By awslabs
End-to-end SageMaker AI model lifecycle management: validate datasets, select and fine-tune models (SFT, DPO, RLVR, RLAIF), deploy to SageMaker endpoints or Bedrock, diagnose HyperPod cluster and NCCL failures, and evaluate model quality — all through natural-language instructions.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Manages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/, manifests/, agent_memory/) and resolves project naming.
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.
External network access
Connects to servers outside your machine
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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[!IMPORTANT] Generative AI can make mistakes. You should consider reviewing all output and costs generated by your chosen AI model and agentic coding assistant. See AWS Responsible AI Policy.
[!TIP] The Agent Toolkit for AWS is now live! The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs, and was informed by feedback from customers like you. If you're building production software using coding agents or building agents for your own customers, we recommend Agent Toolkit for AWS. It includes IAM condition keys to distinguish agent actions from human ones, CloudWatch and CloudTrail visibility, and skills that have been evaluated for accuracy and effectiveness. This repo continues to work and accept contributions. Over time, the most useful projects here will move into Agent Toolkit for AWS.
Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS. Agent plugins are currently supported by Claude Code, Codex, and Cursor.
AI coding agents are increasingly used in software development, helping developers write, review, and deploy code more efficiently. Agent skills and the broader agent plugin packaging model are emerging as best practices for steering coding agents toward reliable outcomes without bloating model context. Instead of repeatedly pasting long AWS guidance into prompts, developers can now encode that guidance as reusable, versioned capabilities that agents invoke when relevant. This improves determinism, reduces context overhead, and makes agent behavior easier to standardize across teams. Agent plugins act as containers that package different types of expertise artifacts together. A single agent plugin can include:
As new types of expertise artifacts emerge in this space, they can be packaged into agent plugins, making the evolution transparent to developers.
To maximize the benefits of plugin-assisted development while maintaining security and code quality, follow these essential guidelines:
claude plugin install sagemaker-ai@claude-plugins-officialDesign, build, deploy, test, and debug serverless applications with AWS Serverless services.
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Migrate, modernize, and upgrade codebases to AWS. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. AWS Transform - continuous modernization analyzes codebases for tech debt, security issues, and upgrade opportunities, then remediates them.
Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning).
AIDLC Operations phase automation — self-improving loops, autonomous deploys, continuous evaluation, incident response, and cost governance on AWS. Humans approve at checkpoints; agents execute diagnosis, proposal, and remediation between gates.
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Authoritative, source-cited playbook that lets a coding agent autonomously design, configure, deploy, and troubleshoot production-grade AI agents on AWS - Strands Agents SDK, Amazon Bedrock (Converse, Guardrails, Knowledge Bases), and Bedrock AgentCore (Runtime, Memory, Gateway, Identity, Browser/Code Interpreter) - with Terraform-first IaC and CloudWatch/OpenTelemetry observability. Every recommendation traces to an official AWS source.
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ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows