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multi-cloud-architecture▌

wshobson/agents · updated Apr 8, 2026

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$npx skills add https://github.com/wshobson/agents --skill multi-cloud-architecture
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summary

Decision framework and service comparison patterns for architecting across AWS, Azure, GCP, and OCI.

  • ›Includes detailed service mapping tables across compute, storage, and database categories to identify equivalent offerings and best-of-breed selections
  • ›Four core multi-cloud patterns: single provider with disaster recovery, best-of-breed service selection, geographic distribution, and cloud-agnostic abstraction layers
  • ›Cloud-agnostic alternatives using Kubernetes, PostgreSQL, Apache Ka
skill.md

Multi-Cloud Architecture

Decision framework and patterns for architecting applications across AWS, Azure, GCP, and OCI.

Purpose

Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.

When to Use

  • Design multi-cloud strategies
  • Migrate between cloud providers
  • Select cloud services for specific workloads
  • Implement cloud-agnostic architectures
  • Optimize costs across providers

Cloud Service Comparison

Compute Services

AWS Azure GCP OCI Use Case
EC2 Virtual Machines Compute Engine Compute IaaS VMs
ECS Container Instances Cloud Run Container Instances Containers
EKS AKS GKE OKE Kubernetes
Lambda Functions Cloud Functions Functions Serverless
Fargate Container Apps Cloud Run Container Instances Managed containers

Storage Services

AWS Azure GCP OCI Use Case
S3 Blob Storage Cloud Storage Object Storage Object storage
EBS Managed Disks Persistent Disk Block Volumes Block storage
EFS Azure Files Filestore File Storage File storage
Glacier Archive Storage Archive Storage Archive Storage Cold storage

Database Services

AWS Azure GCP OCI Use Case
RDS SQL Database Cloud SQL MySQL HeatWave Managed SQL
DynamoDB Cosmos DB Firestore NoSQL Database NoSQL
Aurora PostgreSQL/MySQL Cloud Spanner Autonomous Database Distributed SQL
ElastiCache Cache for Redis Memorystore OCI Cache Caching

Reference: See references/service-comparison.md for complete comparison

Multi-Cloud Patterns

Pattern 1: Single Provider with DR

  • Primary workload in one cloud
  • Disaster recovery in another
  • Database replication across clouds
  • Automated failover

Pattern 2: Best-of-Breed

  • Use best service from each provider
  • AI/ML on GCP
  • Enterprise apps on Azure
  • Regulated data platforms on OCI
  • General compute on AWS

Pattern 3: Geographic Distribution

  • Serve users from nearest cloud region
  • Data sovereignty compliance
  • Global load balancing
  • Regional failover

Pattern 4: Cloud-Agnostic Abstraction

  • Kubernetes for compute
  • PostgreSQL for database
  • S3-compatible storage (MinIO)
  • Open source tools

Cloud-Agnostic Architecture

Use Cloud-Native Alternatives

  • Compute: Kubernetes (EKS/AKS/GKE/OKE)
  • Database: PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL/MySQL HeatWave)
  • Message Queue: Apache Kafka or managed streaming (MSK/Event Hubs/Confluent/OCI Streaming)
  • Cache: Redis (ElastiCache/Azure Cache/Memorystore/OCI Cache)
  • Object Storage: S3-compatible API
  • Monitoring: Prometheus/Grafana
  • Service Mesh: Istio/Linkerd

Abstraction Layers

Application Layer
    ↓
Infrastructure Abstraction (Terraform)
    ↓
Cloud Provider APIs
    ↓
AWS / Azure / GCP / OCI

Cost Comparison

Compute Pricing Factors

  • AWS: On-demand, Reserved, Spot, Savings Plans
  • Azure: Pay-as-you-go, Reserved, Spot
  • GCP: On-demand, Committed use, Preemptible
  • OCI: Pay-as-you-go, annual commitments, burstable/flexible shapes, preemptible instances

Cost Optimization Strategies

  1. Use reserved/committed capacity (30-70% savings)
  2. Leverage spot/preemptible instances
  3. Right-size resources
  4. Use serverless for variable workloads
  5. Optimize data transfer costs
  6. Implement lifecycle policies
  7. Use cost allocation tags
  8. Monitor with cloud cost tools

Reference: See references/multi-cloud-patterns.md

Migration Strategy

Phase 1: Assessment

  • Inventory current infrastructure
  • Identify dependencies
  • Assess cloud compatibility
  • Estimate costs

Phase 2: Pilot

  • Select pilot workload
  • Implement in target cloud
  • Test thoroughly
  • Document learnings

Phase 3: Migration

  • Migrate workloads incrementally
  • Maintain dual-run period
  • Monitor performance
  • Validate functionality

Phase 4: Optimization

  • Right-size resources
  • Implement cloud-native services
  • Optimize costs
  • Enhance security

Best Practices

  1. Use infrastructure as code (Terraform/OpenTofu)
  2. Implement CI/CD pipelines for deployments
  3. Design for failure across clouds
  4. Use managed services when possible
  5. Implement comprehensive monitoring
  6. Automate cost optimization
  7. Follow security best practices
  8. Document cloud-specific configurations
  9. Test disaster recovery procedures
  10. Train teams on multiple clouds

Related Skills

  • terraform-module-library - For IaC implementation
  • cost-optimization - For cost management
  • hybrid-cloud-networking - For connectivity
how to use multi-cloud-architecture

How to use multi-cloud-architecture on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add multi-cloud-architecture
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill multi-cloud-architecture

The skills CLI fetches multi-cloud-architecture from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/multi-cloud-architecture

Reload or restart Cursor to activate multi-cloud-architecture. Access the skill through slash commands (e.g., /multi-cloud-architecture) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.6★★★★★41 reviews
  • ★★★★★Arya Reddy· Dec 16, 2024

    Useful defaults in multi-cloud-architecture — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Naina Gonzalez· Dec 12, 2024

    Solid pick for teams standardizing on skills: multi-cloud-architecture is focused, and the summary matches what you get after install.

  • ★★★★★Chaitanya Patil· Dec 4, 2024

    I recommend multi-cloud-architecture for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Piyush G· Nov 23, 2024

    Useful defaults in multi-cloud-architecture — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Arjun Martinez· Nov 7, 2024

    I recommend multi-cloud-architecture for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Arya Taylor· Nov 3, 2024

    Keeps context tight: multi-cloud-architecture is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Neel Zhang· Nov 3, 2024

    multi-cloud-architecture is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Daniel Johnson· Oct 26, 2024

    multi-cloud-architecture reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Amina Agarwal· Oct 22, 2024

    Registry listing for multi-cloud-architecture matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Neel Khan· Oct 22, 2024

    multi-cloud-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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