Cloud, CI/CD, monitoring, and deployment (6.8K skills)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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.
Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.
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.
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.
Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
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.
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.
Turn existing PromQL or ListTimeSeries queries into Cloud Monitoring chart definitions in textproto, a text format for Protocol Buffers. Generate readable titles, axis labels, units, and plot types, then validate the resulting widget files.
Configure Cloud Logging within one Google Cloud project. Set storage locations, retention periods, routing rules, and viewing permissions; create metrics from logs; and adjust stored volume through exclusions or sampling, which retains only a portion of entries.