Decision framework and patterns for architecting applications across AWS, Azure, and GCP.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmulti-cloud-architectureExecute the skills CLI command in your project's root directory to begin installation:
Fetches multi-cloud-architecture from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate multi-cloud-architecture. Access via /multi-cloud-architecture in your agent's command palette.
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.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Decision framework and patterns for architecting applications across AWS, Azure, and GCP.
resources/implementation-playbook.md.Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.
| 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 |
| 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 |
| 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
Application Layer
↓
Infrastructure Abstraction (Terraform)
↓
Cloud Provider APIs
↓
AWS / Azure / GCP
Reference: See references/multi-cloud-patterns.md
references/service-comparison.md - Complete service comparisonreferences/multi-cloud-patterns.md - Architecture patternsterraform-module-library - For IaC implementationcost-optimization - For cost managementhybrid-cloud-networking - For connectivityPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
sickn33/antigravity-awesome-skills
sickn33/antigravity-awesome-skills
sickn33/antigravity-awesome-skills
sickn33/antigravity-awesome-skills
sickn33/antigravity-awesome-skills
sickn33/antigravity-awesome-skills
multi-cloud-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for multi-cloud-architecture matched our evaluation — installs cleanly and behaves as described in the markdown.
We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: multi-cloud-architecture is focused, and the summary matches what you get after install.
multi-cloud-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: multi-cloud-architecture is focused, and the summary matches what you get after install.
We added multi-cloud-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for multi-cloud-architecture matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend multi-cloud-architecture for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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