computer-vision-expert
This skill provides strategies for building vision systems, including real-time detection, segmentation, and 3D reconstruction.
Install
mkdir -p .claude/skills/computer-vision-expert && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1065" && unzip -o skill.zip -d .claude/skills/computer-vision-expert && rm skill.zipInstalls to .claude/skills/computer-vision-expert
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.Key capabilities
- →NMS-free end-to-end inference
- →Text-to-mask object extraction
- →Single-view 3D scene reconstruction
- →Edge-device model optimization
How it works
Applies SOTA model architectures like YOLO26 and SAM 3 to process visual inputs via optimized geometric and neural pipelines.
Inputs & outputs
When to use computer-vision-expert
- →Implement real-time object detection with YOLO26
- →Perform text-to-mask image segmentation with SAM 3
- →Optimize vision models for edge device hardware
- →Develop 3D scene reconstruction from visual data
About this skill
Computer Vision Expert (SOTA 2026)
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: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images.
- Unified Logic: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.
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.
- Sub-pixel Calibration: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs.
- Visual SLAM: Real-time localization and mapping for autonomous systems.
Patterns
1. Text-Guided Vision Pipelines
- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.
- Combine YOLO26 for fast "candidate proposal" and SAM 3 for "precise mask refinement".
2. Deployment-First Design
- Leverage YOLO26's simplified ONNX/TensorRT exports (NMS-free).
- Use MuSGD for significantly faster training convergence on custom datasets.
3. Progressive 3D Scene Reconstruction
- Integrate monocular depth maps with geometric homographies to build accurate 2.5D/3D representations of scenes.
Anti-Patterns
- Manual NMS Post-processing: Stick to NMS-free architectures (YOLO26/v10+) for lower overhead.
- Click-Only Segmentation: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.
- Legacy DFL Exports: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.
Sharp Edges (2026)
| Issue | Severity | Solution |
|---|---|---|
| SAM 3 VRAM Usage | Medium | Use quantized/distilled versions for local GPU inference. |
| Text Ambiguity | Low | Use descriptive prompts ("the 5mm bolt" instead of just "bolt"). |
| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |
| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU/TPUs. |
Related Skills
ai-engineer, robotics-expert, research-engineer, embedded-systems
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
When not to use it
- →Simple image filtering tasks
- →Non-visual data processing
Prerequisites
Limitations
- →High hardware requirements for training
- →Zero-shot accuracy varies with domain context
How it compares
It integrates high-level natural language reasoning with low-level spatial geometry, skipping traditional intermediate steps like Non-Maximum Suppression.
Compared to similar skills
computer-vision-expert side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| computer-vision-expert (this skill) | 3 | 4mo | No flags | Advanced |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 2mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by sickn33
View all by sickn33 →You might also like
quant-analyst
zenobi-us
Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
umap-learn
K-Dense-AI
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
embedding-strategies
wshobson
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
building-automl-pipelines
jeremylongshore
Build automated machine learning pipelines, including feature engineering, model selection, and performance evaluation.
model-compare
rawwerks
Compare 3D CAD models using boolean operations (IoU, Dice, precision/recall). Use when evaluating generated models against gold references, diffing CAD revisions, or computing similarity metrics for ML training. Triggers on: model diff, compare models, IoU, intersection over union, model similarity, CAD comparison, STEP diff, 3D evaluation, gold reference, generated model, precision recall 3D.
matchms
davila7
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.