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Found 72 Skills
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems.
This skill should be used when implementing fault tolerance and resilience patterns in Spring Boot applications using the Resilience4j library. Apply this skill to add circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback mechanisms to prevent cascading failures, handle transient errors, and manage external service dependencies gracefully in microservices architectures.
Build microservices - Spring Cloud, service mesh, event-driven, resilience patterns
Expert in resilience testing, fault injection, and building anti-fragile systems using controlled experiments.
Distributed systems patterns for locking, resilience, idempotency, and rate limiting. Use when implementing distributed locks, circuit breakers, retry policies, idempotency keys, token bucket rate limiters, or fault tolerance patterns.
Review distributed systems patterns, concurrency, and resilience. Analyzes retry policies, idempotency, timeouts, circuit breakers, and race conditions. Use when reviewing async code, workers, queues, or distributed transactions.
Chaos engineering principles, controlled failure injection, resilience testing, and system recovery validation. Use when testing distributed systems, building confidence in fault tolerance, or validating disaster recovery.
Create and manage chaos experiments using Harness Chaos Engineering via MCP. Run resilience tests like pod deletion, CPU stress, and network faults. Use when user says "chaos experiment", "chaos engineering", "resilience test", "chaos test", or wants to test system reliability.
Implement the circuit breaker pattern to prevent cascade failures in distributed systems. Use when adding resilience to API clients, external service calls, or any operation that can fail and should fail fast.
When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
Design state machines, orchestration workflows, saga patterns, and resilience strategies for distributed systems, AI agents, and complex async processes. Use when asking for a workflow, state machine, orchestration design, saga, HITL checkpoint, or process resilience strategy.