Analyze deployed Azure resource groups and generate detailed Mermaid architecture diagrams showing relationships between resources. Use for post-deployment visualization, understanding existing infrastructure, or documenting live Azure environments.
npx skills add https://github.com/Azure/git-ape --skill azure-resource-visualizer
Analyze deployed Azure resource groups and generate comprehensive Mermaid architecture diagrams showing resource relationships, configurations, and data flows.
Adapted from github/awesome-copilot azure-resource-visualizer skill.
If not specified, list available resource groups:
az group list \
--query "[].{Name:name, Location:location, Tags:tags}" \
--output table
Present a numbered list and ask the user to select.
Query all resources in the selected resource group:
az resource list \
--resource-group {rg-name} \
--query "[].{Name:name, Type:type, Location:location, SKU:sku.name, Kind:kind}" \
--output json
For each resource, gather details:
Identify connections between resources:
| Relationship Type | How to Detect |
|-------------------|---------------|
| Network | VNet peering, subnet assignments, NSG rules, private endpoints |
| Data flow | App → Database connection strings, Function → Storage bindings |
| Identity | Managed identity role assignments |
| Configuration | App Settings referencing Key Vault, instrumentation keys |
| Dependencies | Parent-child relationships (SQL Server → Database) |
Create a detailed diagram using graph TB or graph LR:
graph TB
subgraph "Resource Group: rg-webapp-prod-eastus"
subgraph "Compute Layer"
APP["🌐 app-webapp-prod-eastus<br/>Plan: B1 Basic"]
FUNC["⚡ func-api-prod-eastus<br/>Runtime: Python 3.11"]
end
subgraph "Data Layer"
SQL["🗄️ sql-webapp-prod-eastus<br/>Tier: Standard S1"]
STORAGE["💾 stwebappprod8k3m<br/>Standard LRS"]
end
subgraph "Monitoring & Security"
APPI["📊 appi-webapp-prod-eastus"]
KV["🔑 kv-webapp-prod-eus"]
end
end
Internet["🌐 Internet"] --> APP
APP -->|"connection string"| SQL
APP -->|"blob storage"| STORAGE
APP -.->|"instrumentation key"| APPI
FUNC -->|"trigger"| STORAGE
FUNC -.->|"instrumentation key"| APPI
APP -->|"secrets"| KV
FUNC -->|"secrets"| KV
classDef internet fill:#e0e7ff,stroke:#4338ca,color:#1e1b4b
classDef compute fill:#dbeafe,stroke:#1f6feb,stroke-width:2px,color:#0b3d91
classDef data fill:#dcfce7,stroke:#15803d,color:#14532d
classDef storage fill:#fef3c7,stroke:#92400e,color:#78350f
classDef monitor fill:#ede9fe,stroke:#7c3aed,color:#4c1d95
classDef secret fill:#fde68a,stroke:#b45309,stroke-width:2px,color:#7c2d12
class Internet internet
class APP,FUNC compute
class SQL data
class STORAGE storage
class APPI monitor
class KV secret
Diagram Rules:
<br/>--> for data flow/dependencies, -.-> for optional/monitoring, ==> for critical pathssubgraph for logical groupingGenerate a markdown file named {rg-name}-architecture.md:
# Architecture: {rg-name}
**Subscription:** {subscription-name}
**Region:** {location}
**Analyzed:** {timestamp}
## Overview
{2-3 paragraph summary of the architecture}
## Architecture Diagram
{mermaid diagram}
## Resource Inventory
| # | Resource | Type | SKU | Location | Tags |
|---|----------|------|-----|----------|------|
| 1 | app-webapp-prod | App Service | B1 | East US | env=prod |
| 2 | sql-webapp-prod | SQL Server | S1 | East US | env=prod |
| ... | ... | ... | ... | ... | ... |
## Relationships
| Source | Target | Connection Type | Details |
|--------|--------|-----------------|---------|
| App Service | SQL Server | Connection String | SQL authentication |
| App Service | Storage | Blob Access | Managed Identity |
| Function App | Storage | Queue Trigger | Storage binding |
## Notes
- {observations about the architecture}
- {potential improvements}
- {security considerations}
.azure/deployments/{id}/architecture-live.mddocs/ folderPost-deployment visualization:
Deployment succeeds → /azure-resource-visualizer {rg-name} → Live architecture diagram
Drift detection enhancement:
/azure-drift-detector detects drift → /azure-resource-visualizer → Compare expected vs actual diagram
Import workflow:
/azure-iac-exporter imports resources → /azure-resource-visualizer → Document imported architecture
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 azure/azure-resource-visualizer 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.