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Google Cloud Solution Architecture

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  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
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From the author

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use google-cloud-recipe-*), Well-Architected Framework reviews or audits (use google-cloud-waf-*), or workloads covered by specialized solution skills.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a rigorous, multi-phase workflow for architects and engineers to design holistic Google Cloud solutions. It enforces a structured approach starting with detailed requirements discovery, followed by iterative solution architecture, validation, and final packaging. The workflow emphasizes grounding all recommendations in official Google Cloud documentation and requires explicit user approval at every major milestone to ensure alignment and prevent premature or incorrect implementation.

What it does

  • Conducts systematic requirements discovery for functional and non-functional needs.
  • Generates technical decompositions and multi-product Google Cloud architectures.
  • Produces Mermaid-formatted architecture diagrams and detailed design descriptions.
  • Provides infrastructure-as-code and deployment guidance for the proposed solution.
  • Facilitates static pre-deployment validation and runtime verification planning.
  • Consolidates all architectural artifacts into a structured Markdown guide.

When to use it

  • When designing end-to-end, multi-product cloud solutions on Google Cloud.
  • When planning migrations or new complex workloads requiring architectural blueprints.
  • When you need to select and integrate multiple Google Cloud services based on specific constraints.
  • When you require a formal, step-by-step process to ensure architectural alignment and approval.

Advantages

  • Prevents premature solution anchoring by enforcing a strict requirements-first phase.
  • Ensures architectural decisions are grounded in official Google Cloud best practices.
  • Reduces risk through mandatory user approval gates at every design and deployment stage.
  • Promotes transparency and safety by requiring explicit permission before executing any infrastructure commands.

Usage examples

“I need to design a scalable, secure data processing pipeline on Google Cloud. Can you help me architect this?”
The agent initiates Phase 1, asking for functional requirements, non-functional constraints (security, cost, performance), and current system dependencies.
“My requirements are defined. What Google Cloud products should I use for a web application with a global database?”
The agent analyzes the requirements, proposes a set of Google Cloud products with pros/cons, and waits for your approval before proceeding to diagramming.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/google-cloud-solution-architecture/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip google-cloud-solution-architecture.zip -d .agents/skills

Documentation by Google

Google Cloud solution-architecture workflow

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

Overview of the workflow

The workflow consists of the following phases:

  • Phase 1: Requirements discovery. Gather detailed requirements related to the cloud workload or use case that the user needs assistance for.

  • Phase 2: Solution architecture. Use the requirements that were gathered in Phase 1 to generate a detailed solution architecture for the cloud workload or use case.

  • Phase 3: Solution validation. Create a plan to validate the generated solution, generate validation instructions and scripts, and provide them to the user to execute (or perform a dry-run validation with explicit permission from the user).

  • Phase 4: Solution packing and presentation. Consolidate the generated content and present the solution.

Important notes about the workflow:

  • Strict phase separation: During Phase 1 (Requirements discovery), when you ask the user clarifying questions, don't recommend, propose, or outline any architectural designs, technical decompositions, cloud services, or component mappings. Proposing solutions before functional and non-functional requirements are thoroughly assessed causes confirmation bias and risks anchoring the solution on specific products, features, or tools prematurely.

  • Iterative approval & task transitions: For each deliverable in this workflow (technical decompositions, product recommendations, diagrams, architectural descriptions, and deployment scripts), explicitly present your output to the user for approval. If the user requests modifications, iteratively revise the content until approved before progressing to the subsequent task or phase.

  • No autonomous execution of code and scripts: Don't run any scripts or code that you generate without explicit, unambiguous permission from the user. Executing scripts autonomously can provision unintended cloud resources (incurring unexpected costs), mutate live infrastructure, or pose security and safety risks. Always offer the option for the user to execute the commands manually.

  • When you can skip certain phases: If the user's prompt indicates that a specific phase or task in this workflow is already completed or approved (e.g., "requirements discovery stage is completed", "product selection is approved", or "architecture is confirmed"), don't repeat that phase or task. Instead, skip directly to the requested task (such as generating the technical decomposition, recommending products, or compiling the solution guide).

Phase 1: Requirements discovery

  1. Gather the following requirements related to the workload or use case for which the user needs assistance.

    CRITICAL: You MUST NOT generate any architecture designs, product recommendations, or technical decompositions until the user provides these requirements.

