Model Deployment

Organization

awslabs

model-deployment

Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.

Overview

Publisher	awslabs
Repository	`agent-plugins`
Skill name	`model-deployment`
Stars	917
Forks	162
Bundled files	10
License	Apache-2.0
Links	Skill source Repository

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.
10 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 Deployment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/model-deployment
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/model-deployment

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-deployment .claude/skills/model-deployment

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Deployment 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 Deployment 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 Deployment 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 Deployment

Identifies the correct deployment pathway based on model characteristics and generates deployment code.

Scope

This skill supports deploying Nova and OSS models that were fine-tuned through SageMaker Serverless Model Customization only.

Not supported:

Base models (not fine-tuned)
Models fine-tuned through other processes
Full Fine-Tuning (FFT) — only LoRA fine-tuned models are supported

Prerequisites

The SDK environment has been verified (SDK version, region, execution role). If not done, activate the sdk-getting-started skill first.

Principles

One thing at a time. Each response advances exactly one decision.
Confirm before proceeding. Wait for the user to agree before moving on. But don't re-ask questions already answered in the conversation — use what you know.
Don't read files until you need them. Only read pathway references after the pathway is confirmed.
Use what you know. If conversation history or artifacts already answer a question, confirm your understanding instead of asking again.

Workflow

Step 1: Identify the Training Job

You need the training job name or ARN. Check the conversation history first — the user may have already mentioned it, or it may be available from earlier steps in the workflow (e.g., fine-tuning). If not, ask the user.

Once you have the training job name or ARN, use the AWS MCP tool to look it up:

Use the AWS MCP tool describe-training-job and extract:
- S3 output path (from ModelArtifacts.S3ModelArtifacts or OutputDataConfig.S3OutputPath)
- IAM role ARN (from RoleArn)
- Region
Use the AWS MCP tool list-tags on the training job ARN and extract:
- Model ID from the sagemaker-studio:jumpstart-model-id tag
Determine the model type from the model ID:
- Contains "nova" (nova-micro, nova-lite, nova-pro) → Nova
- Llama, Mistral, Qwen, GPT-OSS, DeepSeek, etc. → OSS

Unsupported models: This skill only supports OSS and Nova models that were LoRA fine-tuned through SageMaker Serverless Model Customization. If the model doesn't match, tell the user this skill can't help and suggest the finetuning skill.

Step 2: Determine Eligible Deployment Targets

Use the following table:

Model Type	Eligible Targets
OSS	SageMaker, Bedrock
Nova	SageMaker, Bedrock

If only one target is eligible, confirm it with the user. Use details from Step 5.

If multiple targets are eligible, help the user decide. Use details from Step 5.

If no targets are eligible, tell the user and explain why.

Step 3: Let the User Choose a Deployment Target

Present the eligible options to the user. Present these details to help them decide between SageMaker and Bedrock, if both are available options:

SageMaker Endpoint:

Dedicated compute resources for consistent performance
Control instance types and scaling
Best for predictable workloads with specific latency requirements

Bedrock:

Fully managed serverless inference
Auto-scales instantly with no capacity planning
Pay per request
Best for variable workloads with fluctuating demand

Do NOT make a recommendation. Let the user choose.

Do NOT mention technical details like merged/unmerged weights, reference files, or APIs, unless the user asks.

⏸ Wait for user to select a deployment option.

Step 4: Display License Agreement

Before proceeding to deployment, display the model's license or service terms to the user.

Read references/model-licenses.md and look up the model by its model ID (determined in Step 1).
Follow the instructions in the Notes column — use the exact phrasing provided.
If the model ID is not found in the table, warn the user that you could not find license information for their model and recommend they verify the license independently before proceeding.

⏸ Wait for the user to confirm before proceeding.

Step 5: Follow Pathway Workflow

Read the reference file for the selected pathway and follow its instructions.

Model Type	Deployment Target	Reference
OSS	SageMaker	`references/deploy-oss-sagemaker.md`
OSS	Bedrock	`references/deploy-oss-bedrock.md`
Nova	SageMaker	`references/deploy-nova-sagemaker.md`
Nova	Bedrock	`references/deploy-nova-bedrock.md`

Step 6: Post-Deployment Summary

After deployment completes, provide the user with a summary. Cover these topics, using details from the pathway reference doc you followed in Step 5:

What was deployed — endpoint or model name, ARN, status
How to use it — sample invoke code for the specific deployment target
Cost — billing model (instance-based vs. pay-per-request) and what to expect
Cleanup — how to delete the endpoint or model when done

Troubleshooting

How to check if a model was LoRA or FFT fine-tuned

If deployment fails unexpectedly, the model may have been full fine-tuned (FFT) rather than LoRA. To check, download the training job's hydra config from its S3 output path at .hydra/config.yaml:

peft_config populated (r, alpha, dropout, etc.) → LoRA (supported)
peft_config: null → FFT (not supported by this skill)

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 Deployment AI skill do?

Why use Model Deployment on TypingMind?

Because you install it once and use it with any model. Model Deployment 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 Deployment 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-deployment. 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 Deployment?

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 Deployment?

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

Is the Model Deployment 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Model Deployment

Scope

Prerequisites

Principles

Workflow

Step 1: Identify the Training Job

Step 2: Determine Eligible Deployment Targets

Step 3: Let the User Choose a Deployment Target

Step 4: Display License Agreement

Step 5: Follow Pathway Workflow

Step 6: Post-Deployment Summary

Troubleshooting

How to check if a model was LoRA or FFT fine-tuned

Bundled files

Frequently asked questions

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