Claude Skills Guide

Serverless architecture has revolutionized how developers build and deploy applications. When combined with Claude Code and the ARC (Architectural Runtime Components) pattern, you can create powerful, scalable serverless workflows that are both maintainable and efficient. This guide walks you through using Claude Code to architect ARC-based serverless workflows, complete with practical examples and actionable advice.

Understanding ARC Serverless Architecture

ARC (Architectural Runtime Components) is a framework for building serverless applications that emphasizes modularity, scalability, and operational excellence. It provides a structured approach to designing serverless systems by separating concerns into distinct runtime components that can be independently developed, deployed, and scaled.

The core philosophy behind ARC is treating infrastructure as code while maintaining developer productivity. Each component in the ARC pattern serves a specific purpose:

Claude Code excels at helping architects design and implement these patterns because it can understand the relationships between components, generate boilerplate code, and suggest optimizations based on best practices.

Setting Up Your Claude Code Environment for ARC Development

Before diving into serverless workflow design, ensure your Claude Code environment is properly configured for ARC development. You’ll want to create a dedicated skill that understands your serverless stack and project conventions.

# Create the .claude/ directory and add an arc-architect skill file
mkdir -p .claude/skills
# Create arc-architect.md in .claude/skills/ with ARC-specific instructions

Create a CLAUDE.md file in your project root that defines your ARC architecture preferences:

# Project: ARC Serverless Application

## Architecture Pattern
We use the ARC (Architectural Runtime Components) pattern with:
- TypeScript for all handler and process functions
- AWS Lambda as the compute runtime
- DynamoDB for persistent storage
- SQS for async queue processing

## Code Conventions
- All handlers must implement the Handler interface
- Use dependency injection for testability
- Include comprehensive TypeScript types
- Follow the single-responsibility principle

## Deployment
- Use the serverless framework
- Stage-based deployments (dev, staging, production)
- Enable detailed CloudWatch logging

Designing Serverless Workflows with Claude Code

When architecting serverless workflows, Claude Code can help you design component interactions, generate implementation code, and ensure best practices are followed. Here’s how to approach workflow design systematically.

Define Your Event Triggers

Start by identifying what events trigger your serverless workflow. In ARC, triggers can come from HTTP requests, file uploads, scheduled cron jobs, message queue events, or database changes. Claude Code can help you scaffold the appropriate handler for each trigger type.

// Example: HTTP API Handler for order processing
import { APIGatewayProxyHandler } from 'aws-lambda';
import { OrderProcessor } from './processes/order-processor';
import { OrderStore } from './stores/order-store';

export const handler: APIGatewayProxyHandler = async (event) => {
  const orderProcessor = new OrderProcessor(new OrderStore());
  
  try {
    const order = JSON.parse(event.body || '{}');
    const result = await orderProcessor.process(order);
    
    return {
      statusCode: 201,
      body: JSON.stringify({ success: true, orderId: result.id })
    };
  } catch (error) {
    return {
      statusCode: 400,
      body: JSON.stringify({ error: error.message })
    };
  }
};

Claude Code can generate this boilerplate and guide you through customizing it based on your specific requirements. Simply describe your workflow requirements and let Claude Code scaffold the initial structure.

Implement Async Processing Queues

One of ARC’s strengths is handling asynchronous workloads through queues. When processing involves long-running operations or external API calls, queue-based processing prevents timeouts and improves reliability.

// Example: Queue Producer for async order processing
import { SQS } from 'aws-sdk';

export class OrderQueueProducer {
  private sqs: SQS;
  private queueUrl: string;

  constructor(queueUrl: string) {
    this.sqs = new SQS();
    this.queueUrl = queueUrl;
  }

  async enqueue(order: Order): Promise<void> {
    await this.sqs.sendMessage({
      QueueUrl: this.queueUrl,
      MessageBody: JSON.stringify({
        orderId: order.id,
        type: 'PROCESS_ORDER',
        payload: order
      }),
      // Enable FIFO for ordered processing
      MessageGroupId: order.customerId,
      // Ensure exactly-once processing
      MessageDeduplicationId: `order-${order.id}-${Date.now()}`
    }).promise();
  }
}

Handle Failed Operations with Dead Letter Queues

Robust serverless workflows must handle failures gracefully. ARC recommends implementing dead letter queues (DLQ) for failed messages that cannot be processed after maximum retry attempts.

// Example: Queue Consumer with DLQ handling
export class OrderQueueConsumer {
  private readonly maxRetries = 3;
  
  async processMessage(message: SQS.Message): Promise<void> {
    const attempt = this.getAttemptCount(message);
    const order = JSON.parse(message.Body || '{}');
    
    try {
      await this.processOrder(order);
    } catch (error) {
      if (attempt >= this.maxRetries) {
        // Send to DLQ after max retries
        await this.sendToDeadLetterQueue(message, error);
      }
      throw error; // Re-throw to trigger Lambda retry
    }
  }
  
  private getAttemptCount(message: SQS.Message): number {
    const attempts = message.Attributes?.ApproximateReceiveCount;
    return attempts ? parseInt(attempts, 10) : 1;
  }
}

Practical Example: E-Commerce Order Processing Workflow

Let’s walk through a complete ARC serverless workflow for processing e-commerce orders. This example demonstrates how different components work together to create a robust, scalable system.

