aws-ami-builder

hashicorp/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/hashicorp/agent-skills --skill aws-ami-builder
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summary

Build custom Amazon Machine Images with Packer's amazon-ebs builder.

  • Automates AMI creation from source AMIs using HCL templates with provisioners for customization (shell scripts, file uploads, configuration management)
  • Supports multi-region AMI distribution via ami_regions and flexible source AMI filtering by name, owner, and virtualization type
  • Authenticates via environment variables, AWS credentials file, or IAM instance profiles; includes validation and build commands for templa
skill.md

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

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

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

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

Multi-Region AMI

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)
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

# 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

how to use aws-ami-builder

How to use aws-ami-builder on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add aws-ami-builder
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/hashicorp/agent-skills --skill aws-ami-builder

The skills CLI fetches aws-ami-builder from GitHub repository hashicorp/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/aws-ami-builder

Reload or restart Cursor to activate aws-ami-builder. Access the skill through slash commands (e.g., /aws-ami-builder) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.644 reviews
  • Ganesh Mohane· Dec 20, 2024

    Useful defaults in aws-ami-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Meera Singh· Dec 20, 2024

    aws-ami-builder reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aisha Abbas· Dec 8, 2024

    aws-ami-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mei Garcia· Dec 4, 2024

    aws-ami-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Li Thomas· Dec 4, 2024

    aws-ami-builder fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aanya Verma· Nov 27, 2024

    Useful defaults in aws-ami-builder — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Michael Menon· Nov 23, 2024

    Registry listing for aws-ami-builder matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sakshi Patil· Nov 11, 2024

    aws-ami-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mei Torres· Nov 11, 2024

    We added aws-ami-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aisha Choi· Nov 3, 2024

    Solid pick for teams standardizing on skills: aws-ami-builder is focused, and the summary matches what you get after install.

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