Azure Developer Cli logo

Azure Developer Cli

OrganizationPopular
github
azure-developer-cli

Design, create, review, migrate, or troubleshoot Azure Developer CLI (azd) projects using current Microsoft guidance. Use for azd, azure.yaml, AZD templates, Bicep or Terraform under infra, AZD environments and secrets, hooks, deployment workflows, and azd-managed CI/CD.

Overview

Publishergithub
Repositoryawesome-copilot
Skill nameazure-developer-cli
Stars
39.1K
Forks
5K
Bundled files
5
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.

  • 5 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 Developer Cli 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-developer-cli .claude/skills/azure-developer-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Developer Cli 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 Developer Cli 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 Developer Cli 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 best practices

Use this skill to produce maintainable, secure, environment-aware azd projects. Prefer repository conventions when they are already coherent, and make the smallest complete change that improves the project.

Start with repository discovery

Before editing:

  1. Find azure.yaml, the configured infra.path, source projects, deployment scripts, .gitignore, and pipeline definitions.
  2. Read azure.yaml before inferring services or the IaC provider.
  3. Identify whether the task is to create, migrate, review, deploy, or troubleshoot.
  4. Identify the active environment only when an environment-specific operation is required.
  5. Read the relevant reference:

Do not assume the default infra path, the default Bicep provider, or a single service when azure.yaml says otherwise.

Apply safety guardrails

  • Never commit .azure, environment .env files, credentials, deployment outputs containing secrets, local Terraform state, or generated deployment artifacts.
  • Never put literal secrets in azure.yaml, IaC parameter files, hooks, source control, command arguments that will be logged, or IaC outputs.
  • Prefer managed identities and RBAC. Use Key Vault references and azd env set-secret when a secret is unavoidable.
  • Before a command that can create, modify, or delete Azure resources, confirm the target environment, subscription, tenant, region, and expected scope.
  • Treat an explicit user request to deploy, provision, destroy, or configure a pipeline as approval for that named action. Otherwise, ask before running azd up, azd provision, azd deploy, azd down, or azd pipeline config.
  • Do not replace Bicep with Terraform, Terraform with Bicep, or an established hosting service unless the user requests that architectural change.
  • Preserve resources and state owned outside the current azd project.

Use these defaults

ConcernPreferred default
Project manifestOne azure.yaml at the repository root
Application codesrc/<service-name> per independently deployable service
Infrastructureinfra with a thin entry point and reusable modules
IaC providerBicep unless the repository or user chooses Terraform
Deployment environmentsSeparate named environments for dev, test, staging, and production
Local AZD state.azure/<environment-name> and excluded from source control
Shared environment stateAZD remote environments backed by Azure Blob Storage
SecretsManaged identity/RBAC first, then Key Vault references
Automation scriptsShort, idempotent scripts under scripts/azd
CI authenticationWorkload identity federation/OIDC where supported
Routine developmentazd up for simple workflows; separate phases for controlled workflows

Implementation workflow

1. Model the application

  • Define one services entry for each independently deployable component.
  • Keep service keys stable because they participate in resource discovery and deployment.
  • Map each service to its actual project, language, and host.
  • Keep shared infrastructure in IaC rather than inventing a fake deployable service.
  • Declare dependencies with supported azure.yaml fields instead of relying on file order.

2. Model infrastructure

  • Keep main.bicep or main.tf as the orchestration entry point.
  • Split reusable or independently understandable infrastructure into modules.
  • Parameterize environment-specific values; do not fork the IaC tree per environment.
  • Output only stable, nonsecret values required by deployment or application configuration.
  • Use deterministic naming and consistent tags that include the project and environment.
  • Add role assignments to identities rather than distributing service keys.
  • Use infrastructure layers only when separate scopes or lifecycle dependencies justify them.

3. Model environments

  • Use predictable names such as <project>-dev for shared environments and <alias>-dev for personal environments.
  • Use azd env set, azd env unset, and azd env set-secret rather than editing .env directly.
  • Use -e or --environment in scripts and automation so the target is explicit.
  • Use azd env refresh to synchronize deployment outputs after another actor changes an environment.
  • Configure AZD remote state when a team shares environment state.

4. Add hooks only for lifecycle gaps

  • Prefer declarative IaC and native service configuration over hooks.
  • Use root hooks for project-wide behavior and service hooks for service-specific behavior.
  • Keep nontrivial hook logic in versioned scripts under scripts/azd.
  • Set shell explicitly. Provide windows and posix variants when necessary.
  • Make hooks idempotent, noninteractive in CI, and fail on errors unless failure is intentionally nonblocking.
  • Test a hook independently with azd hooks run <hook-name>.

5. Build CI/CD deliberately

  • Keep the pipeline definition with the template and review generated changes from azd pipeline config.
  • Use short-lived federated credentials where the provider supports them.
  • Run tests and IaC validation before provisioning.
  • Use explicit environments and --no-prompt in automation.
  • Add protected production environments and approval gates.
  • For Terraform, configure protected remote state before pipeline setup and account for current AZD authentication limitations.

Validate before finishing

Run only checks applicable to the repository:

text
Application: existing formatter, linter, type-check, build, and tests
Bicep:      az bicep build --file infra/main.bicep
Terraform:  terraform fmt -check -recursive
            terraform init -backend=false
            terraform validate
AZD hooks:  azd hooks run <hook-name>
Packaging:  azd package

For a Bicep what-if or Terraform plan, choose the correct deployment scope and environment. These checks can authenticate to Azure or read remote state, so follow the safety guardrails.

Verify that:

  • azure.yaml paths exist and service settings match the source projects.
  • The IaC entry point and provider agree with azure.yaml.
  • Required deployment outputs match the variables consumed by services, hooks, and pipelines.
  • .gitignore excludes .azure, secrets, local state, and generated artifacts.
  • No secret appears in tracked content or command output.
  • Documentation explains prerequisites, environment creation, deployment, verification, and cleanup.

Report the result

State:

  • The files and behavior changed.
  • The IaC provider and environment assumptions.
  • The checks performed.
  • Any cloud-changing command deliberately not run.
  • Any beta or preview feature the solution relies on.

Do not claim deployment success unless the target environment was actually deployed and verified.

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 Developer Cli AI skill do?

Design, create, review, migrate, or troubleshoot Azure Developer CLI (azd) projects using current Microsoft guidance. Use for azd, azure.yaml, AZD templates, Bicep or Terraform under infra, AZD environments and secrets, hooks, deployment workflows, and azd-managed CI/CD.

Why use Azure Developer Cli on TypingMind?

Because you install it once and use it with any model. Azure Developer Cli 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 Developer Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/azure-developer-cli. 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 Developer Cli?

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 Developer Cli?

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

Is the Azure Developer Cli 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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