cloud-k8s-deployment
Deploy to cloud Kubernetes clusters - DigitalOcean DOKS primary, with multi-cloud patterns for AWS EKS and GKE. Use when deploying Phase 5 to production cloud environments. (project)
Deploy to cloud Kubernetes clusters - DigitalOcean DOKS primary, with multi-cloud patterns for AWS EKS and GKE. Use when deploying Phase 5 to production cloud environments. (project)
Container strategies for .NET libraries and test environments. Use when working with Docker test environments, container-based integration testing, or NuGet package container builds. Not typically for production deployment of libraries.
Complete Maven standards covering build processes, POM maintenance, dependency management, and Maven integration for CUI projects
Kubernetes deployment best practices including resource management, security, and observability.
Internal skill used by dev-implement during Phase 5 of /dev workflow. NOT user-facing - should only be invoked by dev-ralph-loop inside each implementation iteration. Handles Task agent spawning with TDD enforcement and two-stage review (spec compliance + code quality).
Deploys multiple components to an environment. Use for coordinated deployments of backend, frontend, and landing.
Use when needing workflow job results and log commands from PR - automatically waits for workflows to complete, then returns job summaries with gh commands to retrieve logs without loading large logs into context
Undeploy the latest deployment. Requires authentication. Use for Agentuity cloud platform operations
Use when multiple independent tasks can run simultaneously. Maximizes throughput through parallel Task tool invocations.
Discovery phase for /verify command - finds all components to validate
REQUIRED Phase 5 of /dev workflow. Orchestrates per-task ralph loops with delegated TDD implementation.
JupyterLab ML/AI development environment management via Podman Quadlet. Supports multi-instance deployment, GPU acceleration (NVIDIA/AMD/Intel), token authentication, and per-instance configuration. Use when users need to configure, start, stop, or manage JupyterLab containers for ML development.
Expert guide for building production-ready Kubernetes operators using Go, controller-runtime, and Kubebuilder. Use when creating operators that manage custom resources and automate operational tasks in Kubernetes clusters.
Kubernetes operations, testing, and validation. Use when working with Kubernetes clusters for deploying resources, verifying deployments, testing operators/CRDs, debugging pods, monitoring workloads, or performing end-to-end testing and validation of K8s applications.
KEDA event-driven autoscaling for Kubernetes. Use when installing KEDA, configuring scalers (Prometheus, RabbitMQ, Kafka, etc.), setting up HPA, or implementing autoscaling best practices.
Automatically deploys to Vercel production, uses Vercel MCP to fetch build logs, analyzes errors, fixes them, and retries until successful deployment. Use when deploying to production or fixing deployment issues.
Design versioning, cache busting, progressive rollout, and rollback for remote WebF bundles. Use when the user mentions version pinning, cache busting, force update, rollback, feature flags, or staged rollout.
Deploys features to staging environment with database migrations, health checks, and deployment verification. Use during /ship-staging phase or when deploying to staging environments for validation before production release.