Skill detail
cloud-solution-architect
>- Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.
Why we recommend it: Provides Azure Architecture Center design principles, architecture styles, cloud design patterns, technology choices, and a Well-Architected review workflow.
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SKILL.md
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--- name: cloud-solution-architect description: >- Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews. --- # Cloud Solution Architect ## Overview Design well-architected, production-grade cloud systems following Azure Architecture Center best practices. This skill provides: - **10 design principles** for Azure applications - **6 architecture styles** with selection guidance - **44 cloud design patterns** mapped to WAF pillars - **Technology choice frameworks** for compute, storage, data, messaging - **Performance antipatterns** to avoid - **Architecture review workflow** for systematic design validation --- ## Ten Design Principles for Azure Applications | # | Principle | Key Tactics | |---|-----------|-------------| | 1 | **Design for self-healing** | Retry with backoff, circuit breaker, bulkhead isolation, health endpoint monitoring, graceful degradation | | 2 | **Make all things redundant** | Eliminate single points of failure, use availability zones, deploy multi-region, replicate data | | 3 | **Minimize coordination** | Decouple services, use async messaging, embrace eventual consistency, use domain events | | 4 | **Design to scale out** | Horizontal scaling, autoscaling rules, stateless services, avoid session stickiness, partition workloads | | 5 | **Partition around limits** | Data partitioning (shard/hash/range), respect compute & network limits, use CDNs for static content | | 6 | **Design for operations** | Structured logging, distributed tracing, metrics & dashboards, runbook automation, infrastructure as code | | 7 | **Use managed services** | Prefer PaaS over IaaS, reduce operational burden, leverage built-in HA/DR/scaling | | 8 | **Use an identity service** | Microsoft Entra ID, managed identity, RBAC, avoid storing credentials, zero-trust principles | | 9 | **Design for evolution** | Loose coupling, versioned APIs, backward compatibility, async messaging for integration, feature flags | | 10 | **Build for business needs** | Define SLAs/SLOs, establish RTO/RPO targets, domain-driven design, cost modeling, composite SLAs | --- ## Architecture Styles | Style | Description | When to Use | Key Services | |-------|-------------|-------------|--------------| | **N-tier** | Horizontal layers (presentation, business, data) | Traditional enterprise apps, lift-and-shift | App Service, SQL Database, VNets | | **Web-Queue-Worker** | Web frontend → message queue → backend worker | Moderate-complexity apps with long-running tasks | App Service, Service Bus, Functions | | **Microservices** | Small autonomous services, bounded contexts, independent deploy | Complex domains, independent team scaling | AKS, Container Apps, API Management | | **Event-driven** | Pub/sub model, event producers/consumers | Real-time processing, IoT, reactive systems | Event Hubs, Event Grid, Functions | | **Big data** | Batch + stream processing pipeline | Analytics, ML pipelines, large-scale data | Synapse, Data Factory, Databricks | | **Big compute** | HPC, parallel processing | Simulations, modeling, rendering, genomics | Batch, CycleCloud, HPC VMs | ### Selection Criteria - **Domain complexity** → Microservices (high), N-tier (low-medium) - **Team autonomy** → Microservices (independent teams), N-tier (single team) - **Data volume** → Big data (TB+), others (GB) - **Latency requirements** → Event-driven (real-time), Web-Queue-Worker (tolerant) --- ## Cloud Design Patterns 44 patterns organized by primary concern. WAF pillar mapping: **R**=Reliability, **S**=Security, **CO**=Cost Optimization, **OE**=Operational Excellence, **PE**=Performance Efficiency. ### Messaging & Communication | Pattern | Summary | Pillars | |---------|---------|---------| | **Asynchronous Request-Read the full source on GitHub (opens external page)