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hf-cloud-sagemaker-deployment-planner

Skill

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 t

0Machine Learning

Install

git clone https://github.com/huggingface/skills.git ~/.claude/skills/hf-cloud-sagemaker-deployment-planner

What is hf-cloud-sagemaker-deployment-planner?

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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~238 tokens of context used while enabled, before you invoke anything

Documentation

README · ~4 min read

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.

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