About

Imported from borghei/claude-skills (engineering/senior-computer-vision/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill senior-computer-vision. Copyright stays with the author (MIT + Commons Clause).

Senior Computer Vision Engineer

Name: senior-computer-vision
Author: borghei

Design end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.

Core Capabilities

Detection pipeline design — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.
Model optimization & deployment — baseline benchmarking, ONNX export, INT8/FP16 quantization, and conversion to TensorRT/OpenVINO/CoreML/TFLite per target platform.
Dataset engineering — audit, cleaning, format conversion (COCO/YOLO/VOC/CVAT/LabelMe), augmentation config, and stratified train/val/test splits.
Architecture guidance — detection and segmentation architecture trade-offs plus CNN vs Vision Transformer selection.
Production targets — FPS, mAP, latency P99, memory, and model-size budgets for real-time, high-accuracy, and edge deployments.

When to Use

Building an object detection or segmentation system from scratch.
Optimizing and deploying a trained model to GPU, edge, or mobile.
Preparing, converting, or auditing a computer vision dataset.
Choosing an architecture for a speed/accuracy/deployment trade-off.

Clarify First

Before generating training configs or pipelines, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Task — detection / instance or semantic segmentation / classification (selects the architecture and --task)
Dataset — location and format (COCO / YOLO / VOC) to analyze or convert (the input to dataset_pipeline_builder.py)
Deployment target — GPU / edge / mobile (drives architecture choice and inference_optimizer --target)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Tool	Purpose	Command
`vision_model_trainer.py`	Generate training configs for YOLO / Detectron2 / MMDetection	`python scripts/vision_model_trainer.py data/coco/ --task detection --arch yolov8m -o configs/train.yaml`
`inference_optimizer.py`	Analyze, benchmark, and recommend optimizations for a model	`python scripts/inference_optimizer.py model.pt --analyze --benchmark --recommend --target edge`
`dataset_pipeline_builder.py`	Analyze/convert/split/augment/validate CV datasets (subcommands)	`python scripts/dataset_pipeline_builder.py analyze --input data/coco/`

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

references/detection-workflows.md — quick-start commands and the three end-to-end workflows (detection pipeline, model optimization/deployment, dataset prep) plus the architecture selection guide. Read when executing a pipeline.
references/commands-targets-and-troubleshooting.md — framework command catalogs (YOLO/Detectron2/MMDetection/optimization), performance targets, anti-patterns, troubleshooting table, and success criteria. Read while running training or deployment.
references/tool-reference.md — full parameter, example, and output-format reference for the three scripts. Read when scripting the tools.
references/computer_vision_architectures.md — CNN backbones (ResNet, EfficientNet, ConvNeXt), ViT variants (ViT, DeiT, Swin), detection heads, and FPN/BiFPN/PANet necks. Read when choosing or tuning architectures.
references/object_detection_optimization.md — NMS variants, anchor optimization, loss design (focal, GIoU/CIoU/DIoU), training strategies, and detection augmentation. Read when improving detection accuracy.
references/production_vision_systems.md — ONNX/TensorRT export, batch inference, edge deployment (Jetson, Intel NCS), Triton serving, and video pipelines. Read when deploying to production.

Scope & Limitations

This skill covers:

End-to-end object detection and segmentation pipeline design (data preparation through production deployment)
Training configuration generation for Ultralytics YOLO, Detectron2, and MMDetection frameworks
Model optimization and export to ONNX, TensorRT, OpenVINO, and CoreML runtimes
Dataset format conversion (COCO, YOLO, Pascal VOC, CVAT), splitting, validation, and augmentation configuration

This skill does NOT cover:

Generative vision tasks (image generation, style transfer, super-resolution) -- see dedicated generative AI skills
3D reconstruction, SLAM, or point cloud processing beyond basic depth estimation
Medical imaging regulatory compliance (DICOM, FDA 510(k)) -- see ra-qm-team/ compliance skills
Real-time video streaming infrastructure (RTSP, WebRTC, GStreamer pipeline design) -- see senior-devops for infrastructure

Integration Points

Skill	Integration	Data Flow
`senior-ml-engineer`	Model serving and MLOps pipeline setup	Trained model artifacts (.pt, .onnx) flow into `model_deployment_pipeline.py` for containerized serving and monitoring
`senior-data-engineer`	Dataset ETL and storage pipelines	Raw image data ingested via `pipeline_orchestrator.py`; cleaned datasets flow into `dataset_pipeline_builder.py` for CV formatting
`senior-data-scientist`	Experiment design and statistical analysis	Experiment parameters from `experiment_designer.py` guide hyperparameter search; model metrics feed back for significance testing
`senior-devops`	CI/CD and GPU infrastructure provisioning	Optimized model artifacts deployed via CI/CD pipelines; GPU node scaling managed through infrastructure-as-code
`senior-prompt-engineer`	Multimodal RAG and vision-language integration	Vision model embeddings and detections feed into `rag_system_builder.py` for multimodal retrieval pipelines
`senior-cloud-architect`	Cloud GPU resource planning and cost optimization	Benchmark results from `inference_optimizer.py` inform instance type selection and auto-scaling policies

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/borghei-claude-skills-senior-computer-vision/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

borghei-claude-skills-senior-computer-vision.ocm.jsonjson

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  "instructions": "# Senior Computer Vision Engineer\n\nDesign end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.\n\n## Core Capabilities\n\n- **Detection pipeline design** — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.\n- **Model optimization & deployment** — baseli",
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Fetch it by URL: GET /api/v1/registry/borghei-claude-skills-senior-computer-vision/manifest?version=1.0.0

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