    • Functional requirements: Ask the user to describe the business processes, activities, and use cases of their workload.

    • Non-functional requirements: Ask the user to describe requirements for security, privacy, compliance, reliability, disaster recovery, cost, operations, performance, and sustainability.

      • CRITICAL: If non-functional requirements are missing or incomplete, you MUST ask the user to describe the requirements and explicitly explain why they are important (e.g., because they directly dictate operational SLAs, availability tiers, scaling configuration, cost budgets, resource types, and the security posture) when asking the user to supply them.

    • Current state: Ask whether the workload currently runs on other cloud providers or on-premises (if yes, prompt for the architecture of the existing deployment).

    • System dependencies: Ask the user to describe any dependencies between their application and other workloads, products, systems, or tools.

  2. Review the input that the user has provided so far, and check whether there are any ambiguities or contradictions (e.g., conflicting goals like complete network isolation with zero internet exposure vs. real-time ingestion from public APIs).

    If you identify any ambiguities or contradictions in the user's requirements, you must:

    • Clearly describe the ambiguities and contradictions.

    • Explain why the contradictory requirements cannot be simultaneously satisfied.

    • Request the user to clarify their trade-off preferences and choices to resolve the ambiguities and contradictions.

      • If the user delegates the choice to you (e.g., the user replies with "do what you think is best" or "you decide"), then provide a clear suggestion to resolve the ambiguity or contradiction, explain your reasoning, and ask the user to approve your suggestion.

    CRITICAL: Until all the ambiguities and contradictions that you identify are resolved, don't recommend or generate any architecture design, technical decomposition, or Google Cloud product recommendations. Ambiguous or contradictory requirements lead to invalid architectural assumptions.

  3. Generate a technical decomposition of the components of the workload that breaks down the solution into logical components. Present it to the user and obtain approval before proceeding to Phase 2.

    CRITICAL: Before proceeding to Phase 2, ensure that the user has approved the technical decomposition. Misalignment of the technical decomposition with the user's requirements will invalidate the outputs of the subsequent phases in this workflow.

Phase 2: Solution architecture

Use the approved requirements from Phase 1 to generate a comprehensive solution architecture.

Ground all generated content

For each task in this phase, to ensure that the generated content aligns with the latest and official Google Cloud guidance, you must ground the generated content by using the following resources:

  • Google Developer Knowledge MCP server

  • Relevant skills from https://github.com/google/skills

  • Official Google Cloud documentation, including the following:

    • Reference architectures and design guides that are relevant to the technology category of the workload: references/architecture-guides.md

    • Decision-making guides for the products and topics that are relevant to the workload: references/decision-making-guides.md

    • Best-practices guides for the products and topics that are relevant to the workload: references/best-practices-guides.md

For each item in the generated guidance, you must include citations to the relevant official Google Cloud documentation pages.

Task 2.1: Identify Google Cloud products and features required for the workload.
  1. Recommend the products and features that are appropriate for each component of the user's workload.

    CRITICAL:

    • Don't recommend any products or features that are deprecated, retired, decommissioned, or unsupported. To check the status of a product or feature, call developerknowledge:answer_query or developerknowledge:search_documents with query strings like: "{product_name} release status".

    • If multiple products or features can be used for a component of the workload, then do the following:

      • Recommend the most appropriate product or feature. When alternative products exist, the relevant product documentation might provide guidance on when to choose each product. Follow that guidance.

      • Mention the available alternative products or features.

      • Explain the pros and cons of each alternative product or feature.

  2. Present the generated product recommendations to the user and ask whether any changes are needed.

    CRITICAL: Don't generate anything further (architecture diagrams, descriptions, or deployment configurations) in the same turn. Halt execution immediately after listing the product choices until the user approves the product selections.

  3. After the user approves the product selections, proceed to Task 2.2.

Task 2.2: Generate an architecture diagram.
  1. Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.

  2. Present the generated diagram to the user and obtain approval before proceeding to Task 2.3.

Task 2.3: Generate an architecture description.
  1. Generate a description that explains the purpose of each component, the relationships between the components, and the task flow or data flow.

  2. Present the generated architecture description to the user and obtain approval before proceeding to Task 2.4.

Task 2.4: Generate design recommendations.
  1. Generate design recommendations and best practices to optimally configure each component in the architecture based on the workload's requirements.

    Important:

    • When generating design recommendations, incorporate the following:

      • Functional requirements that were gathered in Phase 1.

