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Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.

This skill provides expert guidance on creating, reviewing, and managing production-grade AWS infrastructure using Terraform. It focuses on best practices for reusable module design, robust remote state management (S3 + DynamoDB), and adhering to modern HashiCorp Configuration Language (HCL) patterns like using 'for_each'. Ideal for teams building or auditing complex, scalable AWS environments.

Expert in implementing Infrastructure as Code (IaC) using Terraform across multi-cloud environments, including AWS, Azure, and GCP. Specializes in creating reusable, modular components, advanced state management (backends, locking, migrations), ensuring security best practices, and developing robust, production-grade cloud infrastructure pipelines.

Automates terraform module creation within the DevOps Advanced workflow, guiding through best practices, patterns, and production-ready code plus validations.

A comprehensive collection of production-ready, reusable Terraform modules designed for standardized infrastructure provisioning across major cloud platforms: AWS, Azure, and GCP. This library enforces Infrastructure as Code (IaC) best practices, allowing users to build, test, and deploy multi-cloud compatible components efficiently and reliably.

Provides Terraform and OpenTofu best-practice guidance for module design, testing selection, CI/CD pipeline setup, security scanning, and architecture decisions in production infrastructure-as-code projects.

Test Data Builder Guidance auto-activates when you ask about test data strategies, best practices, or configuration for automation frameworks, delivering step-by-step patterns, production-ready samples, and validation against common standards.

This comprehensive workflow provides a complete cycle of quality assurance for software development. It covers every stage from defining the test strategy (TDD, Unit Testing) through integration, E2E testing, browser automation, performance profiling, security scanning, and final code review. Use this when setting up robust testing infrastructure or ensuring production-ready software quality.

This skill generates code to create complex, interactive 3D scenes and visualizations on the web using the Three.js library and WebGL. It covers the entire development lifecycle, including setting up the core components (Scene, Camera, Renderer), defining geometry, applying materials, adding lighting, and implementing animation loops. Ideal for web developers needing to display product mockups, scientific data visualizations, or immersive user experiences.