Official agent skill

Avm Tf Process

by Azure in Azure/terraform-azurerm-avm-ptn-alz

A skill your agent uses for the AVM Terraform contribution process from module proposal and repository setup through implementation, Avm.Authoring validation, pull request review, and release.

OfficialMITAuto-check passedDevOps & Cloud

Install Avm Tf Process

skills CLI
$ npx skills add Azure/terraform-azurerm-avm-ptn-alz --skill avm-tf-process -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Azure/terraform-azurerm-avm-ptn-alz avm-tf-process --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Azure/terraform-azurerm-avm-ptn-alz.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/avm-tf-process .claude/skills/avm-tf-process && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
avm-tf-process
GitHub stars
135
Token cost
~1.5k tokens
SKILL.md length
730 words
Files
1
Skills in repo
13
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses for the AVM Terraform contribution process from module proposal and repository setup through implementation, Avm.Authoring validation, pull request review, and release.

  • Works in 9 steps: Confirm classification and ownership → Establish the managed repository baseline → Implement from current specifications → …
  • The AVM Terraform contribution process from module proposal and repository setup through implementation
  • SKILL.md covers 1. Confirm classification and…, 2. Establish the managed…, 3. Implement from current… and 4. Develop on a focused branch, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Avm Tf Process is an agent skill from Azure/terraform-azurerm-avm-ptn-alz, published by the product's own GitHub organization. Use for the AVM Terraform contribution process from module proposal and repository setup through implementation, Avm.Authoring validation, pull request review, and release.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Infrastructure as code and Pull requests. It works with Terraform and Microsoft Azure. The repository describes itself as: Terraform Azure Verified Pattern Module for Azure Landing Zone Management Groups and Policy. The licence is MIT.

When your agent uses it

  • The AVM Terraform contribution process from module proposal and repository setup through implementation
  • Avm.Authoring validation
  • Pull request review

Example prompts

  • “/avm-tf-process”

Requirements

  • Docker

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Confirm classification and ownership
  2. Establish the managed repository baseline
  3. Implement from current specifications
  4. Develop on a focused branch
  5. Run targeted tests
  6. Apply pre-commit changes
  7. Commit, then run the full PR gate
  8. Open and review the pull request
  9. Release

What it can do on your machine

Read from SKILL.md and the folder at commit e2a318c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are powershell).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • azure.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Avm Tf Process loads about 1.5k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 730 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Azure/terraform-azurerm-avm-ptn-alz at commit e2a318c, republished under its MIT licence (© Azure). 730 words, ~1,529 tokens.

Download SKILL.mdSave it as .claude/skills/avm-tf-process/SKILL.md (or your agent's skills folder).
name
avm-tf-process
description
Use for the AVM Terraform contribution process from module proposal and repository setup through implementation, Avm.Authoring validation, pull request review, and release.

AVM Terraform Contribution Process

Use the current AVM process and specification pages from https://azure.github.io/Azure-Verified-Modules/llms.txt. Older public setup pages or module repositories may still contain the retired launcher, Makefile, container, or Porch flow.

1. Confirm classification and ownership

Determine whether the work is a resource, pattern, or utility module. Confirm the approved module name and ownership from the AVM indexes and proposal rather than inventing a name.

Before creating or restructuring a repository, read the current requirements for:

  • repository naming and visibility;
  • owner and AVM team permissions;
  • CODEOWNERS;
  • branch protection and required checks;
  • issue and pull request templates;
  • release and publishing; and
  • support lifecycle.

2. Establish the managed repository baseline

Start from the current AVM Terraform template or governance-managed repository state. Import Avm.Authoring and synchronize managed files:

pwsh
Install-PSResource -Name Avm.Authoring -Repository PSGallery -TrustRepository
Import-Module Avm.Authoring
avm version
avm sync

Review synchronized changes before continuing. Do not restore files from the retired Make, Porch, or container workflow.

3. Implement from current specifications

Build every new resource-deploying module repository on AzAPI. Do not declare or configure hashicorp/azurerm, and do not create any azurerm_* resource or data source for control-plane operations, convenience, or ordinary supporting infrastructure in implementation, submodules, examples, E2E configurations, Terraform tests, fixtures, setup or teardown Terraform, migration examples, documentation examples, or generated snippets.

When supporting configuration needs a direct Azure resource that the module under test does not supply, use an AzAPI resource, data source, or action. Each standalone Terraform root that performs direct Azure operations includes Azure/azapi in required_providers.

Permit hashicorp/azurerm ~> 4.0 only when required by an independently justified azurerm_* resource or data-source block. Each block scopes to one specific unsupported data-plane/non-ARM operation, documents the exact block and why AzAPI cannot implement it with an upstream AzAPI issue or pull request, and is replaced when support ships. Prefer an AVM TFLint override file, but use a justified line-level annotation when it avoids suppressing unrelated findings in the same scope. One valid block does not authorize another. Examples and tests may configure AzureRM only to exercise such blocks. Follow avm-tf-tflint.

Fetch llms.txt, then read each applicable raw spec page. At minimum, review:

  • module classification and composition rules;
  • TFFR3-TFFR8 for AzAPI;
  • TFRMFR1 and TFNFR38 for parent and resource IDs;
  • TFRMNFR1 and TFRMNFR2 for submodules and resource labels;
  • TFNFR39 for file layout;
  • standard interfaces and telemetry;
  • tests and examples;
  • documentation; and
  • semantic versioning and breaking changes.

Use the local specialized skills for implementation details, but resolve any conflict in favor of the current published spec.

4. Develop on a focused branch

Keep changes scoped and preserve unrelated history. Add or update:

  • Terraform implementation and exact types;
  • root and submodule tests;
  • representative examples;
  • _header.md and _footer.md;
  • migration or upgrade-path coverage when state or addresses change; and
  • generated README files through Avm.Authoring.

