Gke Observability
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive reference for configuring and diagnosing observability in Google Kubernetes Engine (GKE). It covers the setup of Cloud Logging, Cloud Monitoring, and Managed Service for Prometheus (GMP). It includes guidance on monitoring control-plane components, worker nodes, and pod health, as well as troubleshooting common issues like missing metrics, alert evaluation failures, and API server latency. The skill emphasizes the 'golden path' for configuration while highlighting critical caveats regarding billing, IAM permissions, and the non-default status of control-plane metrics.
What it does
- Configures GKE logging and monitoring components via gcloud commands.
- Provides LQL queries for diagnosing control-plane logs and Kubernetes events.
- Offers PromQL queries for monitoring cluster health, latency, and resource usage.
- Troubleshoots Managed Service for Prometheus (GMP) issues like unhealthy scrape targets.
- Explains how to interpret control-plane health metrics and maintenance windows.
- Guides users on enabling Dataplane V2 flow observability.
When to use it
- When setting up monitoring and logging for a new or existing GKE cluster.
- When diagnosing GKE control-plane issues like API server latency or admission webhook failures.
- When troubleshooting missing metrics or misconfigured PodMonitoring resources.
- When investigating pod eviction, preemption, or scheduling bottlenecks.
- When verifying cluster health during maintenance windows or upgrades.
Advantages
- Provides a standardized 'golden path' for comprehensive GKE observability.
- Includes specific guidance on avoiding common pitfalls like accidental billing or IAM permission errors.
- Offers actionable diagnostic queries for both Cloud Logging and Prometheus metrics.
- Clearly distinguishes between system-level metrics and control-plane specific observability.
- Includes mandatory usage attribution for CLI commands to ensure proper tracking.
Usage examples
- “How do I enable full monitoring and logging for my GKE cluster?”
- The agent provides the gcloud command to update the cluster with the recommended monitoring and logging components, while warning about billing impacts and the need for specific IAM permissions.
- “Why are my Prometheus metrics missing in GKE?”
- The agent guides the user to check PodMonitoring configurations, rule evaluation failures, and monitoring permission errors using the provided troubleshooting reference.
- “How can I check if my GKE control plane is healthy?”
- The agent explains how to use the kubernetes.io/cluster/kcp_healthchecks metric and provides a PromQL query to calculate the success rate of control-plane probes.
- “Show me how to find OOMKilled events in my cluster logs.”
- The agent provides an LQL query for Cloud Logging that filters for resource type k8s_event and the specific reason OOMKilling.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/gke-observability/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gke-observability.zip -d .agents/skills
Documentation by Google
GKE Observability
This reference covers monitoring, logging, and metrics configuration and troubleshooting for Google Kubernetes Engine (GKE). The golden path enables comprehensive observability including control-plane metrics and Managed Service for Prometheus (GMP).
MCP Tools:
get_cluster,list_k8s_events,get_k8s_logs,get_k8s_cluster_info,describe_k8s_resource. CLI-only:gcloud container clusters update --monitoring=...,gcloud logging read
Usage Attribution
Do not run live kubectl, gcloud, or curl commands unless the user names a
target cluster/project or asks to inspect a live cluster (output commands and
queries with placeholders, and skip Usage Attribution).
Before running any live command, read Usage Attribution,
prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-observability)",
and append agent-skills/1.0 (skill:gke-observability) to User-Agent headers
(never in generated scripts, Terraform, or user-facing code).
