Azd Deployment
Azd Deployment is an writing AI skill with a core value of Deploy containerized applications to Azure Container Apps using Azure Developer CLI (azd). It
helps developers solve real-world problems in the writing domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
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...
Quick Facts
mkdir -p ./skills/azd-deployment && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azd-deployment/SKILL.md -o ./skills/azd-deployment/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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
# 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 + deployCore 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/Dockerfileazure.yaml Configuration
Minimal Configuration
name: Azd Deployment
services:
backend:
project: ./src/backend
language: python
host: containerapp
docker:
path: ./Dockerfile
remoteBuild: trueFull Configuration with Hooks
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: ${SERVICE_BACKEND_URI}"Key azure.yaml Options
| Option | Description |
|--------|-------------|
| `remoteBuild: true` | Build images in Azure Container Registry (recommended) |
| `context: .` | Docker build context relative to project path |
| `host: containerapp` | Deploy to Azure Container Apps |
| `infra.provider: bicep` | Use Bicep for infrastructure |
Environment Variables Flow
Three-Level Configuration
1. **Local `.env`** - For local development only
2. **`.azure/<env>/.env`** - azd-managed, auto-populated from Bicep outputs
3. **`main.parameters.json`** - Maps env vars to Bicep parameters
Parameter Injection Pattern
// infra/main.parameters.json
{
"parameters": {
"environmentName": { "value": "${AZURE_ENV_NAME}" },
"location": { "value": "${AZURE_LOCATION=eastus2}" },
"azureOpenAiEndpoint": { "value": "${AZURE_OPENAI_ENDPOINT}" }
}
}Syntax: `${VAR_NAME}` or `${VAR_NAME=default_value}`
Setting Environment Variables
# 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
// 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.principalIdIdempotent 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 ca
🎯 Best For
- Claude users
- Content creators
- Writers
- Editors
💡 Use Cases
- Content creation
- Style guide enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azd Deployment to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Can Azd Deployment maintain my brand voice?
Yes — provide style guides or example content in your prompt for consistent brand-aligned output.
How do I install Azd Deployment?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azd-deployment/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Publishing unedited drafts
AI writing needs human editing for facts, flow, and authentic voice.