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Audits Google Cloud Filestore instances for missing or outdated backups, overly broad file share access, and zone isolation requirements. Produces a health scorecard, prioritized findings, an instance inventory, and proposed backup remediation.

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• Finding file shares without recent backups
• Auditing NFS access ranges and root privileges
• Checking physical zone isolation and separation

Design and deploy a Google Cloud global external Application Load Balancer with caching and web attack protection. Matches settings to workload types, discovers existing backends, and generates Terraform or gcloud deployment scripts.

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• Design global web delivery across multiple backends
• Choose caching settings for static content and APIs
• Configure Cloud Armor protection for each workload

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

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

Design and create new Cloud Storage buckets for uses such as backups, media, and AI data. Check project security and name availability, choose storage and protection settings, and produce creation commands or code in the preferred format.

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• Creating storage for application media
• Designing buckets for sensitive data
• Preparing backup and long-term archive storage

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

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

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

Design and implement Google Cloud serverless web apps with separate presentation, application, and data layers. Produces architecture diagrams, Terraform infrastructure code, deployment instructions, and validation scripts for private networking and controlled access between layers.

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• Design a serverless web app with separate application layers
• Generate Terraform for private Cloud Run and Cloud SQL connections
• Plan regional data residency requirements

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

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

Set up Google's agents-cli and find the right workflow for building AI agents. Connects requirements planning, project creation, implementation, evaluation, deployment, publishing, and monitoring through specialized skills for the Gemini Enterprise Agent Platform.

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• Set up agents-cli and its specialized skills
• Document agent requirements before writing code
• Find the workflow for creating and evaluating an ADK agent

Review Google Cloud workloads against the security principles of the Well-Architected Framework. Identify gaps in access control, network protection, data handling, and security operations, then receive prioritized recommendations linked to relevant Google Cloud products.

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• Reviewing security during Google Cloud architecture design
• Checking access permissions and network defenses
• Identifying gaps in data protection and security monitoring

Investigate Google Cloud access denials and choose a resolution path based on the caller’s permissions. Supports temporary access activation, structured escalation tickets, policy analysis, and administrator selection of roles that grant only the permissions needed.

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• Investigating Google Cloud access denials
• Activating temporary access for development
• Preparing access issue tickets for administrators

Define reliability goals for Google Cloud services and generate Terraform files for Cloud Monitoring SLO alerts. A guided conversation gathers service scope, measurement metrics, targets, and alert conditions, then explains the PromQL queries used to evaluate them.

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• Create availability SLO alerts for a service
• Define latency targets and measurement windows
• Configure SLO alerts for App Hub resources

Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.

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• Designing a data platform across multiple clouds
• Planning data ingestion and AI serving components
• Preparing infrastructure code and deployment instructions

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

Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.

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• Reviewing production readiness
• Planning monitoring and logging
• Improving incident response procedures

Design AI agent analytics workflows across distributed tables and documents. Combine Knowledge Catalog for metadata, meaning information about data and its usage rules, with managed Spark processing to produce architecture diagrams, deployment guidance, and validation plans.

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• Design analytics across data stored in multiple clouds
• Define metadata and governance with Knowledge Catalog
• Plan Data Agent Kit use in VS Code or Antigravity

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

Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.

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• Design a data analysis system with multiple AI agents
• Choose Google Cloud products for analytical requirements
• Prepare an architecture diagram and deployment guide

Check Google Cloud IAM allow policy changes against the last 90 days of access logs. The skill identifies access that could be lost and applies changes only after a successful simulation and user approval.

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• Checking access impact before removing a role
• Reviewing allow policy changes across resource scopes
• Applying policy changes after simulation and approval

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

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.

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• Find and reserve a remote Android device
• Connect to a reserved device through ADB
• Check session status and expiration