Image Processing

Use img-process (shipped in bin/) for common operations. For complex or custom workflows, generate a Pillow script adapted to the user's environment.

Quick Reference — img-process CLI

img-process resize hero.png --width 1920
img-process convert logo.png --format webp
img-process trim logo-raw.jpg -o logo-clean.png --padding 10
img-process thumbnail photo.jpg --size 200
img-process optimise hero.jpg --quality 85 --max-width 1920
img-process og-card -o og.png --title "My App" --subtitle "Built for speed"
img-process batch ./images --action convert --format webp -o ./optimised

Use img-process when: the operation is standard (resize, convert, trim, thumbnail, optimise, OG card, batch). This is faster and avoids generating a script each time.

Generate a custom script when: the operation needs logic img-process doesn't cover (compositing multiple images, watermarks, complex text layouts, conditional processing).

Prerequisites

Pillow is required for both img-process and custom scripts:

pip install Pillow

If Pillow is unavailable, use alternatives:

Output Format Guide

Core Patterns

Save with Format-Specific Quality

Different formats need different save parameters. Always handle RGBA-to-JPG compositing — JPG does not support transparency, so composite onto a white background first.

from PIL import Image
import os

def save_image(img, output_path, quality=None):
    os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
    kwargs = {}
    ext = output_path.lower().rsplit(".", 1)[-1]

    if ext == "webp":
        kwargs = {"quality": quality or 85, "method": 6}
    elif ext in ("jpg", "jpeg"):
        kwargs = {"quality": quality or 90, "optimize": True}
        # RGBA → RGB: composite onto white background
        if img.mode == "RGBA":
            bg = Image.new("RGB", img.size, (255, 255, 255))
            bg.paste(img, mask=img.split()[3])
            img = bg
    elif ext == "png":
        kwargs = {"optimize": True}

    img.save(output_path, **kwargs)

Resize with Aspect Ratio

When only width or height is given, calculate the other from aspect ratio. Use Image.LANCZOS for high-quality downscaling.

def resize_image(img, width=None, height=None):
    if width and height:
        return img.resize((width, height), Image.LANCZOS)
    elif width:
        ratio = width / img.width
        return img.resize((width, int(img.height * ratio)), Image.LANCZOS)
    elif height:
        ratio = height / img.height
        return img.resize((int(img.width * ratio), height), Image.LANCZOS)
    return img

Trim Whitespace (Auto-Crop)

Remove surrounding whitespace from logos and icons. Convert to RGBA first, then use getbbox() to find content bounds.

img = Image.open(input_path)
if img.mode != "RGBA":
    img = img.convert("RGBA")
bbox = img.getbbox()  # Bounding box of non-zero pixels
if bbox:
    img = img.crop(bbox)

Thumbnail

Fit within max dimensions while maintaining aspect ratio:

img.thumbnail((size, size), Image.LANCZOS)

Optimise for Web

Resize + compress in one step. Convert to WebP for best compression. Typical settings: width 1920, quality 85.

Cross-Platform Font Discovery

System font paths differ by OS. Try multiple paths, fall back to Pillow's default. On Linux, fc-list can discover fonts dynamically.

from PIL import ImageFont

def get_font(size):
    font_paths = [
        # macOS
        "/System/Library/Fonts/Helvetica.ttc",
        "/System/Library/Fonts/SFNSText.ttf",
        # Linux
        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
        "/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf",
        # Windows
        "C:/Windows/Fonts/arial.ttf",
    ]
    for path in font_paths:
        if os.path.exists(path):
            try:
                return ImageFont.truetype(path, size)
            except Exception:
                continue
    return ImageFont.load_default()

OG Card Generation (1200x630)

Composite text on a background image or solid colour. Apply semi-transparent overlay for text readability. Centre text horizontally.

from PIL import Image, ImageDraw, ImageFont

width, height = 1200, 630

# Background: image or solid colour
if background_path:
    img = Image.open(background_path).resize((width, height), Image.LANCZOS)
else:
    img = Image.new("RGB", (width, height), bg_color or "#1a1a2e")

# Semi-transparent overlay for text readability
overlay = Image.new("RGBA", (width, height), (0, 0, 0, 128))
img = img.convert("RGBA")
img = Image.alpha_composite(img, overlay)

draw = ImageDraw.Draw(img)
font_title = get_font(48)
font_sub = get_font(24)

# Centre title
if title:
    bbox = draw.textbbox((0, 0), title, font=font_title)
    tw = bbox[2
how to use image-processing
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use image-processing on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add image-processing
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/jezweb/claude-skills --skill image-processingcopy
The skills CLI fetches image-processing from GitHub repository jezweb/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/image-processing
Reload or restart Cursor to activate image-processing. Access the skill through slash commands (e.g., /image-processing) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?