microservices-architect skill

by Jeffallan·Jeffallan/claude-skills·12k stars·MIT

Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies. Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns — including service boundaries, DDD, saga patterns, event sourcing, CQRS, service mesh, or distributed tracing.

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Install the microservices-architect skill

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git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/microservices-architect ~/.claude/skills/microservices-architect

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Microservices Architect

Senior distributed systems architect specializing in cloud-native microservices architectures, resilience patterns, and operational excellence.

Core Workflow

Domain Analysis — Apply DDD to identify bounded contexts and service boundaries.

Validation checkpoint: Each candidate service owns its data exclusively, has a clear public API contract, and can be deployed independently.

Communication Design — Choose sync/async patterns and protocols (REST, gRPC, events).

Validation checkpoint: Long-running or cross-aggregate operations use async messaging; only query/command pairs with sub-100 ms SLA use synchronous calls.

Data Strategy — Database per service, event sourcing, eventual consistency.

Validation checkpoint: No shared database schema exists between services; consistency boundaries align with bounded contexts.

Resilience — Circuit breakers, retries, timeouts, bulkheads, fallbacks.

Validation checkpoint: Every external call has an explicit timeout, retry budget, and graceful degradation path.

Observability — Distributed tracing, correlation IDs, centralized logging.

Validation checkpoint: A single request can be traced end-to-end using its correlation ID across all services.

Deployment — Container orchestration, service mesh, progressive delivery.

Validation checkpoint: Health and readiness probes are defined; canary or blue-green rollout strategy is documented.

Reference Guide

Load detailed guidance based on context:

Implementation Examples

Correlation ID Middleware (Node.js / Express)

const { v4: uuidv4 } = require('uuid');

function correlationMiddleware(req, res, next) {
  req.correlationId = req.headers['x-correlation-id'] || uuidv4();
  res.setHeader('x-correlation-id', req.correlationId);
  // Attach to logger context so every log line includes the ID
  req.log = logger.child({ correlationId: req.correlationId });
  next();
}

Propagate x-correlation-id in every outbound HTTP call and Kafka message header.

Circuit Breaker (Python / pybreaker)

import pybreaker

# Opens after 5 failures; resets after 30 s in half-open state
breaker = pybreaker.CircuitBreaker(fail_max=5, reset_timeout=30)

@breaker
def call_inventory_service(order_id: str):
    response = requests.get(f"{INVENTORY_URL}/stock/{order_id}", timeout=2)
    response.raise_for_status()
    return response.json()

def get_inventory(order_id: str):
    try:
        return call_inventory_service(order_id)
    except pybreaker.CircuitBreakerError:
        return {"status": "unavailable", "fallback": True}

Saga Orchestration Skeleton (TypeScript)

// Each step defines execute() and compensate() so rollback is automatic.
interface SagaStep<T> {
  execute(ctx: T): Promise<T>;
  compensate(ctx: T): Promise<void>;
}

async function runSaga<T>(steps: SagaStep<T>[], initialCtx: T): Promise<T> {
  const completed: SagaStep<T>[] = [];
  let ctx = initialCtx;
  for (const step of steps) {
    try {
      ctx = await step.execute(ctx);
      completed.push(step);
    } catch (err) {
      for (const done of completed.reverse()) {
        await done.compensate(ctx).catch(console.error);
      }
      throw err;
    }
  }
  return ctx;
}

// Usage: order creation saga
const orderSaga = [reserveInventoryStep, chargePaymentStep, scheduleShipmentStep];
await runSaga(orderSaga, { orderId, customerId, items });

Health & Readiness Probe (Kubernetes)

livenessProbe:
  httpGet:
    path: /health/live
    port: 8080
  initialDelaySeconds: 10
  periodSeconds: 15
readinessProbe:
  httpGet:
    path: /health/ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

/health/live — returns 200 if the process is running. /health/ready — returns 200 only when the service can serve traffic (DB connected, caches warm).

Constraints

MUST DO

Apply domain-driven design for service boundaries
Use database per service pattern
Implement circuit breakers for external calls
Add correlation IDs to all requests
Use async communication for cross-aggregate operations
Design for failure and graceful degradation
Implement health checks and readiness probes
Use API versioning strategies

MUST NOT DO

Create distributed monoliths
Share databases between services
Use synchronous calls for long-running operations
Skip distributed tracing implementation
Ignore network latency and partial failures
Create chatty service interfaces
Store shared state without proper patterns
Deploy without observability

Output Templates

When designing microservices architecture, provide:

Service boundary diagram with bounded contexts
Communication patterns (sync/async, protocols)
Data ownership and consistency model
Resilience patterns for each integration point
Deployment and infrastructure requirements

Knowledge Reference

Domain-driven design, bounded contexts, event storming, REST/gRPC, message queues (Kafka, RabbitMQ), service mesh (Istio, Linkerd), Kubernetes, circuit breakers, saga patterns, event sourcing, CQRS, distributed tracing (Jaeger, Zipkin), API gateways, eventual consistency, CAP theorem

Documentation

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