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Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default.
Passed 22 of 23 test cases with the skill, and 10 of 23 without it.
and 16 more in the full report
13 fixed9 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
When deploying an async endpoint using deploy_async.py without specifying backlog or concurrency flags, what are the default backlog target per instance and max concurrent invocations per instance?
| Checked | Without skill | With skill |
|---|---|---|
| States default backlog per instance target is 5 | Passed | Passed |
| States default max concurrent invocations per instance is 4 | Missed | Passed |
The instructions your model reads when the skill loads.
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.
For every endpoint, the skill creates these as a unit:
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.
For a text-generation LLM (vLLM):
python scripts/deploy.py \
--model-name qwen3-medical \
--image-uri "$IMAGE_URI" \
--inference-ami-version "$AMI" \
--role-arn "$ROLE_ARN" \
--instance-type ml.g5.xlarge \
--region "$REGION" \
--env SM_VLLM_MODEL=Qwen/Qwen3-0.6B \
--env SM_VLLM_HOST=0.0.0.0 \
--env SM_VLLM_TRUST_REMOTE_CODE=true \
--env SM_VLLM_MAX_MODEL_LEN=4096
test cases passed, vs 10 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.