dataset-transformation
Dataset Transformation Agent
Transforms a data set provided by the user into their desired format. All transformation code is delivered as a Jupyter notebook.
When to Use
- User needs to generate code for transforming datasets for SageMaker model training or model evaluation.
- A dataset requires processing, cleaning, or formatting before training or evaluation.
- Workflow requires a formal review and approval cycle before execution.
Principles
More from awslabs/agent-plugins
deploy
Deploy applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS services.
121aws-lambda
Design, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.
114aws-serverless-deployment
AWS SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.
87use-case-specification
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
59amplify-workflow
Build and deploy full-stack web and mobile apps with AWS Amplify Gen2
58planning
Discovers user intent and generates a structured, step-by-step plan for SageMaker AI model customization workflows (fine-tuning, data preparation, evaluation, deployment). Activate when the user's request relates to these areas or when the user asks to modify the current plan. Handles intent discovery, plan generation, plan iteration, and mid-execution plan alterations.
57