processing-images/SKILL.md
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 skillsauth add oaustegard/claude-skills processing-imagesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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)development
Write effective instructions for Claude: project instructions, standalone prompts, and skill content. Use when users need help writing prompts, setting up project instructions, choosing between instruction formats, or improving how they communicate with Claude. Covers writing principles, model-aware calibration, and format selection. For building and testing complete skills, use skill-creator instead.
data-ai
Discover and load skills on demand from /mnt/skills/user/. Use when you need a capability but don't know which skill provides it, when the boot-emitted skill list is names-only and you need a full description, or when you want to list the catalog. Verbs are list (names only), search (rank by name/description match against a query), and show (emit the full SKILL.md for a named skill).
documentation
Reads the visual content of slides, pages, and images the way a human would, not just their embedded text. Use when a PPTX or PDF has image slides, screenshots, charts, scanned figures, or flattened-to-image layouts that the built-in pptx/pdf skills read as empty; when asked to transcribe, describe, OCR, or extract what is shown in an image, slide deck, or document page; or when embedded-text extraction returned little or nothing from a visually rich file. Triggers on 'read this deck', 'what's on these slides', 'transcribe', 'OCR', 'extract text from image', 'describe this chart/diagram', .pptx/.pdf/.png/.jpg with visual content.
development
Portrait Mode for SVGs — foveated vectorization with 4-zone selective detail. Combines vision annotations, MediaPipe segmentation/landmarks, and optional saliency. Like phone portrait mode, but vectorized. Use when vectorizing a portrait or photo where subject detail should outrank background detail.