Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
npx skills add https://github.com/github/awesome-copilot --skill azure-deployment-preflight
This skill validates Bicep deployments before execution, supporting both Azure CLI (az) and Azure Developer CLI (azd) workflows.
azd up, azd provision, or az deployment commandsFollow these steps in order. Continue to the next step even if a previous step fails—capture all issues in the final report.
Determine the deployment workflow by checking for project indicators:
azure.yaml in the project root.bicep files to validateinfra/ directory first, then project rootinfra/, deploy/, project root)<filename>.bicepparam (Bicep parameters - preferred)<filename>.parameters.json (JSON parameters)parameters.json or parameters/<env>.json in same directoryRun Bicep CLI to check template syntax before attempting deployment validation:
bicep build <bicep-file> --stdout
What to capture:
If Bicep CLI is not installed:
Choose the appropriate validation based on project type detected in Step 1.
Use azd provision --preview to validate the deployment:
azd provision --preview
If an environment is specified or multiple environments exist:
azd provision --preview --environment <env-name>
Determine the deployment scope from the Bicep file's targetScope declaration:
| Target Scope | Command |
|--------------|---------|
| resourceGroup (default) | az deployment group what-if |
| subscription | az deployment sub what-if |
| managementGroup | az deployment mg what-if |
| tenant | az deployment tenant what-if |
Run with Provider validation level first:
# Resource Group scope (most common)
az deployment group what-if \
--resource-group <rg-name> \
--template-file <bicep-file> \
--parameters <param-file> \
--validation-level Provider
# Subscription scope
az deployment sub what-if \
--location <location> \
--template-file <bicep-file> \
--parameters <param-file> \
--validation-level Provider
# Management Group scope
az deployment mg what-if \
--location <location> \
--management-group-id <mg-id> \
--template-file <bicep-file> \
--parameters <param-file> \
--validation-level Provider
# Tenant scope
az deployment tenant what-if \
--location <location> \
--template-file <bicep-file> \
--parameters <param-file> \
--validation-level Provider
Fallback Strategy:
If --validation-level Provider fails with permission errors (RBAC), retry with ProviderNoRbac:
az deployment group what-if \
--resource-group <rg-name> \
--template-file <bicep-file> \
--validation-level ProviderNoRbac
Note the fallback in the report—the user may lack full deployment permissions.
Parse the what-if output to categorize resource changes:
| Change Type | Symbol | Meaning |
|-------------|--------|---------|
| Create | + | New resource will be created |
| Delete | - | Resource will be deleted |
| Modify | ~ | Resource properties will change |
| NoChange | = | Resource unchanged |
| Ignore | * | Resource not analyzed (limits reached) |
| Deploy | ! | Resource will be deployed (changes unknown) |
For modified resources, capture the specific property changes.
Create a Markdown report file in the project root named:
preflight-report.mdUse the template structure from references/REPORT-TEMPLATE.md.
Report sections:
Before running validation, gather:
| Information | Required For | How to Obtain |
|-------------|--------------|---------------|
| Resource Group | az deployment group | Ask user or check existing .azure/ config |
| Subscription | All deployments | az account show or ask user |
| Location | Sub/MG/Tenant scope | Ask user or use default from config |
| Environment | azd projects | azd env list or ask user |
If required information is missing, prompt the user before proceeding.
See references/ERROR-HANDLING.md for detailed error handling guidance.
Key principle: Continue validation even when errors occur. Capture all issues in the final report.
| Error Type | Action |
|------------|--------|
| Not logged in | Note in report, suggest az login or azd auth login |
| Permission denied | Fall back to ProviderNoRbac, note in report |
| Bicep syntax error | Include all errors, continue to other files |
| Tool not installed | Note in report, skip that validation step |
| Resource group not found | Note in report, suggest creating it |
This skill uses the following tools:
az) - Version 2.76.0+ recommended for --validation-levelazd) - For projects with azure.yamlbicep) - For syntax validationCheck tool availability before starting:
az --version
azd version
bicep --version
azure.yaml → azd projectinfra/main.bicep and infra/main.bicepparambicep build infra/main.bicep --stdoutazd provision --previewpreflight-report.md in project rootAssess 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 github/azure-deployment-preflight 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.