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Manage vCluster (virtual Kubernetes clusters) instances using vind. Use when creating, managing, or operating lightweight virtual clusters for development, testing, or multi-tenancy.
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Kubernetes backup and restore with Velero. Use when creating backups, restoring applications, managing disaster recovery, or migrating workloads between clusters.
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Debug Kubernetes pods, nodes, and workloads. Use when pods are failing, containers crash, nodes are unhealthy, or users mention debugging, troubleshooting, or diagnosing Kubernetes issues.
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Kubernetes storage management for PVCs, storage classes, and persistent volumes. Use when provisioning storage, managing volumes, or troubleshooting storage issues.
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Manage Istio service mesh for traffic management, security, and observability. Use for traffic shifting, canary releases, mTLS, and service mesh troubleshooting.
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Audit Kubernetes RBAC, enforce policies, and manage secrets. Use for security reviews, permission audits, policy enforcement with Kyverno/Gatekeeper, and secret management.
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Progressive delivery with Argo Rollouts and Flagger. Use when implementing canary deployments, blue-green deployments, or traffic shifting strategies.
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Kubernetes policy management with Kyverno and Gatekeeper. Use when enforcing security policies, validating resources, or auditing policy compliance.
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Kubernetes networking management for services, ingresses, endpoints, and network policies. Use when configuring connectivity, load balancing, or network isolation.
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Manage multiple Kubernetes clusters, switch contexts, and perform cross-cluster operations. Use when working with multiple clusters, comparing environments, or managing cluster lifecycle.
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Manage kind (Kubernetes IN Docker) local clusters. Use when creating, testing, or developing with local Kubernetes clusters in Docker containers.
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Respond to Kubernetes incidents with runbooks and diagnostics. Use for outages, pod failures, node issues, network problems, and emergency response.
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Manage Helm charts, releases, and repositories. Use for Helm installations, upgrades, rollbacks, chart development, and release management.
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Manage GitOps workflows with Flux and ArgoCD. Use for sync status, reconciliation, app management, source management, and GitOps troubleshooting.
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Deploy and manage Kubernetes workloads with progressive delivery. Use for deployments, rollouts, blue-green, canary releases, scaling, and release management.
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Core Kubernetes resource management for pods, namespaces, configmaps, secrets, and nodes. Use when listing, inspecting, or managing fundamental K8s objects.
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Cilium and Hubble network observability for Kubernetes. Use when managing network policies, observing traffic flows, or troubleshooting connectivity with eBPF-based networking.
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Kubernetes certificate management with cert-manager. Use when managing TLS certificates, configuring issuers, or troubleshooting certificate issues.
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Cluster API lifecycle management for provisioning, scaling, and upgrading Kubernetes clusters. Use when managing cluster infrastructure or multi-cluster operations.
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Configure Kubernetes autoscaling with HPA, VPA, and KEDA. Use for horizontal/vertical pod autoscaling, event-driven scaling, and capacity management.
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Build MCP (Model Context Protocol) servers with tool definitions, resource providers, prompt templates, and transports. Use when exposing APIs to AI agents, building tool servers, converting OpenAPI to MCP, or creating MCP integrations.
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Use to build, wire up, or debug an MCP (Model Context Protocol) server that gives Claude access to an internal API, database, or SaaS tool. Trigger when the user wants Claude to connect to their own system, asks for an MCP server, needs custom tools for Claude Code or the Claude app, or an existing server's tools are not showing up.
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Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
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Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.
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