AWS AI ML

AWSOfficialPublished by AWS

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

From the author

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

— the description by AWS

AutoGineer summary

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

This skill provides a structured framework for managing the Amazon SageMaker model lifecycle. It covers planning, dataset preparation, fine-tuning, model evaluation, and endpoint deployment. The skill assists users in navigating SageMaker-specific workflows, including selecting base models from catalogs, configuring training and processing jobs, and diagnosing endpoint health. It supports various fine-tuning techniques and deployment pathways, ensuring users follow validated procedures for their machine learning projects on AWS.

What it does

Guides users through the end-to-end lifecycle of SageMaker model customization.
Assists in selecting and evaluating base models from SageMaker JumpStart and public hubs.
Provides workflows for validating and transforming training and evaluation datasets.
Supports configuration of fine-tuning techniques like SFT, DPO, RLVR, and RLAIF.
Facilitates the deployment of models to SageMaker endpoints or Bedrock.
Diagnoses endpoint issues including latency, errors, and container logs.

When to use it

When planning a new model customization or deployment project on SageMaker.
When you need to validate or transform datasets for training or evaluation.
When selecting a base model or choosing a fine-tuning technique.
When you need to configure training, processing, or evaluation jobs.
When deploying a model to a SageMaker endpoint or troubleshooting an existing one.
When setting up or managing a SageMaker Managed MLflow environment.

Advantages

Provides structured, step-by-step workflows for complex SageMaker tasks.
Ensures dataset formatting meets specific SageMaker requirements.
Offers clear routing to relevant documentation based on user intent.
Helps resolve configuration issues for IAM, S3, and SDK settings.
Simplifies the diagnosis of endpoint performance and failure logs.