Sub-Skill Reference Routing (Progressive Disclosure)
Complete Reference Index (no
scripts/directory or unlisted reference files exist — do not runlson the skill folder; read only the matching reference below when answering deep diagnostic, metric, log, or GMP questions):
Control-Plane Metrics, Storage, Webhooks & Capacity: Read for GKE control-plane (KCP) VM CPU or RAM/memory usage questions (not publicly exposed) and customer-visible alternatives; cluster etcd / etcd-compatible storage left, quota & per-resource scalability limits; admission webhook latency, system namespace/Lease/upgrade exclusions,
APIServicediscovery bloat, client discovery caching bursts (/apis/custom.metrics.k8s.io,/apis/metrics.k8s.io) & proactive GKE Recommender insights; watch storms; controller-manager workqueue & scheduler lag; or control-plane headroom, VM autoscaling & node-count pre-scaling, network egress, connections (65k, no per-replica metric) & Protobuf.Cloud Logging, Audit Attribution, APF & API Server Traces: Read for
kube-apiserverslow-requestTrace[logs (trace.go& KEP-647);httplog.goclient load (user_agent,verb,apf_iseats); Cloud Audit Log noisy-neighbor, heavyLIST& unindexed CRDfieldSelectorscan tail-latency attribution; API Priority and Fairness (APF)429/504/404throttling;409 Conflicthot objects (ConfigMap->Leaseheartbeats) &RenewDeadline / 2leader-election timeouts;pods/execsaturation, Konnectivity (No agent available), disabled Cloud KMS keys, or in-cluster GCP IAM OAuth token webhook authorizer saturation.Cluster/Node/Pod Health, Maintenance & Managed Prometheus (GMP): Read for control-plane availability (
kubernetes.io/cluster/kcp_healthchecksrate ratio & ruling out KCP outages); maintenance windows (in_maintenance_window), zonal resize downtime & cluster access errors (EOF/masterAuthorizedNetworksConfig); node readiness, registration (AUTO_REPAIR_NODES, Private Google Access) & persistent node-pool labels; pod startup timelines, crashing pod logs,tolerationSeconds: 0mass eviction & Autopilot preemption; or multi-burn-rate SLO alerting (GlobalRules) & GMP / Grafana /PodMonitoringmanifests & troubleshooting.
Golden Path Observability Defaults
| Setting | Golden Path Value | Notes |
|---|---|---|
loggingConfig components | SYSTEM_COMPONENTS, WORKLOADS | Full workload logging |
monitoringConfig components | SYSTEM_COMPONENTS, STORAGE, POD, DEPLOYMENT, STATEFULSET, DAEMONSET, HPA, JOBSET, CADVISOR, KUBELET, DCGM, APISERVER, SCHEDULER, CONTROLLER_MANAGER | Full suite including control-plane |
managedPrometheusConfig.enabled | true | Google-managed Prometheus |
advancedDatapathObservabilityConfig.enableMetrics | true | Dataplane V2 flow metrics |
loggingService | logging.googleapis.com/kubernetes | Cloud Logging |
monitoringService | monitoring.googleapis.com/kubernetes | Cloud Monitoring |
Enabling Full Monitoring & Logging
Include all four caveats whenever providing a --monitoring or --logging
command so users avoid accidental metric loss, surprise billing, or silent IAM
failures:
Control-plane metrics and logs are NOT enabled by default. State this outright:
API_SERVER,SCHEDULER, andCONTROLLER_MANAGERare off on every new cluster (as areDCGM,CADVISOR,KUBELET, and kube-statePOD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,STORAGE,JOBSET). OnlySYSTEMis enabled by default. Link to https://docs.cloud.google.com/kubernetes-engine/docs/how-to/control-plane-metrics and https://docs.cloud.google.com/kubernetes-engine/docs/how-to/view-logs#control_plane_logs.The flag replaces, it does not append. Passing
--monitoringor--loggingoverrides the existing component list; pass the full desired list includingSYSTEM.Control-plane and kube-state metrics bill per sample ingested via Managed Service for Prometheus (GMP). Mention the cost impact on large clusters; use
--monitoring=SYSTEMin non-production to reduce cost.Verify APIs and IAM permissions (
PermissionDenied). Checkgcloud container clusters describe {cluster_name} --region {region} --format="yaml(loggingConfig,monitoringConfig)", ensurelogging.googleapis.comandmonitoring.googleapis.comare enabled, and verify the node service account / GKE service agent hasroles/monitoring.metricWriterandroles/container.defaultNodeServiceAgent.
CLI flag vs. API enum spelling (mixing them fails the command):
Component gcloud --monitoring=monitoringConfigAPI enumSystem SYSTEMSYSTEM_COMPONENTSAPI server API_SERVERAPISERVERController mgr CONTROLLER_MANAGERCONTROLLER_MANAGER
# Enable golden path monitoring and control-plane logging suite
gcloud container clusters update {cluster_name} --region {region} \
--monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,JOBSET,CADVISOR,KUBELET,DCGM \
--logging=SYSTEM,WORKLOAD,API_SERVER,SCHEDULER,CONTROLLER_MANAGER \
--quiet
# Enable Managed Prometheus and Dataplane V2 flow/policy observability
gcloud container clusters update {cluster_name} --region {region} \
--enable-managed-prometheus \
--enable-dataplane-v2-flow-observability \
--quietCore GKE Metrics & Alerting Baseline
Explicitly name Google Cloud Monitoring when proposing dashboards and
alerts, and cover cluster/node CPU and memory capacity, pod restarts
(CrashLoopBackOff), volume space exhaustion, and API server latency
(apiserver_request_duration_seconds). Direct control-plane VM CPU/RAM metrics
are not publicly exposed — read
Control-Plane Metrics, Storage, Webhooks & Capacity
whenever asked about control-plane VM CPU/memory or deep control-plane metrics.
