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"). Wo
Profile →npx skills add waybarrios/opencode-power-pack --skill hf-cloud-sagemaker-deployment-planner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 — us
- Workflow phases
- Discovery: ask only what you need
- Pathway selection
- Instance selection: check quota before recommending
- Style
aws service-quotas list-service-quotas --service-code sagemaker --region <region> \
What does the hf-cloud-sagemaker-deployment-planner skill do?
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"). Wo
How do I install it?
Run `npx skills add waybarrios/opencode-power-pack --skill hf-cloud-sagemaker-deployment-planner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From waybarrios/opencode-power-pack, a repository with 443 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.