What does the Aws Ec2 AI skill do?

Manage EC2 instances, AMIs, and auto-scaling groups. Configure security groups, key pairs, and instance types. Use when deploying compute resources on AWS.

Why use Aws Ec2 on TypingMind?

Because you install it once and use it with any model. Aws Ec2 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 Ec2 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-aws/aws-ec2. 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 Aws Ec2?

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 Ec2?

As many as you like. As long as a model supports skills, you can use Aws Ec2 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Ec2 AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

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.

Aws Ec2 by BagelHole — AI Skill for Any LLM

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)
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

bash
# 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

bash
#!/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

bash
# 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": [{
      "ResourceType": "instance",
      "Tags": [
        {"Key": "Environment", "Value": "production"},
        {"Key": "ManagedBy", "Value": "launch-template"}
      ]
    }],
    "Monitoring": {"Enabled": true},
    "UserData": "'"$(base64 -w0 userdata.sh)"'"
  }'

# Create a new version of the launch template
aws ec2 create-launch-template-version \
  --launch-template-name web-server-template \
  --source-version 1 \
  --version-description "v2 - updated AMI" \
  --launch-template-data '{"ImageId": "ami-0newami1234567890"}'

# Set the default version
aws ec2 modify-launch-template \
  --launch-template-name web-server-template \
  --default-version 2

Auto Scaling Group

bash
# Create ASG with mixed instances (on-demand + spot)
aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name web-asg \
  --mixed-instances-policy '{
    "LaunchTemplate": {
      "LaunchTemplateSpecification": {
        "LaunchTemplateName": "web-server-template",
        "Version": "$Default"
      },
      "Overrides": [
        {"InstanceType": "t3.medium"},
        {"InstanceType": "t3a.medium"},
        {"InstanceType": "m5.large"}
      ]
    },
    "InstancesDistribution": {
      "OnDemandBaseCapacity": 2,
      "OnDemandPercentageAboveBaseCapacity": 25,
      "SpotAllocationStrategy": "capacity-optimized"
    }
  }' \
  --min-size 2 --max-size 10 --desired-capacity 4 \
  --vpc-zone-identifier "subnet-aaa,subnet-bbb" \
  --target-group-arns "arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/web-tg/abc123" \
  --health-check-type ELB \
  --health-check-grace-period 300 \
  --tags '[
    {"Key":"Name","Value":"web-asg","PropagateAtLaunch":true},
    {"Key":"Environment","Value":"production","PropagateAtLaunch":true}
  ]'

# Create target tracking scaling policy (target 60% CPU)
aws autoscaling put-scaling-policy \
  --auto-scaling-group-name web-asg \
  --policy-name cpu-target-tracking \
  --policy-type TargetTrackingScaling \
  --target-tracking-configuration '{
    "PredefinedMetricSpecification": {
      "PredefinedMetricType": "ASGAverageCPUUtilization"
    },
    "TargetValue": 60.0,
    "ScaleInCooldown": 300,
    "ScaleOutCooldown": 60
  }'

# Create scheduled scaling for known traffic patterns
aws autoscaling put-scheduled-update-group-action \
  --auto-scaling-group-name web-asg \
  --scheduled-action-name scale-up-morning \
  --recurrence "0 8 * * MON-FRI" \
  --min-size 4 --max-size 20 --desired-capacity 8

aws autoscaling put-scheduled-update-group-action \
  --auto-scaling-group-name web-asg \
  --scheduled-action-name scale-down-evening \
  --recurrence "0 20 * * MON-FRI" \
  --min-size 2 --max-size 10 --desired-capacity 2

Spot Instances

bash
# Request Spot instances
aws ec2 request-spot-instances \
  --spot-price "0.05" \
  --instance-count 3 \
  --type "one-time" \
  --launch-specification '{
    "ImageId": "ami-0abcdef1234567890",
    "InstanceType": "c5.xlarge",
    "KeyName": "my-key",
    "SecurityGroupIds": ["sg-12345678"],
    "SubnetId": "subnet-12345678"
  }'

# Check current Spot prices
aws ec2 describe-spot-price-history \
  --instance-types t3.medium t3a.medium m5.large \
  --availability-zone us-east-1a \
  --product-descriptions "Linux/UNIX" \
  --start-time "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
  --query "SpotPriceHistory[].{Type:InstanceType,Price:SpotPrice,AZ:AvailabilityZone}" \
  --output table

# Create a Spot Fleet request
aws ec2 request-spot-fleet \
  --spot-fleet-request-config '{
    "IamFleetRole": "arn:aws:iam::123456789012:role/aws-ec2-spot-fleet-role",
    "TargetCapacity": 10,
    "SpotPrice": "0.10",
    "AllocationStrategy": "capacityOptimized",
    "LaunchSpecifications": [
      {"ImageId": "ami-xxx", "InstanceType": "c5.xlarge", "SubnetId": "subnet-aaa"},
      {"ImageId": "ami-xxx", "InstanceType": "c5a.xlarge", "SubnetId": "subnet-bbb"}
    ]
  }'

AMI Management

bash
# Create an AMI from a running instance
aws ec2 create-image \
  --instance-id i-0abc123def456 \
  --name "web-server-$(date +%Y%m%d)" \
  --description "Web server golden AMI" \
  --no-reboot \
  --tag-specifications 'ResourceType=image,Tags=[
    {Key=Name,Value=web-server-golden},
    {Key=Version,Value=2026.03.24}
  ]'

