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. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions,...
risk	unknown
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	"2026-07-01T00:00:00.000Z"
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'],
    detailType: ['OrderPlaced'],
  },
});

rule.addTarget(new targets.LambdaFunction(processOrderFunction));

Benefits:

• Loose coupling between services
• Asynchronous processing
• Better fault tolerance
• Independent scaling

7. Design for Failures and Duplicates

Operations must be idempotent

// ✅ GOOD - Idempotent operation
export const handler = async (event: SQSEvent) => {
  for (const record of event.Records) {
    const orderId = JSON.parse(record.body).orderId;

    // Check if already processed (idempotency)
    const existing = await dynamodb.getItem({
      TableName: process.env.TABLE_NAME,
      Key: { orderId },
    });

    if (existing.Item) {
      console.log('Order already processed:', orderId);
      continue; // Skip duplicate
    }

    // Process order
    await processOrder(orderId);

    // Mark as processed
    await dynamodb.putItem({
      TableName: process.env.TABLE_NAME,
      Item: { orderId, processedAt: Date.now() },
    });
  }
};

Implement retry logic with exponential backoff:

async function withRetry<T>(fn: () => Promise<T>, maxRetries = 3): Promise<T> {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, i) * 1000));
    }
  }
  throw new Error('Max retries exceeded');
}

Architecture Patterns

For detailed implementation patterns with full code examples, see the reference documentation:

Event-Driven Architecture Patterns

File: references/eda-patterns.md

• Event Router with EventBridge (custom event bus, schema registry, rule-based routing)
• Queue-Based Processing with SQS (standard/FIFO, DLQ, Lambda consumers)
• Pub/Sub Fan-Out with SNS + SQS (multi-consumer, filtering)
• Saga Pattern with Step Functions (distributed transactions, compensating actions)
• Event Sourcing with DynamoDB Streams (append-only event store, projections)

Serverless Architecture Patterns

File: references/serverless-patterns.md

• API-Driven Microservices (REST API + Lambda backend)
• Stream Processing with Kinesis (real-time, batch windowing, bisect on error)
• Async Task Processing with SQS (background jobs, concurrency control)
• Scheduled Jobs with EventBridge (cron/rate schedules)
• Webhook Processing (signature validation, async queue forwarding)

Important: When using CDK code examples from references, avoid hardcoding resource names (e.g., restApiName, eventBusName). Let CDK generate unique names automatically to enable reusability and parallel deployments. See aws-cdk-development skill for details.

Best Practices

Error Handling

Implement comprehensive error handling:

export const handler = async (event: SQSEvent) => {
  const failures: SQSBatchItemFailure[] = [];

  for (const record of event.Records) {
    try {
      await processRecord(record);
    } catch (error) {
      console.error('Failed to process record:', record.messageId, error);
      failures.push({ itemIdentifier: record.messageId });
    }
  }

  // Return partial batch failures for retry
  return { batchItemFailures: failures };
};

Dead Letter Queues

Always configure DLQs for error handling:

const dlq = new sqs.Queue(this, 'DLQ', {
  retentionPeriod: Duration.days(14),
});

const queue = new sqs.Queue(this, 'Queue', {
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// Monitor DLQ depth
new cloudwatch.Alarm(this, 'DLQAlarm', {
  metric: dlq.metricApproximateNumberOfMessagesVisible(),
  threshold: 1,
  evaluationPeriods: 1,
  alarmDescription: 'Messages in DLQ require attention',
});

Observability

Enable tracing and monitoring:

new NodejsFunction(this, 'Function', {
  entry: 'src/handler.ts',
  tracing: lambda.Tracing.ACTIVE, // X-Ray tracing
  environment: {
    POWERTOOLS_SERVICE_NAME: 'order-service',
    POWERTOOLS_METRICS_NAMESPACE: 'MyApp',
    LOG_LEVEL: 'INFO',
  },
});

Using MCP Servers Effectively

Use the CDK MCP server (via aws-cdk-development dependency) for construct recommendations and CDK-specific guidance when building serverless infrastructure.

Use AWS Documentation MCP to verify service features, regional availability, and API specifications before implementing.

Additional Resources

This skill includes comprehensive reference documentation based on AWS best practices:

• Serverless Patterns: references/serverless-patterns.md

  • Core serverless architectures and API patterns
  • Data processing and integration patterns
  • Orchestration with Step Functions
  • Anti-patterns to avoid

• Event-Driven Architecture Patterns: references/eda-patterns.md

  • Event routing and processing patterns
  • Event sourcing and saga patterns
  • Idempotency and error handling
  • Message ordering and deduplication

• Security Best Practices: references/security-best-practices.md

  • Shared responsibility model
  • IAM least privilege patterns
  • Data protection and encryption
  • Network security with VPC

• Observability Best Practices: references/observability-best-practices.md

  • Three pillars: metrics, logs, traces
  • Structured logging with Lambda Powertools
  • X-Ray distributed tracing
  • CloudWatch alarms and dashboards

• Performance Optimization: references/performance-optimization.md

  • Cold start optimization techniques
  • Memory and CPU optimization
  • Package size reduction
  • Provisioned concurrency patterns

• Deployment Best Practices: references/deployment-best-practices.md

  • CI/CD pipeline design
  • Testing strategies (unit, integration, load)
  • Deployment strategies (canary, blue/green)
  • Rollback and safety mechanisms

External Resources:

• AWS Well-Architected Serverless Lens: https://docs.aws.amazon.com/wellarchitected/latest/serverless-applications-lens/
• ServerlessLand.com: Pre-built serverless patterns
• AWS Serverless Workshops: https://serverlessland.com/learn?type=Workshops

For detailed implementation patterns, anti-patterns, and code examples, refer to the comprehensive references in the skill directory.

Limitations

• Use this skill only when the task clearly matches its upstream source and local project context.
• Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
• Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.