Deploying Hugging Face models on Amazon SageMaker AI can require decisions such as choosing the right serving container, confirming the current image tag for an AWS Region, matching an instance type to memory footprint, wiring autoscaling, and setting Amazon CloudWatch alarms. Amazon SageMaker AI collapses that work into hours, and coding agents like Kiro and Claude Code are built for such structured, repeatable work. However, an unguided coding agent might make wrong decisions, producing endpoints that are fragile, costly, or quietly wrong, especially for newer models whose training data may lack the latest deployment knowledge. The post explains how to deploy production-ready Hugging Face models on SageMaker AI using agent skills: install six skills from Hugging Face Skills, point a coding agent at a Hugging Face model, and get a real-time endpoint with autoscaling, Amazon CloudWatch alarms, the correct serving container from the AWS Deep Learning Containers (DLC) catalog, and a verified teardown path. Real-time endpoint is the default, but the skills also support real-time with scale-to-zero, serverless inference, asynchronous inference, batch transform, and Amazon Bedrock Custom Model Import. The skills are open source, use only Python and the AWS Command Line Interface (AWS CLI), and work unchanged on macOS, Linux, and Windows. In testing, Kiro and Claude Code initially chose Text Generation Inference (TGI) for the small Qwen/Qwen3-0.6B model,