sagemaker-deploy

A deployment tool for supported open-weight Hugging Face models, which are models whose weights can be downloaded and run by others. It transfers a model to Amazon S3 and deploys it to a real-time Amazon SageMaker AI endpoint using vLLM or SGLang containers.

In plain words
What is it for?
Use it to stage models in S3, create and smoke-test SageMaker endpoints, and remove the deployed endpoint when finished.
Why use it?
It covers the path from model files to a live SageMaker endpoint, including a basic test and cleanup after deployment.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/aws-samples/sample-sagemaker-agentic-model-deployment/sagemaker-deploy
Any agent
npx skills add aws-samples/sample-sagemaker-agentic-model-deployment --skill sagemaker-deploy
Clone the repo
git clone --depth 1 https://github.com/aws-samples/sample-sagemaker-agentic-model-deployment

Made for: Claude Code, Codex.

Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,445 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00067 $0.02445
Opus 5 $0.00034 $0.01222
Sonnet 5 $0.00013 $0.00489
Haiku 4.5 $0.00007 $0.00245

Measured 2d ago against content hash 742e0287c5e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sagemaker-deploy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/config.py, scripts/deploy.py, scripts/smoke_test.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.kiro/skills/sagemaker-deploy/SKILL.md · 230 lines

The source is not reproduced here

Licensed MIT-0

The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 230 lines · 67 tokens per session scan A 742e0287c5e0

Subscribe to this mod's changes

sagemaker-deploy is a skill published in the GitHub repository aws-samples/sample-sagemaker-agentic-model-deployment (5 stars, last pushed 1mo ago), licensed MIT-0. It adds 67 tokens to every session and 2,445 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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