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Official agent skill

Model Deployment

by awslabs in awslabs/agent-plugins

Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.

OfficialApache-2.0Auto-check passedBackend & APIs

Install Model Deployment

skills CLI
$ npx skills add awslabs/agent-plugins --skill model-deployment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install awslabs/agent-plugins model-deployment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sagemaker-ai/skills/model-deployment .claude/skills/model-deployment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
model-deployment
GitHub stars
915
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
731 words
Files
11 (incl. references)
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.

  • Works in 6 steps: Identify the Training Job → Determine Eligible Deployment Targets → Let the User Choose a Deployment Target → …
  • The user says deploy my model
  • SKILL.md covers Scope, Prerequisites, Principles and Workflow, plus 1 more section
  • Runs Python scripts from its folder

What it does

Model Deployment is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. 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.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `code_templates/deploy-nova-bedrock.py`, `code_templates/deploy-nova-sagemaker.py` and `code_templates/deploy-oss-bedrock.py`).

It sits in Backend & APIs, covering Deployment, Serverless and Fine-tuning. It works with Amazon SageMaker and Amazon Web Services. The repository describes itself as: Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS. The licence is Apache-2.0.

When your agent uses it

  • The user says deploy my model
  • Create an endpoint
  • Make it available
  • Asks about deployment options

Example prompts

  • “deploy my model”
  • “create an endpoint”
  • “make it available”
  • “/model-deployment”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Identify the Training Job
  2. Determine Eligible Deployment Targets
  3. Let the User Choose a Deployment Target
  4. Display License Agreement
  5. Follow Pathway Workflow
  6. Post-Deployment Summary

What it can do on your machine

Read from SKILL.md and the folder at commit da51970. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Deployment loads about 1.5k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 731 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from awslabs/agent-plugins at commit da51970, republished under its Apache-2.0 licence (© awslabs). 731 words, ~1,475 tokens.

Download SKILL.mdSave it as .claude/skills/model-deployment/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
model-deployment
description
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.
metadata.version
1.0.0

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

  1. One thing at a time. Each response advances exactly one decision.
  2. 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.
  3. Don't read files until you need them. Only read pathway references after the pathway is confirmed.
  4. 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:

  1. 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
  2. Use the AWS MCP tool list-tags on the training job ARN and extract:
    • Model ID from the sagemaker-studio:jumpstart-model-id tag
  3. 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 TypeEligible Targets
OSSSageMaker, Bedrock
NovaSageMaker, 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.

Show full SKILL.md (250 more words)Show less
Step 4: Display License Agreement

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

  1. Read references/model-licenses.md and look up the model by its model ID (determined in Step 1).
  2. Follow the instructions in the Notes column — use the exact phrasing provided.
  3. 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 TypeDeployment TargetReference
OSSSageMakerreferences/deploy-oss-sagemaker.md
OSSBedrockreferences/deploy-oss-bedrock.md
NovaSageMakerreferences/deploy-nova-sagemaker.md
NovaBedrockreferences/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)

© awslabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in plugins/sagemaker-ai/skills/model-deployment of awslabs/agent-plugins.

  • SKILL.md
  • code_templates/deploy-nova-bedrock.py
  • code_templates/deploy-nova-sagemaker.py
  • code_templates/deploy-oss-bedrock.py
  • code_templates/deploy-oss-sagemaker.py
  • references/code_output_guide.md
  • references/deploy-nova-bedrock.md
  • references/deploy-nova-sagemaker.md
  • references/deploy-oss-bedrock.md
  • references/deploy-oss-sagemaker.md
  • references/model-licenses.md

Open the folder on GitHubat commit da51970

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in awslabs/agent-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Model Deployment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Model Deployment compared with similar skills
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Model Deployment this skillawslabs/agent-plugins9151 repos~1.5kAutomated safety check: PassApache-2.0
AWS AI MLaws/agent-toolkit-for-aws2.8k—~1.7kAutomated safety check: PassApache-2.0
Cloud Provisioningelastic/agent-skills592—~5.4kAutomated safety check: PassApache-2.0
AWS Sam Bootstrapgiuseppe-trisciuoglio/developer-kit355—~954Automated safety check: NotesMIT
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0
Python Environment Setup for SageMakerhuggingface/skills11k2 repos~1.7kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Model Deployment

What does Model Deployment do?

Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Model Deployment is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.

When should I use Model Deployment?

Model Deployment fits situations like: the user says deploy my model; create an endpoint; make it available; asks about deployment options.

How do I install Model Deployment in Claude Code?

Run `npx skills add awslabs/agent-plugins --skill model-deployment -a claude-code`. Or copy the skill folder (plugins/sagemaker-ai/skills/model-deployment in awslabs/agent-plugins) into .claude/skills/model-deployment in your project. Claude Code loads it when a task matches its description.

How do I install Model Deployment in Codex?

Run `npx skills add awslabs/agent-plugins --skill model-deployment -a codex`. Or copy the skill folder (plugins/sagemaker-ai/skills/model-deployment in awslabs/agent-plugins) into .agents/skills/model-deployment in your project. Codex loads it when a task matches its description.

Can I use Model Deployment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add awslabs/agent-plugins --skill model-deployment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-deployment, .gemini/skills/model-deployment, .github/skills/model-deployment and .opencode/skills/model-deployment in your project.

What does Model Deployment need to run?

Going by SKILL.md and its folder, Model Deployment needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Model Deployment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Model Deployment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Model Deployment use?

Model Deployment is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Deployment use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.9k tokens, read only when the agent opens those files.

What are the alternatives to Model Deployment?

Skills that share tags, products or a category with Model Deployment: AWS AI ML (aws/agent-toolkit-for-aws, 2.8k stars), Cloud Provisioning (elastic/agent-skills, 592 stars), AWS Sam Bootstrap (giuseppe-trisciuoglio/developer-kit, 355 stars) and SageMaker Serving Image Selection (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Deployment?

awslabs (a GitHub organization, an official publisher) maintains it in awslabs/agent-plugins, which has 915 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 5, 2026.

Source: awslabs/agent-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.