Aws Ami Builder logo

Aws Ami Builder

Organization
hashicorp
aws-ami-builder

Build Amazon Machine Images (AMIs) with Packer using the amazon-ebs builder. Use when creating custom AMIs for EC2 instances.

Overview

Publisherhashicorp
Repositoryagent-skills
Skill nameaws-ami-builder
Stars
880
Forks
130
Bundled files
Instructions only
LicenseMPL-2.0
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 hashicorp on GitHub. Read the source before you install it.

Installation

Install the Aws Ami Builder 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/hashicorp/agent-skills/tree/main/plugins/packer/skills/aws-ami-builder
  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/hashicorp/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/packer/skills/aws-ami-builder .claude/skills/aws-ami-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Ami Builder 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 Aws Ami Builder 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 Aws Ami Builder 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.

AWS AMI Builder

Build Amazon Machine Images (AMIs) using Packer's amazon-ebs builder.

Reference: Amazon EBS Builder

Note: Building AMIs incurs AWS costs (EC2 instances, EBS storage, data transfer). Builds typically take 10-30 minutes depending on provisioning complexity.

Basic AMI Template

hcl
packer {
  required_plugins {
    amazon = {
      source  = "github.com/hashicorp/amazon"
      version = "~> 1.3"
    }
  }
}

variable "region" {
  type    = string
  default = "us-west-2"
}

locals {
  timestamp = regex_replace(timestamp(), "[- TZ:]", "")
}

source "amazon-ebs" "ubuntu" {
  region        = var.region
  instance_type = "t3.micro"

  source_ami_filter {
    filters = {
      name                = "ubuntu/images/*ubuntu-jammy-22.04-amd64-server-*"
      root-device-type    = "ebs"
      virtualization-type = "hvm"
    }
    most_recent = true
    owners      = ["099720109477"] # Canonical
  }

  ssh_username = "ubuntu"
  ami_name     = "my-app-${local.timestamp}"

  tags = {
    Name      = "my-app"
    BuildDate = local.timestamp
  }
}

build {
  sources = ["source.amazon-ebs.ubuntu"]

  provisioner "shell" {
    inline = [
      "sudo apt-get update",
      "sudo apt-get upgrade -y",
    ]
  }
}

Common Source AMI Filters

Ubuntu 22.04 LTS

hcl
source_ami_filter {
  filters = {
    name                = "ubuntu/images/*ubuntu-jammy-22.04-amd64-server-*"
    root-device-type    = "ebs"
    virtualization-type = "hvm"
  }
  most_recent = true
  owners      = ["099720109477"] # Canonical
}

Amazon Linux 2023

hcl
source_ami_filter {
  filters = {
    name                = "al2023-ami-*-x86_64"
    root-device-type    = "ebs"
    virtualization-type = "hvm"
  }
  most_recent = true
  owners      = ["amazon"]
}

Multi-Region AMI

hcl
source "amazon-ebs" "ubuntu" {
  region        = "us-west-2"
  instance_type = "t3.micro"

  source_ami_filter {
    filters = {
      name = "ubuntu/images/*ubuntu-jammy-22.04-amd64-server-*"
    }
    most_recent = true
    owners      = ["099720109477"]
  }

  ssh_username = "ubuntu"
  ami_name     = "my-app-${local.timestamp}"

  # Copy to additional regions
  ami_regions = ["us-east-1", "us-east-2", "eu-west-1"]
}

Authentication

Packer uses AWS credential resolution:

  1. Environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
  2. AWS credentials file: ~/.aws/credentials
  3. IAM instance profile (when running on EC2)
bash
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="us-west-2"

packer build .

Build Commands

bash
# Initialize plugins
packer init .

# Validate template
packer validate .

# Build AMI
packer build .

# Build with variables
packer build -var "region=us-east-1" .

Common Issues

SSH Timeout

  • Ensure security group allows SSH (port 22)
  • Verify subnet has internet access

AMI Already Exists

  • AMI names must be unique
  • Use timestamp in name: my-app-${local.timestamp}

Volume Size Too Small

  • Check source AMI's volume size
  • Set launch_block_device_mappings.volume_size accordingly

References

Frequently asked questions

What does the Aws Ami Builder AI skill do?

Build Amazon Machine Images (AMIs) with Packer using the amazon-ebs builder. Use when creating custom AMIs for EC2 instances.

Why use Aws Ami Builder on TypingMind?

Because you install it once and use it with any model. Aws Ami Builder 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 Aws Ami Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hashicorp/agent-skills/tree/main/plugins/packer/skills/aws-ami-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Ami Builder?

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 Aws Ami Builder?

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

Is the Aws Ami Builder AI skill free?

Yes. It is published on GitHub by hashicorp under the MPL-2.0 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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