      • Non-functional requirements that were gathered in Phase 1.

    • To generate guidance for non-functional requirements, use the following resources:

      1. Best-practices guides for the products and topics that are relevant to the workload: references/best-practices-guides.md

      2. Well-Architected Framework pillar skills:

        • For security requirements: google-cloud-waf-security

        • For reliability requirements: google-cloud-waf-reliability

        • For cost optimization requirements: google-cloud-waf-cost-optimization

        • For operational excellence requirements: google-cloud-waf-operational-excellence

        • For performance optimization requirements: google-cloud-waf-performance-optimization

        • For sustainability requirements: google-cloud-waf-sustainability

  2. Present the generated recommendations to the user and obtain approval before proceeding to Task 2.5.

Task 2.5: Generate deployment guidance.
  1. Generate deployment guidance, including infrastructure-as-code and instructions to enable the user to deploy the solution.

  2. Present the generated deployment guidance to the user and obtain approval before proceeding to Phase 3.

Phase 3: Solution validation

Task 3.1: Pre-deployment validation
  1. Create a pre-deployment plan to statically validate the generated solution and verify that it meets the workload's requirements without provisioning live resources:

    • Deployment dry-run: Validate infrastructure syntax and preview the resources that will be provisioned using dry-run commands (e.g., terraform plan or (where supported) gcloud ... --dry-run).

    • Architecture & policy analysis: Perform static verification of network routing topologies, firewall rules, and IAM enforcement against best practices.

  2. Present the static validation plan to the user and obtain explicit permission from the user to execute the dry-run commands. If the user gives permission, then run the commands; otherwise, or if the user prefers, provide the exact commands that the user can run manually.

  3. Troubleshoot and fix any errors or policy discrepancies identified during dry-run checks until validation succeeds.

  4. Proceed to Task 3.2

Task 3.2: Runtime validation (Post-deployment)
  1. Ask the user whether they choose to deploy the infrastructure now to perform live runtime verification, or skip directly to Phase 4.

  2. If the user chooses to deploy the infrastructure:

    • After the user deploys the infrastructure, generate runtime verification commands (using tools like curl, ping, or gcloud) and provide them to the user to execute, to test live endpoint reachability, networking paths, and load balancer routing.

    • Troubleshoot any deployment or runtime routing issues until checks pass.

  3. Proceed to Phase 4.

Phase 4: Solution packaging and presentation

Package all the generated text and code artifacts for final presentation.

  1. Consolidate the text artifacts that were generated in Phase 2 and Phase 3 into a single Markdown file named solution-architecture-guide.md, based on the template in assets/output-template.md.

