How does this skill optimize Lambda performance?

It implements ARM64 architecture for 34% better price-performance, provides precise memory-to-vCPU mapping guides, and offers strategies for cold start mitigation such as Provisioned Concurrency.

Does it handle IAM roles and security?

The skill includes parameters for Execution Role ARNs and follows the principle of least privilege, ensuring functions have necessary permissions for VPCs and other AWS services.

Can I use this skill to troubleshoot existing Lambda functions?

Yes, it includes a comprehensive troubleshooting guide for common issues like OOM errors, timeouts, and VPC connectivity problems, complete with CloudWatch log patterns.

Does this skill support multiple runtimes?

Yes, it provides specific implementation templates and best practices for popular environments including Python 3.12 and Node.js 20.x.

AWS Lambda Functions

Name: AWS Lambda Functions
Author: pluginagentmarketplace

bypluginagentmarketplace

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Cloud Infrastructure

Builds and optimizes high-performance serverless functions with production-ready templates and best-practice configurations.

About

The AWS Lambda Functions skill empowers Claude to architect, deploy, and fine-tune serverless infrastructure with precision. It provides standardized handler templates for Python and Node.js, automates the configuration of memory and timeout settings, and implements advanced performance optimizations like ARM64 architecture and cold start mitigation. Designed for developers building everything from simple APIs to complex event-driven pipelines, this skill ensures your serverless code is cost-effective, scalable, and follows AWS security and observability standards.

Key Features

Optimized handler templates for Python 3.12 and Node.js 20.x
Automated function creation and deployment package generation
1 GitHub stars
Integrated observability with CloudWatch log patterns and X-Ray tracing
Cold start mitigation strategies including SnapStart and ARM64 usage
Resource-to-workload mapping for Memory and CPU optimization

Use Cases

Building scalable REST APIs and backend services
Creating scheduled cloud maintenance and infrastructure automation tasks
Automating event-driven data processing from S3, DynamoDB, or Kinesis

About

Key Features

Optimized handler templates for Python 3.12 and Node.js 20.x
Automated function creation and deployment package generation
1 GitHub stars
Integrated observability with CloudWatch log patterns and X-Ray tracing
Cold start mitigation strategies including SnapStart and ARM64 usage
Resource-to-workload mapping for Memory and CPU optimization

Use Cases

Building scalable REST APIs and backend services
Creating scheduled cloud maintenance and infrastructure automation tasks
Automating event-driven data processing from S3, DynamoDB, or Kinesis