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Model Selection

AWS LabsOfficialPublished by AWS Labs

  • Skill
  • Research and analysis
  • Data and AI
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

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.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The model-selection skill streamlines the process of choosing a base model for AI development. It interacts with the user to identify their requirements, queries available SageMaker Hubs to list compatible models, and provides transparency regarding model licenses and performance benchmarks. By ensuring the exact Hub model ID is resolved, it prepares the user for subsequent development or fine-tuning tasks.

What it does

  • Verifies the active AWS region for SageMaker access.
  • Discovers and lists available SageMaker Hubs for model retrieval.
  • Retrieves and presents a list of available models with their associated licenses.
  • Cross-references model options with provided benchmark data.
  • Confirms the final model selection with the user to ensure alignment with their goals.

When to use it

  • When you need to select or change your base model for a project.
  • When you are unsure which model best fits your specific use case.
  • When you need to resolve a specific Hub model ID for a known model family.
  • When you want to evaluate base models before proceeding to fine-tuning.

Advantages

  • Ensures model selection is based on available resources in the user's specific AWS region.
  • Provides clear visibility into model licensing requirements.
  • Integrates benchmark data to support informed decision-making.
  • Standardizes the model selection process to prevent configuration errors.

Usage examples

“I need to pick a base model for my new project, what are my options?”
The agent will check your AWS region, query the available SageMaker Hubs, and present a list of models with their licenses and benchmark context to help you decide.
“I want to use a Llama model, can you help me set that up?”
The agent will identify the available Llama models in your Hub, display their specific license details, and ask you to confirm the exact model ID to proceed.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/awslabs/model-selection/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip model-selection.zip -d .agents/skills

Documentation by AWS Labs

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

Files

references/benchmarks/agenticIndex.md

Agentic Index

AA's composite agentic score: GDPval-AA (real-world tasks, 44 occupations) + τ²-bench Telecom.

Use this for: Autonomous agents, workflow automation, tool-using assistants.

Source: Artificial Analysis (artificialanalysis.ai), June 2026. "—" = no data — infer from similar models in the same family, but tell the user you're inferring.

#ModelFamilyScore
1Qwen3.6 27B (mode: reasoning)Qwen62.9
2Qwen3.5 27B (mode: reasoning)Qwen54.6
3Gemma 4 31B (mode: reasoning)Google40.9
4Qwen3.5 9B (mode: reasoning)Qwen37.4
5Nova 2.0 Lite (mode: high)Amazon Nova37.3
6GPT-OSS 120B (mode: medium (averaged))OpenAI33.0
7Qwen3.5 4B (mode: reasoning)Qwen32.5
8GPT-OSS 20B (mode: medium (averaged))OpenAI24.7
9Qwen3 14B (mode: reasoning)Qwen14.4
10Qwen3 32B (mode: reasoning)Qwen13.5
11Qwen3 8B (mode: reasoning)Qwen12.6
12Llama 3.3 70B InstructMeta Llama9.1
13Qwen3 1.7B (mode: reasoning)Qwen8.7
14Nemotron 3 Nano 30BNVIDIA8.5
15Qwen3 0.6B (mode: reasoning)Qwen7.0
16Nova LiteAmazon Nova5.8
17Llama 3.1 8B InstructMeta Llama5.5
18Llama 4 Scout 17BMeta Llama5.2
19Nova ProAmazon Nova4.7
20Nova MicroAmazon Nova4.7
21Llama 3.2 1B InstructMeta Llama0.0
—Llama 3.2 3B InstructMeta Llama—
—Qwen3 4B (mode: reasoning)Qwen—
—Qwen2.5 72B InstructQwen—
—Qwen2.5 32B InstructQwen—
—DeepSeek R1 Distill Llama 70BDeepSeek—
—DeepSeek R1 Distill Llama 8BDeepSeek—
—DeepSeek R1 Distill Qwen 32BDeepSeek—
—DeepSeek R1 Distill Qwen 14BDeepSeek—
—DeepSeek R1 Distill Qwen 1.5BDeepSeek—
—Qwen2.5 14B InstructQwen—
—Qwen2.5 7B InstructQwen—
—DeepSeek R1 Distill Qwen 7BDeepSeek—

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS Labs
Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/sagemaker-ai/skills/model-selection · commit fbefb37
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download model-selection.zip