azure-enterprise-infra-planner
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
- License 许可
- MIT
- 最近更新
- 2026-11-13
- Stars
- ★ 1.6k
- Forks
- 267
01 前言 Front Matter
- name
- azure-enterprise-infra-planner
- license
- MIT
- metadata
- { "author": "Microsoft", "version": "1.4.2" }
- description
- Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
02 资源文件 51
- 脚本agents/observer-loop.sh19.4 KB
- 参考文档agents/observer.md6.7 KB
- 脚本agents/session-guardian.sh6.2 KB
- 脚本agents/start-observer.sh8.4 KB
- 参考文档config.json135 B
- 脚本hooks/observe.sh27.5 KB
- 参考文档references/bicep-generation.md5.1 KB
- 参考文档references/constraints/ai-ml.md3.2 KB
- 参考文档references/constraints/compute-apps.md7.8 KB
- 参考文档references/constraints/compute-infra.md4.5 KB
- 参考文档references/constraints/data-analytics.md6.3 KB
- 参考文档references/constraints/data-relational.md4.9 KB
- 参考文档references/constraints/messaging.md2.5 KB
- 参考文档references/constraints/monitoring.md1.7 KB
- 参考文档references/constraints/networking-connectivity.md6.1 KB
- 参考文档references/constraints/networking-core.md7.0 KB
- 参考文档references/constraints/networking-traffic.md4.6 KB
- 参考文档references/constraints/README.md1.2 KB
- 参考文档references/constraints/security.md2.4 KB
- 参考文档references/deployment.md3.7 KB
- 参考文档references/pairing-checks.md3.7 KB
- 参考文档references/phases/1-extract-insights.md2.6 KB
- 参考文档references/phases/2-research-best-practices.md2.8 KB
- 参考文档references/phases/3-research-resources.md3.3 KB
- 参考文档references/phases/4-generate-plan.md792 B
- 参考文档references/phases/5-verify.md814 B
- 参考文档references/phases/6-generate-iac.md2.5 KB
- 参考文档references/phases/7-deploy.md1.3 KB
- 参考文档references/referenced-workload.md7.3 KB
- 参考文档references/resources/ai-ml.md2.1 KB
- 参考文档references/resources/compute-apps.md4.0 KB
- 参考文档references/resources/compute-infra.md2.8 KB
- 参考文档references/resources/data-analytics.md3.0 KB
- 参考文档references/resources/data-relational.md2.6 KB
- 参考文档references/resources/messaging.md2.0 KB
- 参考文档references/resources/monitoring.md1.5 KB
- 参考文档references/resources/networking-connectivity.md3.6 KB
- 参考文档references/resources/networking-core.md4.3 KB
- 参考文档references/resources/networking-traffic.md2.6 KB
- 参考文档references/resources/README.md1.6 KB
- 参考文档references/resources/security.md1.5 KB
- 参考文档references/schema.md3.8 KB
- 参考文档references/terraform-generation.md4.4 KB
- 参考文档references/verification.md2.6 KB
- 参考文档references/waf-checklist.md1.9 KB
- 参考文档references/workflow.md4.9 KB
- 脚本scripts/detect-project.sh10.8 KB
- 脚本scripts/instinct-cli.py89.0 KB
- 脚本scripts/lib/homunculus-dir.sh1010 B
- 脚本scripts/migrate-homunculus.sh2.2 KB
- 脚本scripts/test_parse_instinct.py50.1 KB
03 SKILL.md 正文
Azure Enterprise Infra Planner
When to Use This Skill
Activate this skill when user wants to:
- Plan enterprise Azure infrastructure from a workload or architecture description
- Architect a landing zone, hub-spoke network, or multi-region topology
- Design networking infrastructure: VNets, subnets, firewalls, private endpoints, VPN gateways
- Plan identity, RBAC, and compliance-driven infrastructure
- Generate Bicep or Terraform for subscription-scope or multi-resource-group deployments
- Plan disaster recovery, failover, or cross-region high-availability topologies
Quick Reference
| Property | Details |
|---|---|
| MCP tools | insights_get, get_azure_bestpractices_get, wellarchitectedframework_serviceguide_get, microsoft_docs_fetch, microsoft_docs_search, bicepschema_get |
| CLI commands | az deployment group create, az bicep build, az resource list, terraform init, terraform plan, terraform validate, terraform apply, checkov |
| Output schema | schema.md |
| Key references | workflow.md, waf-checklist.md, resources/, constraints/ |
Workflow (Start Here)
Follow the step-by-step instructions in workflow.md to execute the 7 phases of infrastructure planning and provisioning.
