model-selection
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.
Profile →npx skills add awslabs/agent-plugins --skill model-selection --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.
# 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.md` file 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 via `export AWS_DEFAULT_REGION=us-west-2` or `aws configure`." - Set → store REGION in context, continue. ### Step 2: Discover Hub 1. List all available SageMaker Hubs in the user's region by calling the SageMaker `ListHubs` API using the `aws___call_aws` tool. 2. From the results, filter out any hub whose `HubDescription` contains "AI Registry" — these do not contain JumpStart models. 3. The remaining hubs are eligible (e.g., `SageMakerPublicHub` and any private hubs). 4. If exactly one eligible hub exists, use it auto
- When to Use
- Prerequisites
- Workflow
- Step 1: Check Region
- Step 2: Discover Hub
- Step 3: Select Base Model
- Step 4: Confirm Selection
- References
What does the model-selection skill do?
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.
How do I install it?
Run `npx skills add awslabs/agent-plugins --skill model-selection --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 awslabs/agent-plugins, a repository with 850 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.