sagemaker ai — Agent Skill

Overview

This plugin brings deep AWS AI/ML expertise directly into your coding assistant, covering the surface area of Amazon SageMaker AI; currently, skills are provided to assist with the following capability areas:

• Model Customization — End-to-end guided workflows for fine-tuning foundation models, from use case definition through data preparation, training, evaluation, and deployment on Amazon SageMaker AI.
• HyperPod Cluster Operations — Remote command execution on nodes via SSM, version checking, diagnostic reporting, and deep debugging for SageMaker HyperPod training clusters.

Agent Skills

| # | Skill | Description | Documentation | | -- | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------- | | 1 | planning | Builds a dynamic, step-by-step plan tailored to your intents | SKILL.md | | 2 | directory-management | Manages project directory setup, artifact organization, and plan association for new or existing projects | SKILL.md | | 3 | use-case-specification | Guided, conversational process to define your model customization use case goals, key stakeholders, and success criteria | SKILL.md | | 4 | dataset-evaluation | Dataset quality validation, format detection, and data requirements analysis | SKILL.md | | 5 | dataset-transformation | Dataset format conversion and preparation for SageMaker-compatible training formats | SKILL.md | | 6 | finetuning-setup | Fine-tuning technique selection (SFT, DPO, RLVR, etc.) and base model selection | SKILL.md | | 7 | finetuning | Hyperparameter configuration and training job execution | SKILL.md | | 8 | model-evaluation | Evaluation design, benchmark selection, LLM-as-a-judge, and model comparison | SKILL.md | | 9 | model-deployment | Deployment configuration and endpoint setup (SageMaker or Bedrock) | SKILL.md | | 10 | hyperpod-ssm | Remote command execution and file transfer on HyperPod cluster nodes via SSM | SKILL.md | | 11 | hyperpod-version-checker | Check and compare software component versions across HyperPod cluster nodes | SKILL.md | | 12 | hyperpod-issue-report | Generate diagnostic reports for HyperPod troubleshooting and support cases | SKILL.md | | 13 | hyperpod-cluster-debugger | Diagnose cluster-wide HyperPod problems — creation failures, EFA health, lifecycle scripts, capacity | SKILL.md | | 14 | hyperpod-nccl | Diagnose NCCL failures — training hangs, AllReduce timeouts, EFA errors, rendezvous failures | SKILL.md | | 15 | hyperpod-node-debugger | Diagnose per-node issues — GPU hardware, EFA, disk/memory pressure, container runtime | SKILL.md | | 16 | hyperpod-performance-debugger | Diagnose performance issues — uneven NCCL bandwidth, filesystem throughput, straggler nodes | SKILL.md | | 17 | hyperpod-slurm-debugger | Diagnose Slurm scheduler issues — nodes stuck down/drain, jobs pending, GRES miscounts, auto-resume | SKILL.md |

MCP Servers

| # | Server | Description | | - | --------- | ----------------------------------------------------------- | | 1 | aws-mcp | AWS documentation and SOP retrieval via mcp-proxy-for-aws |

Installation

Prerequisite: Install uv

Claude Code

Run in your terminal:

claude plugins marketplace add awslabs/agent-plugins
claude plugins install sagemaker-ai@agent-plugins-for-aws

Or if you're already inside Claude Code, run:

/plugin marketplace add awslabs/agent-plugins
/plugin install sagemaker-ai@agent-plugins-for-aws