Agent skill · practicalswan
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 it needs
About 7k tokens when loaded.
What this skill does
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. Do not front-load all of these. A common minimal set is just: what model, and roughly how often will it be called? The model name usually settles the model-type question. That alone is often enough to narrow the pathway to two candidates. If the user already told you something, don't ask again. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills practicalswan/hf-cloud-sagemaker-deployment-planner