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sagemaker-ai
Build, train, and deploy AI models on AWS SageMaker with deep ML expertise: validate datasets, fine-tune models (SFT, DPO, RLVR), generate Jupyter notebooks, evaluate model quality, and diagnose HyperPod cluster issues (NCCL, GPU, Slurm, EKS) — all from your coding assistant.
$
npx claudepluginhub awslabs/agent-plugins --plugin sagemaker-aiPopularity
Stars
Med: 0·Avg: 267
Installs
Med: 0·Avg: 1
What's Inside
Skills17
dataset-evaluation
/dataset-evaluation
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
dataset-transformation
/dataset-transformation
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
directory-management
/directory-management
Manages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/) and resolves project naming.
finetuning-setup
/finetuning-setup
Selects a base model and fine-tuning technique (SFT, DPO, or RLVR) for the user's use case by querying SageMaker Hub. Use when the user asks which model or technique to use, wants to start fine-tuning, or mentions a model name or family (e.g., "Llama", "Mistral") — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, validates technique compatibility, and confirms selections.
finetuning
/finetuning
Generates a Jupyter notebook that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, and RLVR trainers, including RLVR Lambda reward function creation.
MCP Servers1
Stats
Version1.1.0
Released
LanguageShell
Stars748
Forks102
Copy clicks2
MaintenanceExcellent
LicenseApache-2.0
Last Commit
Added
Safety Signals
Caution
External network access
Connects to servers outside your machine
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