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Azure Well Architected Review

OrganizationPopular
github
azure-well-architected-review

Perform an Azure Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.

Overview

Publishergithub
Repositoryawesome-copilot
Skill nameazure-well-architected-review
Stars
39.1K
Forks
5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by github on GitHub. Read the source before you install it.

Installation

Install the Azure Well Architected Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p .claude/skills
cp -r /tmp/awesome-copilot/skills/azure-well-architected-review .claude/skills/azure-well-architected-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Well Architected Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Well Architected Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Well Architected Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Azure Well-Architected Review

This workflow performs a structured Azure Well-Architected Framework (WAF) review against your workload's IaC files and deployed infrastructure. It identifies risks across all 5 WAF pillars and creates GitHub issues to track remediation.

Prerequisites

  • Azure CLI (az) configured and authenticated
  • IaC files present in the repository (Bicep, Terraform, or ARM templates)
  • GitHub MCP server configured and authenticated

Workflow Steps

Step 1: Load Well-Architected Framework Reference

Fetch current Azure WAF best practices:

  • https://learn.microsoft.com/en-us/azure/well-architected/
  • Service guides for the Azure services in use (https://learn.microsoft.com/en-us/azure/well-architected/service-guides/)
  • Workload-specific guidance relevant to the workload type (SaaS, mission-critical, AI, etc.)

If the microsoft.docs.mcp MCP server is available, use it to query the latest pillar checklists and service-specific recommendations.

Step 2: Discover IaC & Architecture

Establish the review scope, then inventory both the code and the live environment:

  1. Confirm the Azure scope: Ask the user which subscription(s)/resource group(s) are in scope, or infer them from IaC parameters and confirm.
  2. Scan the repository for IaC files:
    • Bicep: **/*.bicep, bicepconfig.json
    • Terraform: **/*.tf (azurerm/azapi providers)
    • ARM templates: **/azuredeploy*.json, **/*.template.json, files with $schema containing deploymentTemplate
  3. Inventory live resources (always, even when IaC exists): az resource list --resource-group <rg> --output json (or subscription-wide), plus targeted az <service> show calls for configuration details the pillar checks need.
  4. Compare IaC with live inventory: Flag drift — resources present in Azure but absent from IaC (portal-created), resources defined in IaC but not deployed, and configuration mismatches. Record drift findings for Step 3 (they typically map to the Operational Excellence pillar).

Identify key Azure services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.

Step 3: Pillar-by-Pillar Review

Pillar 1: Reliability
  • Availability zones enabled for zonal services (VMs, VMSS, AKS node pools, App Service, SQL, Storage ZRS)
  • Production SKUs support the required SLA (no Basic/Free tiers on critical paths)
  • Azure SQL / Cosmos DB backup and point-in-time restore configured with appropriate retention
  • Geo-redundancy configured where RPO requires it (GRS/RA-GRS storage, SQL failover groups, Cosmos DB multi-region)
  • Autoscale rules configured for App Service plans, VMSS, AKS (no fixed single instance for production)
  • Health probes configured on Load Balancer / Application Gateway / Front Door backends
  • Dead-lettering enabled for Service Bus queues/subscriptions and Event Grid subscriptions
  • Retry policies with exponential backoff implemented for transient fault handling
  • Disaster recovery plan defined (documented RTO/RPO, tested failover)
Pillar 2: Security
  • Managed identities used instead of service principals with secrets or connection strings
  • No hardcoded credentials, keys, or connection strings in IaC or code
  • Secrets stored in Azure Key Vault with RBAC authorization (not access policies)
  • Storage accounts deny public blob access and disallow shared key access where possible
  • Private endpoints (or at minimum service endpoints + firewall rules) for PaaS data services
  • NSGs restrict inbound traffic to minimum required ports/CIDRs (no ** allow rules)
  • TLS 1.2+ enforced on all endpoints (minimumTlsVersion, httpsOnly)
  • Azure RBAC follows least privilege (no Owner/Contributor at subscription scope for workload identities)
  • Microsoft Defender for Cloud enabled on relevant resource types (az security pricing list)
  • Azure WAF (Application Gateway or Front Door) configured for public-facing web endpoints
  • Diagnostic settings send security logs to Log Analytics / Microsoft Sentinel
Pillar 3: Cost Optimization
  • Reservations or savings plans evaluated for steady-state compute (VMs, App Service, SQL)
  • Storage lifecycle management policies move blobs to cool/archive tiers
  • Right-sized SKUs based on actual utilization (no oversized VMs/App Service plans)
  • Dev/test environments use auto-shutdown schedules and Dev/Test pricing where eligible
  • Azure Budgets and cost alerts configured (az consumption budget list)
  • Unattached managed disks and orphaned public IPs identified and removed
  • Consumption/serverless tiers used for spiky or low-volume workloads (Functions, Container Apps, SQL serverless)
  • Log Analytics retention and data-cap settings tuned to avoid ingestion overruns
Pillar 4: Operational Excellence
  • All infrastructure defined as IaC (no manual portal changes; deny assignments or policy where feasible)
  • Consistent tagging strategy applied across all resources (owner, environment, cost center)
  • Azure Monitor alerts defined for key metrics and service health
  • Automated deployment pipeline present (GitHub Actions / Azure Pipelines, no manual deployments)
  • Azure Activity Log and resource diagnostic settings routed to Log Analytics
  • Application Insights (or OpenTelemetry equivalent) instrumented for application workloads
  • Azure Policy assignments enforce organizational standards (allowed locations, SKUs, tags)
  • Runbooks or operational documentation present
Pillar 5: Performance Efficiency
  • Right-sized compute SKUs validated against load requirements
  • Caching implemented where beneficial (Azure Cache for Redis, CDN/Front Door caching)
  • Azure Front Door or CDN used for global static content delivery
  • Autoscale based on load metrics rather than fixed instance counts
  • Database performance tier appropriate (DTU vs vCore, elastic pools, Cosmos DB RU autoscale)
  • Premium/zone-redundant storage used for latency-sensitive disk workloads
  • Connection pooling and async patterns used for database and HTTP clients

