engineering-team/skills/azure-cloud-architect/SKILL.md
Design Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost optimization.
npx skillsauth add alirezarezvani/claude-skills azure-cloud-architectInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Design scalable, cost-effective Azure architectures for startups and enterprises with Bicep infrastructure-as-code templates.
Collect application specifications:
- Application type (web app, mobile backend, data pipeline, SaaS, microservices)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and Azure experience level
- Compliance requirements (GDPR, HIPAA, SOC 2, ISO 27001)
- Availability requirements (SLA, RPO/RTO)
- Region preferences (data residency, latency)
Run the architecture designer to get pattern recommendations:
python scripts/architecture_designer.py \
--app-type web_app \
--users 10000 \
--requirements '{"budget_monthly_usd": 500, "compliance": ["SOC2"]}'
Example output:
{
"recommended_pattern": "app_service_web",
"service_stack": ["App Service", "Azure SQL", "Front Door", "Key Vault", "Entra ID"],
"estimated_monthly_cost_usd": 280,
"pros": ["Managed platform", "Built-in autoscale", "Deployment slots"],
"cons": ["Less control than VMs", "Platform constraints", "Cold start on consumption plans"]
}
Select from recommended patterns:
See references/architecture_patterns.md for detailed pattern specifications.
Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.
Create infrastructure-as-code for the selected pattern:
# Web app stack (Bicep)
python scripts/bicep_generator.py --arch-type web-app --output main.bicep
Example Bicep output (core web app resources):
@description('The environment name')
param environment string = 'dev'
@description('The Azure region for resources')
param location string = resourceGroup().location
@description('The application name')
param appName string = 'myapp'
// App Service Plan
resource appServicePlan 'Microsoft.Web/serverfarms@2023-01-01' = {
name: '${environment}-${appName}-plan'
location: location
sku: {
name: 'P1v3'
tier: 'PremiumV3'
capacity: 1
}
properties: {
reserved: true // Linux
}
}
// App Service
resource appService 'Microsoft.Web/sites@2023-01-01' = {
name: '${environment}-${appName}-web'
location: location
properties: {
serverFarmId: appServicePlan.id
httpsOnly: true
siteConfig: {
linuxFxVersion: 'NODE|20-lts'
minTlsVersion: '1.2'
ftpsState: 'Disabled'
alwaysOn: true
}
}
identity: {
type: 'SystemAssigned'
}
}
// Azure SQL Database
resource sqlServer 'Microsoft.Sql/servers@2023-05-01-preview' = {
name: '${environment}-${appName}-sql'
location: location
properties: {
administrators: {
azureADOnlyAuthentication: true
}
minimalTlsVersion: '1.2'
}
}
resource sqlDatabase 'Microsoft.Sql/servers/databases@2023-05-01-preview' = {
parent: sqlServer
name: '${appName}-db'
location: location
sku: {
name: 'GP_S_Gen5_2'
tier: 'GeneralPurpose'
}
properties: {
autoPauseDelay: 60
minCapacity: json('0.5')
}
}
Full templates including Front Door, Key Vault, Managed Identity, and monitoring are generated by
bicep_generator.pyand also available inreferences/architecture_patterns.md.
Bicep is the recommended IaC language for Azure. Prefer Bicep over ARM JSON templates: Bicep compiles to ARM JSON, has cleaner syntax, supports modules, and is first-party supported by Microsoft.
Analyze estimated costs and optimization opportunities:
python scripts/cost_optimizer.py \
--config current_resources.json \
--json
Example output:
{
"current_monthly_usd": 2000,
"recommendations": [
{ "action": "Right-size SQL Database GP_S_Gen5_8 to GP_S_Gen5_2", "savings_usd": 380, "priority": "high" },
{ "action": "Purchase 1-year Reserved Instances for AKS node pools", "savings_usd": 290, "priority": "high" },
{ "action": "Move Blob Storage to Cool tier for objects >30 days old", "savings_usd": 65, "priority": "medium" }
],
"total_potential_savings_usd": 735
}
Output includes:
Set up Azure DevOps Pipelines or GitHub Actions with Azure:
# GitHub Actions — deploy Bicep to Azure
name: Deploy Infrastructure
on:
push:
branches: [main]
permissions:
id-token: write
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: azure/login@v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- uses: azure/arm-deploy@v2
with:
resourceGroupName: rg-myapp-dev
template: ./infra/main.bicep
parameters: environment=dev
# Azure DevOps Pipeline
trigger:
branches:
include:
- main
pool:
vmImage: 'ubuntu-latest'
steps:
- task: AzureCLI@2
inputs:
azureSubscription: 'MyServiceConnection'
scriptType: 'bash'
scriptLocation: 'inlineScript'
inlineScript: |
az deployment group create \
--resource-group rg-myapp-dev \
--template-file infra/main.bicep \
--parameters environment=dev
Validate security posture before production:
If deployment fails:
az deployment group show \
--resource-group rg-myapp-dev \
--name main \
--query 'properties.error'
az bicep build --file main.bicep
az deployment group validate \
--resource-group rg-myapp-dev \
--template-file main.bicep
Common failure causes:
az provider register --namespace Microsoft.WebGenerates architecture pattern recommendations based on requirements.
