Agent skill · practicalswan
hf-cloud-sagemaker-production-defaults
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
What it needs
About 11k tokens when loaded.
What this skill does
SageMaker Production Defaults The difference between a demo endpoint and one you can leave running is: it scales with traffic, it tells you when it breaks, and you can debug it later. This skill makes those three the default rather than optional extras. By the time this skill runs, the planner has chosen a real-time endpoint, IAM has a usable role, and image-selection has resolved a container URI + AMI version. This skill turns those into an actual deployment. What gets created For every endpoint, the skill creates these as a unit: 1. SageMaker Model — image + env vars + execution role + S3 artifacts 2. Endpoint config — instance type, initial count, optional data capture 3. Endpoint — the real-time endpoint serving inference 4. Autoscaling target + policy — target tracking on invocations per instance 5. CloudWatch alarms — latency, errors, platform overhead An inference-component deployment (deployic.py) creates the same set with two changes: the endpoint config carries the execution role and ManagedInstanceScaling, and an inference component carries the model. Its autoscaling target is the component, not the variant. Data capture (logging requests/responses to S3) is off by default — useful for debugging but creates ongoing S3 costs the user didn't necessarily ask for. Enable with --enable-data-capture. All resources get a consistent tag set including CreatedBy=agentic-deploy-skills for later cleanup. Defaults and reasoning in references/deployment-template.md. Running the deployment For a text-generation LLM (vLLM): For an embedding model (TEI, often on CPU): Note: TEI deployments do not need --inference-ami-version. That flag is vLLM-specific. TEI env vars are also simpler (HFMODELID instead of SMVLLM, no host or trust-remote-code to configure). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills practicalswan/hf-cloud-sagemaker-production-defaults