Agent skill · research science · sickn33
computer-vision-expert
Computer vision pipeline design and evaluation. Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
Why this skill is useful
Provides advanced techniques and proprietary workflows for state-of-the-art computer vision tasks that the AI cannot generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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 1. Unified Real-Time Detection (YOLO26) NMS-Free Architecture: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity). Edge Deployment: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer. Improved Small-Object Recognition: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings. 2. Promptable Segmentation (SAM 3) Text-to-Mask: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right"). SAM 3D: separate Objects/Body repositories and checkpoints address 3D reconstruction; they are not an automatic SAM 3 segmentation capability. See SAM 3D Body. Unified Logic: Use SAM 3’s documented task interfaces and task-specific benchmark conditions; do not generalize an upstream metric into “2x accuracy” for every dataset. 3. Vision Language Models (VLMs) Visual Grounding: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding. Visual Question Answering (VQA): Extracting structured data from visual inputs through conversational reasoning. 4. Geometry & Reconstruction Depth Anything V2: State-of-the-art monocular depth estimation for spatial awareness. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/computer-vision-expert--86408e