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Azd Deployment

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benjaminasterA
azd-deployment

Deploy containerized applications to Azure Container Apps using Azure Developer CLI (azd). Use when setting up azd projects, writing azure.yaml configuration, creating Bicep infrastructure for Cont...

Overview

PublisherbenjaminasterA
Repositoryantigravity-awesome-skills
Skill nameazd-deployment
Stars
419
Forks
42
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Azd Deployment 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.
    https://github.com/benjaminasterA/antigravity-awesome-skills/tree/main/skills/azd-deployment
  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/benjaminasterA/antigravity-awesome-skills.git /tmp/antigravity-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/antigravity-awesome-skills/skills/azd-deployment .claude/skills/azd-deployment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azd Deployment 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 Azd Deployment 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 Azd Deployment 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 Developer CLI (azd) Container Apps Deployment

Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure.

Quick Start

bash
# Initialize and deploy
azd auth login
azd init                    # Creates azure.yaml and .azure/ folder
azd env new <env-name>      # Create environment (dev, staging, prod)
azd up                      # Provision infra + build + deploy

Core File Structure

project/
├── azure.yaml              # azd service definitions + hooks
├── infra/
│   ├── main.bicep          # Root infrastructure module
│   ├── main.parameters.json # Parameter injection from env vars
│   └── modules/
│       ├── container-apps-environment.bicep
│       └── container-app.bicep
├── .azure/
│   ├── config.json         # Default environment pointer
│   └── <env-name>/
│       ├── .env            # Environment-specific values (azd-managed)
│       └── config.json     # Environment metadata
└── src/
    ├── frontend/Dockerfile
    └── backend/Dockerfile

azure.yaml Configuration

Minimal Configuration

yaml
name: azd-deployment
services:
  backend:
    project: ./src/backend
    language: python
    host: containerapp
    docker:
      path: ./Dockerfile
      remoteBuild: true

Full Configuration with Hooks

yaml
name: azd-deployment
metadata:
  template: my-project@1.0.0

infra:
  provider: bicep
  path: ./infra

azure:
  location: eastus2

services:
  frontend:
    project: ./src/frontend
    language: ts
    host: containerapp
    docker:
      path: ./Dockerfile
      context: .
      remoteBuild: true

  backend:
    project: ./src/backend
    language: python
    host: containerapp
    docker:
      path: ./Dockerfile
      context: .
      remoteBuild: true

hooks:
  preprovision:
    shell: sh
    run: |
      echo "Before provisioning..."
      
  postprovision:
    shell: sh
    run: |
      echo "After provisioning - set up RBAC, etc."
      
  postdeploy:
    shell: sh
    run: |
      echo "Frontend: ${SERVICE_FRONTEND_URI}"
      echo "Backend: ${SERVICE_BACKEND_URI}"

Key azure.yaml Options

OptionDescription
remoteBuild: trueBuild images in Azure Container Registry (recommended)
context: .Docker build context relative to project path
host: containerappDeploy to Azure Container Apps
infra.provider: bicepUse Bicep for infrastructure

Environment Variables Flow

Three-Level Configuration

  1. Local .env - For local development only
  2. .azure/<env>/.env - azd-managed, auto-populated from Bicep outputs
  3. main.parameters.json - Maps env vars to Bicep parameters

Parameter Injection Pattern

json
// infra/main.parameters.json
{
  "parameters": {
    "environmentName": { "value": "${AZURE_ENV_NAME}" },
    "location": { "value": "${AZURE_LOCATION=eastus2}" },
    "azureOpenAiEndpoint": { "value": "${AZURE_OPENAI_ENDPOINT}" }
  }
}

Syntax: ${VAR_NAME} or ${VAR_NAME=default_value}

Setting Environment Variables

bash
# Set for current environment
azd env set AZURE_OPENAI_ENDPOINT "https://my-openai.openai.azure.com"
azd env set AZURE_SEARCH_ENDPOINT "https://my-search.search.windows.net"

# Set during init
azd env new prod
azd env set AZURE_OPENAI_ENDPOINT "..." 

Bicep Output → Environment Variable

bicep
// In main.bicep - outputs auto-populate .azure/<env>/.env
output SERVICE_FRONTEND_URI string = frontend.outputs.uri
output SERVICE_BACKEND_URI string = backend.outputs.uri
output BACKEND_PRINCIPAL_ID string = backend.outputs.principalId

