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/azure-architecture-autopilot

@4214189 official
by githubgithub/awesome-copilot40k stars
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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/azure-architecture-autopilot

This session only. Nothing lands on disk.

referencesarchitecture-guidance-sources.md

≈1.8k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Architecture Guidance Sources (For Design Direction Decisions)

A source registry for using Azure official architecture guidance only for design direction decisions.

The URLs in this document are a list of sources for "where to look". Do not hardcode the contents of these URLs as fixed facts. Do not use for SKU, API version, region, model availability, or PE mapping decisions — those are handled exclusively via azure-dynamic-sources.md.


Purpose Separation

Purpose Document to Use Decidable Items
Design direction decisions This document (architecture-guidance-sources) Architecture patterns, best practices, service combination direction, security boundary design
Deployment spec verification azure-dynamic-sources.md API version, SKU, region, model availability, PE groupId, actual property values

What must NOT be decided using this document:

  • API version
  • SKU names/pricing
  • Region availability
  • Model names/versions/deployment types
  • PE groupId / DNS Zone mapping
  • Specific values for resource properties

Primary Sources

Targeted fetch targets for design direction decisions.

ID Document URL Purpose
A1 Azure Architecture Center https://learn.microsoft.com/en-us/azure/architecture/ Hub — Entry point for finding domain-specific documents
A2 Well-Architected Framework https://learn.microsoft.com/en-us/azure/architecture/framework/ Security/reliability/performance/cost/operations principles
A3 Cloud Adoption Framework / Landing Zone https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ready/landing-zone/ Enterprise governance, network topology, subscription structure
A4 Azure AI/ML Architecture https://learn.microsoft.com/en-us/azure/architecture/ai-ml/ AI/ML workload reference architecture hub
A5 Basic Foundry Chat Reference Architecture https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/basic-azure-ai-foundry-chat Basic Foundry-based chatbot structure
A6 Baseline AI Foundry Chat Reference Architecture https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-openai-e2e-chat Foundry chatbot enterprise baseline (including network isolation)
A7 RAG Solution Design Guide https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide RAG pattern design guide
A8 Microsoft Fabric Overview https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview Fabric platform overview and workload understanding
A9 Fabric Governance / Adoption https://learn.microsoft.com/en-us/power-bi/guidance/fabric-adoption-roadmap-governance Fabric governance, adoption roadmap

Secondary Sources (awareness only)

Not direct fetch targets; referenced only for change awareness.

Document URL Notes
Azure Updates https://azure.microsoft.com/en-us/updates/ Service changes/new feature announcements. Not a targeted fetch target

Fetch Trigger — When to Query

Architecture guidance documents are not queried on every request. Only perform targeted fetch when the following triggers apply.

Trigger Conditions

  1. When the user's workload type is identified in Phase 1 (automatic)
    • Pre-query the relevant workload's reference architecture to adjust question depth
    • Triggers automatically even if the user doesn't mention "best practice" etc.
    • Purpose: Reflect official architecture-based design decision points in questions, beyond SKU/region spec questions
  2. When the user requests design direction justification
    • Keywords such as "best practice", "reference architecture", "recommended structure", "baseline", "well-architected", "landing zone", "enterprise pattern"
  3. When architecture boundaries for a new service combination are ambiguous
    • Inter-service relationships that cannot be determined from existing reference files/service-gotchas
  4. When enterprise-level security/governance design is needed
    • Subscription structure, network topology, landing zone patterns

When Triggers Do Not Apply

  • Simple resource creation (SKU/API version/region questions) → Use only azure-dynamic-sources.md
  • Service combinations already covered in domain-packs → Prioritize reference files
  • Bicep property value verification → service-gotchas.md or MS Docs Bicep reference

Fetch Budget

Scenario Max Fetch Count
Default (when trigger fires) Architecture guidance documents up to 2
Additional fetch allowed when Conflicts between documents / core design uncertainty remains / user explicitly requests deeper justification
Simple deployment spec questions 0 (no architecture guidance queries)

Decision Rule by Question Type

Question Type Documents to Query Design Decision Points to Extract Documents NOT to Query
RAG / chatbot / Foundry app A5 or A6 + A7 Network isolation level, authentication method (managed identity vs key), indexing strategy (push vs pull), monitoring scope Do not traverse entire Architecture Center
Enterprise security / governance / landing zone A2 + A3 Subscription structure, network topology (hub-spoke etc.), identity/governance model, security boundary AI/ML domain documents not needed
Fabric data platform A8 + A9 Capacity model (SKU selection criteria), governance level, data boundary (workspace separation etc.) AI-related documents not needed
Ambiguous service combination (unclear pattern) A1 (find closest domain document from hub) + that document Key design decision points identified from the document Do not traverse all sub-documents
Simple resource creation values (SKU/API/region) No query — All architecture guidance
General AI/ML architecture A4 (hub) + closest reference architecture Compute isolation, data boundary, model serving approach Do not crawl entirely

URL Fallback Rule

  1. Use en-us Learn URLs by default
  2. If a specific URL returns 404 / redirect / deprecated → Fall back to the parent hub page
    • Example: If A5 fails → Search for "foundry chat" keyword on A4 (AI/ML hub)
  3. If not found on the parent hub either → Search by title keyword on A1 (Architecture Center main)
  4. Do not use the contents of a URL as fixed rules just because the URL exists

Full Traversal Prohibited

  • Do not broadly traverse (crawl) Architecture Center sub-documents
  • Only targeted fetch 1–2 related documents according to the decision rule by question type
  • Even within fetched documents, only reference relevant sections; do not read the entire document
  • Unlimited fetching, recursive link following, and sub-page enumeration are prohibited

Source: SKILL.md on GitHub

1 warning16d4 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill is a professional tool for Azure infrastructure management, providing design advice, architecture visualization, and Bicep-based deployment automation. It integrates with official Azure CLI tools and relies on trusted Microsoft documentation. The security analysis found no malicious patterns, with all operations being consistent with the skill's administrative purpose.

  • Socket16d

    1 alert: gptAnomaly

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 4214189. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 months ago
  • azure
  • bicep
  • infrastructure-as-code
  • architecture-design
  • resource-analysis
  • diagram-generation
  • deployment
  • iac

README badge

README badge for github/awesome-copilot/azure-architecture-autopilot

Designs and deploys Azure infrastructure from natural language descriptions, or analyzes existing resources to generate architecture diagrams and Bicep IaC code. Includes an embedded diagram engine with 605+ Azure icons and supports modification workflows across Microsoft Foundry, Azure OpenAI, AI Search, and 200+ other Azure services.

Generated from the current SKILL.md.

Does this skill generate actual Bicep code for deployment?
Yes. After designing or analyzing an architecture, the skill generates Bicep templates and validates them before deployment via Phase 2 (bicep-generator.md) and Phase 3 (bicep-reviewer.md).
Can I use this to analyze my existing Azure infrastructure?
Yes. Path B (Phase 0) scans existing Azure resources, auto-generates a diagram, and then lets you modify the architecture through natural language conversation.
Do I need to install Python or other tools separately?
No. The diagram engine is embedded in the skill's scripts folder and runs without requiring pip install or network access.
What Azure services are supported?
All Azure services are supported. The skill includes optimized patterns for Microsoft Foundry, Azure OpenAI, AI Search, ADLS Gen2, Key Vault, Fabric, ADF, and VNet/Private Endpoint configurations.
What languages does this skill support?
The skill auto-detects your input language and responds in that same language for all interactions, prompts, and generated code.

Generated from the current SKILL.md. These answers refresh after source changes.