Azure Scaffold Wizard

Azure Scaffold Wizard — Universal Project Generator

This skill generates a complete, production-ready Azure project deployable with azd up. It is fully domain-agnostic and applies to any use case. It produces every file needed: requirements documentation, project structure with all source files, README, Azure Bicep infrastructure, azure.yaml, Docker configuration, CI/CD workflows, architecture diagrams, observability setup, and Responsible AI documentation.

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Required Workflow

Follow these steps in order. Do not skip or reorder steps. Each step's output feeds the next.

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Step 1 — Identify Project Type and Gather Requirements

This is the most critical step. Do not generate any files until this step is complete.

1A — Project Type Selection

Ask the user: "What type of Azure project do you want to build?"

Present these options:

| # | Project Type | Description | When to Choose | |---|---|---|---| | 1 | RAG Chatbot | Retrieval-Augmented Generation chatbot with vector search | Conversational AI grounded in your own data | | 2 | Multi-Agent System | Multiple AI agents coordinated by an orchestrator | Parallel/sequential AI reasoning pipeline (Foundry compatible) | | 3 | API Backend | REST or GraphQL API service | Backend service with database and Azure deployment | | 4 | Data Pipeline | ETL/ELT data processing pipeline | Batch or streaming data transformation | | 5 | Azure Functions | Serverless event-driven functions | Lightweight event triggers and integrations | | 6 | Full-Stack Web App | Frontend + Backend web application | Complete web application with UI | | 7 | ML Training & Inference | Model training and serving endpoint | Custom ML model lifecycle on Azure ML | | 8 | Event-Driven Microservices | Message-based distributed system | Async decoupled services at scale |

If the user's request does not fit these categories exactly, choose the closest match or combine patterns. For detailed descriptions, read references/project-types.md.

1B — Universal Requirements (ask for ALL project types)

| # | Question | Guidance | |---|---|---| | U1 | What business problem does this solve? | One sentence. Drives README opening and all documentation. | | U2 | Who are the end users? | Drives UI design, access control decisions, RBAC roles. | | U3 | What is the project name? | kebab-case slug for folder names, azure.yaml, and resource naming. | | U4 | Programming language? | Python (default), TypeScript, C#, or multi-language. Drives all source file templates. | | U5 | Azure region? | Default: eastus2. Constrain by AI model availability if applicable. | | U6 | Authentication required? | None (default scaffold), Microsoft Entra ID (production). | | U7 | What Azure services beyond the defaults? | e.g., Cosmos DB, Service Bus, Key Vault, Storage, Redis. Drives extra Bicep modules. | | U8 | Frontend required? | Yes/No. If yes: Next.js (default), React SPA, or other. | | U9 | What compliance or regulatory requirements? | e.g., HIPAA, SOC2, GDPR. Drives TRANSPARENCY_FAQ.md and security docs. | | U10 | Target deployment platform? | Azure Container Apps (default), Azure Functions, AKS, App Service. | | U11 | Use Azure AI Foundry Agent Service? | Yes (default for AI-capable types): AI processing via Foundry Hosted Agents with structured output, managed deployment, and MAF. No: hand-rolled code using Azure OpenAI SDK directly. When to ask: Type 1 (RAG) — always ask. Types 3, 4, 5, 6, 8 — ask only after the type-specific "Include AI?" question is answered Yes. Skip for: Type 2 (Multi-Agent, always Foundry) and Type 7 (ML Training, uses Azure ML). |

1C — Type-Specific Requirements

Based on the project type selected in 1A, read the corresponding reference file and ask the type-specific questions defined there:

| Project Type | Reference File | What It Adds | |---|---|---| | RAG Chatbot | references/rag-chatbot.md | Vector store, embedding model, chunking strategy, data sources, conversation memory | | Multi-Agent System | references/multi-agent.md | Agent names/roles, orchestration topology, MCP tools, Foundry model choice | | API Backend | references/api-backend.md | Endpoints, database, API style (REST/GraphQL), rate limiting | | Data Pipeline | references/data-pipeline.md | Data sources/sinks, scheduling, batch vs. stream, transformations | | Azure Functions | references/function-app.md | Triggers, bindings, Durable Functions, runtime stack | | Full-Stack Web App | references/full-stack-app.md | Frontend framework, SSR/SPA, state management, API integration | | ML Training & Inference | references/ml-training.md | ML framework, compute targets, model registry, managed endpoints | | Event-Driven Microservices | references/event-driven.md | Message broker, event schemas, KEDA scaling rules |

The selected reference file contains:

• Type-specific qualifying questions
• The project folder structure for that type
• Source file templates and generation instructions
• Required Bicep modules list
• Type-specific quality checklist

1C-bis — Load Foundry Agent Patterns (Conditional)

If U11 = Yes (or if the project type is Multi-Agent), also read references/foundry-agent-patterns.md. This file provides the shared Foundry agent scaffolding patterns (agent.yaml, MAF main.py, schemas.py, skills, two-mode dispatcher, registration script) used by the type-specific reference file's "Foundry Mode" section. Exception: Multi-Agent projects use their own complete patterns in references/multi-agent.md — do not load foundry-agent-patterns.md for Multi-Agent.

