multi-cloud-architecture

Overview of multi-cloud-architecture skill

What the multi-cloud-architecture skill does

The multi-cloud-architecture skill helps an AI agent design and compare architectures that span AWS, Azure, GCP, and OCI. Its value is not just listing equivalent services; it gives the agent a decision frame for choosing where each workload should live, when portability matters, and which multi-cloud pattern fits the business goal.

Who should use this skill

This multi-cloud-architecture skill is best for cloud architects, platform engineers, SRE teams, migration leads, and technical decision-makers who need to answer questions like:

• which provider should host each workload
• how to reduce provider lock-in without rebuilding everything from scratch
• how to split primary, DR, analytics, identity, or customer-facing traffic across clouds
• when to use portable building blocks versus provider-native services

It is especially useful when a generic architecture prompt would miss provider tradeoffs.

The real job-to-be-done

Most users are not looking for a textbook multi-cloud diagram. They need a defendable architecture choice under constraints such as compliance, latency, existing team skills, Oracle dependency, Microsoft ecosystem fit, Kubernetes preference, or DR requirements. This skill is built for that decision-making step.

What makes it different from a normal prompt

The main differentiator is structure. The skill gives the model:

• cross-provider service mappings
• practical multi-cloud patterns
• a bias toward matching architecture style to operating constraints
• a portable baseline mindset using tools like Kubernetes, Terraform/OpenTofu, PostgreSQL, Redis, and OpenTelemetry

That makes the output more useful for Cloud Architecture work than a broad “design me a multi-cloud system” prompt.

What it does well and where it is thinner

The repository is strongest on service comparison and pattern selection. It is lighter on implementation depth, governance detail, networking topologies, and step-by-step deployment mechanics. Use it to frame decisions and draft architecture options, then validate provider-specific details separately.

How to Use multi-cloud-architecture skill

multi-cloud-architecture install context

This skill lives in the wshobson/agents repository under:

plugins/cloud-infrastructure/skills/multi-cloud-architecture

If your agent framework supports repository-based skills, add or sync the source repository first, then enable the multi-cloud-architecture skill in the way your host agent expects. The baseline directory install pattern is:

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

The upstream SKILL.md does not include its own install command, so treat the exact command as host-tool dependent.

Read these files before relying on output

For a fast, high-signal review, read these in order:

1. SKILL.md
2. references/multi-cloud-patterns.md
3. references/service-comparison.md

This gives you the skill’s purpose, the recommended architecture patterns, and the provider mapping tables that shape its answers.

What input the skill needs to work well

The multi-cloud-architecture usage quality depends heavily on the constraints you provide. At minimum, give:

• workload type: web app, API, data platform, batch, ERP, ML, event-driven
• business goal: DR, cost optimization, exit strategy, regional expansion, best-of-breed
• current estate: existing cloud commitments, identity platform, databases, IaC, observability
• non-functional requirements: RTO, RPO, latency, compliance, data residency, throughput
• portability tolerance: fully portable, partially portable, or provider-native acceptable
• team reality: Kubernetes maturity, DB expertise, on-call capacity, budget discipline

Without that, the skill can only produce generic mappings.

Turn a rough idea into a strong prompt

Weak prompt:

“Design a multi-cloud architecture for our app.”

Stronger prompt:

“Use the multi-cloud-architecture skill to propose 2 architecture options for a customer-facing SaaS platform. We currently run on AWS, use Azure AD for workforce identity, need warm DR in a second cloud, target RTO under 2 hours and RPO under 15 minutes, want PostgreSQL and Redis, prefer Terraform/OpenTofu, and want to avoid deep lock-in except where it materially reduces ops burden. Compare AWS+Azure vs AWS+GCP and recommend one.”

This works better because it gives the skill a decision target, not just a topic.

Best prompt structure for this skill

A practical prompt template:

1. state the workload
2. define the business driver
3. list hard constraints
4. list current tools and cloud affinities
5. ask for 2–3 architecture options
6. require tradeoffs, risks, and recommendation
7. ask for service mappings by provider

Example request:

“Use multi-cloud-architecture for Cloud Architecture planning. Recommend a portable baseline and a provider-specific exception list. Include compute, storage, database, messaging, observability, IAM touchpoints, and DR pattern.”

Suggested workflow for real projects

Use this sequence:

1. ask for candidate patterns first
2. narrow to one primary pattern
3. ask for provider service mapping
4. ask which components should stay portable
5. ask which components can be provider-native
6. review DR, identity, networking, and data replication assumptions
7. convert the chosen option into a migration or implementation backlog

This is better than asking for a final architecture in one shot because the skill’s source material is strongest at comparison and pattern framing.

