القائمة
Answer natural language questions about GKE-related costs by leveraging BigQuery export and cost allocation data.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | gke-cost-analysis |
| description | Answer natural language questions about GKE-related costs by leveraging BigQuery export and cost allocation data. |
This skill provides guidance on answering natural language questions about GKE-related costs, optimization, or billing.
When users ask about GKE costs (e.g., "What are my costs across projects?", "What's my most expensive namespace?"), use this skill to provide a structured and expert response.
When handling a cost-related question:
bq) over BigQuery Studio when available.bq) is preferred. When writing Standard SQL queries, use a dot (.) instead of a colon (:) to separate the project ID and dataset name.Use these queries as templates to answer questions. All parameters (dataset, table, project, cluster, etc.) need to be replaced.
bq query --nouse_legacy_sql '
SELECT
SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS cost,
SUM(cost) AS cost_before_credits
FROM {{.BQDatasetProjectID}}.{{.BQDatasetName}}.gcp_billing_export_resource_v1_XXXXXX_XXXXXX_XXXXXX AS bqe
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
AND project.id = "sample-project-id"
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-location" AND l.value = "us-central1")
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name" AND l.value = "sample-cluster-name")
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-namespace" AND l.value = "sample-namespace")
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-workload-type" AND l.value = "apps/v1-Deployment")
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "k8s-workload-name" AND l.value = "sample-workload-name")
;
'
bq query --nouse_legacy_sql '
SELECT
project.id AS project_id,
(SELECT l.value FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-location" LIMIT 1) AS cluster_location,
(SELECT l.value FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name" LIMIT 1) AS cluster_name,
(SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-namespace" LIMIT 1) AS k8s_namespace,
(SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-workload-type" LIMIT 1) AS k8s_workload_type,
(SELECT l.value FROM bqe.labels AS l WHERE l.key = "k8s-workload-name" LIMIT 1) AS k8s_workload_name,
SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS cost,
SUM(cost) AS cost_before_credits
FROM {{.BQDatasetProjectID}}.{{.BQDatasetName}}.gcp_billing_export_resource_v1_XXXXXX_XXXXXX_XXXXXX AS bqe
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
AND EXISTS(SELECT * FROM bqe.labels AS l WHERE l.key = "goog-k8s-cluster-name")
GROUP BY 1, 2, 3, 4, 5, 6
ORDER BY 7 DESC
LIMIT 10
;
'
Note: Checking that the "goog-k8s-cluster-name" label exists scopes the total billing data to just GKE costs.
Systematically diagnose GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads. Identifies preemption events, maintenance interruptions, bad host VMs, unhealthy pods, and coordinator worker failures.
Diagnose and predict node disruption during Compute Engine host maintenance for GPU and TPU workloads.
Diagnose and prevent `vbar_control_agent` segfaults and OOMs caused by race conditions during TPU device resets and frequent metrics collection (e.g. every 3s). Use when TPU slice initialization fails or `vbar_control_agent` crashes on TPU v6e nodes.
Verifies if a GKE or Kubernetes cluster is unused (no active compute, external exposure, or persistent data) before allowing deletion. Evaluates external exposure (LoadBalancer Service, Ingress, Gateway, MultiClusterIngress), persistent data (Bound PVC), and active compute (Running/Pending Pods in user namespaces) with low-overhead queries and fail-close timeouts.
Monitor and manage GKE TPU Dynamic Slices custom resources. Use when checking slice lifecycle states, troubleshooting failed slice creations (e.g. SliceCreationFailed, FAILED), running single or multi-slice workloads, or safely deleting/disabling slices.
Monitor and troubleshoot GKE TPU workloads using GKE system metrics and PromQL.