Cloud, CI/CD, monitoring, and deployment (6.9K skills)
Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.
Plan and configure security across Google Kubernetes Engine clusters. Review access permissions, enable Secret Manager integration, and configure protections such as Shielded Nodes and Binary Authorization to strengthen the platform supporting your applications.
Reserve remote Android devices on Google's Developer Device Platform and connect through ADB, Android's device control tool. Check session status and expiration, extend usage time, or cancel sessions and release resources.
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.
Review GKE reliability settings for applications running in Kubernetes. Configure availability safeguards during maintenance, health checks, replica counts, graceful shutdown, and distribution across zones and nodes to help workloads handle disruptions.
Create and connect to managed MySQL, PostgreSQL, or SQL Server databases on Google Cloud. This skill guides instance setup, database creation, access permissions, and connection steps, with references for backups, security, and Terraform configuration.
Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.
Plan and configure GKE cluster networks, including private nodes, IP address ranges, DNS, and outbound connectivity. Use the guidance to set up VPC-native networking and Dataplane V2, which provides built-in network policy enforcement.
Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.
Design production GKE clusters, which run containerized applications on Google Cloud, using recommended Autopilot settings. Review networking, security, monitoring, and upgrade choices, then compare existing clusters against the configuration and identify deviations and remedies.
Diagnose interrupted or repeatedly restarting AI training jobs managed by JobSet on GKE. Correlate restart metrics, node events, host health, and worker logs to distinguish Spot VM preemptions, infrastructure failures, and application crashes, then recommend responses.
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.
Inspect GKE TPU Dynamic Slices, diagnose provisioning failures, and validate workload configuration for single slices or JobSet workloads spanning multiple slices. Get cleanup commands, with confirmation required before destructive changes.
Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.
Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.
Diagnose why GKE Pods cannot authenticate to Google Cloud APIs. Inspect cluster settings, service account bindings, resource permissions, and metadata server health, then provide evidence and concrete fixes for a human to apply.
Answers questions about GKE spending using BigQuery billing exports and live cluster metrics. It breaks down costs by project, namespace, or workload and compares requested computing resources with actual usage to investigate cost increases.
Plan and configure a shared GKE cluster for multiple teams or environments. Set up namespaces, team permissions, resource limits, and network separation, then use labels and GKE cost allocation to track spending by team.
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.
Diagnose GKE nodes marked NotReady or Unknown using node conditions, events, logs, and resource metrics. The skill gathers evidence, distinguishes temporary transitions from faults, and proposes fixes for a person to apply.
Configure Backup for GKE to protect Kubernetes configurations and persistent data. Set backup schedules, retention periods, and customer-managed encryption keys, then prepare restore plans with explicit policies for handling existing resources.
Assess whether a GKE cluster or application is ready for production. Coordinates specialist skills to review scaling, security, reliability, monitoring, recovery, costs, and maintenance, then reports domain status, a weighted readiness score, and remediation priorities.
Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.
Configure how GKE applications receive traffic from the internet or internal networks. The skill provides configuration templates for routing, load balancing, certificates, and access protection, and guides diagnosis of failed health checks, dropped requests, and TLS connection problems.