Usage examples

“I need to fine-tune a model on SageMaker. Where do I start?”: The agent will initiate the planning workflow, prompting you to define your use case and requirements before guiding you through model selection and dataset preparation.
“My SageMaker endpoint is returning errors. Can you help?”: The agent will route you to the endpoint diagnostics workflow to collect logs, check instance status, and analyze latency or failure metrics.
“How do I convert my CSV training data for SageMaker?”: The agent will guide you through the dataset transformation workflow, recommending the appropriate tools to convert your data into the required JSONL format.
“Which base model should I use for a text summarization task?”: The agent will trigger the model selection workflow to help you evaluate available models and resolve the correct Hub model ID for your specific requirements.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/aws-ai-ml/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip aws-ai-ml.zip -d .agents/skills
```

Documentation by AWS

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.
Train a model with a SageMaker Training job	references/training-jobs/	Use when the goal is to fit or update model parameters and produce model artifacts or checkpoints. Covers recipes, JumpStart traditional ML, custom training scripts or containers, distributed training, and training-job diagnosis.
Process data with a SageMaker Processing job	references/processing-jobs/	Use when the goal is preprocessing, feature engineering, evaluation, transformation, or ETL without model training as the primary operation. Covers scripts, framework processors, Spark, custom containers, Athena, Redshift, and processing-job diagnosis.
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.

Rules

Service boundary. This skill covers SageMaker, including deployment of a customized model to Bedrock. Bedrock-native work (invoking Bedrock's managed foundation models, Bedrock Agents, Knowledge Bases, Guardrails, AgentCore) is supported by AWS but has no validated workflow in this skill: let the user know and proceed with best-effort guidance using general AWS knowledge. If the user asks for a model on AWS without naming a service, compare SageMaker and Bedrock or ask one service-choice question. Do not select a service by default. The two services have different model catalogs, deployment paths, and pricing, and a default selection can send the user into an incorrect workflow.
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.

Files

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LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
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references/processing-jobs/overview.md

Processing Jobs

Run arbitrary scripts and distributed data workloads on SageMaker managed processing compute with SageMaker Python SDK v3. Covers plain scripts, framework environments, Spark, custom containers, Athena/Redshift dataset definitions, and failure diagnosis.

When to use

Use this capability when the user asks to:

"run my script on SageMaker" or execute Python on managed compute with S3 I/O
run a transform with PyTorch, TensorFlow, Hugging Face, XGBoost, or sklearn dependencies
run PySpark/Spark JAR distributed ETL or process large datasets
run a custom processing container
query Athena or Redshift directly into a processing job
diagnose a failed, stopped, or stuck processing job

Dataset format conversion that is already covered by dataset-transformation stays there; that capability may call SageMaker Processing internally.

Operating assumptions

Area	Assumption
Runtime	Python supported by `sagemaker>=3.22.0,<4.0`; templates install dependencies at use time
Credentials	Ambient AWS credentials plus a SageMaker execution role; caller needs processing create/describe, S3, logs, ECR/DLC, `iam:PassRole`, and Athena/Redshift permissions when selected
Storage	Small scripts/configs may be local; processor uploads code and stages data through S3; container paths live under `/opt/ml/processing/`
Region	Resolve from the AWS session; processing images and S3 query/output locations must be usable in that region
Network	PyPI access is needed for installation; runtime egress depends on the selected container and VPC/network isolation settings

Decision flow

Check conversation and project files before asking for values.
Choose one branch:
- Single Python script or BYOC → code_templates/script_processing.py
- Framework source directory + requirements → code_templates/framework_processing.py
- PySpark or Spark JAR → code_templates/spark_processing.py
- Athena or Redshift query input → code_templates/dataset_definition_processing.py
- Existing job failure/status → references/troubleshooting.md
Use a prebuilt image resolved through image_uris.retrieve when it fits. Follow the managed-image resolution and reproducibility rules in references/configuration.md; review the resolved URI before launch.
For a custom ECR image, use the read-only aws ecr describe-images procedure in references/configuration.md, present candidate tags/digests and timestamps, obtain user selection, and pin the selected digest. Never silently select a mutable latest tag.
For Athena/Redshift, SageMaker executes the query and stages results to the configured S3 URI; do not add a separate manual export step.

Gather inputs

Use cascading fallback: conversation → project files → ask one unresolved question at a time.

Required for launch: script/app or image, role, instance type/count, max runtime, input/output mapping, and wait behavior. Additionally:

Framework: framework, exact supported version, Python version, source directory, optional requirements.txt
Spark: PySpark script or JAR, optional Spark/Python/container version overrides, dependencies, multi-instance count. Apply the version policy from references/configuration.md.
BYOC: ECR image URI and command/entrypoint expectations
Athena: catalog, database, workgroup (optional), SQL, query-result S3 URI, output format
Redshift: cluster, database, DB user, cluster role, SQL, query-result S3 URI, output format

For a Spark JAR, infer the fully qualified main class from the JAR manifest, build file (pom.xml, build.sbt, Gradle), or application documentation. Ask the user if it remains unresolved. SPARK_MAIN_CLASS is required only for APPLICATION_KIND="jar"; clear it for PySpark.

GPU-backed Processing Jobs are supported only with a GPU processing instance, compatible CUDA/framework or BYOC image, and sufficient regional quota. This reference has not validated a dedicated GPU-processing branch, so state that limitation and verify image compatibility/quota rather than assuming a CPU image will work.

Preflight and confirmation

Before generating code or submitting a job:

Verify region, execution-role trust, S3 paths, exact image availability, query permissions, and requested compute quota.
Validate that input/output local paths start with /opt/ml/processing/, because SageMaker Processing mounts input channels and collects declared outputs under that container path; paths outside it are not automatically populated or uploaded.
For GPU requests, additionally verify GPU-compatible image metadata and the relevant Processing instance quota.
Summarize branch, image/framework/version, script/query, S3 I/O, instance type/count, volume, max runtime, and wait behavior.
Link to SageMaker pricing rather than calculating a dollar estimate.
Ask for explicit approval. Stop until approved.

Generate and launch

Read references/configuration.md and the selected template.
Write the customized output under the project directory in notebook or script mode.
Keep WAIT_FOR_COMPLETION=False unless the user asks to block.
Templates submit with wait=False, print job name/ARN immediately, and use logs_for_processing_job only when waiting.
Report output S3 locations after completion.

Diagnose an existing job

Read references/troubleshooting.md. Start with DescribeProcessingJob, then inspect the job's CloudWatch stream. Return failure signature, root cause category, evidence, and fix; state what was not checked.

Out of Scope

The following are supported by SageMaker and AWS but do not have a validated workflow in this reference. If the user's request matches one of these, let them know and proceed with best-effort guidance using general AWS knowledge:

scheduled orchestration through SageMaker Pipelines, Step Functions, or EventBridge
Ground Truth labeling jobs
EMR/Glue cluster administration outside the processing job
a dedicated, end-to-end validated GPU Processing branch

References

code_templates/script_processing.py — plain script and BYOC
code_templates/framework_processing.py — framework image, source directory, requirements
code_templates/spark_processing.py — PySpark or Spark JAR
code_templates/dataset_definition_processing.py — Athena or Redshift query input
references/configuration.md — placeholders and branch settings
references/troubleshooting.md — status and failure classification
SageMaker Processing SDK reference
SageMaker prebuilt containers

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

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Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · skills/core-skills/aws-ai-ml · commit 0a218f8

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
NOTICE (added by AutoGineer from NOTICE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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aws/agent-toolkit-for-aws

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