Agent skill

aws-serverless-eda

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, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

View SKILL.md on GitHub Repository
Stars 152
Forks 20

Install this agent skill to your Project

npx add-skill https://github.com/Microck/ordinary-claude-skills/tree/main/skills_all/aws-skills/skills/aws-serverless-eda

SKILL.md

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.

Integrated MCP Servers

This skill includes 5 MCP servers for serverless development:

AWS Documentation MCP Server

When to use: Always verify AWS service information before implementation

  • Search AWS documentation for latest features and best practices
  • Check regional availability of AWS services
  • Verify service limits and quotas
  • Confirm API specifications and parameters
  • Access up-to-date AWS service information

AWS Serverless MCP Server

Purpose: Complete serverless application lifecycle with SAM CLI

  • Initialize new serverless applications
  • Deploy serverless applications
  • Test Lambda functions locally
  • Generate SAM templates
  • Manage serverless application lifecycle

AWS Lambda Tool MCP Server

Purpose: Execute Lambda functions as tools

  • Invoke Lambda functions directly
  • Test Lambda integrations
  • Execute workflows requiring private resource access
  • Run Lambda-based automation

AWS Step Functions MCP Server

Purpose: Execute complex workflows and orchestration

  • Create and manage state machines
  • Execute workflow orchestrations
  • Handle distributed transactions
  • Implement saga patterns
  • Coordinate microservices

Amazon SNS/SQS MCP Server

Purpose: Event-driven messaging and queue management

  • Publish messages to SNS topics
  • Send/receive messages from SQS queues
  • Manage event-driven communication
  • Implement pub/sub patterns
  • Handle asynchronous processing

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

typescript
// ✅ 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
typescript
// 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

typescript
// ❌ 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

typescript
// ❌ 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

typescript
// 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

typescript
// ✅ 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:

typescript
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');
}

Event-Driven Architecture Patterns

Pattern 1: Event Router (EventBridge)

Use EventBridge for event routing and filtering:

typescript
// Create custom event bus
const eventBus = new events.EventBus(this, 'AppEventBus', {
  eventBusName: 'application-events',
});

// Define event schema
const schema = new events.Schema(this, 'OrderSchema', {
  schemaName: 'OrderPlaced',
  definition: events.SchemaDefinition.fromInline({
    openapi: '3.0.0',
    info: { version: '1.0.0', title: 'Order Events' },
    paths: {},
    components: {
      schemas: {
        OrderPlaced: {
          type: 'object',
          properties: {
            orderId: { type: 'string' },
            customerId: { type: 'string' },
            amount: { type: 'number' },
          },
        },
      },
    },
  }),
});

// Create rules for different consumers
new events.Rule(this, 'ProcessOrderRule', {
  eventBus,
  eventPattern: {
    source: ['orders'],
    detailType: ['OrderPlaced'],
  },
  targets: [new targets.LambdaFunction(processOrderFunction)],
});

new events.Rule(this, 'NotifyCustomerRule', {
  eventBus,
  eventPattern: {
    source: ['orders'],
    detailType: ['OrderPlaced'],
  },
  targets: [new targets.LambdaFunction(notifyCustomerFunction)],
});

Pattern 2: Queue-Based Processing (SQS)

Use SQS for reliable asynchronous processing:

typescript
// Standard queue for at-least-once delivery
const queue = new sqs.Queue(this, 'ProcessingQueue', {
  visibilityTimeout: Duration.seconds(300),
  retentionPeriod: Duration.days(14),
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// FIFO queue for ordered processing
const fifoQueue = new sqs.Queue(this, 'OrderedQueue', {
  fifo: true,
  contentBasedDeduplication: true,
  deduplicationScope: sqs.DeduplicationScope.MESSAGE_GROUP,
});

// Lambda consumer
new lambda.EventSourceMapping(this, 'QueueConsumer', {
  target: processingFunction,
  eventSourceArn: queue.queueArn,
  batchSize: 10,
  maxBatchingWindow: Duration.seconds(5),
});

Pattern 3: Pub/Sub (SNS + SQS Fan-Out)

Implement fan-out pattern for multiple consumers:

typescript
// Create SNS topic
const topic = new sns.Topic(this, 'OrderTopic', {
  displayName: 'Order Events',
});

