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Azure Deployment Preflight

OrganizationPopular
github
azure-deployment-preflight

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.

Overview

Publishergithub
Repositoryawesome-copilot
Skill nameazure-deployment-preflight
Stars
39.1K
Forks
5K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by github on GitHub. Read the source before you install it.

Installation

Install the Azure Deployment Preflight AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p .claude/skills
cp -r /tmp/awesome-copilot/skills/azure-deployment-preflight .claude/skills/azure-deployment-preflight
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Deployment Preflight in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Deployment Preflight on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Deployment Preflight is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Azure Deployment Preflight Validation

This skill validates Bicep deployments before execution, supporting both Azure CLI (az) and Azure Developer CLI (azd) workflows.

When to Use This Skill

  • Before deploying infrastructure to Azure
  • When preparing or reviewing Bicep files
  • To preview what changes a deployment will make
  • To verify permissions are sufficient for deployment
  • Before running azd up, azd provision, or az deployment commands

Validation Process

Follow these steps in order. Continue to the next step even if a previous step fails—capture all issues in the final report.

Step 1: Detect Project Type

Determine the deployment workflow by checking for project indicators:

  1. Check for azd project: Look for azure.yaml in the project root

    • If found → Use azd workflow
    • If not found → Use az CLI workflow
  2. Locate Bicep files: Find all .bicep files to validate

    • For azd projects: Check infra/ directory first, then project root
    • For standalone: Use the file specified by the user or search common locations (infra/, deploy/, project root)
  3. Auto-detect parameter files: For each Bicep file, look for matching parameter files:

    • <filename>.bicepparam (Bicep parameters - preferred)
    • <filename>.parameters.json (JSON parameters)
    • parameters.json or parameters/<env>.json in same directory

Step 2: Validate Bicep Syntax

Run Bicep CLI to check template syntax before attempting deployment validation:

bash
bicep build <bicep-file> --stdout

What to capture:

  • Syntax errors with line/column numbers
  • Warning messages
  • Build success/failure status

If Bicep CLI is not installed:

  • Note the issue in the report
  • Continue to Step 3 (Azure will validate syntax during what-if)

Step 3: Run Preflight Validation

Choose the appropriate validation based on project type detected in Step 1.

For azd Projects (azure.yaml exists)

Use azd provision --preview to validate the deployment:

bash
azd provision --preview

If an environment is specified or multiple environments exist:

bash
azd provision --preview --environment <env-name>
For Standalone Bicep (no azure.yaml)

Determine the deployment scope from the Bicep file's targetScope declaration:

Target ScopeCommand
resourceGroup (default)az deployment group what-if
subscriptionaz deployment sub what-if
managementGroupaz deployment mg what-if
tenantaz deployment tenant what-if

Run with Provider validation level first:

bash
# 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:

bash
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.

Step 4: Capture What-If Results

Parse the what-if output to categorize resource changes:

Change TypeSymbolMeaning
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.

Step 5: Generate Report

Create a Markdown report file in the project root named:

  • preflight-report.md

Use the template structure from references/REPORT-TEMPLATE.md.

Report sections:

  1. Summary - Overall status, timestamp, files validated, target scope
  2. Tools Executed - Commands run, versions, validation levels used
  3. Issues - All errors and warnings with severity and remediation
  4. What-If Results - Resources to create/modify/delete/unchanged
  5. Recommendations - Actionable next steps

Required Information

Before running validation, gather:

InformationRequired ForHow to Obtain
Resource Groupaz deployment groupAsk user or check existing .azure/ config
SubscriptionAll deploymentsaz account show or ask user
LocationSub/MG/Tenant scopeAsk user or use default from config
Environmentazd projectsazd env list or ask user

If required information is missing, prompt the user before proceeding.

Error Handling

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 TypeAction
Not logged inNote in report, suggest az login or azd auth login
Permission deniedFall back to ProviderNoRbac, note in report
Bicep syntax errorInclude all errors, continue to other files
Tool not installedNote in report, skip that validation step
Resource group not foundNote in report, suggest creating it

Tool Requirements

This skill uses the following tools:

  • Azure CLI (az) - Version 2.76.0+ recommended for --validation-level
  • Azure Developer CLI (azd) - For projects with azure.yaml
  • Bicep CLI (bicep) - For syntax validation
  • Azure MCP Tools - For documentation lookups and best practices

Check tool availability before starting:

bash
az --version
azd version
bicep --version

Example Workflow

  1. User: "Validate my Bicep deployment before I run it"
  2. Agent detects azure.yaml → azd project
  3. Agent finds infra/main.bicep and infra/main.bicepparam
  4. Agent runs bicep build infra/main.bicep --stdout
  5. Agent runs azd provision --preview
  6. Agent generates preflight-report.md in project root
  7. Agent summarizes findings to user

Reference Documentation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Azure Deployment Preflight AI skill do?

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.

Why use Azure Deployment Preflight on TypingMind?

Because you install it once and use it with any model. Azure Deployment Preflight is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Deployment Preflight in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/azure-deployment-preflight. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure Deployment Preflight?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Deployment Preflight?

As many as you like. As long as a model supports skills, you can use Azure Deployment Preflight with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Deployment Preflight AI skill free?

Yes. It is published on GitHub by github under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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