| Metric | Source | Use / Alert Threshold |
|---|---|---|
apiserver_request_duration_seconds | API Server (GMP) | Control-plane latency (Alert: P99 > 5s) |
scheduler_scheduling_attempt_duration_seconds | Scheduler (GMP) | Current GKE scheduling latency (scheduler_schedule_attempts_total{result="error"} > 0) |
container_cpu_usage_seconds_total | cAdvisor (Node) | Worker-node container CPU usage |
container_memory_working_set_bytes | cAdvisor (Node) | Worker-node container memory usage |
kubernetes.io/node/cpu/core_usage_time | Cloud Monitoring | Node CPU usage (kubernetes.io/node/memory/used_bytes for memory) |
kube_pod_status_phase | kube-state-metrics | Pod lifecycle phase (requires deploying GMP or self-managed kube-state-metrics) |
kube_pod_container_status_restarts_total | kube-state-metrics | Pod crash loops (Alert: > 5 in 10m; requires kube-state-metrics) |
kube_node_status_condition | kube-state-metrics | Node readiness (condition="Ready", status!="True"; or kubernetes.io/node/status_condition) |
kubelet_volume_stats_used_bytes / capacity | Kubelet | PVC capacity utilization (Alert: > 85%) |
DCGM_FI_DEV_GPU_UTIL | DCGM (GMP) | GPU utilization (Alert: > 95% sustained) |
Live Resource Usage & Cloud Logging Queries
Use kubectl top for live usage (no MCP or gcloud equivalent):
kubectl top pods --all-namespaces --sort-by=cpu
kubectl top nodes
kubectl top pods --containers -n {namespace}Use gcloud logging read (or Cloud Logging Log Explorer) with LQL filters:
# Control-plane component logs (requires API_SERVER / SCHEDULER / CONTROLLER_MANAGER enabled)
gcloud logging read 'resource.type="k8s_control_plane_component" AND resource.labels.cluster_name="{cluster_name}"' --project {project_id} --limit 50 --quiet
# Container errors: resource.type="k8s_container" AND resource.labels.container_name="{container_name}" AND severity>=ERROR
# OOMKilled events: resource.type="k8s_event" AND jsonPayload.reason="OOMKilling"
# FailedScheduling: resource.type="k8s_event" AND jsonPayload.reason="FailedScheduling"
# Cloud Audit Logs: resource.type="k8s_cluster" AND logName:"cloudaudit.googleapis.com"
# Dataplane V2 network policy / packet drop logs: logName:"policy_action" AND jsonPayload.disposition="DENY"Distributed Tracing, Profiling & Recent Additions
Cloud Trace & OpenTelemetry: Deploy the OpenTelemetry Collector (
googlecloudexporter with Workload Identity) or enable Managed OpenTelemetry for GKE (Preview) on GKE 1.34.1-gke.2178000+ (gcloud beta container clusters update {cluster_name} --region {region} --managed-otel-scope=COLLECTION_AND_INSTRUMENTATION_COMPONENTS).Cloud Profiler: Continuous low-overhead production CPU/memory profiling.
PSI (Pressure Stall Information) metrics: cAdvisor
container_pressure_{cpu,memory,io}_{waiting,stalled}_seconds_totalvia GMPClusterNodeMonitoring(GKE 1.35+).
Supporting Links
Files
- ATTRIBUTION.mdAdded by AutoGineer852 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md13.3 KiB
- references/attribution.md1.8 KiB
- references/cluster-node-pod-health-and-gmp.md12.9 KiB
- references/control-plane-metrics-and-storage.md14.2 KiB
- references/logging-audit-and-apiserver-traces.md13.8 KiB
LICENSE
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool, Needs Node.js
Security
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Attribution and licence
- Published by
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- Google (Official)
- Source
- google/skills · skills/cloud/gke-observability · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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