/aws-serverless-eda

name: aws-serverless-eda
description: "AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows."
risk: critical
source: https://github.com/zxkane/aws-skills/tree/main/plugins/serverless-eda/skills/aws-serverless-eda
source_repo: zxkane/aws-skills
source_type: community
date_added: 2026-07-01
license: MIT
license_source: https://github.com/zxkane/aws-skills/blob/main/LICENSE

AWS Serverless & Event-Driven Architecture

This skill provides comprehensive guidance for building serverless applications and event-driven architectures on AWS based on Well-Architected Framework principles.

AWS Documentation Requirement

Always verify AWS facts using MCP tools (`mcp__aws-mcp__*` or `mcp__*awsdocs*__*`) before answering. The `aws-mcp-setup` dependency is auto-loaded — if MCP tools are unavailable, guide the user through that skill's setup flow.

Serverless MCP Servers

This skill leverages the CDK MCP server (provided via `aws-cdk-development` dependency) and AWS Documentation MCP for serverless guidance.

> **Note**: The following AWS MCP servers are available separately via the Full AWS MCP Server (see `aws-mcp-setup` skill) and are not bundled with this plugin: > - AWS Serverless MCP — SAM CLI lifecycle (init, deploy, local test) > - AWS Lambda Tool MCP — Direct Lambda invocation > - AWS Step Functions MCP — Workflow orchestration > - Amazon SNS/SQS MCP — Messaging and queue management

When to Use This Skill

Use this skill when:

• Building serverless applications with Lambda
• Designing event-driven architectures
• Implementing microservices patterns
• Creating asynchronous processing workflows
• Orchestrating multi-service transactions
• Building real-time data processing pipelines
• Implementing saga patterns for distributed transactions
• Designing for scale and resilience

AWS Well-Architected Serverless Design Principles

1. Speedy, Simple, Singular

**Functions should be concise and single-purpose**

// ✅ GOOD - Single purpose, focused function
export const processOrder = async (event: OrderEvent) => {
  // Only handles order processing
  const order = await validateOrder(event);
  await saveOrder(order);
  await publishOrderCreatedEvent(order);
  return { statusCode: 200, body: JSON.stringify({ orderId: order.id }) };
};

// ❌ BAD - Function does too much
export const handleEverything = async (event: any) => {
  // Handles orders, inventory, payments, shipping...
  // Too many responsibilities
};

**Keep functions environmentally efficient and cost-aware**:

• Minimize cold start times
• Optimize memory allocation
• Use provisioned concurrency only when needed
• Leverage connection reuse

2. Think Concurrent Requests, Not Total Requests

**Design for concurrency, not volume**

Lambda scales horizontally - design considerations should focus on:

• Concurrent execution limits
• Downstream service throttling
• Shared resource contention
• Connection pool sizing

// Consider concurrent Lambda executions accessing DynamoDB
const table = new dynamodb.Table(this, 'Table', {
  billingMode: dynamodb.BillingMode.PAY_PER_REQUEST, // Auto-scales with load
});

// Or with provisioned capacity + auto-scaling
const table = new dynamodb.Table(this, 'Table', {
  billingMode: dynamodb.BillingMode.PROVISIONED,
  readCapacity: 5,
  writeCapacity: 5,
});

// Enable auto-scaling for concurrent load
table.autoScaleReadCapacity({ minCapacity: 5, maxCapacity: 100 });
table.autoScaleWriteCapacity({ minCapacity: 5, maxCapacity: 100 });

3. Share Nothing

**Function runtime environments are short-lived**

// ❌ BAD - Relying on local file system
export const handler = async (event: any) => {
  fs.writeFileSync('/tmp/data.json', JSON.stringify(data)); // Lost after execution
};

// ✅ GOOD - Use persistent storage
export const handler = async (event: any) => {
  await s3.putObject({
    Bucket: process.env.BUCKET_NAME,
    Key: 'data.json',
    Body: JSON.stringify(data),
  });
};

**State management**:

• Use DynamoDB for persistent state
• Use Step Functions for workflow state
• Use ElastiCache for session state
• Use S3 for file storage

4. Assume No Hardware Affinity

**Applications must be hardware-agnostic**

Infrastructure can change without notice:

• Lambda functions can run on different hardware
• Container instances can be replaced
• No assumption about underlying infrastructure

**Design for portability**:

• Use environment variables for configuration
• Avoid hardware-specific optimizations
• Test across different environments

5. Orchestrate with State Machines, Not Function Chaining

**Use Step Functions for orchestration**

// ❌ BAD - Lambda function chaining
export const handler1 = async (event: any) => {
  const result = await processStep1(event);
  await lambda.invoke({
    FunctionName: 'handler2',
    Payload: JSON.stringify(result),
  });
};

// ✅ GOOD - Step Functions orchestration
const stateMachine = new stepfunctions.StateMachine(this, 'OrderWorkflow', {
  definition: stepfunctions.Chain
    .start(validateOrder)
    .next(processPayment)
    .next(shipOrder)
    .next(sendConfirmation),
});

**Benefits of Step Functions**:

• Visual workflow representation
• Built-in error handling and retries
• Execution history and debugging
• Parallel and sequential execution
• Service integrations without code

6. Use Events to Trigger Transactions

**Event-driven over synchronous request/response**

// Pattern: Event-driven processing
const bucket = new s3.Bucket(this, 'DataBucket');

bucket.addEventNotification(
  s3.EventType.OBJECT_CREATED,
  new s3n.LambdaDestination(processFunction),
  { prefix: 'uploads/' }
);

// Pattern: EventBridge integration
const rule = new events.Rule(this, 'OrderRule', {
  eventPattern: {
    source: ['orders'],
    de