Azure-Resource-Visualizer
Azure-Resource-Visualizer是一款engineering方向的AI技能,核心价值是Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources,可用于解决开发者在engineering领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. Use this skill when the user asks for a diagram of their Azure
mkdir -p ./skills/azure-resource-visualizer && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/azure-resource-visualizer/SKILL.md -o ./skills/azure-resource-visualizer/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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
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
Step 3: Diagram Construction
Create a **detailed Mermaid diagram** using the `graph TB` (top-to-bottom) or `graph LR` (left-to-right) format:
**Diagram Structure Guidelines:**
graph TB
%% Use subgraphs to group related resources
subgraph "Resource Group: [name]"
subgraph "Network Layer"
VNET[Virtual Network<br/>10.0.0.0/16]
SUBNET1[Subnet: web<br/>10.0.1.0/24]
SUBNET2[Subnet: data<br/>10.0.2.0/24]
NSG[Network Security Group]
end
subgraph "Compute Layer"
APP[App Service<br/>Plan: P1v2]
FUNC[Function App<br/>Runtime: .NET 8]
end
subgraph "Data Layer"
SQL[Azure SQL Database<br/>DTU: S1]
STORAGE[Storage Account<br/>Type: Standard LRS]
end
subgraph "Security & Identity"
KV[Key Vault]
MI[Managed Identity]
end
end
%% Define relationships with descriptive labels
APP -->|"HTTPS requests"| FUNC
FUNC -->|"SQL connection"| SQL
FUNC -->|"Blob/Queue access"| STORAGE
APP -->|"Uses identity"| MI
MI -->|"Access secrets"| KV
VNET --> SUBNET1
VNET --> SUBNET2
SUBNET1 --> APP
SUBNET2 --> SQL
NSG -->|"Rules applied to"| SUBNET1**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
🎯 Best For
- Developers scaffolding new projects
- Prototype builders
- Data analysts
- Business intelligence teams
- Data visualization designers
💡 Use Cases
- Bootstrapping React components
- Creating API route handlers
- Finding patterns in customer data
- Creating automated dashboards
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure-Resource-Visualizer to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Can I customize the generated output?
Yes — modify the skill's prompt instructions to match your project conventions and coding style.
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
What chart types does this support?
Check the skill content for supported chart types — most support bar, line, scatter, and heatmap.
How do I install Azure-Resource-Visualizer?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-resource-visualizer/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Using generated code without understanding
Understand what generated code does before shipping it to production.
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Choosing wrong chart types
Let the data shape dictate the visualization, not the other way around.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.