sagemaker-optimize

A workflow for finding and deploying a suitable Amazon SageMaker AI model-serving setup. SageMaker AI is AWS's managed service for deploying machine-learning models.

In plain words
What is it for?
It is for searching instance and serving settings, applying techniques such as quantization or speculative decoding, deploying the result, and comparing benchmark performance.
Why use it?
It helps compare serving settings and optimization techniques against a baseline instead of relying on untested configuration choices.

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

Made for: Claude Code, Codex.

Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,563 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 $0.00090 $0.01563
Opus 5 $0.00045 $0.00781
Sonnet 5 $0.00018 $0.00313
Haiku 4.5 $0.00009 $0.00156

Measured 3d ago against content hash 27684f70500b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sagemaker-optimize 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 3d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/config.py, scripts/deploy_recommendation.py, scripts/recommend.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-optimize/SKILL.md · 105 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

7 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. 3d ago First seen · 105 lines · 90 tokens per session scan A 27684f70500b

Subscribe to this mod's changes

sagemaker-optimize 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 90 tokens to every session and 1,563 once invoked, about $0.0005 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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