基础设施与 DevOps技能

Evaluate Google Cloud workloads against built-in best practices or custom organizational rules using Workload Manager. Use public client libraries or the REST API to run checks, review violations and scanned resources, and export results to BigQuery.

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• Evaluate workloads against Google Cloud best practices
• Inspect rules for SAP and SQL Server workloads
• Run evaluations using custom organizational rules

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

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• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

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• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Evaluate Google Cloud workloads and develop reliability recommendations covering user experience goals, redundancy, monitoring, and recovery. The skill applies the Google Cloud Well-Architected Framework to help identify gaps and plan improvements suited to business requirements.

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• Reviewing a Google Cloud architecture for reliability gaps
• Setting reliability targets based on user experience
• Planning redundancy and behavior during partial failures

Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.

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• Plan an AI agent architecture on Google Cloud
• Choose between single-agent and multi-agent designs
• Prepare deployment code and step-by-step instructions

Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.

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• Migrating an existing Cloud Run AI service to GKE
• Planning a self-hosted replacement for Gemini API inference
• Sizing GPUs and preparing deployment configuration

Inspect and manage Google Cloud IAM policies that grant roles or explicitly deny permissions. The skill prepares policy changes for user approval, supports organization, folder, project, and supported resource scopes, and verifies the resulting policy after execution.

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• Reviewing existing allow and deny policies
• Adding or removing role bindings for specific identities
• Creating or updating organization, folder, or project deny policies

Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.

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• Reviewing Google Cloud spending by team or project
• Finding idle resources and oversized instances
• Comparing CUDs, SUDs, and Spot VMs

Diagnose Google Cloud Filestore mount failures by checking client access rules, network firewalls, and audit logs. Distinguish permission errors from connection timeouts, then provide a structured report and remediation commands that require confirmation before execution.

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• Investigating a Filestore mount that hangs or fails
• Diagnosing access-denied errors from a GKE workload
• Checking client permissions and VPC firewall rules

Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.

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• Investigating a failed DAG run
• Diagnosing task timeouts and queue delays
• Checking service errors against DAG code

Investigate Google Cloud network problems using traffic logs, performance metrics, and connection path tests. Identify blocked traffic, packet loss, and latency, summarize traffic volumes, or estimate VPC Flow Logs costs.

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• Investigate blocked traffic and packet loss
• Check latency and throughput trends
• Analyze Cloud NAT port exhaustion

Diagnoses applications that fail to start or repeatedly crash on GKE, Google's managed Kubernetes service. It examines logs, events, memory use, image pulls, and connectivity, then proposes configuration patches or commands without applying live changes.

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• Investigate repeated container restarts
• Diagnose memory exhaustion
• Find causes of image pull or scheduling failures

Set up an organization-wide Google Cloud foundation with 17 baseline security policies, environment folders and projects, billing links, and centralized audit logging and monitoring. Check the resulting setup against a defined validation checklist.

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• Set up the initial foundation for a Google Cloud organization
• Organize production, non-production, and development resources
• Apply baseline security policies across an organization

Browse and search files on Google Cloud Filestore, read selected log lines, and inspect permissions and timestamps. Uses a Cloud Run bridge or an existing virtual machine without requiring local NFS client packages or root access.

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• Explore the folder structure of a Filestore share
• Find backup files by filename pattern
• Search remote logs for errors and read selected lines

Manage files in Google Cloud's Cloud Storage buckets, the containers that hold stored data. Upload, download, and organize content, configure access and time-limited sharing links, and manage storage lifecycle, costs, and data protection.

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• Upload, download, and organize stored files
• Share files through time-limited signed URLs
• Review storage classes and lifecycle rules

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

Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.

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• Configuring fallback from Spot VMs to on-demand capacity
• Selecting GPUs, TPUs, or machine families
• Restricting ComputeClass editing and workload usage

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

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.

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• Reviewing GKE high availability
• Maintaining Pod availability during upgrades
• Configuring application health checks

Set up Firebase command-line tools, sign in, and create or switch projects. Retrieve Android and iOS app configuration files through commands, including guidance for signing in from remote environments.

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• Set up Firebase tools and sign in
• Create or switch Firebase projects
• Retrieve Android or iOS configuration files

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

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 create Google Kubernetes Engine clusters using templates for production, development, or AI workloads. Compare Google-managed and self-managed node options, review configurations before creation, and check settings for production readiness.

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• Create a production Autopilot cluster
• Compare Autopilot and Standard modes
• Prepare a development or test cluster

Finds the base image needed to create a custom GKE node image. It translates requirements such as GKE version, operating system, processor architecture, accelerators, and sandbox settings into a matching image name.

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• Finding a base image for custom GKE nodes
• Looking up images for GPU or TPU configurations
• Matching images to COS, Ubuntu, ARM64, or X86_64