Model Selection
This does the legwork of matching your use case to an actual SageMaker Hub model ID. It queries available models in your region, filters out the AI Registry hubs that don't have JumpStart models, and shows you benchmarks and licenses before you commit. The workflow is sensible: it checks your AWS region first, discovers eligible hubs, then walks you through selection with license info displayed inline. One nice touch is that it doesn't bother you with hub selection if there's only one option. Use this when you're starting a project or switching models, especially if you're not sure which Llama or Mistral variant is actually available in your setup.
- GitHub stars
- 783
- last updated
- Jun 11, 2026
Install
$ npx -y skills add awslabs/agent-plugins --skill model-selection --agent claude-codeInstalls into .claude/skills of the current project.
Files
Model Selection
Guides the user through selecting a base model based on their use case.
When to Use
- User asks which model to use
- User wants to select or change their base model
- User mentions a model name or family (e.g., "Llama", "Mistral", "Nova") — the exact Hub model ID still needs to be resolved
- User wants to evaluate a base model before deciding whether to finetune
Prerequisites
- A
use_case_spec.mdfile exists. If not, activate the use-case-specification skill to generate it first.
Workflow
Step 1: Check Region
Run:
python -c "import boto3; print(boto3.session.Session().region_name)"
None→ STOP. Tell user: "Set your region viaexport AWS_DEFAULT_REGION=us-west-2oraws configure."- Set → store REGION in context, continue.
Step 2: Discover Hub
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- Repository
- github.com/awslabs/agent-plugins
- Category
- AI & Agent Building
- License
- Apache-2.0
- First seen
- Jun 11, 2026
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