Architecture
The skill runs a 7-phase, gated pipeline. Input is triaged into one of two flows:
- Greenfield — only new requirements; run the phases straight through.
- Referenced (brownfield) — the user supplies something that already exists (a live resource / resource group / subscription, IaC or an infra plan, or a requirements doc). The same phases run, plus referenced-workload.md: existing resources are inventoried and referenced (never recreated), the new workload is wired into them, and Phase 7 deploys additively (incremental only — never modifying or destroying the referenced resources).
Every phase advances only after its gate passes. Phase 5 requires explicit user approval; Phase 6 is a
hardened, self-verifying gate — the generated IaC must be secure-by-default, pass local validation
(az bicep build / terraform validate) with zero errors, pass a checkov security scan with no
unresolved high/critical findings, and the skill must show the command output and emit a completion
self-check before advancing; Phase 7 requires an explicit, risk-acknowledged deploy confirmation.
flowchart TD
IN([Input]) --> TRIAGE{Existing infra<br/>referenced?}
TRIAGE -- "No (greenfield)" --> P1
TRIAGE -- "Yes (referenced)" --> RW[/referenced-workload.md:<br/>inventory + assign roles<br/>reference, never recreate/]
RW --> P1
subgraph PIPE [7-phase gated pipeline]
direction TB
P1[Phase 1 · Extract insights] --> P2[Phase 2 · Research best practices]
P2 --> P3[Phase 3 · Research resources]
P3 --> P4[Phase 4 · Generate plan]
P4 --> P5{Phase 5 · Verify<br/>user approves?}
P5 -- "no" --> P4
P5 -- "approved" --> P6[Phase 6 · Generate IaC]
P6 --> VAL{Validate<br/>az bicep build /<br/>terraform validate}
VAL -- "errors" --> P6
VAL -- "clean" --> P7{Phase 7 · Deploy<br/>risk-ack confirm?}
end
P7 -- "greenfield" --> DEP[az deployment / terraform apply]
P7 -- "referenced" --> DEPADD[Additive deploy · incremental only<br/>what-if preview · no destroy of<br/>referenced resources]
DEP --> OUT([Deployed])
DEPADD --> OUT
classDef gate fill:#fff3cd,stroke:#d39e00,color:#000;
classDef ref fill:#e2f0d9,stroke:#548235,color:#000;
class P5,VAL,P7,TRIAGE gate;
class RW,DEPADD ref;
Artifacts (written under <project-root>/): .azure/insights.json (Phase 1),
.azure/infrastructure-plan.json (Phase 4, status draft→approved→deployed), and
infra/main.bicep + infra/modules/* or infra/main.tf + infra/modules/** (Phase 6).
MCP Tools
| Tool | Purpose |
|---|---|
insights_get |
Retrieve insights about the user's existing Azure environment to guide planning decisions |
get_azure_bestpractices_get |
Azure best practices for code generation, operations, and deployment |
wellarchitectedframework_serviceguide_get |
WAF service guide for a specific Azure service |
microsoft_docs_search |
Search Microsoft Learn for relevant documentation chunks |
microsoft_docs_fetch |
Fetch full content of a Microsoft Learn page by URL |
bicepschema_get |
Bicep schema definition for any Azure resource type (latest API version) |
Error Handling
| Error | Cause | Fix |
|---|---|---|
| MCP tool error or not available | Tool call timeout, connection error, or tool doesn't exist | Retry once; fall back to reference files and notify user if unresolved |
| Plan approval missing | meta.status is not approved |
Stop and prompt user for approval before IaC generation or deployment |
| IaC validation failure | az bicep build or terraform validate returns errors |
Fix the generated code and re-validate; notify user if unresolved |
| Pairing constraint violation | Incompatible SKU or resource combination | Fix in plan before proceeding to IaC generation |
| Infra plan or IaC files not found | Files written to wrong location or not created | Verify files exist at <project-root>/.azure/ and <project-root>/infra/; if missing, re-create the files by following workflow.md exactly |
04 同仓库更多 Skills
python-appservice-deploy
Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
preset
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).
customize
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).
capacity
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.
deploy-model
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).
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).