By tonone-ai
Audits queue and streaming infrastructure for missing DLQs, scaling gaps, and reliability issues in Kafka, RabbitMQ, and Redis deployments.
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npx claudepluginhub tonone-ai/tonone --plugin queue-reconInfrastructure 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.
This skill should be used when the user asks to "set up Cloudflare Queues", "create a message queue", "implement queue consumer", "process background jobs", "configure queue retry logic", "publish messages to queue", "implement dead letter queue", or encountering "queue timeout", "message retry", "throughput exceeded", "queue backlog" errors.
Implement event-driven APIs with message queues and event streaming
Use this agent when monitoring system health, optimizing performance, managing scaling, or ensuring infrastructure reliability. This agent excels at keeping studio applications running smoothly while preparing for growth and preventing disasters. Examples:\n\n<example>\nContext: App experiencing slow performance
Lenses Kafka agent skills (topic audit, consumer lag, perf review, schema, security, connectors, DLQ, python client scaffold) powered by the Lenses MCP server
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".