Design Azure cloud architectures from requirements and generate High-Level Design (HLD) documentation with service selection, patterns, cost estimates, and WAF alignment. Use this when asked to design or architect Azure solutions.
npx skills add https://github.com/thomast1906/github-copilot-agent-skills --skill architecture-design
Design comprehensive Azure architectures and produce HLD documentation following Well-Architected Framework and Cloud Adoption Framework best practices.
Ask clarifying questions about:
Service Selection Priority: PaaS > Containers > IaaS
Refer to references.md for detailed guidance on:
Key Decision Criteria:
Apply patterns based on requirements:
N-Tier (Traditional):
Microservices:
Event-Driven:
Serverless:
Address all five pillars (detailed checklists in waf-assessment skill):
Reliability:
Security:
Cost Optimization:
Operational Excellence:
Performance Efficiency:
Follow Cloud Adoption Framework:
{resource-type}-{workload}-{environment}-{region}-{instance}
Examples:
- rg-ecommerce-prod-eastus-001
- app-ecommerce-prod-eastus-001
- sql-ecommerce-prod-eastus-001
- kv-ecommerce-prod-eastus
- func-orderproc-prod-eastus-001
Standard Tags:
Environment: Production | Staging | Development | Test
Owner: [email protected]
CostCenter: IT-12345
Project: ProjectName
BusinessUnit: Sales | Marketing | Engineering
Criticality: Critical | High | Medium | Low
DataClassification: Public | Internal | Confidential | Restricted
Invoke the azure-pricing skill to retrieve live retail pricing. Never estimate costs from memory.
The azure-pricing skill will:
azure-mcp/pricing (pricing_get) per billable resource SKU and region> Confirm all service SKUs in step 3 before requesting pricing — the tool requires a specific SKU or service name.
Structure the cost output by category:
Generate comprehensive HLD documents with these sections:
For each component:
azure-pricing skill to retrieve live retail prices per service SKU and target region# High-Level Design: E-Commerce Web Platform
## 1. Executive Summary
This HLD describes a scalable e-commerce platform on Azure supporting up to 100K concurrent users
with 99.95% availability. The solution uses proven PaaS services with multi-region capabilities,
comprehensive security controls, and cost-optimized infrastructure.
**Key Benefits:**
- Global reach with Azure Front Door CDN
- Auto-scaling for traffic spikes (Black Friday, holidays)
- PCI-DSS compliant payment processing
- **Estimated cost**: see Section 10 — priced live via the `azure-pricing` skill per SKU and target region
**Timeline:** 8-week implementation with phased rollout
## 3. Architecture Overview
**Pattern:** N-Tier with asynchronous order processing
**Components:**
Azure Front Door (Global CDN + WAF)
└─ Application Gateway (Regional WAF + LB)
├─ App Service (Web Frontend - 3 instances, P2v3)
├─ App Service (API Backend - 3 instances, P2v3)
├─ Azure Functions (Order Processor, Premium)
├─ Azure SQL Database (S2 DTU, 50GB)
├─ Redis Cache (Basic C1, 1GB)
└─ Blob Storage (Hot tier, product images)
**Rationale:** N-tier provides proven scalability, PaaS reduces operational overhead,
Functions handle asynchronous order processing, Azure SQL provides ACID guarantees.
## 4. Component Design
**Frontend Web App**
- Service: Azure App Service (Linux)
- SKU: P2v3 (2 vCores, 8GB RAM)
- Instances: 3 (Availability Zones 1, 2, 3)
- Auto-scale: 3-10 instances based on CPU > 70%
- Naming: app-ecommerce-web-prod-eastus-001
- Purpose: Serves customer-facing website
[Continue with all components...]
Be Specific: Use exact service names and SKUs (not "database" but "Azure SQL Database S2 DTU")
Show Trade-offs: Explain why you chose service X over Y
Include Diagrams: Describe architecture visually with clear component relationships
Live Pricing: Invoke the azure-pricing skill for every cost section — never guess prices from memory; always pass the target region and currency (e.g. GBP for UK workloads)
Cost-Aware: Always provide cost estimates and optimization opportunities
Security First: Address authentication, authorization, encryption, network security
WAF Alignment: Reference specific WAF principles in design decisions
Naming Standards: Use CAF conventions consistently
Implementation-Ready: Provide enough detail for IaC generation
Avoid: Vague terms, missing costs, ignoring security, skipping WAF, incomplete components, no rationale
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take thomast1906/architecture-design from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.