computer-vision-pipeline
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
npx skills add majiayu000/claude-skill-registry --skill computer-vision-pipeline-curiositech-some-claude-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computer Vision Pipeline Expert in building production-ready computer vision systems for object detection, tracking, and video analysis. ## When to Use ✅ **Use for**: - Drone footage analysis (archaeological surveys, conservation) - Wildlife monitoring and tracking - Real-time object detection systems - Video preprocessing and analysis - Custom model training and inference - Multi-object tracking (MOT) ❌ **NOT for**: - Simple image filters (use Pillow/PIL) - Photo editing (use Photoshop/GIMP) - Face recognition APIs (use AWS Rekognition) - Basic OCR (use Tesseract) --- ## Technology Selection ### Object Detection Models | Model | Speed (FPS) | Accuracy (mAP) | Use Case | |-------|-------------|----------------|----------| | YOLOv8 | 140 | 53.9% | Real-time detection | | Detectron2 | 25 | 58.7% | High accuracy, research | | EfficientDet | 35 | 55.1% | Mobile deployment | | Faster R-CNN | 10 | 42.0% | Legacy systems | **Timeline**: - 2015: Faster R-CNN (two-stage detection) - 2016: YOLO v1 (one-stage, real-time) - 2020: YOLOv5 (PyTorch, production-ready) - 2023: YOLOv8 (state-of-the-art) - 2024: YOLOv8 is industry standard for real-time **Decision tree**: ``` Need real-time (>30
- When to Use
- Technology Selection
- Object Detection Models
- Common Anti-Patterns
- Anti-Pattern 1: Not Preprocessing Frames Before Detection
- Anti-Pattern 2: Processing Every Frame in Video
- Anti-Pattern 3: Not Using Batch Inference
- Anti-Pattern 4: Ignoring Non-Maximum Suppression (NMS) Tuning
- Anti-Pattern 5: No Tracking Between Frames
- Production Checklist
- When to Use vs Avoid
- References
- Scripts
What does the computer-vision-pipeline skill do?
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill computer-vision-pipeline-curiositech-some-claude-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.