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skillboardWorld-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
World-class senior computer vision engineer skill for production-grade AI/ML/Data systems.
# Core Tool 1
python scripts/vision_model_trainer.py --input data/ --output results/
# Core Tool 2
python scripts/inference_optimizer.py --target project/ --analyze
# Core Tool 3
python scripts/dataset_pipeline_builder.py --config config.yaml --deploy
This skill covers world-class capabilities in:
Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Comprehensive guide available in references/computer_vision_architectures.md covering:
Complete workflow documentation in references/object_detection_optimization.md including:
Technical reference guide in references/production_vision_systems.md with:
Enterprise-scale data processing with distributed computing:
Production ML system with high availability:
High-throughput inference system:
Latency:
Throughput:
Availability:
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
references/computer_vision_architectures.mdreferences/object_detection_optimization.mdreferences/production_vision_systems.mdscripts/ directoryAs a world-class senior professional:
Technical Leadership
Strategic Thinking
Collaboration
Innovation
Production Excellence
Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/senior-computer-vision · pinned to the source commit
# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/development/senior-computer-vision" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/development/senior-computer-vision" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/senior-computer-vision
Scanner static-checks@0.1.0 · commit 5cc17cf26713. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
No static rules matched. This is not a safety guarantee.