# Hf Cloud Sagemaker Deployment Planner

Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and...

- Repository: https://github.com/waybarrios/opencode-power-pack (549 stars)
- Category: Media
- License: MIT
- Last updated: 2026-10-28
- Runs scripts: no, instructions only

## Install

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

## Description

Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation 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/waybarrios/opencode-power-pack/blob/HEAD/skills/hf-cloud-sagemaker-deployment-planner/SKILL.md