1D — Document the Requirements

Create docs/requirements.md with a structured summary of ALL answers organized by section: Universal (U1–U11) and Type-Specific.

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Step 2 — Generate Project Structure

Based on the requirements from Step 1, generate the folder layout.

Every project, regardless of type, includes these common root files and directories:

/
├── azure.yaml                          # azd project descriptor + hooks
├── docker-compose.yml                  # Local dev: all containers without Azure
├── docker-compose.override.yml         # Local overrides (ports, volumes, hot-reload)
├── .env.example                        # All env vars documented with descriptions
├── README.md
├── TRANSPARENCY_FAQ.md                 # Responsible AI FAQ
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── SECURITY.md
├── SUPPORT.md
├── LICENSE                             # MIT
│
├── .github/
│   └── workflows/
│       ├── ci.yml                      # PR validation: lint, type-check, test, Bicep build
│       └── deploy.yml                  # Push to main: build, push, deploy
│
├── infra/
│   ├── main.bicep                      # Subscription-scoped entry point
│   ├── main.parameters.json            # azd parameter bindings
│   ├── abbreviations.json              # Azure resource name prefix map
│   └── modules/                        # (varies by project type + U7 choices)
│
├── scripts/
│   ├── preprovision.sh                 # Region + model validation (posix)
│   ├── preprovision.ps1                # Region + model validation (windows)
│   ├── postprovision.sh                # Build, push, register (posix)
│   └── postprovision.ps1               # Build, push, register (windows)
│
├── docs/
│   ├── requirements.md                 # Output of Step 1
│   ├── architecture.md                 # Narrative architecture description
│   ├── DeploymentGuide.md              # Step-by-step azd up walkthrough
│   ├── production-migration.md         # Scaffold → production gaps
│   ├── troubleshooting.md              # Common errors + fixes
│   └── images/
│       └── readme/
│           ├── solution-architecture.svg
│           └── pipeline-flow.svg       # (if applicable)
│
└── 

Read the type-specific reference file (loaded in Step 1C) for the additional directories and files specific to the selected project type.

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Step 3 — Generate Type-Specific Source Files

Follow the generation instructions in the type-specific reference file loaded in Step 1C. This step produces the core application code.

Examples of what each type generates:

• RAG Chatbot: ingestion pipeline, vector store client, retrieval chain, chat API, prompt templates
• Multi-Agent System: agent containers with agent.yaml, schemas, skills, orchestrator backend, two-mode dispatcher
• API Backend: routers, models, services, middleware, database migrations
• Data Pipeline: pipeline definitions, transformers, connectors, scheduling config
• Azure Functions: function triggers, bindings, orchestrations, shared utilities
• Full-Stack Web App: frontend components, API client, backend endpoints, shared types
• ML Training: training scripts, model definitions, inference endpoints, evaluation
• Event-Driven: message handlers, event schemas, saga orchestrators, dead-letter processors

Generate ALL source files with complete, functional code — not placeholders or stubs.

When U11 = Yes (Foundry mode): The Foundry agent patterns from references/foundry-agent-patterns.md produce additional files: an agents/ directory with agent.yaml, main.py, schemas.py, skills/, plus a dispatcher (hosted_agents.py) and registration script (register_agents.py). The type-specific reference file specifies exactly which parts of the existing code the Foundry agent replaces vs which parts it augments.

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Step 4 — Generate Azure Infrastructure and Deployment

4A — Bicep Infrastructure

Read references/bicep-patterns.md for universal Bicep patterns.

Generate infra/main.bicep (subscription-scoped) and all required modules.

Base modules for ALL project types:

• monitoring.bicep — App Insights + Log Analytics workspace
• container-registry.bicep — ACR (if containerized, which is most types)
• container-apps-env.bicep — Managed Environment (if U10 = ACA)
• container-app.bicep — Reusable per-app module
• role-assignments.bicep — RBAC for all managed identities

Add type-specific modules as listed in the loaded reference file (e.g., ai-foundry.bicep for RAG/Multi-Agent, ai-search.bicep for RAG, function-app.bicep for Functions).

Add optional service modules based on U7 answers: cosmos.bicep, storage.bicep, keyvault.bicep, servicebus.bicep, etc.

4B — azure.yaml and Hook Scripts

Read references/azure-yaml-patterns.md.

Generate:

• azure.yaml with preprovision and postprovision hooks
• scripts/preprovision.sh + scripts/preprovision.ps1 — validate region, model availability, exit non-zero on failure
• scripts/postprovision.sh + scripts/postprovision.ps1 — ACR login, image build with timestamp tag, registration

4C — Docker Configuration

Read references/docker-patterns.md.

