By tonone-ai
Design message queuing and streaming architectures with technology selection, topic/queue structure, consumer groups, DLQ configuration, and retry strategies
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npx claudepluginhub tonone-ai/tonone --plugin queue-designInfrastructure Specialist Team — Queue: Message queuing and streaming — Kafka, SQS, RabbitMQ design, consumer group strategy, dead letter queues
Message queues and distributed systems expertise. Master queue theory, RabbitMQ, SQS, Kafka, async processing, backpressure, and distributed system patterns.
Implement event-driven APIs with message queues and event streaming
Enterprise microservices architecture design and implementation expert for scalable distributed systems
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
Technical architecture skills for system design, API design, database design, event-driven systems, and migration planning.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".