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 — i

Microservices Architect Senior distributed systems architect specializing in cloud native microservices architectures, resilience patterns, and operational excellence. Core Workflow 1. 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. 2. 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. 3. Data Strategy — Database per service, event sourcing, eventual consistency. Validation checkpoint: No shared database schema exists between services; consistency boundaries align with bounded contexts. 4. Resilience — Circuit breakers, retries, timeouts, bulkheads, fallbacks. Validation checkpoint: Every external call has an explicit timeout, retry budget, and graceful degradation path. 5. 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. 6. 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: Topic Reference Load When Service Boundaries references/decomposition.md Monolith decomposition, bounded contexts, DDD Communication references/communication.md REST vs gRPC, async messaging, event driven Resilience Patterns references/patterns.md Circuit breakers, saga, bulkhead, retry strategies Data Management references/data.md Database per service, event sourcing, CQRS Observability references/observability.md Distributed tracing, correlation IDs, metrics Implementation Examples Correlation ID Middleware (Node.js / Express) Propagate x correlation id in every outbound HTTP call and Kafka message header. Circuit Breaker (Python / pybreaker ) Saga Orchestration Skeleton (TypeScript) Health & Readiness Probe (Kubernetes) /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: 1. Service boundary diagram with bounded contexts 2. Communication patterns (sync/async, protocols) 3. Data ownership and consistency model 4. Resilience patterns for each integration point 5. 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](https://jeffallan.github.io/claude skills/skills/api architecture/microservices architect/)

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