# Hf Cloud Sagemaker Deployment Planner

Plan and coordinate the deployment of a model to Amazon SageMaker AI.

- Repository: https://github.com/huggingface/skills (11249 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add huggingface/skills --skill hf-cloud-sagemaker-deployment-planner -g -a claude-code -y`
Codex: `npx skills add huggingface/skills --skill hf-cloud-sagemaker-deployment-planner -g -a codex -y`
Cursor: `npx skills add huggingface/skills --skill hf-cloud-sagemaker-deployment-planner -g -a cursor -y`
Gemini CLI: `npx skills add huggingface/skills --skill hf-cloud-sagemaker-deployment-planner -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add huggingface/skills`

## Description

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS, including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models, picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work, it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# SageMaker Deployment Planner

You are helping a user deploy a model to Amazon SageMaker. Most users invoking this skill want the model deployed with reasonable defaults, in as few questions as possible. Ask only what you need, recommend a pathway honestly, and hand off to the specialized skills.

## Workflow phases

1. **Discovery**, what is being deployed and what are the constraints (this skill)
2. **Pathway selection**, real-time / serverless / async / batch / Bedrock CMI (this skill)
3. **Context preflight**, `hf-cloud-aws-context-discovery`, then `hf-cloud-python-env-setup`
4. **IAM preflight**, `hf-cloud-sagemaker-iam-preflight`
5. **Image selection**, `hf-cloud-serving-image-selection`
6. **Deployment**, `hf-cloud-sagemaker-production-defaults`

Phases 1, 2 are this skill's job. The others activate when their patterns match.

## Discovery: ask only what you need

You will eventually need to know:

- **What model**: HuggingFace ID, S3 path to artifacts, or model name. If the user is vague ("the model I fine-tuned"), ask for the artifact location.
- **Model type**: text-generation LLM, embedding/reranker, or other (classifier, NER, etc.). This determines the serving stack, usually inferable from the model name (anything ending in `-embed-*`, starting with `BAAI/bge-`, `sentence-transformers/*` etc. is embeddings; chat/instruct models are LLMs). Only ask if it's genuinely ambiguous.
- **Traffic shape**: roughly how often will this be called?
- **Latency tolerance**: interactive, near-real-time, or async?
- **Cost sensitivity**: ask only if the user signals it or the traffic pattern is ambiguous.

Region comes from `hf-cloud-aws-context-discovery`, don't ask unless the user volunteers it.

The rest: https://github.com/huggingface/skills/blob/HEAD/skills/hf-cloud-sagemaker-deployment-planner/SKILL.md