aws-ec2
aws-ec2 is an engineering AI skill with a core value of Manage EC2 instances, AMIs, and auto-scaling groups. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Manage EC2 instances, AMIs, and auto-scaling groups. Configure security groups, key pairs, and instance types. Use when deploying compute resources on AWS.
Quick Facts
mkdir -p ./skills/aws-ec2 && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/aws-ec2/SKILL.md -o ./skills/aws-ec2/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# AWS EC2
Deploy and manage Amazon EC2 compute instances for production, staging, and development workloads.
When to Use This Skill
- Launching new compute instances for application hosting
- Building golden AMIs for consistent deployments
- Setting up auto-scaling groups behind load balancers
- Migrating workloads to Spot instances for cost savings
- Troubleshooting instance connectivity, performance, or launch failures
- Creating launch templates for repeatable infrastructure
Prerequisites
- AWS CLI v2 installed and configured (`aws configure`)
- IAM permissions: `ec2:*`, `autoscaling:*`, `elasticloadbalancing:*`, `iam:PassRole`
- An existing VPC with subnets (see aws-vpc (`aws-vpc`))
- SSH key pair created (`aws ec2 create-key-pair --key-name my-key --query 'KeyMaterial' --output text > my-key.pem`)
Instance Type Selection Guide
| Category | Types | Use Case |
|---|---|---|
| General Purpose | t3, t3a, m6i, m7g | Web servers, small databases, dev/test |
| Compute Optimized | c6i, c7g | Batch processing, media encoding, ML inference |
| Memory Optimized | r6i, r7g, x2idn | In-memory caches, large databases |
| Storage Optimized | i3, i4i, d3 | Data warehousing, distributed file systems |
| Accelerated | p4d, g5, inf2 | ML training, GPU rendering, inference |
| Burstable | t3.micro-t3.2xlarge | Low-steady-state with occasional bursts |
Launch an Instance
# Launch a production web server
aws ec2 run-instances \
--image-id ami-0abcdef1234567890 \
--instance-type t3.medium \
--key-name my-key \
--security-group-ids sg-12345678 \
--subnet-id subnet-12345678 \
--iam-instance-profile Name=EC2AppProfile \
--metadata-options "HttpTokens=required,HttpEndpoint=enabled" \
--block-device-mappings '[{
"DeviceName": "/dev/xvda",
"Ebs": {
"VolumeSize": 30,
"VolumeType": "gp3",
"Iops": 3000,
"Throughput": 125,
"Encrypted": true
}
}]' \
--tag-specifications 'ResourceType=instance,Tags=[
{Key=Name,Value=web-server-01},
{Key=Environment,Value=production},
{Key=Team,Value=platform}
]' \
--user-data file://userdata.sh
# Launch with IMDSv2 required (security best practice)
aws ec2 run-instances \
--image-id ami-0abcdef1234567890 \
--instance-type t3.micro \
--metadata-options "HttpTokens=required,HttpPutResponseHopLimit=1,HttpEndpoint=enabled" \
--tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=secure-instance}]'User Data Scripts
#!/bin/bash
# userdata.sh - Bootstrap a web server on Amazon Linux 2023
set -euxo pipefail
# System updates
dnf update -y
# Install and start web server
dnf install -y nginx
systemctl enable nginx
systemctl start nginx
# Install CloudWatch agent
dnf install -y amazon-cloudwatch-agent
/opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl \
-a fetch-config -m ec2 \
-s -c ssm:AmazonCloudWatch-linux
# Install CodeDeploy agent
dnf install -y ruby wget
cd /home/ec2-user
wget https://aws-codedeploy-us-east-1.s3.us-east-1.amazonaws.com/latest/install
chmod +x ./install
./install auto
# Signal CloudFormation (if launched via CFN)
# /opt/aws/bin/cfn-signal -e $? --stack ${AWS::StackName} --resource ASG --region ${AWS::Region}Launch Templates
# Create a launch template with full configuration
aws ec2 create-launch-template \
--launch-template-name web-server-template \
--version-description "v1 - AL2023 with nginx" \
--launch-template-data '{
"ImageId": "ami-0abcdef1234567890",
"InstanceType": "t3.medium",
"KeyName": "my-key",
"SecurityGroupIds": ["sg-12345678"],
"IamInstanceProfile": {"Name": "EC2AppProfile"},
"MetadataOptions": {
"HttpTokens": "required",
"HttpEndpoint": "enabled"
},
"BlockDeviceMappings": [{
"DeviceName": "/dev/xvda",
"Ebs": {
"VolumeSize": 30,
"VolumeType": "gp3",
"Encrypted": true
}
}],
"TagSpecifications": [{
"ResourceTyp🎯 Best For
- Security auditors
- DevSecOps teams
- Compliance officers
- Claude users
- AI users
💡 Use Cases
- Auditing dependencies for known CVEs
- Scanning API endpoints for auth gaps
- Using aws-ec2 in daily workflow
- Automating repetitive engineering tasks
📖 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 aws-ec2 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 this replace a dedicated SAST tool?
AI-based security review is complementary to SAST tools. Use it as a first-pass filter, not a replacement.
How do I install aws-ec2?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/aws-ec2/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
Only scanning surface-level issues
Deep security review requires understanding your app architecture, not just regex patterns.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.