Computer Vision Expert
If you're working with cutting-edge vision systems in 2026, this covers the full stack from YOLO26's NMS-free detection to SAM 3's text-guided segmentation and VLM-based reasoning. The standout is how it bridges deployment reality (TensorRT exports, edge optimization, MuSGD training speedups) with modern foundation models. You'll get practical patterns like using YOLO26 for fast proposals then SAM 3 for precise masks, plus the geometry fundamentals (depth estimation, calibration, SLAM) that still matter. The sharp edges table is honest about VRAM and motion blur trade-offs. Best for anyone building real-time vision pipelines where you need both the latest models and actual production knowledge, not just research paper summaries.
- skills.sh installs
- 317
- GitHub stars
- 39.4K
- last updated
- Jun 3, 2026
Install
$ npx -y skills add sickn33/antigravity-awesome-skills --skill computer-vision-expert --agent claude-codeInstalls into .claude/skills of the current project.
Files
Computer Vision Pipeline Review
Role: Advanced Vision Systems Architect & Spatial Intelligence Expert
Purpose
To provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.
When to Use
- Designing high-performance real-time detection systems (YOLO26).
- Implementing zero-shot or text-guided segmentation tasks (SAM 3).
- Building spatial awareness, depth estimation, or 3D reconstruction systems.
- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).
- Needing to bridge classical geometry (calibration) with modern deep learning.
Capabilities
Sponsors
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- Repository
- github.com/sickn33/antigravity-awesome-skills
- First seen
- Jun 3, 2026
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