Model Selection logo

Model Selection

Organization
awslabs
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.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill namemodel-selection
Stars
917
Forks
162
Bundled files
11
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Model Selection AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/model-selection
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/sagemaker-ai/skills/model-selection .claude/skills/model-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Selection in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Model Selection on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Model Selection is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 automatically — do not ask the user.

  5. 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
    Which hub would you like to use?
  6. Store the selected hub name for use in subsequent steps.

Step 3: Select Base Model

First, retrieve all available SageMaker Hub model names by running: python model-selection/scripts/get_model_names.py <hub-name>.

Present all available models to the user with their licenses before making any recommendations. Cross-reference the model list with references/model-licenses.md and display each as <model name> - [<license>](<url>). For example: "Qwen3-4B - Apache 2.0"

If you already know the model the user wants to use (from conversation context or planning files), confirm that it's in the list, display its license, and move on. Otherwise, help the user pick a model following the instructions in references/model-selection.md. Important: Make sure to remember this list of available models when helping with model selection. Don't recommend a model that's not available to the user.

Step 4: Confirm Selection

Present a summary to the user:

Here's what we've selected:
- Base model: [model name]

Ask if they'd like to proceed with this model.

References

  • references/model-selection.md — Model selection instructions and benchmark descriptions
  • references/model-licenses.md — Model license information for display during model selection

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Model Selection AI 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.

Why use Model Selection on TypingMind?

Because you install it once and use it with any model. Model Selection is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Model Selection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/model-selection. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Model Selection?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Model Selection?

As many as you like. As long as a model supports skills, you can use Model Selection with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Model Selection AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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