  2. Request the user's permission to write the code files in the user's workspace.

  3. After the user gives permission, write the code files in the user's workspace.

Files

references/decision-making-guides.md

Decision-making guides

TopicDocumentDescription
Agentic AI: Architecturehttps://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txtProvides architectural guidance for selecting the optimal building blocks and services on Google Cloud to power your agentic AI applications.
Agentic AI: Design patternhttps://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system.md.txtHelps you analyze your workload and select the best design pattern (single-agent, multi-agent, etc.) for constructing your generative AI system.
AlloyDB Omni: Deployment environmenthttps://docs.cloud.google.com/alloydb/omni/docs/choose-deployment.md.txtProvides guidance on choosing the best target environment (single container, Kubernetes, or serverless) for deploying AlloyDB Omni.
AlloyDB: Connectivityhttps://docs.cloud.google.com/alloydb/docs/choose-alloydb-connectivity.md.txtCompares AlloyDB connectivity methods, including AlloyDB Auth Proxy, Private Service Connect (PSC), and direct VPC peering, to help you select the appropriate method.
AlloyDB: Machine typeshttps://docs.cloud.google.com/alloydb/docs/choose-machine-type.md.txtExplains how to select the appropriate machine type for your AlloyDB primary and read instances based on vCPUs, memory, and performance.
AlloyDB: Vector indexhttps://docs.cloud.google.com/alloydb/docs/ai/choose-index-strategy.md.txtCompares Approximate Nearest Neighbors (ANN) and K-Nearest Neighbors (KNN) indexing strategies to help you choose the appropriate model for vector search.
App Engine: Environmenthttps://docs.cloud.google.com/appengine/docs/the-appengine-environments.md.txtCompares the App Engine Standard and Flexible environments, helping you select the appropriate environment based on language support, scaling speed, and customization requirements.
App Hub: Setup modelhttps://docs.cloud.google.com/app-hub/docs/set-up-app-hub.md.txtHelps you select the best approach (single-project, multi-project, or organization-level) for setting up and configuring App Hub.
BigQuery: ETL vs. ELThttps://docs.cloud.google.com/bigquery/docs/load-transform-export-intro.md.txtDescribes the data integration approaches to load and transform data in BigQuery using the extract, load, and transform (ELT) or the extract, transform, load (ETL) processes.
BigQuery: Natural language processing functionhttps://docs.cloud.google.com/bigquery/docs/choose-ml-text-function.md.txtCompares the capabilities of BigQuery ML text functions, such as AI.GENERATE_TEXT, ML.TRANSLATE, and ML.UNDERSTAND_TEXT, to help you choose between them.
BigQuery: Text generation functionhttps://docs.cloud.google.com/bigquery/docs/choose-text-generation-function.md.txtCompares the different text generation options in BigQuery ML (such as using Gemini models vs. other LLMs) to select the appropriate fit.
Bigtable: SSD vs HDDhttps://docs.cloud.google.com/bigtable/docs/choosing-ssd-hdd.md.txtCompares the performance, cost, and use cases of SSD versus HDD storage to help you select the best disk layout for your Cloud Bigtable instances.
CDN products: Cloud CDN vs. Media CDNhttps://docs.cloud.google.com/cdn/docs/choose-cdn-product.md.txtHelps you select the best CDN product (Cloud CDN vs. Media CDN) based on your content delivery requirements and architecture.
Cloud Run: When to usehttps://docs.cloud.google.com/run/docs/fit-for-run.md.txtProvides a checklist and technical assessment guide to determine if your containerized application is well-suited for deployment on Cloud Run.
Cloud SQL for MySQL: Editionshttps://docs.cloud.google.com/sql/docs/mysql/choose-edition.md.txtProvides guidance on choosing between Enterprise and Enterprise Plus editions for Cloud SQL for MySQL.
Cloud SQL for MySQL: Machine serieshttps://docs.cloud.google.com/sql/docs/mysql/machine-series-overview.md.txtGuides you in choosing the optimal machine family and series (general-purpose, memory-optimized, etc.) for your Cloud SQL for MySQL instances.
Cloud SQL for MySQL: Storagehttps://docs.cloud.google.com/sql/docs/mysql/storage-options-overview.md.txtHelps you select the best storage options (SSD vs. HDD, automatic storage increase) for Cloud SQL for MySQL databases based on IOPS and throughput needs.
Cloud SQL for MySQL: Region availabilityhttps://docs.cloud.google.com/sql/docs/mysql/region-availability-overview.md.txtProvides information on regional availability and feature support across Google Cloud regions for Cloud SQL for MySQL.
Cloud SQL for PostgreSQL: Editionshttps://docs.cloud.google.com/sql/docs/postgres/choose-edition.md.txtProvides guidance on choosing between Enterprise and Enterprise Plus editions for Cloud SQL for PostgreSQL.
Cloud SQL for PostgreSQL: Machine serieshttps://docs.cloud.google.com/sql/docs/postgres/machine-series-overview.md.txtGuides you in choosing the optimal machine family and series (general-purpose, memory-optimized, etc.) for your Cloud SQL for PostgreSQL instances.