# Copy AMI to another region for disaster recovery
aws ec2 copy-image \
  --source-image-id ami-0abcdef1234567890 \
  --source-region us-east-1 \
  --region us-west-2 \
  --name "web-server-dr-copy"

# Deregister old AMIs and delete associated snapshots
aws ec2 deregister-image --image-id ami-old123
aws ec2 delete-snapshot --snapshot-id snap-old123

# Share AMI with another AWS account
aws ec2 modify-image-attribute \
  --image-id ami-0abcdef1234567890 \
  --launch-permission "Add=[{UserId=987654321098}]"

Instance Management

bash
# List running instances with key details
aws ec2 describe-instances \
  --filters "Name=instance-state-name,Values=running" \
  --query "Reservations[].Instances[].{ID:InstanceId,Type:InstanceType,IP:PrivateIpAddress,Name:Tags[?Key=='Name']|[0].Value,State:State.Name}" \
  --output table

# Stop and start instances
aws ec2 stop-instances --instance-ids i-0abc123def456
aws ec2 start-instances --instance-ids i-0abc123def456

# Resize an instance (stop first)
aws ec2 stop-instances --instance-ids i-0abc123def456
aws ec2 wait instance-stopped --instance-ids i-0abc123def456
aws ec2 modify-instance-attribute \
  --instance-id i-0abc123def456 \
  --instance-type '{"Value": "t3.large"}'
aws ec2 start-instances --instance-ids i-0abc123def456

# Get console output for debugging boot issues
aws ec2 get-console-output --instance-id i-0abc123def456

# Get instance screenshot (helps debug GUI issues)
aws ec2 get-console-screenshot --instance-id i-0abc123def456

Terraform EC2 with Auto Scaling

hcl
resource "aws_launch_template" "web" {
  name_prefix   = "web-"
  image_id      = data.aws_ami.amazon_linux.id
  instance_type = "t3.medium"

  iam_instance_profile {
    name = aws_iam_instance_profile.web.name
  }

  metadata_options {
    http_tokens   = "required"
    http_endpoint = "enabled"
  }

  block_device_mappings {
    device_name = "/dev/xvda"
    ebs {
      volume_size = 30
      volume_type = "gp3"
      encrypted   = true
    }
  }

  user_data = base64encode(file("userdata.sh"))

  tag_specifications {
    resource_type = "instance"
    tags = {
      Name        = "web-server"
      Environment = "production"
    }
  }
}

resource "aws_autoscaling_group" "web" {
  name                = "web-asg"
  min_size            = 2
  max_size            = 10
  desired_capacity    = 4
  vpc_zone_identifier = [aws_subnet.private_a.id, aws_subnet.private_b.id]
  target_group_arns   = [aws_lb_target_group.web.arn]
  health_check_type   = "ELB"

  launch_template {
    id      = aws_launch_template.web.id
    version = "$Latest"
  }

  tag {
    key                 = "Name"
    value               = "web-asg"
    propagate_at_launch = true
  }
}

resource "aws_autoscaling_policy" "cpu" {
  name                   = "cpu-target-tracking"
  autoscaling_group_name = aws_autoscaling_group.web.name
  policy_type            = "TargetTrackingScaling"

  target_tracking_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ASGAverageCPUUtilization"
    }
    target_value = 60.0
  }
}

Troubleshooting

Problem	Cause	Fix
Instance stuck in `pending`	Insufficient capacity	Try a different AZ or instance type
Cannot SSH to instance	Security group or NACL blocks port 22	Check SG ingress rules and route tables
Instance immediately terminates	EBS volume limit or AMI issue	Check `describe-instances` for StateReason
IMDSv1 deprecation warnings	Metadata options not set	Set `HttpTokens=required` in launch template
User data not running	Script missing shebang or not base64	Verify `#!/bin/bash` header; check `/var/log/cloud-init-output.log`
Spot instance terminated	Capacity reclaimed by AWS	Use capacity-optimized allocation and diversify types
ASG not replacing unhealthy	Health check grace period too short	Increase grace period to cover app boot time
EBS throughput bottleneck	gp2 volume too small for IOPS	Migrate to gp3 and set explicit IOPS/throughput

Related Skills

aws-vpc - VPC networking, subnets, and security groups
aws-iam - Instance profiles and roles
aws-cost-optimization - Rightsizing and Spot strategies
terraform-aws - Infrastructure as Code deployment
cloudformation - AWS-native IaC templates

Publisher	BagelHole
Repository	`DevOps-Security-Agent-Skills`
Skill name	`aws-ec2`
Stars	1.1K
Forks	161
Bundled files	1
License	MIT
Links	Skill source Repository

Aws Ec2

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AWS EC2

When to Use This Skill

Prerequisites

Instance Type Selection Guide

Launch an Instance

User Data Scripts

Launch Templates

Auto Scaling Group

Spot Instances

AMI Management

Instance Management

Terraform EC2 with Auto Scaling

Troubleshooting

Related Skills

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AWS EC2

When to Use This Skill

Prerequisites

Instance Type Selection Guide

Launch an Instance

User Data Scripts

Launch Templates

Auto Scaling Group

Spot Instances

AMI Management

Instance Management

Terraform EC2 with Auto Scaling

Troubleshooting

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now