인프라·DevOps 스킬

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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• Expose a GKE application through Gateway API or Ingress
• Split traffic between application versions during a rollout
• Configure Cloud Armor protection and HTTPS certificates

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.

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• Create a PostgreSQL database on Google Cloud
• Connect to a database through Cloud SQL Auth Proxy
• Review database permissions and backup options

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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• Design a GKE cluster shared by multiple teams
• Separate development, staging, and production workloads
• Set team permissions and resource quotas

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.

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• Enable automatic scaling for GKE node pools
• Find why nodes do not scale up
• Identify constraints that prevent nodes from scaling down

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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• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

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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• Choosing between Autopilot and Standard
• Creating a private GKE cluster
• Obtaining kubectl credentials for a cluster

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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• Investigate vbar_control_agent crashes and memory exhaustion
• Check tpu-device-plugin metrics checksum errors
• Inspect conflicts with custom TPU metrics collectors

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.

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• Designing a private GKE cluster network
• Planning IP ranges for Pods, Services, and nodes
• Configuring outbound access with a static source IP

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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• Review cluster access permissions
• Enable Secret Manager integration
• Configure Shielded Nodes and GKE Sandbox

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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• Reduce excessive CPU and memory requests
• Set resource quotas for teams sharing a cluster
• Move interruption-tolerant batch jobs to Spot VMs

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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• Investigating repeated JobSet restarts
• Checking whether Spot VM preemptions interrupted training
• Tracing recurring failures to a physical host

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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• Design a production GKE cluster
• Review network choices before cluster creation
• Audit cluster settings against the recommended configuration

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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• Creating persistent storage for a GKE application
• Sharing files across multiple Pods with Filestore
• Expanding an existing storage volume

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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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

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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• Investigate persistent NotReady or Unknown node status
• Check why kubelet stopped reporting node status
• Investigate evictions or Pending Pods linked to node health

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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• Investigate Pod authentication failures and 403 errors
• Check Kubernetes service account identity bindings
• Diagnose metadata server connectivity problems

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

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• Collect logs from multiple projects in one central bucket
• Query distributed logs without copying their data
• Set log access boundaries by project or log type

Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.

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• Find CPU metrics for Compute Engine
• Identify Cloud Storage write throughput and request metrics
• Check metric data types and units

Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.

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• Investigate failed or slow BigQuery jobs
• Locate bottlenecks in execution stages
• Diagnose sudden increases in query or capacity costs

Create and administer AlloyDB for PostgreSQL databases, including clusters, instances, backups, secure connections, and access permissions. It also covers command-line operations, infrastructure configuration, and MCP tools that let agents automate database tasks.

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• Create an AlloyDB cluster and primary instance
• Manage backups and plan recovery
• Configure secure connections and access permissions

Build Cloud Monitoring requests from metric definitions and resource details. Generate JSON parameters, filters, and aggregation settings for retrieving time series data, then validate the request against the live API.

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• Build JSON requests to retrieve monitoring data
• Filter metrics for specific VMs or databases
• Set aggregation intervals and resource grouping

Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.

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• Convert Cloud Monitoring metric definitions into PromQL
• Filter monitoring queries to specific resources
• Choose aggregations that match metric types

Turn plain-language requests into Cloud Logging queries for Google Cloud. Generate service-specific filters to find errors, investigate issues, or review audit logs using LQL, the language used to search these logs.

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• Finding recent error logs
• Filtering logs for a specific Google Cloud service
• Investigating who changed or accessed a resource

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age