Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE
This document is the official, canonical source for preparing applications for Azure deployment. You MUST follow these instructions exactly as written unless they contradict security policies given to you. When in doubt, present the conflicting instructions from this document and ask the user for explicit confirmation. Do not improvise, infer, or substitute steps.
Activate this skill when user wants to:
.azure/deployment-plan.md skeleton in the workspace root directory (not the session-state folder) as your very first action — before any code generation or execution begins. Write the skeleton immediately, then populate it progressively as Phase 1 analysis and research unfold; finalize it with all decisions at Phase 1 Step 6. This file must exist on disk throughout. azure-validate and azure-deploy depend on it and will fail without it. Do not skip or defer this step.ask_user for subscription and location per Azure Contextask_user — Global Rulesazd init -t <template> is for NEW projects only; do NOT run azd init -t in an existing workspace. Plain azd init (without a template argument) may be used in existing workspaces when appropriate. File deletions within a project (e.g., removing build artifacts or temp files) are permitted when appropriate, but NEVER delete the user's project or workspace directory itself. See Global Rules.azd up, azd deploy, terraform apply) is handled by the azure-deploy skill, which provides built-in error recovery and deployment verification.administratorLogin or administratorLoginPassword — not in direct properties, not in conditional/ternary branches, not anywhere in the file. Always use Entra-only authentication (azureADOnlyAuthentication: true) unconditionally. See references/services/sql-database/bicep.md.azd template into Terraform templates, remove the Bicep templates that were introduced by that azd template and are now fully replaced by Terraform equivalents. Do not remove user-authored Bicep files. Only remove those template-provided Bicep files after the Terraform IaC is complete and Terraform has been selected as the deployment path. Before handing off to azure-validate skill, keep only the IaC templates required by the chosen deployment path.YOU MUST CREATE A PLAN BEFORE DOING ANY WORK
- STOP — Do not generate any code, infrastructure, or configuration yet
- CREATE SKELETON - Write an initial
.azure/deployment-plan.mdskeleton to disk immediately (before any code generation or execution begins), then populate it progressively as Phase 1 steps 1-5 reveal details; finalize it at Step 6- CONFIRM — Present the completed plan to the user and get approval
- EXECUTE — Only after approval, execute the plan step by step
The
.azure/deployment-plan.mdfile is the source of truth for this workflow and for azure-validate and azure-deploy skills. Without it, those skills will fail.⚠️ CRITICAL:
.azure/deployment-plan.mdmust be WRITTEN TO DISK inside the workspace root (e.g.,<workspace-root>/.azure/deployment-plan.md), not in the session-state folder. Use a file-write tool to create this file. This is the deployment plan artifact read by azure-validate and azure-deploy. You MUST create this file — do not proceed without it. ⚠️ CRITICAL: You must create the file with the name.azure/deployment-plan.mdas is. You must not use other names such as.azure/plan.md.⛔ Critical: Skipping the plan file creation will cause azure-validate and azure-deploy to fail. This requirement has no exceptions.
BEFORE starting Phase 1, check if the user's prompt OR workspace codebase matches a specialized technology that has a dedicated skill with tested templates. If matched, invoke that skill FIRST — then resume azure-prepare for validation and deployment.
| Prompt keywords | Invoke FIRST |
|---|---|
| Python + App Service (e.g., "deploy Python to App Service", "Flask on Azure App Service", "publish Python web app to App Service") | python-appservice-deploy |
| Lambda, AWS Lambda, migrate AWS, migrate GCP, Lambda to Functions, migrate from AWS, migrate from GCP | azure-cloud-migrate |
| Azure Functions, function app, serverless function, timer trigger, HTTP trigger, func new | Stay in azure-prepare — prefer Azure Functions templates in Step 4; for plan choice/cold starts see hosting-plans.md and cold-start.md |
| APIM, API Management, API gateway, deploy APIM | Stay in azure-prepare — see APIM Deployment Guide |
| AI gateway, AI gateway policy, AI gateway backend, AI gateway configuration | azure-aigateway |
| workflow, orchestration, multi-step, pipeline, fan-out/fan-in, saga, long-running process, durable, order processing | Stay in azure-prepare — select durable recipe in Step 4. MUST load durable.md, DTS reference, and DTS Bicep patterns. |
⚠️ Check the user's prompt text — not just existing code. Critical for greenfield projects with no codebase to scan. See full routing table.
