azure-architecture-autopilot

安装量: 1.1K
排名: #3996

安装

npx skills add https://github.com/github/awesome-copilot --skill azure-architecture-autopilot
Azure Architecture Builder
A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.
The diagram engine is
embedded within the skill
(
scripts/
folder).
No
pip install
needed — it directly uses the bundled Python scripts
to generate interactive HTML diagrams with 605+ official Azure icons.
Ready to use immediately without network access or package installation.
Automatic User Language Detection
🚨 Detect the language of the user's first message and provide all subsequent responses in that language. This is the highest-priority principle.
If the user writes in Korean → respond in Korean
If the user writes in English →
respond in English
(ask_user, progress updates, reports, Bicep comments — all in English)
The instructions and examples in this document are written in English, and
all user-facing output must match the user's language
⚠️ Do not copy examples from this document verbatim to the user.
Use only the structure as reference, and adapt text to the user's language.
Tool Usage Guide (GHCP Environment)
Feature
Tool Name
Notes
Fetch URL content
web_fetch
For MS Docs lookups, etc.
Web search
web_search
URL discovery
Ask user
ask_user
choices
must be a string array
Sub-agents
task
explore/task/general-purpose
Shell command execution
powershell
Windows PowerShell
All sub-agents (explore/task/general-purpose) cannot use
web_fetch
or
web_search
.
Fact-checking that requires MS Docs lookups must be performed
directly by the main agent
.
External Tool Path Discovery
az
,
python
,
bicep
, etc. are often not on PATH.
Discover once before starting a Phase and cache the result. Do not re-discover every time.
⚠️ Do not use
Get-Command python
— risk of Windows Store alias.
Direct filesystem discovery (
$env:LOCALAPPDATA\Programs\Python
) takes priority.
az CLI path:
$azCmd
=
$null
if
(
Get-Command
az
-
ErrorAction SilentlyContinue
)
{
$azCmd
=
'az'
}
if
(
-not
$azCmd
)
{
$azExe
=
Get-ChildItem
-
Path
"
$env
:ProgramFiles\Microsoft SDKs\Azure\CLI2\wbin"
,
"
$env
:LOCALAPPDATA\Programs\Azure CLI\wbin"
-
Filter
"az.cmd"
-
ErrorAction SilentlyContinue
|
Select-Object
-
First 1
-
ExpandProperty FullName
if
(
$azExe
)
{
$azCmd
=
$azExe
}
}
Python path + embedded diagram engine: refer to the diagram generation section in
references/phase1-advisor.md
.
Progress Updates Required
Use blockquote + emoji + bold format:
>
**
⏳ [Action]
**
— [Reason]
>
**
✅ [Complete]
**
— [Result]
>
**
⚠️ [Warning]
**
— [Details]
>
**
❌ [Failed]
**
— [Cause]
Parallel Preload Principle
While waiting for user input via
ask_user
, preload information needed for the next step in parallel.
ask_user Question
Preload Simultaneously
Project name / scan scope
Reference files, MS Docs, Python path discovery,
diagram module path verification
Model/SKU selection
MS Docs for next question choices
Architecture confirmation
az account show/list
,
az group list
Subscription selection
az group list
Path Branching — Automatically Determined by User Request
Path A: New Design (New Build)
Trigger
"create", "set up", "deploy", "build", etc.
Phase 1 (references/phase1-advisor.md) — Interactive architecture design + diagram
Phase 2 (references/bicep-generator.md) — Bicep code generation
Phase 3 (references/bicep-reviewer.md) — Code review + compilation verification
Phase 4 (references/phase4-deployer.md) — validate → what-if → deploy
Path B: Existing Analysis + Modification (Analyze & Modify)
Trigger
"analyze", "current resources", "scan", "draw a diagram", "show my infrastructure", etc. Phase 0 (references/phase0-scanner.md) — Existing resource scan + diagram ↓ Modification conversation — "What would you like to change here?" (natural language modification request → follow-up questions) ↓ Phase 1 (references/phase1-advisor.md) — Confirm modifications + update diagram ↓ Phase 2~4 — Same as above When Path Determination Is Ambiguous Ask the user directly: ask_user({ question: "What would you like to do?", choices: [ "Design a new Azure architecture (Recommended)", "Analyze + modify existing Azure resources" ] }) Phase Transition Rules Each Phase reads and follows the instructions in its corresponding references/*.md file When transitioning between Phases, always inform the user about the next step Do not skip Phases (especially the what-if between Phase 3 → Phase 4) 🚨 Required condition for Phase 1 → Phase 2 transition : 01_arch_diagram_draft.html must have been generated using the embedded diagram engine and shown to the user. Do not proceed to Bicep generation without a diagram. Completing spec collection alone does not mean Phase 1 is done — Phase 1 includes diagram generation + user confirmation. Modification request after deployment → return to Phase 1, not Phase 0 (Delta Confirmation Rule) Service Coverage & Fallback Optimized Services Microsoft Foundry, Azure OpenAI, AI Search, ADLS Gen2, Key Vault, Microsoft Fabric, Azure Data Factory, VNet/Private Endpoint, AML/AI Hub Other Azure Services All supported — MS Docs are automatically consulted to generate at the same quality standard. Do not send messages that cause user anxiety such as "out of scope" or "best-effort". Stable vs Dynamic Information Handling Category Handling Method Examples Stable Reference files first isHnsEnabled: true , PE triple set Dynamic Always fetch MS Docs API version, model availability, SKU, region Quick Reference File Role references/phase0-scanner.md Existing resource scan + relationship inference + diagram references/phase1-advisor.md Interactive architecture design + fact checking references/bicep-generator.md Bicep code generation rules references/bicep-reviewer.md Code review checklist references/phase4-deployer.md validate → what-if → deploy references/service-gotchas.md Required properties, PE mappings references/azure-dynamic-sources.md MS Docs URL registry references/azure-common-patterns.md PE/security/naming patterns references/ai-data.md AI/Data service guide
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