Cloud SQL for PostgreSQL: Region availabilityhttps://docs.cloud.google.com/sql/docs/postgres/region-availability-overview.md.txtProvides information on regional availability and feature support across Google Cloud regions for Cloud SQL for PostgreSQL.
Cloud SQL for PostgreSQL: Storagehttps://docs.cloud.google.com/sql/docs/postgres/storage-options-overview.md.txtHelps you select the best storage options (SSD vs. HDD, automatic storage increase) for Cloud SQL for PostgreSQL databases based on IOPS and throughput needs.
Cloud SQL for SQL Server: Editionshttps://docs.cloud.google.com/sql/docs/sqlserver/choose-edition.md.txtProvides guidance on choosing between Enterprise and Enterprise Plus editions for Cloud SQL for SQL Server.
Cloud SQL for SQL Server: Machine serieshttps://docs.cloud.google.com/sql/docs/sqlserver/machine-series-overview.md.txtGuides you in choosing the optimal machine family and series (general-purpose, memory-optimized, etc.) for your Cloud SQL for SQL Server instances.
Cloud SQL for SQL Server: Region availabilityhttps://docs.cloud.google.com/sql/docs/sqlserver/region-availability-overview.md.txtProvides information on regional availability and feature support across Google Cloud regions for Cloud SQL for SQL Server.
Cloud SQL for SQL Server: Storagehttps://docs.cloud.google.com/sql/docs/sqlserver/storage-options-overview.md.txtHelps you select the best storage options (SSD vs. HDD, automatic storage increase) for Cloud SQL for SQL Server databases based on IOPS and throughput needs.
Cloud Storage: Bucket locationhttps://docs.cloud.google.com/storage/docs/locations.md.txtProvides guidance to help you choose appropriate locations for Cloud Storage buckets.
Compute: Service optionshttps://docs.cloud.google.com/docs/compute-area/choose-compute-options.md.txtCompares core compute options in Google Cloud (Batch, Cloud Run, Cluster Director, Cluster Toolkit, Compute Engine, GKE, and VMware Engine) to help you select the best solution or tool for your application's architecture, scaling, and other requirements.
Compute Engine: Migration pathhttps://docs.cloud.google.com/compute/docs/import.md.txtHelps you evaluate and select the best migration strategy and toolset for migrating your virtual machine workloads into Compute Engine.
Compute Engine: Reservation typehttps://docs.cloud.google.com/compute/docs/instances/choose-reservation-type.md.txtDescribes reservation types to help you decide how to reserve compute capacity for Compute Engine instances based on your workload needs.
Compute Engine: VM deployment strategyhttps://docs.cloud.google.com/compute/docs/choose-compute-deployment-option.md.txtHelps you select an appropriate VM deployment strategy on Compute Engine based on workload resilience, scalability, and cost requirements.
Compute Engine: Region selectionhttps://docs.cloud.google.com/solutions/best-practices-compute-engine-region-selection.md.txtDescribes the criteria that you should consider when you choose Google Cloud regions for your Compute Engine resources
Container runtimeshttps://docs.cloud.google.com/architecture/select-managed-container-runtime-environment.md.txtA selection guide comparing managed container options (GKE Autopilot vs. Cloud Run vs. App Engine Flexible) based on organizational and technical factors.
Container runtimes: GKE vs. Cloud Runhttps://docs.cloud.google.com/kubernetes-engine/docs/concepts/gke-and-cloud-run.md.txtProvides a comparison of GKE and Cloud Run and their advantages, and helps you find out if a single-platform or hybrid strategy will work for you.
Databases: Google Cloud database serviceshttps://github.com/google/skills/blob/main/skills/cloud/cloud-databases-onboarding/references/recommendation_matrix.txtProvides guidance to help you choose appropriate Google Cloud database services based on the requirements of your workload.
Eventarc: Advanced vs. Standardhttps://docs.cloud.google.com/eventarc/standard/docs/choose-product-edition.md.txtCompares the features, scalability, and event-routing models of Eventarc Standard and Eventarc Advanced to help you choose the appropriate edition.
Firestore: Native mode vs. Datastore modehttps://docs.cloud.google.com/datastore/docs/firestore-or-datastore.md.txtDetailed guide comparing Firestore in Native mode versus Datastore mode, including features, scaling limits, and target use cases.
Gemini Enterprise Agent Platform: Gemini model versionhttps://docs.cloud.google.com/gemini-enterprise-agent-platform/models/model-versions.md.txtHelps you choose the appropriate Gemini model version for your use case.
Gemini Enterprise Agent Platform: Serverless training methodhttps://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/custom-training-methods.md.txtHelps you compare custom training options in Agent Platform, such as custom jobs, hyperparameter tuning jobs, and training pipelines.
