image

name	image
title	Image Perception
description	Vision for still images. Use for any task with image reference files (.png/.jpg/.jpeg/.webp/.bmp/.tiff/.gif) or that asks to read text from an image (OCR), analyze colours, detect QR/barcodes, or transform pictures. Provides metadata, OCR, dominant-colour extraction, QR/barcode reading, and resize/grayscale/thumbnail helpers.
modalities	["image"]
file_extensions	[".png",".jpg",".jpeg",".webp",".bmp",".tiff",".tif",".gif"]
keywords	["image","picture","photo","png","jpg","jpeg","logo","screenshot","scan","ocr","text in image","colour","color","palette","qr","barcode","thumbnail","resize","crop","chart image","diagram"]
requires	["Pillow","opencv-python","pytesseract","pyzbar","numpy"]
version	1.0.0

Image Skill — read & transform pictures

Use this skill to see still images: read embedded text, measure colours, decode QR/barcodes, and produce transformed image deliverables.

A typical "image problem" workflow:

1. info = image.image_info(path) — size, mode, format.
2. text = image.ocr_text(path) — pull any text in the picture.
3. colors = image.dominant_colors(path) — brand/palette analysis.
4. Transform / annotate and save the deliverable.

Toolkit API

from skills import image

image.image_info(path) -> dict          # {width, height, mode, format, size_kb}
image.ocr_text(path, lang="eng") -> str # pytesseract OCR
image.dominant_colors(path, k=5) -> list[tuple[int,int,int]]
image.read_codes(path) -> list[dict]    # QR / barcode payloads via pyzbar
image.to_grayscale(path, out) -> str
image.resize(path, width, out) -> str   # keep aspect ratio
image.thumbnail(path, max_px=256, out=None) -> str

Notes:

• OCR needs the system tesseract binary (present in the sandbox image).
• dominant_colors uses k-means over down-sampled pixels (OpenCV).