524 skills · sorted by relevance
Create CI/CD configurations, which automate build, test, and deployment steps, alongside Terraform infrastructure templates and Kubernetes deployment files. Generate health-check and rollback instructions to review before applying changes to cloud infrastructure or application environments.
Plan and create Google Kubernetes Engine clusters using templates for production, development, or AI workloads. Compare Google-managed and self-managed node options, review configurations before creation, and check settings for production readiness.
Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.
Set up a managed Kubernetes environment on Google Cloud, from choosing Autopilot or Standard to creating a GKE cluster, obtaining kubectl connection credentials, and deploying workloads. Includes guidance on private clusters and resource requests.
Create or update Kubernetes YAML configuration files for GKE Autopilot and Standard. Translates application requirements into manifests covering container permissions, CPU and memory allocation, health checks, storage, external routing, and GPU resources for AI model serving.
Inspect KubeSphere clusters with read-only commands. List clusters, view detailed configuration, identify host and member roles, and review connection types, readiness indicators, and KubeSphere and Kubernetes versions without changing cluster resources.
Manage credentials for Git access and deployments in KubeSphere DevOps. Covers SSH keys, passwords, API tokens, and Kubernetes connection settings, including their use in Jenkins pipelines and troubleshooting synchronization failures.
Set up and manage a local Kubernetes development environment for OpenShell using k3d, Skaffold, and Helm. Build and deploy changes, inspect traces, configure optional authentication components, and test behavior across multiple gateway replicas.
Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
Choose diagnostic tools and collect performance traces for production .NET problems such as high CPU usage, memory growth, or slow requests. Get environment-specific collection commands and guidance on which tools to use for later analysis.
Set up mirrord preview environments that run changed services in isolated Kubernetes pods. Configure traffic filters, share previews through HTTPS links, and connect previews to pull request workflows so teams can review changes using shared cluster dependencies.
Set up one mirrord-up.yaml file to debug several related services locally at the same time. Define each application’s command, Kubernetes target, environment settings, and traffic mode, then manage the sessions together with mirrord up.
Configure mirrord to route selected Temporal tasks to a local worker. Generate Kubernetes configuration files, connection settings, and task filters, then check that the settings agree and troubleshoot common setup problems.
Structure authorized container security tests covering Docker escape, Kubernetes privilege escalation, image vulnerabilities, and network isolation. This skill defines scope, alternative investigation paths, and evidence requirements for reporting demonstrated access and its impact.
Set up Auto Ontology with Helm or Docker Compose, or connect an agent to an existing deployment through MCP, an agent connection protocol. Guides database connections, readiness checks, and troubleshooting for startup and sign-in problems.
Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
Run a Kubernetes workflow that changes clothing in cropped person images, searches person attributes, and builds augmented datasets. The skill handles input preparation, run monitoring, and result retrieval for person re-identification data work.
Create videos of anomalous events, such as falls or fires, from seed images using Cosmos3. Run a Kubernetes workflow that generates clips, automatically labels them, and builds an anomaly dataset, with progress monitoring and result retrieval.
Diagnose failed Dynamo deployments using Kubernetes status, events, and logs. Gather evidence without changing the cluster, identify the likely failure layer, and return a specific command or patch to try next.
Build GitLab workflows that automate building, testing, and deployment. The skill covers pipeline stages, dependency caching, runners that execute jobs, and manual approval before production deployment, with examples for Docker, Kubernetes, and Terraform.
Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.
Use Stratus Red Team to simulate attack techniques across AWS, Azure, GCP, and Kubernetes. Prepare test resources, run and repeat simulations, check logs and alerts, and remove resources to assess whether security detections work.
Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.
Plans, executes, and validates Google Kubernetes Engine upgrades for Standard and Autopilot clusters. Creates environment-specific upgrade plans, checklists, and command-based runbooks, with guidance on maintenance timing, node upgrade strategies, workload readiness, and stalled upgrades.