python scripts/architecture_designer.py \
--app-type web_app \
--users 50000 \
--requirements '{"budget_monthly_usd": 1000, "compliance": ["HIPAA"]}' \
--json
Input: Application type, expected users, JSON requirements Output: Recommended pattern, service stack, cost estimate, pros/cons
Analyzes Azure resource configurations for cost savings.
python scripts/cost_optimizer.py --config resources.json --json
Input: JSON file with current Azure resource inventory Output: Recommendations for:
Generates Bicep template scaffolds from architecture type.
python scripts/bicep_generator.py --arch-type microservices --output main.bicep
Output: Production-ready Bicep templates with:
Ask: "Design an Azure web app for a startup with 5000 users"
Result:
- App Service (B1 Linux) for the application
- Azure SQL Serverless for relational data
- Azure Blob Storage for static assets
- Front Door (free tier) for CDN and routing
- Key Vault for secrets
- Estimated: $40-80/month
Ask: "Design a microservices architecture on Azure for a SaaS platform with 50k users"
Result:
- AKS cluster with 3 node pools (system, app, jobs)
- API Management for gateway and rate limiting
- Cosmos DB for multi-model data
- Service Bus for async messaging
- Azure Monitor + Application Insights for observability
- Multi-zone deployment
Ask: "Design an event-driven backend for processing orders"
Result:
- Azure Functions (Consumption plan) for compute
- Event Grid for event routing
- Service Bus for reliable messaging
- Cosmos DB for order data
- Application Insights for monitoring
- Estimated: $30-150/month depending on volume
Ask: "Design a data pipeline for ingesting 10M events/day"
Result:
- Event Hubs for ingestion
- Stream Analytics or Functions for processing
- Data Lake Storage Gen2 for raw data
- Synapse Analytics for warehouse
- Power BI for dashboards
Provide these details for architecture design:
| Requirement | Description | Example | |-------------|-------------|---------| | Application type | What you're building | SaaS platform, mobile backend | | Expected scale | Users, requests/sec | 10k users, 100 RPS | | Budget | Monthly Azure limit | $500/month max | | Team context | Size, Azure experience | 3 devs, intermediate | | Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 | | Availability | Uptime requirements | 99.9% SLA, 1hr RPO |
JSON Format:
{
"application_type": "saas_platform",
"expected_users": 10000,
"requests_per_second": 100,
"budget_monthly_usd": 500,
"team_size": 3,
"azure_experience": "intermediate",
"compliance": ["SOC2"],
"availability_sla": "99.9%"
}
| Anti-Pattern | Why It Fails | Do This Instead | |---|---|---| | ARM JSON templates for new projects | Verbose, hard to read, no modules | Use Bicep — compiles to ARM, cleaner syntax | | Storing secrets in App Settings | Secrets visible in portal, no rotation | Use Key Vault references in App Settings | | Single large AKS node pool | Cannot optimize for different workloads | Use multiple node pools: system, app, jobs | | Public endpoints on PaaS services | Exposed attack surface | Use Private Endpoints + VNet integration | | Over-provisioning "just in case" | Wastes budget month one | Start small, use autoscale, right-size monthly | | Shared resource groups for everything | Blast radius, RBAC nightmares | One resource group per environment per workload | | No tagging strategy | Cannot track costs or ownership | Tag: environment, owner, cost-center, app-name | | Using classic resources | Deprecated, limited features | Use ARM/Bicep resources exclusively |
| Skill | Relationship |
|-------|-------------|
| engineering-team/aws-solution-architect | AWS equivalent — same 6-step workflow, different services |
| engineering-team/gcp-cloud-architect | GCP equivalent — completes the cloud trifecta |
| engineering-team/senior-devops | Broader DevOps scope — pipelines, monitoring, containerization |
| engineering/terraform-patterns | IaC implementation — use for Terraform modules targeting Azure |
| engineering/ci-cd-pipeline-builder | Pipeline construction — automates Azure DevOps and GitHub Actions |
| Document | Contents |
|----------|----------|
| references/architecture_patterns.md | 5 patterns: web app, microservices/AKS, serverless, data pipeline, multi-region |
| references/service_selection.md | Decision matrices for compute, database, storage, messaging, networking |
| references/best_practices.md | Naming conventions, tagging, RBAC, network security, monitoring, DR |
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.