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multi-cloud-architecture

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

sickn33/antigravity-awesome-skillsUpdated Apr 8, 2026

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill multi-cloud-architecture

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Installation Guide

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 machine
  • ›Node.js 16+ with npm — verify with node --version
  • ›Active project directory where you want to add multi-cloud-architecture
2

Run the install command

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

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill multi-cloud-architecture

Fetches multi-cloud-architecture from sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/multi-cloud-architecture

Restart Cursor to activate multi-cloud-architecture. Access via /multi-cloud-architecture in your agent's command palette.

⚠

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub›Skills CLI docs›About Cursor›What are agent skills?

Documentation

Multi-Cloud Architecture

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

Do not use this skill when

  • The task is unrelated to multi-cloud architecture
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Purpose

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

Use this skill when

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

Storage Services

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

Database Services

AWS Azure GCP Use Case
RDS SQL Database Cloud SQL Managed SQL
DynamoDB Cosmos DB Firestore NoSQL
Aurora PostgreSQL/MySQL Cloud Spanner Distributed SQL
ElastiCache Cache for Redis Memorystore 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
  • 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)
  • Database: PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL)
  • Message Queue: Apache Kafka (MSK/Event Hubs/Confluent)
  • Cache: Redis (ElastiCache/Azure Cache/Memorystore)
  • 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

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

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

Reference Files

  • references/service-comparison.md - Complete service comparison
  • references/multi-cloud-patterns.md - Architecture patterns

Related Skills

  • terraform-module-library - For IaC implementation
  • cost-optimization - For cost management
  • hybrid-cloud-networking - For connectivity

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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

Steps

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

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Reviews

4.7★★★★★41 reviews
  • H
    Hiroshi Gill★★★★★Dec 8, 2024

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

  • N
    Noor Bhatia★★★★★Dec 8, 2024

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

  • P
    Pratham Ware★★★★★Dec 4, 2024

    We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Y
    Yusuf Torres★★★★★Nov 27, 2024

    We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • L
    Liam Torres★★★★★Nov 27, 2024

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

  • Y
    Yash Thakker★★★★★Nov 23, 2024

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

  • Z
    Zara Patel★★★★★Oct 18, 2024

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

  • N
    Noor Diallo★★★★★Oct 18, 2024

    We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • D
    Dhruvi Jain★★★★★Oct 14, 2024

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

  • H
    Hiroshi Gupta★★★★★Sep 17, 2024

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

showing 1-10 of 41

1 / 5

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