Gemini Enterprise Agent Platform: Training methodhttps://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/start/training-methods.md.txtGuides you in selecting the appropriate model training approach in Agent Platform, comparing AutoML, custom training, and BigQuery ML.
Gemini Enterprise Agent Platform: Vector database for RAG Enginehttps://docs.cloud.google.com/gemini-enterprise-agent-platform/build/rag-engine/vector-db-choices.md.txtDescribes and compare the vector database options that are supported on Agent Platform RAG Engine.
GKE: Cluster modehttps://docs.cloud.google.com/kubernetes-engine/docs/concepts/choose-cluster-mode.md.txtCompares GKE Autopilot and Standard modes to help you decide which level of operational control and management is best for your cluster.
GKE: Multi-cluster load balancing APIhttps://docs.cloud.google.com/kubernetes-engine/docs/concepts/choose-mc-lb-api.md.txtCompares Multi-Cluster Gateway versus Multi-Cluster Ingress to help you choose the correct multi-cluster load balancing approach for GKE.
GKE: Predefined ComputeClasses for Autopilot Podshttps://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-compute-classes.md.txtExplains GKE Autopilot ComputeClasses (Balanced, Scale-Out, Performance) to help you choose the appropriate hardware profile for your containers.
Google Cloud Observability: Instrumentation strategyhttps://docs.cloud.google.com/stackdriver/docs/instrumentation/choose-approach.md.txtGuides you through the process of choosing a telemetry instrumentation strategy (such as OpenTelemetry vs. legacy agents) for Google Cloud Observability.
Load balancinghttps://docs.cloud.google.com/load-balancing/docs/choosing-load-balancer.md.txtHelps you choose the appropriate Google Cloud load balancer based on client location, protocol, and proxy requirements.
Looker: Hostinghttps://docs.cloud.google.com/looker/docs/choosing-hosting-option.md.txtProvides a comprehensive guide to selecting the optimal hosting model (Looker-hosted vs. customer-hosted) based on your requirements
Model inference on GKE: Load balancing strategyhttps://docs.cloud.google.com/kubernetes-engine/docs/concepts/machine-learning/choose-lb-strategy.md.txtProvides guidance on load balancing strategies for AI/ML inference workloads on Google Kubernetes Engine.
Network Connectivityhttps://docs.cloud.google.com/network-connectivity/docs/how-to/choose-product.md.txtProvides guidance to help you choose appropriate hybrid connectivity options: Cloud Interconnect, Cloud VPN, and Network Connectivity Center.
Pub/Sub vs. Cloud Managed Service for Apache Kafkahttps://docs.cloud.google.com/pubsub/docs/choose-pubsub-kafka.md.txtCompares serverless global Pub/Sub versus Google Cloud's Managed Service for Apache Kafka.
Pub/Sub vs. Cloud Taskshttps://docs.cloud.google.com/pubsub/docs/choosing-pubsub-or-cloud-tasks.md.txtDescribes the fundamental differences (implicit vs. explicit invocation) between Pub/Sub and Cloud Tasks.
Pub/Sub vs. Pub/Sub Litehttps://docs.cloud.google.com/pubsub/docs/choosing-pubsub-or-lite.md.txtHelps you choose between standard Pub/Sub and Pub/Sub Lite based on budget, operations, and scale requirements.
Secret Manager: Replication policyhttps://docs.cloud.google.com/secret-manager/docs/choosing-replication.md.txtCompares automatic replication versus user-managed replication policies in Secret Manager to help you decide how to secure and distribute secrets.
Security Command Center: Security sourceshttps://docs.cloud.google.com/security-command-center/docs/concepts-security-sources.md.txtGuides you on choosing and configuring security sources (built-in, Google-provided, and third-party) to integrate with Security Command Center.
Storage for AI and MLhttps://docs.cloud.google.com/ai-hypercomputer/docs/storage.md.txtIntroduces and compares storage services in Google Cloud that can best help you optimize GPU or TPU performance and provides recommendations on the ideal service for specific AI and ML use cases.
Storage: Service optionshttps://docs.cloud.google.com/architecture/storage-advisor.md.txtHelps you understand and choose appropriate storage services for any workload.
VPC: Private accesshttps://docs.cloud.google.com/vpc/docs/private-access-options.md.txtCompares different private access options on Google Cloud VPC, such as Private Google Access, Private Service Connect, and serverless VPC access.
Workflows vs. Application Integrationhttps://docs.cloud.google.com/workflows/docs/choose-app-integ-or-workflows.md.txtCompares the capabilities and ideal scenarios for Google Cloud Workflows and Application Integration to help you choose the appropriate orchestration service.
Workflows vs. Managed Airflow for service orchestrationhttps://docs.cloud.google.com/workflows/docs/choose-orchestration.md.txtCompares Workflows versus Managed Service for Apache Airflow (Cloud Composer) based on latency, scaling models, and language (YAML vs. Python).

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/google-cloud-solution-architecture · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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