Generate:

• Dockerfile for each service (multi-stage, non-root user, health checks)
• docker-compose.yml for local development
• docker-compose.override.yml for local overrides
• .dockerignore per service

4D — Observability Setup

Read references/observability-patterns.md for OpenTelemetry + Azure Monitor patterns.

Configure observability in all backend/API entry points:

• Azure Application Insights integration via OpenTelemetry
• Structured JSON logging
• Custom trace attributes for domain-specific telemetry
• FastAPI/Express lifespan integration

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Step 5 — Generate Documentation and Architecture

5A — README.md

Read references/readme-template.md for the section template and conventions.

Generate a complete README with all required sections populated from requirements — no placeholder text.

5B — Architecture Diagrams

Read references/architecture-diagrams.md for SVG generation prompts and conventions.

Generate:

• docs/images/readme/solution-architecture.svg — Azure resource topology diagram
• docs/images/readme/pipeline-flow.svg — Data/agent flow diagram (if applicable)

SVG requirements: viewBox + preserveAspectRatio, system-ui fonts, no external imports, under 50KB.

5C — Responsible AI Documents

Read references/responsible-ai.md for the TRANSPARENCY_FAQ.md template.

Generate TRANSPARENCY_FAQ.md addressing all 6 required questions, populated with project-specific content from Step 1.

5D — CI/CD Workflows

Read references/cicd-patterns.md.

Generate .github/workflows/ci.yml and .github/workflows/deploy.yml with language-appropriate steps.

5E — Supporting Documents

Generate:

• docs/architecture.md — narrative description of the architecture
• docs/DeploymentGuide.md — step-by-step azd up walkthrough
• docs/production-migration.md — gaps between scaffold and production (auth, storage, scaling, security)
• docs/troubleshooting.md — common errors and fixes
• CODE_OF_CONDUCT.md, CONTRIBUTING.md, SECURITY.md, SUPPORT.md

5F — Production Security Documentation

Read references/security-patterns.md for authentication, RBAC, Key Vault, and network isolation patterns.

Use these patterns to populate docs/production-migration.md with specific guidance for:

• Microsoft Entra ID integration (app registration, token validation)
• Role-based access control aligned with end users (U2)
• Azure Key Vault secret management
• VNet injection and private endpoints
• Input validation and sanitization

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Step 6 — Quality Checklist

Run through this checklist before delivering. Every item must pass.

Universal Checks (all project types)

Requirements & Documentation:

• [ ] docs/requirements.md exists with all U1–U11 + type-specific answers
• [ ] TRANSPARENCY_FAQ.md covers all 6 required questions
• [ ] README includes all required sections with no placeholder text
• [ ] docs/production-migration.md documents all production gaps

Infrastructure:

• [ ] main.bicep is subscription-scoped (targetScope = 'subscription')
• [ ] All secrets use @secure() — no plain-text secret outputs
• [ ] main.parameters.json binds all params to ${AZURE_*} azd env vars
• [ ] Role assignments grant appropriate roles to all managed identities

azure.yaml & Deployment:

• [ ] IMAGE_TAG is always a timestamp (YYYYMMDDHHmmss), never latest
• [ ] Preprovision hook validates region and exits non-zero on failure
• [ ] Postprovision hook validates images before registration
• [ ] Hooks have both windows (pwsh) and posix (sh) variants

Docker:

• [ ] All Dockerfiles use non-root user
• [ ] docker-compose.yml enables full local development without Azure
• [ ] .env.example documents ALL required environment variables

CI/CD:

• [ ] ci.yml has lint + type-check + test + Bicep validation steps
• [ ] deploy.yml builds, pushes, and deploys on main branch

Architecture Diagrams:

• [ ] All SVGs use viewBox + preserveAspectRatio="xMidYMid meet"
• [ ] No external font imports (use system-ui)
• [ ] SVGs render correctly in a browser

Observability:

• [ ] App Insights connection string configured in all containers
• [ ] OTel trace setup in backend/API entry point

Type-Specific Checks

Run the quality checklist defined in the loaded type-specific reference file from Step 1C.

Foundry Agent Checks (if U11 = Yes)

Run these in addition to Universal and Type-Specific checks:

• [ ] agent.yaml has meaningful description: (2-3 sentences, not a placeholder)
• [ ] main.py uses default_options={"response_format": Schema} for structured output
• [ ] schemas.py has confidence_score, confidence_level, summary, and errors fields
• [ ] skill.md defines role, input contract, step-by-step instructions, and output requirements
• [ ] register_agents.py passes description=agent_def["description"] to create_version()
• [ ] hosted_agents.py correctly switches between Foundry mode and direct HTTP mode
• [ ] docker-compose.yml doe

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: microsoft
• Source: microsoft/Foundry-AI-solution-templates-creation
• License: MIT

Install and usage instructions live in the source repository linked above.