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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

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

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

Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.

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• Designing product search with meaning-based matching and attribute filters
• Planning result reranking and AI quality checks
• Preparing AlloyDB and Cloud Run deployment guidance

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

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

Design security controls for Google Cloud Agent Gateway, covering incoming client requests and outgoing agent tool calls. Produce architecture diagrams, configuration files, deployment commands, and validation scripts using Model Armor, IAP, and Agent Registry.

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• Design incoming and outgoing agent communication controls
• Prepare Model Armor filters and tool access policies
• Generate scripts to check authentication and allowed tool calls

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

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

Design a Google Cloud system that combines multiple services, starting with a discussion of business and technical requirements. The skill produces service recommendations, architecture diagrams, deployment guidance, and validation plans, with user approval at each stage.

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• Gather requirements for a new cloud workload
• Compare and select services for a cloud migration
• Create a diagram and explain system data flows

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

Retrieve security findings from Google Cloud Security Command Center for initial assessment. Filter active vulnerabilities, threats, and external exposures, inspect one finding’s full details, or group counts by category to understand the reported risks.

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• Retrieving full details for a named security finding
• Listing active vulnerabilities or external exposures
• Summarizing active finding counts by category

Helps individual developers get started on Google Cloud by checking local tools and authentication, selecting or creating a project, and linking billing. It verifies the setup and directs users to specialized guides for their first deployment.

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• Setting up a first Google Cloud project
• Reusing an existing project for development
• Checking authentication and billing setup

Guides authentication and access permissions for Google Cloud users and applications. It helps choose credentials for local development, production services, or external clouds, with guidance on short-lived credentials and avoiding downloaded service account keys.

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• Set up credentials for local Python development
• Configure Cloud Run access to Cloud SQL
• Connect workloads on AWS or Azure to Google Cloud

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

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

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

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

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

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

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

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