Skills from AWS Labs’s repositories that passed our trust, licence and security gates. Each keeps its author’s attribution.
| Repository | Tier | Verdict | Authorship | Checked | Skills |
|---|---|---|---|---|---|
| awslabs/agent-plugins | Vendor | Approved · created 2026-02-05, pushed 2026-11-05, 937 stars | Written in-house | 2026-11-07 | 34 |
Files are mirrored only where the licence allows redistribution; otherwise we link to the source.
AWS LabsOfficial
Generates a structured use case specification document for model customization projects based on AWS Responsible AI Lens principles.
AWS LabsOfficial
Diagnose and remediate cluster-wide issues for AWS HyperPod (EKS or Slurm) deployments.
AWS LabsOfficial
Design, build, deploy, and debug serverless applications using AWS Lambda and related serverless services.
AWS LabsOfficial
Generate professional, validated AWS architecture diagrams as draw.io XML files.
AWS LabsOfficial
Generates Python code to evaluate SageMaker models using LLM-as-Judge or custom programmatic scoring.
AWS LabsOfficial
Provides remote command execution and file transfer capabilities for SageMaker HyperPod cluster nodes via AWS Systems Manager.
AWS LabsOfficial
Build, run, and manage Firecracker-isolated, snapshot-resumable serverless compute environments with AWS Lambda MicroVMs.
AWS LabsOfficial
Guides users through selecting and confirming an appropriate base model from SageMaker Hub for their specific use case.
AWS LabsOfficial
Automated AWS deployment workflow that analyzes codebases to recommend, estimate, and generate infrastructure-as-code.
AWS LabsOfficial
A comprehensive guide for building and deploying full-stack web and mobile applications using the AWS Amplify Gen2 TypeScript code-first approach.
AWS LabsOfficial
Guidance for building AWS Step Functions state machines using Amazon States Language and JSONata.
AWS LabsOfficial
Validates dataset formatting and quality for Amazon SageMaker model fine-tuning and evaluation.
AWS LabsOfficial
Generates comprehensive, source-cited technical documentation and architecture diagrams for existing codebases.
AWS LabsOfficial
Comprehensive guide and toolset for developing, migrating, and managing Amazon Aurora DSQL serverless distributed databases.
AWS LabsOfficial
Automates the transformation of machine learning datasets between different schemas for training or evaluation.
AWS LabsOfficial
Guides users in selecting and validating fine-tuning techniques for machine learning models.
AWS LabsOfficial
Orchestrates model customization workflows by generating, tracking, and managing structured task plans.
AWS LabsOfficial
Generates deployment code for fine-tuned Nova and OSS models from SageMaker Serverless Model Customization to SageMaker or Bedrock.
AWS LabsOfficial
Deploy and manage web and worker applications on AWS using Elastic Beanstalk's Standard or Cluster modes.
AWS LabsOfficial
Build resilient, long-running, and stateful serverless workflows on AWS Lambda using the Durable Execution SDK.
AWS LabsOfficial
Standardizes project directory structures and artifact organization for AI-assisted development.
AWS LabsOfficial
Diagnose and remediate per-node health issues on Amazon HyperPod clusters (EKS or Slurm).
AWS LabsOfficial
Generates SageMaker serverless training code for fine-tuning base models using various techniques.
AWS LabsOfficial
Evaluate, configure, and migrate high-volume, predictable workloads to AWS Lambda Managed Instances (LMI).