Architecture Overview

The workflow consists of:

  1. API Handler: Receives order submissions via HTTP
  2. Validation Process: Validates order data and inventory
  3. Order Store: Persists order details to DynamoDB
  4. Payment Queue: Sends payment requests to async processing
  5. Payment Process: Handles payment processing asynchronously
  6. Notification Process: Sends confirmation emails

Implementation with Claude Code

Describe your workflow to Claude Code and let it generate the implementation:

// Generated with Claude Code - Order Processing Workflow
// Claude Code prompt: "Create an ARC workflow for e-commerce 
// order processing with validation, payment, and notifications"

import { DynamoDB } from 'aws-sdk';

interface Order {
  id: string;
  customerId: string;
  items: OrderItem[];
  total: number;
  status: 'PENDING' | 'PAID' | 'SHIPPED' | 'DELIVERED';
}

export class OrderWorkflow {
  private db: DynamoDB.DocumentClient;
  private paymentQueue: any;
  private notificationService: any;

  async createOrder(order: Order): Promise<Order> {
    // Step 1: Validate order
    this.validateOrder(order);
    
    // Step 2: Save initial order state
    const savedOrder = await this.saveOrder({
      ...order,
      status: 'PENDING',
      createdAt: new Date().toISOString()
    });
    
    // Step 3: Queue for payment processing
    await this.paymentQueue.send({
      orderId: savedOrder.id,
      amount: savedOrder.total,
      customerId: savedOrder.customerId
    });
    
    return savedOrder;
  }
  
  private validateOrder(order: Order): void {
    if (!order.items?.length) {
      throw new Error('Order must contain at least one item');
    }
    if (order.total <= 0) {
      throw new Error('Order total must be positive');
    }
  }
  
  private async saveOrder(order: Order): Promise<Order> {
    await this.db.put({
      TableName: process.env.ORDER_TABLE!,
      Item: order
    }).promise();
    return order;
  }
}

Best Practices for ARC Serverless Workflows

Based on real-world implementations and lessons learned, here are actionable best practices when architecting serverless workflows with Claude Code.

Implement Idempotency

Serverless functions can be invoked multiple times due to retries or duplicate events. Always design your processes to be idempotent:

// Example: Idempotent payment processing
export async function processPayment(
  event: PaymentEvent
): Promise<PaymentResult> {
  // Check if payment already processed
  const existing = await paymentStore.findByOrderId(event.orderId);
  if (existing?.status === 'COMPLETED') {
    return { status: 'DUPLICATE', existingPayment: existing };
  }
  
  // Process payment (idempotent operation)
  const result = await paymentGateway.charge({
    amount: event.amount,
    orderId: event.orderId
  });
  
  await paymentStore.save({
    orderId: event.orderId,
    status: 'COMPLETED',
    transactionId: result.transactionId
  });
  
  return { status: 'SUCCESS', transactionId: result.transactionId };
}

Use Structured Logging

Implement structured logging to make debugging serverless workflows easier:

// Structured logging for CloudWatch
export function createLogger(context: string) {
  return {
    info: (message: string, data?: object) => {
      console.log(JSON.stringify({
        level: 'INFO',
        context,
        message,
        timestamp: new Date().toISOString(),
        ...data
      }));
    },
    error: (message: string, error: Error, data?: object) => {
      console.error(JSON.stringify({
        level: 'ERROR',
        context,
        message,
        error: { message: error.message, stack: error.stack },
        timestamp: new Date().toISOString(),
        ...data
      }));
    }
  };
}

Configure Proper Timeout and Memory

Serverless functions need appropriate timeout and memory settings. Use Claude Code to analyze your workflow and recommend optimal configurations:

# serverless.yml - Generated with Claude Code guidance
service: ecommerce-orders

provider:
  name: aws
  runtime: nodejs18.x
  memorySize: 512
  timeout: 30

functions:
  createOrder:
    handler: handlers/order.create
    events:
      - http:
          path: orders
          method: post
    timeout: 10
    memorySize: 256

  processPayment:
    handler: handlers/payment.process
    events:
      - sqs:
          arn: !GetAtt PaymentQueue.Arn
          batchSize: 10
    timeout: 30
    memorySize: 512
    reservedConcurrency: 5

Optimizing Costs and Performance

Serverless doesn’t mean costless. Follow these strategies to optimize your ARC serverless workflows.

Implement Caching

Use Redis or DynamoDB DAX for frequently accessed data:

// Example: Caching layer implementation
export class CachedOrderStore implements OrderStore {
  private cache: Redis;
  private store: DynamoDBOrderStore;
  private ttl = 300; // 5 minutes

  async findById(id: string): Promise<Order | null> {
    // Check cache first
    const cached = await this.cache.get(`order:${id}`);
    if (cached) {
      return JSON.parse(cached);
    }
    
    // Fetch from database
    const order = await this.store.findById(id);
    
    // Cache the result
    if (order) {
      await this.cache.setex(
        `order:${id}`,
        this.ttl,
        JSON.stringify(order)
      );
    }
    
    return order;
  }
}

Conclusion

Architecting ARC serverless workflows with Claude Code combines the structured approach of the ARC pattern with AI-assisted development. By using Claude Code’s understanding of serverless patterns, you can rapidly scaffold components, implement best practices, and optimize for cost and performance.

Key takeaways: define clear event triggers, implement idempotent processing, use structured logging, right-size your functions, and monitor costs continuously. With these practices and Claude Code as your development partner, you’ll build serverless workflows that are robust, scalable, and cost-effective.

Start by setting up your Claude Code environment with ARC-specific configurations, then iteratively build out your workflow components. The combination of structured architecture patterns and AI-assisted development will significantly accelerate your serverless journey.

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