// Multiple SQS queues subscribe to topic
const inventoryQueue = new sqs.Queue(this, 'InventoryQueue');
const shippingQueue = new sqs.Queue(this, 'ShippingQueue');
const analyticsQueue = new sqs.Queue(this, 'AnalyticsQueue');

topic.addSubscription(new subscriptions.SqsSubscription(inventoryQueue));
topic.addSubscription(new subscriptions.SqsSubscription(shippingQueue));
topic.addSubscription(new subscriptions.SqsSubscription(analyticsQueue));

// Each queue has its own Lambda consumer
new lambda.EventSourceMapping(this, 'InventoryConsumer', {
  target: inventoryFunction,
  eventSourceArn: inventoryQueue.queueArn,
});

Pattern 4: Saga Pattern with Step Functions

Implement distributed transactions:

typescript
const reserveFlight = new tasks.LambdaInvoke(this, 'ReserveFlight', {
  lambdaFunction: reserveFlightFunction,
  outputPath: '$.Payload',
});

const reserveHotel = new tasks.LambdaInvoke(this, 'ReserveHotel', {
  lambdaFunction: reserveHotelFunction,
  outputPath: '$.Payload',
});

const processPayment = new tasks.LambdaInvoke(this, 'ProcessPayment', {
  lambdaFunction: processPaymentFunction,
  outputPath: '$.Payload',
});

// Compensating transactions
const cancelFlight = new tasks.LambdaInvoke(this, 'CancelFlight', {
  lambdaFunction: cancelFlightFunction,
});

const cancelHotel = new tasks.LambdaInvoke(this, 'CancelHotel', {
  lambdaFunction: cancelHotelFunction,
});

// Define saga with compensation
const definition = reserveFlight
  .next(reserveHotel)
  .next(processPayment)
  .addCatch(cancelHotel.next(cancelFlight), {
    resultPath: '$.error',
  });

new stepfunctions.StateMachine(this, 'BookingStateMachine', {
  definition,
  timeout: Duration.minutes(5),
});

Pattern 5: Event Sourcing

Store events as source of truth:

typescript
// Event store with DynamoDB
const eventStore = new dynamodb.Table(this, 'EventStore', {
  partitionKey: { name: 'aggregateId', type: dynamodb.AttributeType.STRING },
  sortKey: { name: 'version', type: dynamodb.AttributeType.NUMBER },
  stream: dynamodb.StreamViewType.NEW_IMAGE,
});

// Lambda function stores events
export const handleCommand = async (event: any) => {
  const { aggregateId, eventType, eventData } = event;

  // Get current version
  const items = await dynamodb.query({
    TableName: process.env.EVENT_STORE,
    KeyConditionExpression: 'aggregateId = :id',
    ExpressionAttributeValues: { ':id': aggregateId },
    ScanIndexForward: false,
    Limit: 1,
  });

  const nextVersion = items.Items?.[0]?.version + 1 || 1;

  // Append new event
  await dynamodb.putItem({
    TableName: process.env.EVENT_STORE,
    Item: {
      aggregateId,
      version: nextVersion,
      eventType,
      eventData,
      timestamp: Date.now(),
    },
  });
};

// Projections read from event stream
eventStore.grantStreamRead(projectionFunction);

Serverless Architecture Patterns

Pattern 1: API-Driven Microservices

REST APIs with Lambda backend:

typescript
const api = new apigateway.RestApi(this, 'Api', {
  restApiName: 'microservices-api',
  deployOptions: {
    throttlingRateLimit: 1000,
    throttlingBurstLimit: 2000,
    tracingEnabled: true,
  },
});

// User service
const users = api.root.addResource('users');
users.addMethod('GET', new apigateway.LambdaIntegration(getUsersFunction));
users.addMethod('POST', new apigateway.LambdaIntegration(createUserFunction));

// Order service
const orders = api.root.addResource('orders');
orders.addMethod('GET', new apigateway.LambdaIntegration(getOrdersFunction));
orders.addMethod('POST', new apigateway.LambdaIntegration(createOrderFunction));

Pattern 2: Stream Processing

Real-time data processing with Kinesis:

typescript
const stream = new kinesis.Stream(this, 'DataStream', {
  shardCount: 2,
  retentionPeriod: Duration.days(7),
});

