senior-computer-vision
World-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.
Best use case
senior-computer-vision is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
World-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.
Teams using senior-computer-vision should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/senior-computer-vision/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How senior-computer-vision Compares
| Feature / Agent | senior-computer-vision | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
World-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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Senior Computer Vision Engineer World-class senior computer vision engineer skill for production-grade AI/ML/Data systems. ## Quick Start ### Main Capabilities ```bash # 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 ``` ## Core Expertise This skill covers world-class capabilities in: - Advanced production patterns and architectures - Scalable system design and implementation - Performance optimization at scale - MLOps and DataOps best practices - Real-time processing and inference - Distributed computing frameworks - Model deployment and monitoring - Security and compliance - Cost optimization - Team leadership and mentoring ## Tech Stack **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 ## Reference Documentation ### 1. Computer Vision Architectures Comprehensive guide available in `references/computer_vision_architectures.md` covering: - Advanced patterns and best practices - Production implementation strategies - Performance optimization techniques - Scalability considerations - Security and compliance - Real-world case studies ### 2. Object Detection Optimization Complete workflow documentation in `references/object_detection_optimization.md` including: - Step-by-step processes - Architecture design patterns - Tool integration guides - Performance tuning strategies - Troubleshooting procedures ### 3. Production Vision Systems Technical reference guide in `references/production_vision_systems.md` with: - System design principles - Implementation examples - Configuration best practices - Deployment strategies - Monitoring and observability ## Production Patterns ### Pattern 1: Scalable Data Processing Enterprise-scale data processing with distributed computing: - Horizontal scaling architecture - Fault-tolerant design - Real-time and batch processing - Data quality validation - Performance monitoring ### Pattern 2: ML Model Deployment Production ML system with high availability: - Model serving with low latency - A/B testing infrastructure - Feature store integration - Model monitoring and drift detection - Automated retraining pipelines ### Pattern 3: Real-Time Inference High-throughput inference system: - Batching and caching strategies - Load balancing - Auto-scaling - Latency optimization - Cost optimization ## Best Practices ### Development - Test-driven development - Code reviews and pair programming - Documentation as code - Version control everything - Continuous integration ### Production - Monitor everything critical - Automate deployments - Feature flags for releases - Canary deployments - Comprehensive logging ### Team Leadership - Mentor junior engineers - Drive technical decisions - Establish coding standards - Foster learning culture - Cross-functional collaboration ## Performance Targets **Latency:** - P50: < 50ms - P95: < 100ms - P99: < 200ms **Throughput:** - Requests/second: > 1000 - Concurrent users: > 10,000 **Availability:** - Uptime: 99.9% - Error rate: < 0.1% ## Security & Compliance - Authentication & authorization - Data encryption (at rest & in transit) - PII handling and anonymization - GDPR/CCPA compliance - Regular security audits - Vulnerability management ## Common Commands ```bash # 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 ``` ## Resources - Advanced Patterns: `references/computer_vision_architectures.md` - Implementation Guide: `references/object_detection_optimization.md` - Technical Reference: `references/production_vision_systems.md` - Automation Scripts: `scripts/` directory ## Senior-Level Responsibilities As a world-class senior professional: 1. **Technical Leadership** - Drive architectural decisions - Mentor team members - Establish best practices - Ensure code quality 2. **Strategic Thinking** - Align with business goals - Evaluate trade-offs - Plan for scale - Manage technical debt 3. **Collaboration** - Work across teams - Communicate effectively - Build consensus - Share knowledge 4. **Innovation** - Stay current with research - Experiment with new approaches - Contribute to community - Drive continuous improvement 5. **Production Excellence** - Ensure high availability - Monitor proactively - Optimize performance - Respond to incidents
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