Amazon SageMaker has launched the aws-ai-ml skill, which enhances coding agents like Kiro, Claude Code, and Codex with advanced capabilities for inference optimization and benchmarking. This skill allows agents to generate executable SageMaker Python SDK v3 code based on user requirements and real performance data.
What happened
Amazon SageMaker has introduced the aws-ai-ml skill, a new agent skill that enhances coding agents with deep expertise in inference optimization and benchmarking. This skill is available through the Agent Toolkit for AWS and integrates with any coding agent that supports the Model Context Protocol (MCP). The aws-ai-ml skill enables agents to benchmark endpoints, recommend deployment configurations, and generate executable SageMaker Python SDK v3 code based on user requirements. Users can install the skill on their local machine or within an Amazon SageMaker Studio JupyterLab space, allowing them to quickly start using the enhanced capabilities for their development workflows.
In the Fathom Layer index
- Model Context Protocol (MCP) — MCP Servers
- Claude Code — Developer Tools
What to weigh
- The aws-ai-ml skill requires the Agent Toolkit for AWS.
- Users must manage resources to avoid ongoing charges.
FAQ
How do I install the aws-ai-ml skill?
You can install the aws-ai-ml skill through the Agent Toolkit for AWS on your local machine or within an Amazon SageMaker Studio JupyterLab space.
What can the aws-ai-ml skill do?
The aws-ai-ml skill can benchmark endpoints, recommend deployment configurations, compare performance runs, and generate executable SageMaker Python SDK v3 code.
How long does it take to start using the aws-ai-ml skill?
You can go from zero to a working conversation in 10 minutes.
Source: Amazon Web Services
