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Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.

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• Design real-time technical guidance using live audio and video
• Plan hazard detection and visual inspection from video streams
• Create cloud architecture diagrams and deployment instructions

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

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

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

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

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

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a database for a new Google Cloud application
• Clarifying data models, workloads, and scale requirements
• Preparing and validating database provisioning code for review

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

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• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.

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• Configure alerts for agent latency and error rates
• Monitor rapid increases in token usage
• Monitor safety triggers and permission denials

Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.

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• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections

Verify Kortix changes with focused tests and real inputs and outputs. This skill guides test placement, API and CLI workflows, browser checks, database verification, and reporting of results, including skipped cases.

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• Verify a bug fix with focused tests
• Check API and CLI behavior through real inputs and outputs
• Test database query conditions

Guides a pull request from local checks through a recorded demo, GitHub upload, merge, and development environment verification. Explains the project's test and preview labels, attachment requirements, and rules for keeping the pull request current.

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• Prepare a pull request with local verification results
• Record and attach a demo of changed behavior
• Check when test and preview labels run workflows

Write and manage infrastructure as code with Terraform and OpenTofu. Design reusable modules, review planned changes before applying them, bring existing resources under management, and organize shared state so teams can track infrastructure across environments.

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• Designing reusable infrastructure modules
• Bringing existing resources under Terraform management
• Separating development, staging, and production state

Build infrastructure as code with Terraform across AWS, Azure, and GCP. Create reusable modules, manage state records that track resources, configure environments and providers, and validate proposed changes before applying an explicitly approved plan.

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• Creating reusable Terraform modules
• Migrating state storage or importing existing resources
• Managing settings across development and production

Design cloud architectures and migration plans for AWS, Azure, and GCP. Produce service diagrams, selection rationale, security designs, cost estimates, and recovery strategies, with deployment and rollback plans that account for availability and operational monitoring.

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• Design a cloud architecture and explain service choices
• Plan a cloud migration and cutover checks
• Review cloud spending and resource sizes

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

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• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Create editable draw.io diagrams from descriptions, code, infrastructure settings, or database schemas. Update existing diagrams while preserving manual layout, check their structure and architecture rules, and export them to image or document formats for sharing.

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• Turn a written description into an editable diagram
• Generate architecture diagrams from code or infrastructure settings
• Update a diagram while preserving manual layout

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.

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• Choose services for a Cloudflare application
• Select storage for databases and files
• Build AI agents and semantic search

Deploy applications and infrastructure to Cloudflare by choosing services that fit the project. Covers Workers, Pages, storage, and related platform options, with authentication checks and references for configuring and publishing the selected setup.

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• Publishing a web application on Cloudflare
• Deploying serverless functions with Workers
• Choosing storage services for an application

Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security, service selection, migrations, troubleshooting, or learning AWS. Triggers on AWS, Lambda, S3, EC2, ECS, EKS, DynamoDB, RDS, CloudFormation, CDK, Terraform, Serverless, SAM, IAM, VPC, API Gateway, or any AWS service. Do NOT use for non-AWS cloud providers or general infrastructure without AWS context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the agents-cli skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (ADK projects: use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).

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