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Get Started Free →awslabs/model-selection
Selects a base model for the user's use case by querying SageMaker Hub.
Passed 11 of 17 test cases with the skill, and 4 of 17 without it.
and 13 more in the full report
12 fixed2 passed either way5 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
What exact Python code snippet should be executed inline to detect the current AWS region setting via the AWS SDK?
| Checked | Without skill | With skill |
|---|---|---|
| Uses python -c with boto3 session Session region_name evaluation. | Missed | Passed |
The instructions your model reads when the skill loads.
Guides the user through selecting a base model based on their use case.
use_case_spec.md file exists. If not, activate the use-case-specification skill to generate it first.Run:
python -c "import boto3; print(boto3.session.Session().region_name)"
None → STOP. Tell user: "Set your region via export AWS_DEFAULT_REGION=us-west-2 or aws configure."List all available SageMaker Hubs in the user's region by calling the SageMaker ListHubs API using the aws___call_aws tool.
From the results, filter out any hub whose HubDescription contains "AI Registry" — these do not contain JumpStart models.
The remaining hubs are eligible (e.g., SageMakerPublicHub and any private hubs).
If exactly one eligible hub exists, use it automatically — do not ask the user.
If multiple eligible hubs exist, present them to the user and ask which one to use. Example:
I found the following model hubs:
- SageMakerPublicHub — SageMaker Public Hub
- Private-Hub-XYZ — Private Hub models
test cases passed, vs 4 / 17 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.