Model Deployment
awslabs/agent-plugins
Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.
Selects, deploys, and customizes AI models on Amazon SageMaker.
Install AWS AI ML
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-ml --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .claude/skills/aws-ai-ml && rm -rf skills-srcUse ~/.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/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .claude/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-mlType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-ml --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .agents/skills/aws-ai-ml && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .agents/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-ml --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .cursor/skills/aws-ai-ml && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .cursor/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path skills/core-skills/aws-ai-ml--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-ml --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .gemini/skills/aws-ai-ml && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .gemini/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-mlInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .github/skills/aws-ai-ml && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .github/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-ai-ml --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/core-skills/aws-ai-ml .opencode/skills/aws-ai-ml && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-ai-ml" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-ai-ml into .opencode/skills/aws-ai-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-ai-ml", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
aws-ai-ml- GitHub stars
- 2.8k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 727 words
- Files
- 135 (incl. references)
- Skills in repo
- 138
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Selects, deploys, and customizes AI models on Amazon SageMaker.
- Fine-tuning models on SageMaker
- SKILL.md covers Routing and Rules
- Runs Python scripts from its folder
- Choosing/selecting which base model to customize
What it does
AWS AI ML is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing/selecting which base model to customize or fine-tune from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training…
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 143 other files, including reference files (for example `references/dataset-evaluation/overview.md`, `references/dataset-evaluation/references/custom-scorer-evaluation-dataset-formats.md` and `references/dataset-evaluation/references/strategy_data_requirements.md`).
It sits in AI & LLM Engineering, covering Fine-tuning, Deployment and File uploads and storage. It works with Amazon Web Services, Amazon SageMaker and MLflow. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.
When your agent uses it
- Fine-tuning models on SageMaker
- Choosing/selecting which base model to customize
- Fine-tune from SageMaker Hub
- Finding a model to deploy without fine-tuning
Example prompts
- “/aws-ai-ml”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 188af2f. 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, from the files we listed), 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
AWS AI ML loads about 1.7k tokens when it runs, and up to ~148k if it reads all its reference files. Until then it costs about 257 tokens; SKILL.md has 727 words of instructions outside code blocks.
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 aws/agent-toolkit-for-aws at commit 188af2f, republished under its Apache-2.0 licence (© aws). 727 words, ~1,748 tokens.
Download SKILL.mdSave it as
.claude/skills/aws-ai-ml/SKILL.md (or your agent's skills folder). This skill also uses 134 other files; get the full folder from GitHub.- name
- aws-ai-ml
- description
- Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing/selecting which base model to customize or fine-tune from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, benchmarking or optimizing inference, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
- metadata.version
- 4
AWS AI/ML Model Customization
Domain expertise for fine-tuning and deploying models on Amazon SageMaker. Covers the full model customization lifecycle from planning through production deployment.
Routing
Match the user's intent to the appropriate reference folder and load only that content.
| User intent | Reference | When to use |
|---|---|---|
| Plan a model customization project, discover scope of work, resume or modify a plan | references/planning/ | User's request relates to model customization or deployment (fine-tuning, training, building, customizing, reviewing data, deploying or standing up a model — including selecting or deploying an off-the-shelf or base model with no training — or getting advice on approach). Always co-activate with other intents to discover full scope. Load this reference FIRST when the request matches multiple rows in this table — read its plan templates before routing to a single-action reference. |
| Define the business problem, success criteria, or use case spec | references/use-case-specification/ | User says "define my use case", "capture requirements", "what should I decide up front", or as default first step in any plan. Skip only if user explicitly declines. |
| Select or change a base model | references/model-selection/ | User asks which model to use, mentions a model name or family, or wants to evaluate what's available. Always activate model-selection even for known model names because the exact Hub model ID must be resolved. Recommended: route to use-case-specification first to capture requirements — this produces better filtering results. Routing to use-case-specification first is not required if user provides a specific model name/ID or declines. If intent is ambiguous (fine-tune vs deploy as-is), model-selection MUST confirm which path before proceeding. Base model filtering for deployment MUST go through select-for-deployment.md and its scripts for any final recommendation. |
| Choose a fine-tuning technique (SFT, DPO, RLVR, RLAIF) | references/finetuning-technique/ | User has decided to fine-tune and needs to choose a technique, or technique needs validation against the selected model's recipes. Requires a base model to be selected first. |
| Validate dataset quality and format | references/dataset-evaluation/ | User says "is my dataset okay", "check my training data", "I have my own data", or before starting any fine-tuning job. |
| Transform or convert a dataset between formats | references/dataset-transformation/ | User says "transform", "convert", "reformat", or dataset schema needs to change. Always use this rather than writing inline transformation code. |
