Skill UI

Provides canonical AWS serverless patterns covering Lambda handlers, DynamoDB single-table modeling, CDK infrastructure, and S3 event processing along with anti-patterns and a checklist for production-ready deployments.

This comprehensive guide covers the best practices for building production-ready, scalable serverless applications on Amazon Web Services (AWS). Learn to leverage core services like Lambda, API Gateway, and DynamoDB, and implement advanced patterns such as event-driven architectures (SQS/SNS). The content emphasizes performance optimization, robust error handling, structured logging, and deploying infrastructure using SAM/CDK.

This comprehensive guide covers the best practices for building production-ready, scalable serverless applications on Amazon Web Services (AWS). Learn to leverage core services like Lambda, API Gateway, and DynamoDB, and implement advanced patterns such as event-driven architectures (SQS/SNS). The content emphasizes performance optimization, robust error handling, structured logging, and deploying infrastructure using SAM/CDK.

Automated guidance for designing DynamoDB tables within AWS, offering best-practice patterns, production-ready configurations, and validation tailored to compute, storage, networking, and serverless use cases.

Expert guidance on designing highly scalable, distributed NoSQL databases, specifically covering Apache Cassandra and Amazon DynamoDB. This skill emphasizes the 'Query-First' modeling approach, teaching best practices like partitioning, denormalization, single-table design, and avoiding common anti-patterns (e.g., hot partitions, joins) to ensure optimal read/write performance at massive scale.

This skill provides expert guidance on creating, reviewing, and managing production-grade AWS infrastructure using Terraform. It focuses on best practices for reusable module design, robust remote state management (S3 + DynamoDB), and adhering to modern HashiCorp Configuration Language (HCL) patterns like using 'for_each'. Ideal for teams building or auditing complex, scalable AWS environments.

A comprehensive guide for designing and building scalable, resilient serverless applications and event-driven architectures on AWS. It covers best practices using Lambda, API Gateway, DynamoDB, Step Functions, and adheres to the AWS Well-Architected Framework principles, ensuring optimal performance, cost-efficiency, and maintainability.

Achieve complete integration with Amazon Alexa to build sophisticated, intelligent voice skills. This system transforms a standard Alexa device into an ultra-intelligent assistant by utilizing Claude as the core LLM brain, while deeply integrating with the entire AWS ecosystem (Lambda, DynamoDB, Polly, Smart Home). Ideal for complex, specialized voice interaction applications.

Auri is an advanced, next-generation voice assistant built upon Amazon Alexa and the powerful Claude AI model. Unlike traditional assistants, Auri conducts real, contextual conversations, enabling deep reasoning, remembering user context via DynamoDB, and providing natural, empathetic responses in Brazilian Portuguese. It elevates interactions from simple commands to complex, multi-turn dialogues requiring sophisticated natural language understanding.

A comprehensive guide for authorized red-team assessments using the Pacu framework. This process details how to enumerate IAM policies, scan for privilege escalation paths, test data access across services (S3, DynamoDB), and simulate lateral movement to determine the blast radius of compromised AWS credentials.

This catalog provides essential database abstractions and specialized clients for robust data persistence. It includes core components like DbRegistry, DynamoDBClient, and various filtering utilities. By utilizing these standardized imports, developers can standardize data access logic, ensuring clean code separation, type safety, and scalable architecture in their applications.

This comprehensive workflow analyzes the health status of various AWS resources (EC2, Lambda, RDS, DynamoDB, etc.). It uses AWS CLI to diagnose potential issues by analyzing CloudWatch logs, metrics, and performance data. The process culminates in classifying root causes and generating actionable, tiered remediation plans (Immediate, Short-term, Long-term) to ensure system resilience and stability.