Scripts and lifecycle hooks must be PowerShell. Supported hook names include:

  • tests/unit/setup.ps1;
  • tests/integration/setup.ps1;
  • examples/<name>/pre.ps1;
  • examples/<name>/post.ps1; and
  • examples/<name>/tflint-pre.ps1.

Do not add shell-hook counterparts.

Show full SKILL.md (263 more words)Show less

5. Run targeted tests

Choose the smallest tier that proves the change:

pwsh
avm test unit
avm test integration
avm test e2e --example <example-name>

Unit tests mock all required providers. Integration and E2E tests need real Azure authentication. E2E tests must prove deployment, idempotency, and cleanup.

6. Apply pre-commit changes

Run:

pwsh
avm pre-commit

For Terraform this performs managed-file sync, fixable convention rules, transforms, formatting, and documentation generation. Review every generated or synchronized change and rerun targeted tests if the generated change affects behavior.

7. Commit, then run the full PR gate

Commit the complete worktree before running:

pwsh
avm pr-check

avm pr-check requires a clean Git worktree. It checks sync, formatting, transforms, lint, APRL/AVMSEC policy evaluation, conventions, Terraform validation, and documentation. Unit tests remain a separate test tier and CI job; a passing PR check is not a substitute for required unit, integration, or E2E coverage.

8. Open and review the pull request

The pull request must explain:

  • what changed and why;
  • applicable specification IDs;
  • compatibility or breaking-change impact;
  • state migration steps when addresses or providers changed;
  • tests and examples exercised; and
  • confirmation that control-plane and supporting resources use AzAPI, plus the evidence and upstream link for any narrow data-plane/non-ARM AzureRM exception.

Review the final diff rather than only the hand-authored files. Managed and generated outputs are part of the change.

9. Release

Follow the current AVM semantic-versioning and publishing process. Confirm:

  • the change classification matches the release version;
  • generated documentation is current;
  • required checks and test tiers passed;
  • upgrade notes are included where needed; and
  • repository permissions and release automation remain governance-compliant.

Use Avm.Authoring throughout. Do not substitute ./avm, avm.ps1, Make, Porch, Docker, Podman, or manually installed pinned tools.

© Azure, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/avm-tf-process of Azure/terraform-azurerm-avm-ptn-alz.

Open the folder on GitHubat commit e2a318c

Compare with similar skills

Avm Tf Process next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Avm Tf Process compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Avm Tf Process this skillAzure/terraform-azurerm-avm-ptn-alz135—~1.5kAutomated safety check: PassMIT
Provider Verificationmondoohq/mql411—~3.7kAutomated safety check: PassCustom licence
Terravision Cloud Diagramspatrickchugh/terravision1.6k—~5.6kAutomated safety check: NotesAGPL-3.0-only
Apex GitHub Operationsjonathan-vella/apex217—~1.5kAutomated safety check: PassMIT
Azure CopilotMicrosoftDocs/Agent-Skills777—~1.4kAutomated safety check: PassCC-BY-4.0
TerrasharkLukasNiessen/terrashark714—~843Automated safety check: PassMIT

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  • Avm Tf Codestyle

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  • Avm Tf Conftest

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  • Avm Tf Documentation

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Questions about Avm Tf Process

What does Avm Tf Process do?

A skill your agent uses for the AVM Terraform contribution process from module proposal and repository setup through implementation, Avm.Authoring validation, pull request review, and release. Avm Tf Process is an agent skill from Azure/terraform-azurerm-avm-ptn-alz, published by the product's own GitHub organization.Authoring validation, pull request review, and release.

When should I use Avm Tf Process?

Avm Tf Process fits situations like: the AVM Terraform contribution process from module proposal and repository setup through implementation; avm.Authoring validation; pull request review.

How do I install Avm Tf Process in Claude Code?

Run `npx skills add Azure/terraform-azurerm-avm-ptn-alz --skill avm-tf-process -a claude-code`. Or copy the skill folder (.github/skills/avm-tf-process in Azure/terraform-azurerm-avm-ptn-alz) into .claude/skills/avm-tf-process in your project. Claude Code loads it when a task matches its description.

How do I install Avm Tf Process in Codex?

Run `npx skills add Azure/terraform-azurerm-avm-ptn-alz --skill avm-tf-process -a codex`. Or copy the skill folder (.github/skills/avm-tf-process in Azure/terraform-azurerm-avm-ptn-alz) into .agents/skills/avm-tf-process in your project. Codex loads it when a task matches its description.

Can I use Avm Tf Process in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Azure/terraform-azurerm-avm-ptn-alz --skill avm-tf-process -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/avm-tf-process, .gemini/skills/avm-tf-process, .github/skills/avm-tf-process and .opencode/skills/avm-tf-process in your project.

What does Avm Tf Process need to run?

SKILL.md names no scripts, command-line tools or credentials: Avm Tf Process is instructions for the agent only. Our summary lists: Docker.

Does Avm Tf Process access the network?

SKILL.md names 1 domain. As links in the text: azure.github.io. This is read from the text; nothing was executed.

Is Avm Tf Process safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Avm Tf Process use?

Avm Tf Process is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Avm Tf Process use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Avm Tf Process?

Skills that share tags, products or a category with Avm Tf Process: Provider Verification (mondoohq/mql, 411 stars), Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars), Apex GitHub Operations (jonathan-vella/apex, 217 stars) and Azure Copilot (MicrosoftDocs/Agent-Skills, 777 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Avm Tf Process?

Azure (a GitHub organization, an official publisher) maintains it in Azure/terraform-azurerm-avm-ptn-alz, which has 135 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 6, 2026.

Source: Azure/terraform-azurerm-avm-ptn-alz on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.