Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
Skills 19 in the Claude listing
Dataset evaluation Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Dataset transformation Generates code that transforms datasets between ML schemas for model training or evaluation. Directory management Manages project directory setup and artifact organization. finetuning Generates code that fine-tunes a base model using SageMaker serverless training jobs. Finetuning technique Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Hyperpod cluster debugger Diagnose and remediate cluster-wide HyperPod (EKS or Slurm) problems — creation / deployment failures (CloudFormation, EFA health check, lifecycle scripts, capacity), EKS access, node replacement, Clo Hyperpod issue report Generate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases. Hyperpod nccl Diagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EF Hyperpod node debugger Diagnose and remediate per-node issues on a HyperPod cluster (EKS or Slurm) — a specific node is unhealthy, unresponsive, stuck, or needs replacing. Hyperpod performance debugger Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Hyperpod slurm debugger Diagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Hyperpod ssm Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). Hyperpod version checker Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python Model deployment Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Model evaluation Generates python code that evaluates SageMaker models. Model selection Selects a base model for the user's use case by querying SageMaker Hub. planning Discovers user intent and generates a structured, step-by-step plan for model customization workflows. Sdk getting started Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use case specification Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. 