// Lambda processes stream records
new lambda.EventSourceMapping(this, 'StreamProcessor', {
  target: processFunction,
  eventSourceArn: stream.streamArn,
  batchSize: 100,
  maxBatchingWindow: Duration.seconds(5),
  parallelizationFactor: 10,
  startingPosition: lambda.StartingPosition.LATEST,
  retryAttempts: 3,
  bisectBatchOnError: true,
  onFailure: new lambdaDestinations.SqsDestination(dlq),
});

Pattern 3: Async Task Processing

Background job processing:

typescript
// SQS queue for tasks
const taskQueue = new sqs.Queue(this, 'TaskQueue', {
  visibilityTimeout: Duration.minutes(5),
  receiveMessageWaitTime: Duration.seconds(20), // Long polling
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// Lambda worker processes tasks
const worker = new lambda.Function(this, 'TaskWorker', {
  // ... configuration
  reservedConcurrentExecutions: 10, // Control concurrency
});

new lambda.EventSourceMapping(this, 'TaskConsumer', {
  target: worker,
  eventSourceArn: taskQueue.queueArn,
  batchSize: 10,
  reportBatchItemFailures: true, // Partial batch failure handling
});

Pattern 4: Scheduled Jobs

Periodic processing with EventBridge:

typescript
// Daily cleanup job
new events.Rule(this, 'DailyCleanup', {
  schedule: events.Schedule.cron({ hour: '2', minute: '0' }),
  targets: [new targets.LambdaFunction(cleanupFunction)],
});

// Process every 5 minutes
new events.Rule(this, 'FrequentProcessing', {
  schedule: events.Schedule.rate(Duration.minutes(5)),
  targets: [new targets.LambdaFunction(processFunction)],
});

Pattern 5: Webhook Processing

Handle external webhooks:

typescript
// API Gateway endpoint for webhooks
const webhookApi = new apigateway.RestApi(this, 'WebhookApi', {
  restApiName: 'webhooks',
});

const webhook = webhookApi.root.addResource('webhook');
webhook.addMethod('POST', new apigateway.LambdaIntegration(webhookFunction, {
  proxy: true,
  timeout: Duration.seconds(29), // API Gateway max
}));

// Lambda handler validates and queues webhook
export const handler = async (event: APIGatewayProxyEvent) => {
  // Validate webhook signature
  const isValid = validateSignature(event.headers, event.body);
  if (!isValid) {
    return { statusCode: 401, body: 'Invalid signature' };
  }

  // Queue for async processing
  await sqs.sendMessage({
    QueueUrl: process.env.QUEUE_URL,
    MessageBody: event.body,
  });

  // Return immediately
  return { statusCode: 202, body: 'Accepted' };
};

Best Practices

Error Handling

Implement comprehensive error handling:

typescript
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:

typescript
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:

typescript
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

AWS Serverless MCP Usage

Lifecycle management:

  • Initialize new serverless projects
  • Generate SAM templates
  • Deploy applications
  • Test locally before deployment

Lambda Tool MCP Usage

Function execution:

  • Test Lambda functions directly
  • Execute automation workflows
  • Access private resources
  • Validate integrations

Step Functions MCP Usage

Workflow orchestration:

  • Create state machines for complex workflows
  • Execute distributed transactions
  • Implement saga patterns
  • Coordinate microservices

SNS/SQS MCP Usage

Messaging operations:

  • Test pub/sub patterns
  • Send test messages to queues
  • Validate event routing
  • Debug message processing

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:

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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

Microck/ordinary-claude-skills

nondominium-holochain-dna-dev

Specialized skill for nondominium Holochain DNA development, focusing on zome creation, entry patterns, integrity/coordinator architecture, ValueFlows compliance, and WASM optimization. Use when creating new zomes, implementing entry types, or modifying Holochain DNA code.

152 20
Explore
Microck/ordinary-claude-skills

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

152 20
Explore
Microck/ordinary-claude-skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

152 20
Explore
Microck/ordinary-claude-skills

run-tests

Validate code changes by intelligently selecting and running the appropriate test suites. Use this when editing code to verify changes work correctly, run tests, validate functionality, or check for regressions. Automatically discovers affected test suites, selects the minimal set of venvs needed for validation, and handles test execution with Docker services as needed.

152 20
Explore
Microck/ordinary-claude-skills

skill-navigator

The 100th skill! Your intelligent guide to all 99 other skills. Recommends the perfect skill for any task, creates skill combinations, and helps you discover capabilities you didn't know you had.

152 20
Explore
Microck/ordinary-claude-skills

AgentDB Advanced Features

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

152 20
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results