| Generate fine-tuning code and start training | references/finetuning/ | User says "start training", "fine-tune my model", "I'm ready to train", or plan reaches the finetuning step. Supports SFT, DPO, RLVR, RLAIF trainers. |
| Evaluate or benchmark a trained model | references/model-evaluation/ | User says "evaluate my model", "run a benchmark", "test model performance", "compare models". Supports LLM-as-Judge and Custom Scorer. |
| Deploy, benchmark, or optimize a model on an endpoint or Bedrock | references/model-deployment/ | User says "deploy my model", "create an endpoint", or "make it available" (plain deploy) — or, for the inference-optimization sub-workflows on SageMaker Real-Time Endpoints only, "benchmark my endpoint" / "compare benchmark runs" (benchmarking), or states a performance/cost/latency/throughput goal for a new deployment such as "find the cheapest instance" (recommendations). Handles Nova vs OSS deployment pathways. |
| Set up IAM roles, S3 buckets, SDK configuration | references/sdk-getting-started/ | User says "set up", "getting started", "check my environment", "configure SDK", or as first step in any plan involving SageMaker training/evaluation/deployment. |
| Manage project directory and artifacts | references/directory-management/ | Starting a new project, resuming existing one, or when PLAN.md needs to be associated with a project directory. |
| Set up, update, or delete a SageMaker Managed MLflow app | references/manage-mlflow/ | User says "set up MLflow", "create MLflow app", "update my MLflow app", "delete my MLflow app", "I need an MLflow server", asks "what is SageMaker MLflow", or a workflow needs an MLflow backend and none is connected. |
| Diagnose a failing or unhealthy SageMaker endpoint | references/endpoint-diagnostics/ | User reports endpoint errors, latency, inference failures, or a deployment that failed. "What's the status of my endpoint?", "Is my endpoint erroring?", "My endpoint failed — why?", "How many instances are running behind my endpoint?", "Is the latency my model or SageMaker?", "Show me the container logs for my endpoint." NOT for training-job issues, endpoint deletion, scaling changes, or new deployments. |
Show full SKILL.md (74 more words)Show less
Rules
- Progressive disclosure. Load only the reference folder relevant to the current user intent. Do not load all references at once.
- Best-effort help. If the user's request falls outside this skill's references, do not dead-end the conversation. Help them using general AWS knowledge and documentation, and inform the user that the guidance is not covered by this skill's validated workflows.
- Usage attribution. Before running any AWS CLI command or packaged script, set
export AWS_SDK_UA_APP_ID=AWSSkill-SageMaker.
© aws, 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 134 other files (references) in skills/core-skills/aws-ai-ml of aws/agent-toolkit-for-aws.
- SKILL.md
- references/dataset-evaluation/overview.md
- references/dataset-evaluation/references/custom-scorer-evaluation-dataset-formats.md
- references/dataset-evaluation/references/strategy_data_requirements.md
- references/dataset-evaluation/scripts/format_detector.py
- references/dataset-transformation/code_templates/transformation.py
- references/dataset-transformation/overview.md
- references/dataset-transformation/references/code_output_guide.md
- references/dataset-transformation/references/dataset_transformation_code.md
- references/dataset-transformation/references/sagemaker_dataset_formats.md
- references/dataset-transformation/scripts/transformation_tools.py
- references/directory-management/overview.md
- … and 123 more
Open the folder on GitHubat commit 188af2f
Compare with similar skills
AWS AI ML 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AWS AI ML this skillaws/agent-toolkit-for-aws | 2.8k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Model Deploymentawslabs/agent-plugins | 915 | 1 repos | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Python Environment Setup for SageMakerhuggingface/skills | 11k | 2 repos | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Deployment Plannerhuggingface/skills | 11k | 1 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Production Defaultshuggingface/skills | 11k | 1 repos | ~6.9k | Automated safety check: Pass | Apache-2.0 |
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Related
Questions about AWS AI ML
What does AWS AI ML do?
Selects, deploys, and customizes AI models on Amazon SageMaker. AWS AI ML is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Selects, deploys, and customizes AI models on Amazon SageMaker.
When should I use AWS AI ML?
AWS AI ML fits situations like: fine-tuning models on SageMaker; choosing/selecting which base model to customize; fine-tune from SageMaker Hub; finding a model to deploy without fine-tuning.
How do I install AWS AI ML in Claude Code?
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a claude-code`. Or copy the skill folder (skills/core-skills/aws-ai-ml in aws/agent-toolkit-for-aws) into .claude/skills/aws-ai-ml in your project. Claude Code loads it when a task matches its description.
How do I install AWS AI ML in Codex?
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-ai-ml -a codex`. Or copy the skill folder (skills/core-skills/aws-ai-ml in aws/agent-toolkit-for-aws) into .agents/skills/aws-ai-ml in your project. Codex loads it when a task matches its description.
Can I use AWS AI ML 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 aws/agent-toolkit-for-aws --skill aws-ai-ml -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-ai-ml, .gemini/skills/aws-ai-ml, .github/skills/aws-ai-ml and .opencode/skills/aws-ai-ml in your project.
What does AWS AI ML need to run?
Going by SKILL.md and its folder, AWS AI ML needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AWS AI ML 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 AWS AI ML 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 AWS AI ML use?
AWS AI ML 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 AWS AI ML use?
About 1.7k tokens (SKILL.md is roughly 7k 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 146k tokens, read only when the agent opens those files.
What are the alternatives to AWS AI ML?
Skills that share tags, products or a category with AWS AI ML: Model Deployment (awslabs/agent-plugins, 915 stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Python Environment Setup for SageMaker (huggingface/skills, 11k stars) and SageMaker Deployment Planner (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 AWS AI ML?
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,825 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.
Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.