Run an Azure Deployment Stack create (subscription scope) for a prepared Git-Ape deployment artifact and write state.json (schemaVersion 1.0). Use locally so the result matches the CI deploy workflow.
npx skills add https://github.com/Azure/git-ape --skill azure-stack-deploy
Deploy a Git-Ape deployment artifact as a subscription-scoped Azure Deployment Stack (az stack sub create --action-on-unmanage deleteAll). The stack is the lifecycle owner of every resource the template creates — across resource groups and subscription scope — which makes destroy idempotent in a single call (see azure-stack-destroy).
This skill produces the same state.json schema (schemaVersion: "1.0") as the CI workflow at .github/workflows/git-ape-deploy.yml, so local deployments and pipeline deployments are interchangeable.
git-ape agent invokes this in Stage 3)az deployment sub create against a Git-Ape template.jsonazure-stack-destroy insteadazure-deployment-preflight insteadazure-prepare or another IaC authoring skill| Tool | Why |
|------|-----|
| az (Azure CLI ≥ 2.59) | az stack sub requires CLI ≥ 2.50; 2.59 has the latest stack flags |
| jq | State capture and JSON extraction |
| bash ≥ 4 OR PowerShell 7+ | Either runner works |
| Active az login | Skill exits early if no subscription is selected |
| Existing template.json (and optional parameters.json) under .azure/deployments/<id>/ | Source artifacts |
DEPLOYMENT_ID="deploy-20260506-001"
DEPLOYMENT_PATH=".azure/deployments/$DEPLOYMENT_ID"
[[ -f "$DEPLOYMENT_PATH/template.json" ]] || { echo "template.json missing"; exit 1; }
If parameters.json is present, location, project (or projectName), and environment are read from it. Defaults: eastus / unknown / dev.
.github/skills/azure-stack-deploy/scripts/deploy-stack.sh \
--deployment-id "$DEPLOYMENT_ID"
PowerShell equivalent:
.github/skills/azure-stack-deploy/scripts/deploy-stack.ps1 `
-DeploymentId "$DEPLOYMENT_ID"
The script:
location, project, environment from parameters.json (or defaults)az account show)az stack sub create with the canonical Git-Ape flag set:--action-on-unmanage deleteAll--deny-settings-mode none--description "Git-Ape deployment <id>"--tags managedBy=git-ape deploymentId=<id>--yes --verboseaz deployment sub create and prints ⚠️ FALLBACK: no multi-RG idempotency, no soft-delete tracking so the trade-off is unambiguousaz deployment operation sub list) inline so the root cause is visible without clicking into the Portalaz stack sub show --query "resources[].id" for the live managed-resource list, classifies each resource (type, scope, soft-deletable, purge-protected), and writes the extended state.jsonmetadata.json with status: "succeeded", deployMethod, and resourceGroups[]✅ Deployment succeeded in 142s (method: stack)
State written to: .azure/deployments/deploy-20260506-001/state.json
Stack ID: /subscriptions/<sub>/providers/Microsoft.Resources/deploymentStacks/deploy-20260506-001
To destroy this deployment:
/azure-stack-destroy deploy-20260506-001
After the script returns, your reply MUST mention:
az stack sub create --action-on-unmanage deleteAll (or fallback az deployment sub create)state.json.stackId) — this is the single handle for destroystate.json (schemaVersion 1.0) was written under the deployment folder/azure-stack-destroy <deploymentId>| Flag (bash) | Param (pwsh) | Required | Description |
|-------------|--------------|----------|-------------|
| --deployment-id <id> | -DeploymentId <id> | yes | Folder name under .azure/deployments/ |
| --location <region> | -Location <region> | no | Override the location from parameters.json |
| --no-fallback | -NoFallback | no | Fail loudly if the stack call fails instead of falling back to az deployment sub create |
{
"schemaVersion": "1.0",
"deploymentId": "deploy-20260506-001",
"timestamp": "2026-05-06T12:00:00Z",
"status": "succeeded",
"duration": "142s",
"subscription": "<sub-id>",
"location": "eastus",
"project": "myapp",
"environment": "dev",
"resourceGroup": "rg-myapp-dev-eastus",
"deployMethod": "stack",
"stackId": "/subscriptions/<sub>/providers/Microsoft.Resources/deploymentStacks/deploy-20260506-001",
"managedResources": [
{
"id": "/subscriptions/<sub>/resourceGroups/rg-myapp-dev-eastus/providers/Microsoft.KeyVault/vaults/kv-myapp-dev-eus",
"type": "Microsoft.KeyVault/vaults",
"scope": "resourceGroup",
"softDeletable": true,
"purgeProtected": true
}
],
"resourceGroups": ["rg-myapp-dev-eastus"],
"subscriptions": ["<sub-id>"],
"externalReferences": []
}
See website/docs/deployment/state.md for the full schema reference.
Microsoft.KeyVault/vaults, Microsoft.CognitiveServices/accounts, Microsoft.AppConfiguration/configurationStores, Microsoft.ApiManagement/service, Microsoft.MachineLearningServices/workspaces, Microsoft.RecoveryServices/vaults.
The destroy skill (azure-stack-destroy) consumes the softDeletable and purgeProtected fields to drive its purge sweep.
| Symptom | Likely cause | Recovery |
|---------|--------------|----------|
| Not logged in to Azure | az login missing | Run az login then retry |
| template.json missing | Wrong deployment ID | Check .azure/deployments/ contents |
| Stack create fails immediately | Region/policy blocks Deployment Stacks | Re-run without --no-fallback, accept the legacy path, or pick a supported region |
| Stack succeeds but state.json missing managed resources | az stack sub show race condition | Re-run — the script is idempotent (stacks de-duplicate on --name) |
azure-stack-destroy — the matching destroy skill (single source of truth: stackId)azure-deployment-preflight — what-if and permission checks BEFORE deployazure-security-analyzer — security gate (BLOCKING) before deploy confirmationAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
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.
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).
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).
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).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take azure/azure-stack-deploy from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.