By tonone-ai
Designs a service mesh deployment on Kubernetes with technology selection, mTLS policy, and traffic management configuration.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin mesh-designInfrastructure Specialist Team — Mesh: Service mesh design — Istio/Linkerd/Envoy, mTLS, traffic management, observability integration
Configure service mesh (Istio, Linkerd) for microservices
Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.
Enterprise microservices architecture design and implementation expert for scalable distributed systems
Design cloud infrastructure, deployment topology, disaster recovery, and capacity planning. Master Kubernetes, Terraform, and multi-region architectures.
Generate Kubernetes manifests and debug pod issues with kubectl
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".