Step 4: Risk Classification

For each finding, classify:

  • High Risk: Security vulnerability, single point of failure, no backup/recovery
  • Medium Risk: Suboptimal reliability, cost inefficiency, performance concern
  • Low Risk: Best practice deviation, minor optimization opportunity

Step 5: User Confirmation

🏗️ Azure Well-Architected Review Summary

📊 Review Results:
• IaC Files Analyzed: X
• Azure Services Identified: Y
• Total Findings: Z
  • High Risk: A (immediate action required)
  • Medium Risk: B (should address soon)
  • Low Risk: C (nice to have)

🔴 Top High Risk Findings:
1. [Pillar]: [Finding] — [Why it matters]
2. [Pillar]: [Finding] — [Why it matters]

💡 This will create Z individual GitHub issues + 1 EPIC issue.

❓ Proceed with creating GitHub issues? (y/n)

Gate: Only proceed to Steps 6–7 if the user gives an explicit affirmative response (e.g. "y", "yes"). On a negative, ambiguous, or missing response, do not create any GitHub issues — output the full findings as formatted markdown to the console and stop.

Step 6: Create Individual Finding Issues

Label with "well-architected" and the pillar name (e.g., "security", "reliability").

Title: [WAF-<PILLAR>] [Brief Finding] — [Risk Level]

Body:

markdown
## 🏗️ Well-Architected Finding: [Brief Title]

**Pillar**: [Name] | **Risk Level**: [High/Medium/Low] | **Effort**: [Low/Medium/High]

### 📋 Description
[Clear explanation of the finding and why it matters]

### 🔧 Remediation

**IaC Fix** (preferred):
```bicep
// Bicep example
resource storageAccount 'Microsoft.Storage/storageAccounts@2023-05-01' = {
  name: storageAccountName
  location: location
  sku: { name: 'Standard_ZRS' }
  kind: 'StorageV2'
  properties: {
    minimumTlsVersion: 'TLS1_2'
    allowBlobPublicAccess: false
    supportsHttpsTrafficOnly: true
  }
}
```

**Azure CLI fallback**:
```bash
az storage account update --name  --resource-group  \
  --min-tls-version TLS1_2 --allow-blob-public-access false --https-only true
```

### 📚 Azure Reference
- [WAF Best Practice Link]
- [Microsoft Learn Documentation Link]

### ✅ Validation
- [ ] Change implemented in IaC and deployed
- [ ] Azure Policy compliance passes (if applicable)
- [ ] Microsoft Defender for Cloud recommendation resolved (if applicable)

**Well-Architected Recommendation**: [WAF checklist item this maps to]

Step 7: Create EPIC Tracking Issue

Label with "well-architected" and "epic".

Title: [EPIC] Azure Well-Architected Review — X findings across 5 pillars

Body: Executive summary with pillar breakdown table (finding counts by pillar and risk level), Mermaid architecture diagram, prioritized checklist linking all individual issues (High → Medium → Low), and success criteria:

  • All High-risk findings resolved
  • Medium findings have accepted mitigation plans
  • No regression in existing Azure Monitor alerts or Azure Policy compliance

Error Handling

  • No IaC Files Found: Limit review to live resource discovery via Azure CLI (az resource list) and note the gap
  • Insufficient Azure Permissions: List required read-only roles for the review (Reader, Security Reader)
  • GitHub Creation Failure: Output all findings as formatted markdown to console

Success Criteria

  • ✅ All 5 WAF pillars reviewed against IaC and live infrastructure
  • ✅ All findings classified by risk level and pillar
  • ✅ Actionable remediation steps with IaC examples for each finding
  • ✅ GitHub issues created for team tracking
  • ✅ Architecture diagram generated for EPIC context
  • ✅ Microsoft Learn documentation references included

Frequently asked questions

What does the Azure Well Architected Review AI skill do?

Perform an Azure Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.

Why use Azure Well Architected Review on TypingMind?

Because you install it once and use it with any model. Azure Well Architected Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Well Architected Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/azure-well-architected-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Azure Well Architected Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Well Architected Review?

As many as you like. As long as a model supports skills, you can use Azure Well Architected Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Well Architected Review AI skill free?

Yes. It is published on GitHub by github under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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