By tonone-ai
Designs backpressure and scaling strategies for queue consumer systems, including auto-scaling configuration, lag alerting, and tradeoff analysis.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin queue-scaleDesign 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".
Infrastructure 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.
A divide-and-conquer wiki of system-design skills. Reason about a design problem one part at a time -- requirements, capacity, APIs, storage, caching, load balancing, messaging, consistency, resilience, delivery, and scaling -- each with generic recipes, cloud-provider variants (AWS/Azure/GCP/Temporal), explicit trade-offs, and failure-mode analysis. Optimizes for reasoning over memorized architectures.
Analyze and plan for capacity requirements
Ultra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.