Multi Cloud Architecture — Agent Skill by Seth Hobson

From the author

Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, GCP, and OCI. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.

— the description by Seth Hobson

AutoGineer summary

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

This skill provides a structured approach to multi-cloud architecture, offering decision frameworks, service comparisons, and architectural patterns to help teams build resilient, cloud-agnostic systems. It covers strategies for avoiding vendor lock-in, leveraging best-of-breed services, and optimizing costs across major cloud providers. The content includes comparative tables for compute, storage, and database services, alongside guidance on migration phases and best practices for infrastructure abstraction.

What it does

Provides a comparative mapping of compute, storage, and database services across AWS, Azure, GCP, and OCI.
Defines architectural patterns such as single-provider DR, best-of-breed, and geographic distribution.
Outlines a migration strategy covering assessment, pilot, execution, and optimization phases.
Suggests cloud-agnostic technology stacks using Kubernetes, PostgreSQL, and open-source observability tools.
Offers cost optimization strategies including reserved capacity and right-sizing resources.
Lists best practices for infrastructure as code, CI/CD, and disaster recovery planning.

When to use it

When designing a multi-cloud strategy to avoid vendor lock-in.
When evaluating which cloud provider services best fit specific workload requirements.
When planning a migration between different cloud providers.
When building cloud-agnostic architectures to improve portability.
When optimizing infrastructure costs across multiple cloud environments.

Advantages

Reduces architectural complexity through standardized abstraction layers.
Facilitates informed decision-making with clear service-to-service comparisons.
Improves system resilience by implementing multi-cloud disaster recovery patterns.
Provides actionable guidance for balancing cloud-native features with portability.

Usage examples

“Compare managed Kubernetes offerings across AWS, Azure, GCP, and OCI.”: The agent provides a table showing EKS, AKS, GKE, and OKE as the respective managed Kubernetes services for each provider.
“What are the recommended patterns for a multi-cloud disaster recovery strategy?”: The agent outlines patterns such as Primary/DR pairing and Active-Active regional splits, emphasizing traffic steering and data replication.
“How can I design a cloud-agnostic database layer?”: The agent suggests using PostgreSQL or MySQL across providers and utilizing S3-compatible storage APIs to maintain portability.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/multi-cloud-architecture/download

Unzip it into the cross-client skills folder of your project:

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

Documentation by Seth Hobson

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

Use reserved/committed capacity (30-70% savings)
Leverage spot/preemptible instances
Right-size resources
Use serverless for variable workloads
Optimize data transfer costs
Implement lifecycle policies
Use cost allocation tags
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

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

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

Files

ATTRIBUTION.mdAdded by AutoGineer1012 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md5.6 KiB
references/multi-cloud-patterns.md1.0 KiB
references/service-comparison.md1.7 KiB

references/multi-cloud-patterns.md

Multi-Cloud Architecture Patterns

Active-Active Regional Split

Run customer-facing services in two providers for resiliency
Use global DNS and traffic steering to shift load during incidents
Keep shared data replicated asynchronously unless low-latency writes are mandatory

Best-of-Breed Service Mix

Analytics and ML on GCP
Enterprise identity and Microsoft workloads on Azure
Broad ecosystem integrations on AWS
Oracle-centric databases and regulated transaction systems on OCI

Primary / DR Pairing

Keep primary infrastructure in the provider closest to operational expertise
Use a second provider for cold or warm disaster recovery
Validate RPO/RTO assumptions with regular failover exercises

Portable Platform Baseline

Standardize on Kubernetes, Terraform/OpenTofu, PostgreSQL, Redis, and OpenTelemetry
Abstract cloud differences behind modules, golden paths, and service catalogs
Document provider-specific exceptions such as IAM, networking, and managed database behavior

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.

Security

No issues found by our static scan· No issues found

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download multi-cloud-architecture.zip