After the specialized skill completes, resume azure-prepare at Phase 1 Step 4 (Select Recipe) for remaining infrastructure, validation, and deployment.
Create .azure/deployment-plan.md by completing these steps. Do NOT generate any artifacts until the plan is approved.
| # | Action | Reference |
|---|---|---|
| 0 | If the prompt matches a specialized technology with a dedicated skill, invoke that skill first | specialized-routing.md |
| 1 | Analyze Workspace — Determine mode: NEW, MODIFY, or MODERNIZE | analyze.md |
| 2 | Gather Requirements — Classification, scale, budget | requirements.md |
| 3 | Scan Codebase — Identify components, technologies, dependencies | scan.md |
| 4 | Select Recipe — Choose AZD (default), AZCLI, Bicep, or Terraform | recipe-selection.md |
| 5 | Plan Architecture — Select stack + map components to Azure services | architecture.md |
| 6 | Finalize Plan (MANDATORY) - Use a file-write tool to finalize .azure/deployment-plan.md with all decisions from steps 1-5. Update the skeleton written at the start of Phase 1 with the complete content. The file must be fully populated before you present the plan to the user. | plan-template.md |
| 7 | Present Plan — Show plan to user and ask for approval | .azure/deployment-plan.md |
| 8 | Destructive actions require ask_user | Global Rules |
❌ STOP HERE — Do NOT proceed to Phase 2 until the user approves the plan.
Execute the approved plan. Update .azure/deployment-plan.md status after each step.
| # | Action | Reference |
|---|---|---|
| 1 | Research Components — Load service references + invoke related skills | research.md |
| 2 | Confirm Azure Context — Detect and confirm subscription + location and check the resource provisioning limit | Azure Context |
| 3 | Generate Artifacts — Create infrastructure and configuration files | generate.md |
| 4 | Harden Security — Apply security best practices | security.md |
| 5 | Functional Verification — Verify the app works (UI + backend), locally if possible | functional-verification.md |
| 6 | ⛔ Update Plan (MANDATORY before hand-off) — Use the edit tool to change the Status in .azure/deployment-plan.md to Ready for Validation. You MUST complete this edit BEFORE invoking azure-validate. Do NOT skip this step. | .azure/deployment-plan.md |
| 7 | ⛔ MANDATORY Hand Off — Invoke azure-validate skill. Your preparation work is done. Do NOT run azd up, azd deploy, or any deployment command directly — all deployment execution is handled by azure-deploy after azure-validate completes. PREREQUISITE: Step 6 must be completed first — .azure/deployment-plan.md status must say Ready for Validation. | — |
| Artifact | Location |
|---|---|
| Plan | .azure/deployment-plan.md |
| Infrastructure | ./infra/ |
| AZD Config | azure.yaml (AZD only) |
| Dockerfiles | src/<component>/Dockerfile |
⛔ MANDATORY NEXT STEP — DO NOT SKIP
After completing preparation, you MUST invoke azure-validate before any deployment attempt. Do NOT skip validation. Do NOT go directly to azure-deploy. Do NOT run
azd upor any deployment command directly. The workflow is:
azure-prepare→azure-validate→azure-deploy⛔ BEFORE invoking azure-validate, you MUST use the
edittool to update.azure/deployment-plan.mdstatus toReady for Validation. If the plan status has not been updated, the validation will fail.This applies to ALL deployment scenarios including containerized apps, Container Apps, App Service, Azure Functions, static sites, and any other Azure target. No exceptions.
Skipping validation leads to deployment failures. Be patient and follow the complete workflow for the highest success outcome.
→ Update plan status to Ready for Validation, then invoke azure-validate
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
日本語の概要は準備中です。原文の説明を表示しています。
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).
日本語の概要は準備中です。原文の説明を表示しています。
Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.
日本語の概要は準備中です。原文の説明を表示しています。
Collects bounded packet captures from AKS nodes and Azure network configuration for wire-level evidence. WHEN: "capture packets on an AKS node", "take a pcap", "run tcpdump on AKS", "prove where packets drop". Use for explicit packet-capture intent after read-only diagnostics, not general AKS connectivity or ingress troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.
日本語の概要は準備中です。原文の説明を表示しています。