Convert or migrate Azure ARM (Azure Resource Manager) templates, Bicep templates, or code to Pulumi, including importing existing Azure resources. This skill MUST be loaded whenever a user requests migration, conversion, or import of ARM templates, Bicep templates, ARM code, Bicep code, or Azure resources to Pulumi.
npx skills add https://github.com/pulumi/agent-skills --skill pulumi-arm-to-pulumi
If you have already generated a migration plan before loading this skill, you MUST:
The migration output MUST meet all of the following:
pulumi preview (assuming proper config).pulumi preview must show:If a user-provided ARM template is incomplete, ambiguous, or missing artifacts, ask targeted questions before generating Pulumi code.
If there is ambiguity on how to handle a specific resource property on import, ask targeted questions before altering Pulumi code.
Follow this workflow exactly and in this order:
Running Azure CLI commands (e.g., az resource list, az resource show). Requires initial login using ESC and az login
Setting up Azure CLI using ESC:
pulumi env run {org}/{project}/{environment} -- bash -c 'az login --service-principal -u "$ARM_CLIENT_ID" --tenant "$ARM_TENANT_ID" --federated-token "$ARM_OIDC_TOKEN"'. ESC is not required after establishing the sessionaz account showaz account list --query "[].{Name:name, SubscriptionId:id, IsDefault:isDefault}" -o tableFor detailed ESC information: Load the pulumi-esc skill by calling the tool "Skill" with name = "pulumi-esc"
ARM templates do not have the concept of "stacks" like CloudFormation. Read the ARM template JSON file directly:
# View template structure
cat template.json | jq '.resources[] | {type: .type, name: .name}'
# View parameters
cat template.json | jq '.parameters'
# View variables
cat template.json | jq '.variables'
Extract:
Documentation: ARM Template Structure
If the ARM template has already been deployed and you're importing existing resources:
# List all resources in a resource group
az resource list \
--resource-group <resource-group-name> \
--output json
# Get specific resource details
az resource show \
--ids <resource-id> \
--output json
# Query specific properties using JMESPath
az resource show \
--ids <resource-id> \
--query "{name:name, location:location, properties:properties}" \
--output json
Documentation: Azure CLI Documentation
IMPORTANT: ARM to Pulumi conversion requires manual translation. There is NO automated conversion tool for ARM templates. You are responsible for the complete conversion.
@pulumi/azure-native for full Azure Resource Manager API coverage@pulumi/azure (classic provider) when azure-native doesn't support specific features or when you need simplified abstractionsDocumentation:
Follow conversion patterns in arm-conversion-patterns.md.
arm-conversion-patterns.md provides:
After conversion, you can optionally import existing resources to be managed by Pulumi. If the user does not request this, suggest it as a follow-up step to conversion.
CRITICAL: When the user requests importing existing Azure resources into Pulumi, see arm-import.md for detailed import procedures and zero-diff validation workflows.
arm-import.md provides:
import resource option with Azure Resource IDspulumi-cdk-importer)/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}pulumi preview after importSet up stack configuration matching ARM template parameters:
# Set Azure region
pulumi config set azure-native:location eastus --stack dev
# Set application parameters
pulumi config set storageAccountName mystorageaccount --stack dev
# Set secret parameters
pulumi config set --secret adminPassword MyS3cr3tP@ssw0rd --stack dev
After achieving zero diff in preview (if importing), validate the migration:
pulumi stack output
pulumi stack graph
If the user asks for help planning or performing an ARM to Pulumi migration, use the information above to guide the user through the conversion and import process.
When the user wants additional information, use the web-fetch tool to get content from the official Pulumi documentation:
Microsoft Azure Documentation:
When performing a migration, always produce:
pulumi config set commandsKeep code syntactically valid and clearly separated by files.
Assess 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 pulumi/pulumi-arm-to-pulumi 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.