'gcloud' 검색 결과

Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.

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Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).

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Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.

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Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

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Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.

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Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.

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Use when working with Gcp Secret Manager — google Secret Manager secret lifecycle management, version control, rotation configuration, IAM policy auditing, and access diagnostics via gcloud CLI.

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Use when working with Gcp Bigquery Deep — google BigQuery job analysis, slot utilization, storage optimization, materialized views, BI Engine capacity, and query performance diagnostics via bq CLI and gcloud.

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Use when working with Gcp Cloud Spanner — google Cloud Spanner instance management, query statistics analysis, hot spot detection, schema analysis, and performance diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Storage — google Cloud Storage bucket analysis, lifecycle rule management, access control configuration, transfer service operations, and storage class optimization via gsutil and gcloud CLI.

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Use when working with Gcp Cloud Logging — google Cloud Logging log analysis, log-based metrics, sink management, exclusion filters, and log routing configuration via gcloud CLI.

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Use when working with Gcp Artifact Registry — google Artifact Registry repository management, vulnerability scanning analysis, cleanup policy configuration, and artifact lifecycle management via gcloud CLI.

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Use when working with Gcp Cloud Functions — google Cloud Functions management, execution analysis, scaling configuration, event trigger inspection, and runtime diagnostics via gcloud CLI.

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Use when working with Gcp Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Armor — google Cloud Armor security policy management, WAF rule configuration, adaptive protection analysis, and edge security policy management via gcloud CLI.

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Use when working with Gcp Gke — google Kubernetes Engine cluster operations, node pool management, workload analysis, autopilot configuration, and upgrade planning via gcloud CLI.

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Use when working with Gcp Cloud Deploy — google Cloud Deploy delivery pipeline management, release tracking, rollout status monitoring, approval workflows, and deployment diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Run — google Cloud Run service management, revision traffic splitting, scaling configuration, concurrency tuning, and container diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.

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Use when working with Gcp Alloydb — google AlloyDB cluster management, instance analysis, query insights, maintenance window configuration, and performance diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Build — google Cloud Build trigger management, build history analysis, worker pool configuration, artifact management, and CI/CD pipeline diagnostics via gcloud CLI.

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Perform GCP security testing using GCPBucketBrute for storage bucket enumeration, gcloud IAM privilege escalation path analysis, and service account permission auditing

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Performing comprehensive security assessments of Google Cloud Platform environments using Forseti Security, Security Command Center, and gcloud CLI to audit IAM policies, firewall rules, storage permissions, and compliance against CIS GCP Foundations Benchmark.

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