Process images for web development — resize, crop, trim whitespace, convert formats (PNG/WebP/JPG), optimise file size, generate thumbnails, create OG card images. Uses Pillow (Python) — no ImageMagick needed. Trigger with 'resize image', 'convert to webp', 'trim logo', 'optimise images', 'make thumbnail', 'create OG image', 'crop whitespace', 'process image', or 'image too large'.
npx skills add https://github.com/jezweb/claude-skills --skill 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.
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).
Pillow is required for both img-process and custom scripts:
pip install Pillow
If Pillow is unavailable, use alternatives:
| Alternative | Platform | Install | Best for |
|-------------|----------|---------|----------|
| sips | macOS (built-in) | None | Resize, convert (no trim/OG) |
| sharp | Node.js | npm install sharp | Full feature set, high performance |
| ffmpeg | Cross-platform | brew install ffmpeg | Resize, convert |
| Use case | Format | Why |
|----------|--------|-----|
| Photos, hero images | WebP | Best compression, wide browser support |
| Logos, icons (need transparency) | PNG | Lossless, supports alpha |
| Fallback for older browsers | JPG | Universal support |
| Thumbnails | WebP or JPG | Small file size priority |
| OG cards | PNG | Social platforms handle PNG best |
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)
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
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)
Fit within max dimensions while maintaining aspect ratio:
img.thumbnail((size, size), Image.LANCZOS)
Resize + compress in one step. Convert to WebP for best compression. Typical settings: width 1920, quality 85.
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()
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] - bbox[0]
draw.text(((width - tw) // 2, height // 2 - 60), title, fill="white", font=font_title)
img = img.convert("RGB")
img-process trim logo-raw.jpg -o logo-trimmed.png --padding 10
img-process thumbnail logo-trimmed.png --size 512 -o favicon-512.png
img-process optimise hero.jpg --max-width 1920 --quality 85
# Outputs hero.webp — resized and compressed
img-process batch ./raw-images --action convert --format webp --quality 85 -o ./optimised
img-process batch ./photos --action resize --width 800 -o ./thumbnails
Generate images with the gemini-image-gen skill, then process them:
# After generating with Gemini (raw PNG output):
img-process optimise generated-image.png --max-width 1920 --quality 85
# Or batch process all generated images:
img-process batch ./generated --action optimise -o ./production
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Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take jezweb/image-processing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, npm, brew.
Without those the skill loads but fails at the first command.