1417 个 ・ 集成工具: Kubernetes · 按人气排序
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.
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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.
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Reduce Google Kubernetes Engine costs by adjusting CPU and memory requests, setting resource quotas, and choosing Spot VMs or committed use discounts. Match spending decisions to steady usage, variable demand, and workloads that tolerate interruptions.
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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.
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Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.
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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.
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Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.
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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.
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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.
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Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.
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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.
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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.
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Helps configure persistent storage for GKE applications, including disk selection, storage requests, shared files through Filestore, and volume expansion. It also explains how disk compatibility and zone placement affect workload scheduling.
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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.
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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.
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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.
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Investigate interruptions affecting GPU and TPU workloads on GKE using maintenance labels, monitoring queries, logs, and node scheduling restrictions. The skill helps determine whether host maintenance caused a disruption and recommends workload protection measures when maintenance is confirmed or expected.
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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.
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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.
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Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.
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Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.
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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.
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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.
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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.
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