Patterns the skill is best at selecting

From the repository references, the most useful built-in patterns are:

• active-active regional split across providers
• best-of-breed service mix
• primary / DR pairing
• portable platform baseline

These are useful starting points when the architecture debate is really about resilience, lock-in, or team operating model.

How to use service comparison tables correctly

The tables in references/service-comparison.md are best used to map categories, not claim perfect equivalence. For example, “managed Kubernetes” maps cleanly across providers, but IAM, networking, database semantics, and eventing behavior do not become identical just because names line up.

Use the tables to shortlist services, then ask the model to call out non-portable differences explicitly.

Practical prompts that get better results

Ask for outputs like:

• “Compare portability cost for EKS, AKS, GKE, and OKE for a 20-service platform.”
• “Recommend where to keep provider-native services and where to standardize on open components.”
• “Design a primary/DR multi-cloud-architecture using AWS as primary and Azure as warm standby.”
• “Map our Azure identity dependency and Oracle database requirement into a realistic multi-cloud plan.”

These requests fit the repository evidence better than asking for low-level implementation runbooks.

Common misuses to avoid

Do not use this skill as if it were:

• a security compliance control catalog
• a detailed network reference architecture
• a cost calculator with current pricing
• a deployment automation framework
• a substitute for provider documentation

It helps decide and structure. It does not remove the need for provider-specific validation.

multi-cloud-architecture skill FAQ

Is multi-cloud-architecture worth using over a normal architecture prompt

Yes, if your problem is comparative. A normal prompt may produce plausible diagrams, but this skill gives the model a clearer basis for selecting across AWS, Azure, GCP, and OCI, plus concrete patterns like primary/DR and portable baseline.

Is this skill good for beginners

Partially. Beginners can use it to understand cloud service equivalents and common multi-cloud patterns. But the output quality depends on knowing your own constraints. If you cannot describe RTO/RPO, compliance, or operating model, expect generic answers.

When is the multi-cloud-architecture skill the wrong fit

Skip it when you only need:

• single-cloud optimization
• exact implementation commands
• deep security architecture review
• up-to-date price comparison
• one provider’s managed service tuning

In those cases, provider-specific skills or documentation are usually a better fit.

Does it favor portability over managed services

It leans toward a balanced answer. The source material explicitly supports both approaches: use provider-native managed services when expertise and lock-in tolerance are high, and use portable components when portability matters more. That tradeoff is the point of the skill.

Which clouds does it cover best

It directly covers AWS, Azure, GCP, and OCI. AWS, Azure, and GCP comparisons will usually feel more familiar to most teams, while OCI becomes especially relevant when Oracle affinity, networking economics, or regulated transaction workloads matter.

Can I use multi-cloud-architecture for migration planning

Yes, especially for evaluating target-state options during a migration. It is useful for comparing destination services, identifying portable baselines, and choosing a primary/DR pattern. You will still need a separate migration execution plan.

How to Improve multi-cloud-architecture skill

Give business constraints before technical preferences

The fastest way to improve multi-cloud-architecture usage is to start with business drivers such as resilience target, data sovereignty, procurement constraints, or M&A separation needs. Technical choices become clearer once those are explicit.

Tell it what must stay portable

Be specific about portability boundaries. Example:

• must stay portable: app runtime, PostgreSQL, Redis, observability, IaC
• acceptable lock-in: CDN, IAM integration, queueing, managed analytics

This prevents the model from over-standardizing everything or overusing native services everywhere.

Force explicit tradeoff output

Ask the model to produce sections for:

• recommendation
• rejected options
• lock-in risks
• operational burden
• DR implications
• portability exceptions

This makes the multi-cloud-architecture guide much more decision-ready.

Provide current-state anchors

Better outputs come from details like:

• “We already operate EKS well”
• “Workforce identity is Microsoft-first”
• “Analytics talent is strongest on GCP”
• “Oracle licensing makes OCI attractive”
• “We cannot staff 24/7 operations for two distinct platforms”

These anchors are often more important than abstract architecture ideals.

Watch for common failure modes

The skill can drift into weak recommendations when prompts are missing:

• data gravity constraints
• IAM and identity dependencies
• replication assumptions
• team capability limits
• failover testing expectations

If the answer sounds too clean, ask it to identify the top operational friction points.

Iterate after the first answer

A strong second-pass prompt is:

“Revise the proposed multi-cloud-architecture with stricter operational realism. Reduce platform diversity, call out provider-specific exceptions, and explain what we would actually need to test for DR readiness.”

That usually improves practicality more than asking for extra detail everywhere.

Ask for a final output format you can use

For real adoption, ask the model to end with:

• architecture option table
• recommended provider split
• service mapping by domain
• portability policy
• risks and assumptions
• next validation steps

That turns the multi-cloud-architecture skill from an idea generator into a usable architecture review artifact.