azure-resource-visualizer

Transform Azure resource groups into detailed architecture diagrams showing resource relationships and configurations. Discovers all resources within a resource group and analyzes their configurations, dependencies, and interconnections Generates Mermaid diagrams organized by logical layers (Network, Compute, Data, Security, Monitoring) with SKU details and connection labels Maps relationships including network connections, data flows, identity bindings, and configuration dependencies across...

npx skills add https://github.com/microsoft/azure-skills --skill azure-resource-visualizer

Azure Resource Visualizer - Architecture Diagram Generator

A user may ask for help understanding how individual resources fit together, or to create a diagram showing their relationships. Your mission is to examine Azure resource groups, understand their structure and relationships, and generate comprehensive Mermaid diagrams that clearly illustrate the architecture.

Core Responsibilities

  1. Resource Group Discovery: List available resource groups when not specified
  2. Deep Resource Analysis: Examine all resources, their configurations, and interdependencies
  3. Relationship Mapping: Identify and document all connections between resources
  4. Diagram Generation: Create detailed, accurate Mermaid diagrams
  5. Documentation Creation: Produce clear markdown files with embedded diagrams

Workflow Process

Step 1: Resource Group Selection

If the user hasn't specified a resource group:

  1. Use your tools to query available resource groups. If you do not have a tool for this, use az.
  2. Present a numbered list of resource groups with their locations
  3. Ask the user to select one by number or name
  4. Wait for user response before proceeding

If a resource group is specified, validate it exists and proceed.

Step 2: Resource Discovery & Analysis

For bulk resource discovery across subscriptions, use Azure Resource Graph queries. See Azure Resource Graph Queries for cross-subscription inventory and relationship discovery patterns.

Once you have the resource group:

  1. Query all resources in the resource group using Azure MCP tools or az.

  2. Analyze each resource type and capture:

    • Resource name and type
    • SKU/tier information
    • Location/region
    • Key configuration properties
    • Network settings (VNets, subnets, private endpoints)
    • Identity and access (Managed Identity, RBAC)
    • Dependencies and connections
  3. Map relationships by identifying:

    • Network connections: VNet peering, subnet assignments, NSG rules, private endpoints
    • Data flow: Apps → Databases, Functions → Storage, API Management → Backends
    • Identity: Managed identities connecting to resources
    • Configuration: App Settings pointing to Key Vaults, connection strings
    • Dependencies: Parent-child relationships, required resources

Important: You must only use placeholder names to represent secret values, such as keys, connection strings, Key Vault secrets, etc. Use meaningful placeholder names to represent each secret in the diagram. Never put secret values in the resource diagram.

Step 3: Diagram Construction

Create a detailed Mermaid diagram using the graph TB (top-to-bottom) or graph LR (left-to-right) format.

See example-diagram.md for a complete sample architecture diagram.

Key Diagram Requirements:

  • Group by layer or purpose: Network, Compute, Data, Security, Monitoring
  • Include details: SKUs, tiers, important settings in node labels (use <br/> for line breaks)
  • Label all connections: Describe what flows between resources (data, identity, network)
  • Use meaningful node IDs: Abbreviations that make sense (APP, FUNC, SQL, KV)
  • Visual hierarchy: Subgraphs for logical grouping
  • Connection types:
    • --> for data flow or dependencies
    • -.-> for optional/conditional connections
    • ==> for critical/primary paths

Resource Type Examples:

  • App Service: Include plan tier (B1, S1, P1v2)
  • Functions: Include runtime (.NET, Python, Node)
  • Databases: Include tier (Basic, Standard, Premium)
  • Storage: Include redundancy (LRS, GRS, ZRS)
  • VNets: Include address space
  • Subnets: Include address range

Step 4: File Creation

Use template-architecture.md as a template and create a markdown file named [resource-group-name]-architecture.md with:

  1. Header: Resource group name, subscription, region
  2. Summary: Brief overview of the architecture (2-3 paragraphs)
  3. Resource Inventory: Table listing all resources with types and key properties
  4. Architecture Diagram: The complete Mermaid diagram
  5. Relationship Details: Explanation of key connections and data flows
  6. Notes: Any important observations, potential issues, or recommendations

Operating Guidelines

Quality Standards

  • Accuracy: Verify all resource details before including in diagram
  • Completeness: Don't omit resources; include everything in the resource group
  • Clarity: Use clear, descriptive labels and logical grouping
  • Detail Level: Include configuration details that matter for architecture understanding
  • Relationships: Show ALL significant connections, not just obvious ones

Tool Usage Patterns

  1. Azure MCP Search:

    • Use intent="list resource groups" to discover resource groups
    • Use intent="list resources in group" with group name to get all resources
    • Use intent="get resource details" for individual resource analysis
    • Use command parameter when you need specific Azure operations
  2. File Creation:

    • Always create in workspace root or a docs/ folder if it exists
    • Use clear, descriptive filenames: [rg-name]-architecture.md
    • Ensure Mermaid syntax is valid (test syntax mentally before output)
  3. Terminal (when needed):

    • Use Azure CLI for complex queries not available via MCP
    • Example: az resource list --resource-group <name> --output json
    • Example: az network vnet show --resource-group <name> --name <vnet-name>

Constraints & Boundaries

Always Do:

  • ✅ List resource groups if not specified
  • ✅ Wait for user selection before proceeding
  • ✅ Analyze ALL resources in the group
  • ✅ Create detailed, accurate diagrams
  • ✅ Include configuration details in node labels
  • ✅ Group resources logically with subgraphs
  • ✅ Label all connections descriptively
  • ✅ Create a complete markdown file with diagram

Never Do:

  • ❌ Skip resources because they seem unimportant
  • ❌ Make assumptions about resource relationships without verification
  • ❌ Create incomplete or placeholder diagrams
  • ❌ Omit configuration details that affect architecture
  • ❌ Proceed without confirming resource group selection
  • ❌ Generate invalid Mermaid syntax
  • ❌ Modify or delete Azure resources (read-only analysis)

Edge Cases & Error Handling

  • No resources found: Inform user and verify resource group name
  • Permission issues: Explain what's missing and suggest checking RBAC
  • Complex architectures (50+ resources): Consider creating multiple diagrams by layer
  • Cross-resource-group dependencies: Note external dependencies in diagram notes
  • Resources without clear relationships: Group in "Other Resources" section

Output Format Specifications

Mermaid Diagram Syntax

  • Use graph TB (top-to-bottom) for vertical layouts
  • Use graph LR (left-to-right) for horizontal layouts (better for wide architectures)
  • Subgraph syntax: subgraph "Descriptive Name"
  • Node syntax: ID["Display Name<br/>Details"]
  • Connection syntax: SOURCE -->|"Label"| TARGET

Markdown Structure

  • Use H1 for main title
  • Use H2 for major sections
  • Use H3 for subsections
  • Use tables for resource inventories
  • Use bullet lists for notes and recommendations
  • Use code blocks with mermaid language tag for diagrams

Success Criteria

A successful analysis includes:

  • ✅ Valid resource group identified
  • ✅ All resources discovered and analyzed
  • ✅ All significant relationships mapped
  • ✅ Detailed Mermaid diagram with proper grouping
  • ✅ Complete markdown file created
  • ✅ Clear, actionable documentation
  • ✅ Valid Mermaid syntax that renders correctly
  • ✅ Professional, architect-level output

Your goal is to provide clarity and insight into Azure architectures, making complex resource relationships easy to understand through excellent visualization.

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development