Idempotent Deployments

Why azd up is Idempotent

  1. Bicep is declarative - Resources reconcile to desired state
  2. Remote builds tag uniquely - Image tags include deployment timestamp
  3. ACR reuses layers - Only changed layers upload

Preserving Manual Changes

Custom domains added via Portal can be lost on redeploy. Preserve with hooks:

yaml
hooks:
  preprovision:
    shell: sh
    run: |
      # Save custom domains before provision
      if az containerapp show --name "$FRONTEND_NAME" -g "$RG" &>/dev/null; then
        az containerapp show --name "$FRONTEND_NAME" -g "$RG" \
          --query "properties.configuration.ingress.customDomains" \
          -o json > /tmp/domains.json
      fi

  postprovision:
    shell: sh
    run: |
      # Verify/restore custom domains
      if [ -f /tmp/domains.json ]; then
        echo "Saved domains: $(cat /tmp/domains.json)"
      fi

Handling Existing Resources

bicep
// Reference existing ACR (don't recreate)
resource containerRegistry 'Microsoft.ContainerRegistry/registries@2023-07-01' existing = {
  name: containerRegistryName
}

// Set customDomains to null to preserve Portal-added domains
customDomains: empty(customDomainsParam) ? null : customDomainsParam

Container App Service Discovery

Internal HTTP routing between Container Apps in same environment:

bicep
// Backend reference in frontend env vars
env: [
  {
    name: 'BACKEND_URL'
    value: 'http://ca-backend-${resourceToken}'  // Internal DNS
  }
]

Frontend nginx proxies to internal URL:

nginx
location /api {
    proxy_pass $BACKEND_URL;
}

Managed Identity & RBAC

Enable System-Assigned Identity

bicep
resource containerApp 'Microsoft.App/containerApps@2024-03-01' = {
  identity: {
    type: 'SystemAssigned'
  }
}

output principalId string = containerApp.identity.principalId

Post-Provision RBAC Assignment

yaml
hooks:
  postprovision:
    shell: sh
    run: |
      PRINCIPAL_ID="${BACKEND_PRINCIPAL_ID}"
      
      # Azure OpenAI access
      az role assignment create \
        --assignee-object-id "$PRINCIPAL_ID" \
        --assignee-principal-type ServicePrincipal \
        --role "Cognitive Services OpenAI User" \
        --scope "$OPENAI_RESOURCE_ID" 2>/dev/null || true
      
      # Azure AI Search access
      az role assignment create \
        --assignee-object-id "$PRINCIPAL_ID" \
        --role "Search Index Data Reader" \
        --scope "$SEARCH_RESOURCE_ID" 2>/dev/null || true

Common Commands

bash
# Environment management
azd env list                        # List environments
azd env select <name>               # Switch environment
azd env get-values                  # Show all env vars
azd env set KEY value               # Set variable

# Deployment
azd up                              # Full provision + deploy
azd provision                       # Infrastructure only
azd deploy                          # Code deployment only
azd deploy --service backend        # Deploy single service

# Debugging
azd show                            # Show project status
az containerapp logs show -n <app> -g <rg> --follow  # Stream logs

Reference Files

  • Bicep patterns: See references/bicep-patterns.md for Container Apps modules
  • Troubleshooting: See references/troubleshooting.md for common issues
  • azure.yaml schema: See references/azure-yaml-schema.md for full options

Critical Reminders

  1. Always use remoteBuild: true - Local builds fail on M1/ARM Macs deploying to AMD64
  2. Bicep outputs auto-populate .azure//.env - Don't manually edit
  3. Use azd env set for secrets - Not main.parameters.json defaults
  4. Service tags (azd-service-name) - Required for azd to find Container Apps
  5. || true in hooks - Prevent RBAC "already exists" errors from failing deploy

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Frequently asked questions

What does the Azd Deployment AI skill do?

Deploy containerized applications to Azure Container Apps using Azure Developer CLI (azd). Use when setting up azd projects, writing azure.yaml configuration, creating Bicep infrastructure for Cont...

Why use Azd Deployment on TypingMind?

Because you install it once and use it with any model. Azd Deployment 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 Azd Deployment in TypingMind?

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

Which AI models can use Azd Deployment?

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 Azd Deployment?

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

Is the Azd Deployment AI skill free?

Yes. It is published on GitHub by benjaminasterA 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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