. These contain engine-specific
patterns, gotchas, and helper snippets that prevent common mistakes.
./references/pillow.md — Pillow patterns and helpers./references/cairo.md — PyCairo patterns and helpers./references/matplotlib.md — Matplotlib + Seaborn patterns./references/plotly.md — Plotly static export patterns./references/playwright.md — HTML-to-PNG via Playwrightnpx claudepluginhub suxrobgm/rasterizeGenerates 2D graphics programmatically using SVG construction, diagram layout, image compositing, and batch processing. For diagrams, flowcharts, infographics, scientific figures, and automated visual assets.
Creates polished visuals from concepts using HTML/CSS/SVG as a refineable intermediate, then exports to PNG or SVG. Useful for diagrams, infographics, cards, and charts.
Renders GPT Image 2 prompts via Garden local generation, host-native tools, or advisor mode. Covers 18 template categories including posters, UI, products, infographics, academic figures, comics, storyboards, and editing.