Image processing toolkit awareness. Use when: user uploads images for manipulation, requests format conversion, batch processing, compositing, resizing, optimization, analysis, effects, metadata inspection, montages, animated GIFs, color correction, or any image-related task. Also use when working with screenshots, photos, diagrams, icons, or visual assets. Triggers on 'resize', 'crop', 'convert', 'compress', 'optimize', 'thumbnail', 'watermark', 'montage', 'collage', 'gif', 'sprite sheet', 'color space', 'metadata', 'EXIF', 'compare images', 'diff', 'overlay', 'composite', 'batch process', 'image analysis', 'histogram', 'blur', 'sharpen', 'rotate', 'flip', 'border', 'shadow', 'round corners', 'favicon', 'icon set'.
npx skills add https://github.com/oaustegard/claude-skills --skill processing-images
This container has a rich image processing toolkit. Before writing any image code, scan this inventory to pick the best tool for the job — don't default to Pillow for everything.
ImageMagick convert is the default choice. Handles 260+ formats, single command, no code needed.
convert input.png output.webp
convert input.png -quality 85 output.jpg
mogrify -format webp *.png # batch in-place
For animated formats (GIF↔WebP↔APNG, video↔frames), prefer ffmpeg.
ImageMagick for CLI one-liners. Pillow when already in Python pipeline.
convert input.jpg -resize 800x600 output.jpg # fit within box
convert input.jpg -resize 800x600^ -gravity center -extent 800x600 output.jpg # fill+crop
convert input.jpg -thumbnail 200x200^ -gravity center -extent 200x200 thumb.jpg
IM supports 30+ resize filters: -filter Lanczos (sharp downscale), -filter Mitchell (balanced), -filter Point (nearest-neighbor/pixel art).
ImageMagick composite or convert with -composite. Supports all Porter-Duff modes.
composite -gravity southeast watermark.png photo.jpg output.jpg
convert base.png overlay.png -gravity center -composite result.png
For complex multi-layer work or programmatic positioning, use Pillow (Image.paste, Image.alpha_composite).
ImageMagick montage — purpose-built for grid layouts.
montage *.jpg -geometry 200x200+5+5 -tile 4x3 sheet.jpg
montage *.png -label '%f' -geometry +4+4 catalog.png
ImageMagick for simple GIF assembly. ffmpeg for anything involving timing control, video sources, or optimization.
convert -delay 10 -loop 0 frame_*.png animation.gif # IM
ffmpeg -framerate 10 -i frame_%03d.png -vf palettegen palette.png && \
ffmpeg -framerate 10 -i frame_%03d.png -i palette.png -lavfi paletteuse output.gif # optimized
imageio is convenient for frame-sequence GIFs from Python arrays.
identify for quick metadata and stats. OpenCV for structural analysis. scikit-image for scientific measurement.
identify -verbose image.png # full metadata dump
identify -format '%wx%h %[colorspace] %[depth]bit' image.png # targeted
cv2): histograms, contour detection, template matching, edge detection, color distributionskimage): region properties, morphology, thresholding (Otsu, adaptive), feature detection (SIFT via OpenCV, ORB), SSIM comparisonImageMagick compare produces visual diffs directly.
compare image1.png image2.png diff.png
compare -metric RMSE image1.png image2.png null: 2>&1 # numeric similarity
For structural similarity: skimage.metrics.structural_similarity (SSIM).
ImageMagick handles color space conversion, channel manipulation, color quantization.
convert input.jpg -colorspace Gray output.jpg
convert input.png -colors 16 reduced.png # quantize
convert input.jpg -modulate 110,130,100 output.jpg # brightness,saturation,hue
convert input.png -channel R -separate red_channel.png
For programmatic color analysis (dominant colors, palettes): OpenCV k-means on pixel arrays, or Pillow getcolors().
ImageMagick has extensive built-in effects:
convert in.png -blur 0x3 out.png # Gaussian blur
convert in.png -sharpen 0x1 out.png
convert in.png -shadow 60x4+2+2 out.png # drop shadow
convert in.png -vignette 0x40 out.png
convert in.png -sketch 0x10+120 out.png
convert in.png -charcoal 2 out.png
convert in.png -edge 1 out.png
convert in.png -emboss 1 out.png
convert in.png \( +clone -background black -shadow 60x4+0+0 \) +swap -background none -layers merge +repage rounded.png
For advanced/custom convolution kernels: scipy.ndimage or OpenCV.
identify -verbose reads all metadata. Pillow reads/writes EXIF programmatically.
identify -verbose image.jpg | grep -A20 'Properties:'
from PIL import Image
img = Image.open("photo.jpg")
exif = img.getexif() # dict-like access to EXIF tags
Note: no exiftool in this container. Use identify or Pillow for metadata tasks.
ImageMagick generates multi-size ICO files directly.
convert icon.png -define icon:auto-resize=256,128,64,48,32,16 favicon.ico
For icon sets (iOS/Android), batch-resize with convert or mogrify to each required size.
ImageMagick can read/write PDF and EPS but Ghostscript is not installed — complex PDF rasterization may fail. For PDF-to-image, prefer Pillow (for simple cases), pdfplumber (text/table extraction), or the pdf skill (for full PDF manipulation). IM's built-in PDF delegate handles basic operations.
ImageMagick has Liquid Rescale (LQR) built in:
convert input.jpg -liquid-rescale 80x100%! output.jpg # shrink width 20%, preserve content
ImageMagick rasterizes SVG via its built-in delegate. For SVG→PNG at specific sizes:
convert -density 300 input.svg -resize 800x output.png
No Inkscape or rsvg-convert available. For SVG manipulation, work with the XML directly or use Python's lxml.
| Tool | Type | Key Strength |
|---|---|---|
| ImageMagick 6.9 | CLI suite | 260 formats, effects, compositing, batch ops |
| ffmpeg | CLI | Video/animation, frame extraction, optimized GIFs |
| Graphviz | CLI | Diagram→image rendering (dot, neato, etc.) |
| LibreOffice | CLI | Document→image conversion |
| Pillow 12.1 | Python | General-purpose, EXIF, drawing, WebP/AVIF, freetype |
| OpenCV 4.13 | Python | Computer vision, histograms, contours, morphology |
| scikit-image 0.26 | Python | Scientific analysis, SSIM, segmentation, features |
| Wand 0.6 | Python | Full ImageMagick API from Python |
| imageio 2.37 | Python | Unified I/O, GIF frame sequences |
| scipy.ndimage | Python | Convolution, interpolation, labeling |
| numpy | Python | Raw pixel array math |
| reportlab | Python | PDF generation with embedded images |
identify or Pillow for metadataconvert/identify commands, not magick (v7 syntax)Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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 oaustegard/processing-images 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.