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Multi-Cloud Architecture · sickn33 | DecimalAI

Multi Cloud Architecture

SkillScore18Maintained

multi-cloud-architecture

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

DevOpsby sickn33

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

DevOps & SRE pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/multi-cloud-architecture/SKILL.md

decimalai skills pull multi-cloud-architecture --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/multi-cloud-architecture/SKILL.md https://app.decimal.ai/s/multi-cloud-architecture/SKILL.md

Raw file: /s/multi-cloud-architecture/SKILL.md ↗ · machine-readable summary at /s/multi-cloud-architecture.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 54d ago

Yes — +18% lift · at higher cost+34% tokens0% turns18% of whole cases pass with skill (4/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-08	✗→✓	▲ Improved	—	—
case-15	✗→✓	▲ Improved	—	—
case-11	✗→✓	▲ Improved	—	—
case-19	✗→✓	▲ Improved	—	—
case-12	✗→✗	＝ Same ✗	—	—

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

sickn33 / antigravity-awesome-skills

skills/multi-cloud-architecture/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1

Skill contents

SKILL.md

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

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

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

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Illustrative Want to see what multi-cloud-architecture would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

7/28/2026, 22:51 UTC · 22 cases

Lift

+18%

18% vs 0%

Agent turns

0%

1.0 vs 1.0

Tokens

+34%

3,524.27 vs 2,628.45

fail → pass · 4fail → fail · 18

Per-case results (22)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-12 We are building event-driven micro-functions triggered by object storage uploads on AWS, Azure, and GCP. The developers want to use AWS Fargate for all micro-functions across clouds. Map the serverless function-as-a-service offerings for all three cloud providers.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-03 We are looking to optimize operational costs and avoid vendor lock-in across our public cloud infrastructure. Please draft a recommendation report that outlines an abstracted open-source technology stack for portable deployments, along with a structured cost-reduction checklist tailored for multi-cloud workloads.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-21 I am writing an Azure Bicep template to deploy a single Azure App Service plan in westeurope and getting a syntax error on resource declaration `resource appServicePlan 'Microsoft.Web/serverfarms@2022-03-01'`. Troubleshoot the Azure Bicep template code.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-14 We need to build an application storage layer that can read and write object storage across AWS, Azure, and GCP without cloud-specific SDKs. Recommend the abstraction strategy for object storage access.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-13 Our architects need a multi-region, globally consistent relational database layer across clouds. Developers suggest using AWS RDS PostgreSQL for global distributed transactions across Azure and GCP. Identify the distributed SQL service equivalents across AWS, Azure, and GCP.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-16 We run fault-tolerant batch processing workloads and want to utilize deeply discounted, short-lived, interruptible compute instances across AWS, Azure, and GCP. Developers call all of these 'Preemptible VMs'. Provide the provider-specific terminology for short-lived interruptible instances across all three clouds.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-08 Our team needs to deploy Kubernetes clusters across AWS, Azure, and GCP. A developer suggested running self-managed Kubernetes on bare EC2/VMs on all clouds to maintain consistency. Map the native managed Kubernetes engines for each cloud provider and verify their cloud-agnostic applicability.	fail→pass	—	—	—	—	—	—	—	—	—	—	—	—
case-02 Our company needs a failover framework to ensure high availability across two major cloud providers. Could you provide a multi-provider disaster recovery architecture design, detailing cross-cloud database replication methods, key failover considerations, and a phased migration plan to execute the rollout?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-04 Our enterprise wants to distribute workloads across cloud providers based on specialized vendor strengths: machine learning pipelines, Microsoft enterprise directory apps, and general elastic compute scale. We are tempted to deploy everything into AWS for simplicity. Recommend a multi-cloud architectural allocation pattern that matches provider specializations.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-15 When presenting multi-cloud cost optimization strategies to executive leadership, we need realistic expectation figures for savings achieved by switching from on-demand to committed/reserved compute capacity. What percentage savings range should be projected?	fail→pass	—	—	—	—	—	—	—	—	—	—	—	—
case-20 We are configuring an AWS-only network with VPC peering between subnets in us-east-1. The VPC peering connection status is active, but traffic between private subnet 10.0.1.0/24 and 10.0.2.0/24 times out. Provide AWS route table and Security Group troubleshooting steps specifically for AWS.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-09 We want to purchase long-term capacity discounts across AWS, Azure, and GCP to lower compute expenditures. Our finance team thinks all three providers use the exact same term 'Savings Plans' for discounted compute instances. Clarify the pricing terminology and discount mechanisms across the three clouds.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-11 We are planning a large-scale migration from AWS to GCP. The team wants to begin by instantly migrating critical production databases before running any tests or inventory. Provide the correct 4-phase multi-cloud migration lifecycle.	fail→pass	—	—	—	—	—	—	—	—	—	—	—	—
case-19 We are designing a Single Provider with DR multi-cloud pattern. Engineers suggest keeping the secondary cloud DR environment completely passive without any data sync until a disaster occurs. Detail the critical database and failover requirements for this pattern.	fail→pass	—	—	—	—	—	—	—	—	—	—	—	—
case-01 We are evaluating a transition from a single-cloud vendor to a multi-cloud setup. Please generate a detailed service mapping matrix comparing compute, container orchestration, object storage, NoSQL, and caching options across AWS, Azure, and Google Cloud Platform, followed by actionable guidance on making our infrastructure layer cloud-agnostic.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-17 We need to run standalone containers without managing a Kubernetes cluster across AWS, Azure, and GCP. Engineers recommend using Azure AKS for simple standalone container execution across all clouds. Provide the direct managed container execution service equivalents across AWS, Azure, and GCP.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-22 We need to tune NGINX Ingress Controller annotations inside our single on-prem Kubernetes cluster to adjust client body buffer size `nginx.ingress.kubernetes.io/proxy-body-size` to 50m. Provide the exact Kubernetes YAML manifest snippet for NGINX ingress.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-05 We operate a global SaaS app needing low latency for international users and strict compliance with local European and Asian data sovereignty laws. We are tempted to route all global traffic through a single AWS region using global VPC peering. Design a multi-cloud distribution strategy addressing latency and regulatory requirements.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-18 Our CTO asked for a comprehensive multi-cloud cost optimization strategy checklist beyond instance discounts. Provide concrete actionable practices to reduce spend across cloud providers.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-06 We are building an event-driven microservices architecture across AWS and Azure. Our developers want to use AWS Simple Queue Service directly from Azure workloads via IAM role federation. Recommend a cloud-agnostic messaging alternative to avoid provider lock-in.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-07 We need a service cross-reference matrix for persistent block storage attached to VMs and shared network file systems across AWS, Azure, and GCP. The team assumes EBS on AWS maps to Azure Files. Provide the exact service equivalencies for block and file storage across all three clouds.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-10 We need to archive 500TB of historical audit logs for 7 years across multiple cloud providers for compliance redundancy. Engineers are planning to store these in standard AWS S3 buckets in all clouds. Map the appropriate cold archive storage tier across AWS, Azure, and GCP.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 0%
Pass rate with the skill: 18.2%
Cases attempted: 22
Cases counted toward lift: 22
Cases that regressed: 1
Skill version measured: v1
Run completed: 7/28/2026, 22:51 UTC

The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run18

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 54 days ago

Other measured skills in the registry, with their headline benchmark lift.