This is the standard library for computer vision work. You'll reach for it whenever you need to read images or video, detect edges and contours, track objects, or run face detection. It's built in optimized C++ so it's fast even from Python, with over 2500 algorithms ready to use. One critical gotcha: OpenCV uses BGR instead of RGB by default, so you'll need to convert colors when working with matplotlib or other libraries. The skill documentation covers the essentials well, from basic image I/O patterns to video processing loops, plus anti-patterns like using Python loops over pixels instead of vectorized operations. Works great for real-time applications where performance matters.
- skills.sh installs
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- last updated
- Aug 21, 2026
Install
$ npx -y skills add tondevrel/scientific-agent-skills --skill opencv --agent claude-codeInstalls into .claude/skills of the current project.
Files
OpenCV - Computer Vision and Image Processing
OpenCV (Open Source Computer Vision Library) is the de facto standard library for computer vision tasks. It provides 2500+ optimized algorithms for real-time image and video processing, from basic operations like reading images to advanced tasks like face recognition and 3D reconstruction.
When to Use
- Reading, writing, and displaying images and videos from files or cameras.
- Image preprocessing (resizing, cropping, rotating, color conversion).
- Edge detection (Canny, Sobel) and contour finding.
- Feature detection and matching (SIFT, ORB, AKAZE).
- Object detection (Haar Cascades, HOG, DNN module for YOLO/SSD).
- Face detection and recognition.
- Image segmentation (thresholding, watershed, GrabCut).
- Video analysis (motion detection, object tracking, optical flow).
- Camera calibration and 3D reconstruction.
- Image stitching and panorama creation.
- Real-time applications requiring fast performance.
Reference Documentation
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Details
- Repository
- github.com/tondevrel/scientific-agent-skills
- Categories
- Backend & APIs, AI & Agent Building
- First seen
- Jun 3, 2026
