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Localized reading guide

hf-cloud-sagemaker-deployment-plannerPurpose, usage and source

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.

Source retrieved:

Source repository
huggingface/skills
SKILL.md
skills/hf-cloud-sagemaker-deployment-planner/SKILL.md
Repository stars
11,241
Repository updated
2026-10-30
Categories
Cloud
Last collected

License: Apache-2.0

Source SKILL.md

Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.

The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.

What this Skill does

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.

How the upstream workflow works

Workflow phases

Discovery — what is being deployed and what are the constraints (this skill) Pathway selection — real-time / serverless / async / batch / Bedrock CMI (this skill) Context preflight — hf-cloud-aws-context-discovery, then hf-cloud-python-env-setup IAM preflight — hf-cloud-sagemaker-iam-preflight

Upstream passage · L10 ↗

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.

Upstream passage · L6 ↗

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.

Upstream passage · L21 ↗
Upstream passage · bash ↗
aws service-quotas list-service-quotas --service-code sagemaker --region <region> \
    --query "Quotas[?contains(QuotaName, 'for endpoint usage') && Value > \`0\`].[QuotaName, Value]" \
    --output table

Requirements and setup

No short requirements passage was selected from this file. This does not mean the Skill has no dependencies. Check the repository setup instructions and referenced files.

Limits and quality checks

No separate limits passage was selected from this file. This does not mean the Skill is safe for every input or environment. Review the complete source and validate your own result.

Start with a safe trial

  1. Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
  2. Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
  3. Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
  4. Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.

Skill format and directory specification ↗

Read further in the source

Translation and scope

This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.

This introduction refers to the pinned version below; current repository activity and collection time are shown separately.

Source retrieved
Source commit
fabc634a893f62ad5c2039830397efa35fa62715
Source SHA-256
2538e5f705466ef2912c07eb4311befc29940f026dfd17720242e4c976a8d8e9
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