> AWS EC2 virtual machine management — instances, security groups, key pairs, AMIs, EBS volumes, Auto Scaling Groups, Spot Instances, Session Manager, placement groups, and instance lifecycle automation.
npx skills add https://github.com/itsmostafa/aws-agent-skills --skill ec2
Amazon Elastic Compute Cloud (EC2) provides resizable compute capacity in the cloud.
Advanced patterns (Auto Scaling, Spot Fleets, Session Manager, Instance Connect, IMDS, Placement Groups, scheduled scaling): see instance-management.md.
| Category | Example | Use Case |
|----------|---------|----------|
| General Purpose | t3, m6i, t4g (Graviton) | Web servers, dev environments |
| Compute Optimized | c6i, c7g (Graviton) | Batch processing, gaming |
| Memory Optimized | r6i, r7g (Graviton) | Databases, caching |
| Storage Optimized | i3, d3 | Data warehousing |
| Accelerated | p4d, g5 | ML, graphics |
Graviton (ARM) instances (t4g, m7g, c7g, r7g) are ~20% cheaper than x86 equivalents for the same performance — worth considering for new workloads.
| Option | Description |
|--------|-------------|
| On-Demand | Pay by the hour/second |
| Reserved | 1-3 year commitment, up to 72% discount |
| Spot | Unused capacity, up to 90% discount — can be interrupted with 2-minute notice |
| Savings Plans | Flexible commitment-based discount |
Template containing OS, software, and configuration for launching instances. Use SSM Parameter Store to look up the latest official AMIs rather than hardcoding IDs:
# Latest Amazon Linux 2 AMI
aws ssm get-parameter \
--name /aws/service/ami-amazon-linux-latest/amzn2-ami-hvm-x86_64-gp2 \
--query 'Parameter.Value' --output text
# Latest Amazon Linux 2023
aws ssm get-parameter \
--name /aws/service/ami-amazon-linux-latest/al2023-ami-kernel-default-x86_64 \
--query 'Parameter.Value' --output text
# Latest Ubuntu 22.04
aws ssm get-parameter \
--name /aws/service/canonical/ubuntu/server/22.04/stable/current/amd64/hvm/ebs-gp2/ami-id \
--query 'Parameter.Value' --output text
Virtual firewalls controlling inbound and outbound traffic. Changes take effect immediately — no restart required.
# Create key pair
aws ec2 create-key-pair \
--key-name my-key \
--query 'KeyMaterial' \
--output text > my-key.pem
chmod 400 my-key.pem
# Create security group
aws ec2 create-security-group \
--group-name web-server-sg \
--description "Web server security group" \
--vpc-id vpc-12345678
# Allow SSH and HTTP
aws ec2 authorize-security-group-ingress \
--group-id sg-12345678 \
--protocol tcp \
--port 22 \
--cidr 10.0.0.0/8
aws ec2 authorize-security-group-ingress \
--group-id sg-12345678 \
--protocol tcp \
--port 80 \
--cidr 0.0.0.0/0
# Launch instance
aws ec2 run-instances \
--image-id ami-0123456789abcdef0 \
--instance-type t3.micro \
--key-name my-key \
--security-group-ids sg-12345678 \
--subnet-id subnet-12345678 \
--associate-public-ip-address \
--tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=web-server}]'
# Wait until running, then get IP
aws ec2 wait instance-running --instance-ids i-1234567890abcdef0
aws ec2 describe-instances \
--instance-ids i-1234567890abcdef0 \
--query 'Reservations[].Instances[].PublicIpAddress' --output text
boto3:
import boto3
ec2 = boto3.resource('ec2')
instances = ec2.create_instances(
ImageId='ami-0123456789abcdef0',
InstanceType='t3.micro',
KeyName='my-key',
SecurityGroupIds=['sg-12345678'],
SubnetId='subnet-12345678',
MinCount=1,
MaxCount=1,
TagSpecifications=[{
'ResourceType': 'instance',
'Tags': [{'Key': 'Name', 'Value': 'web-server'}]
}]
)
instance = instances[0]
instance.wait_until_running()
instance.reload()
print(f"Instance ID: {instance.id}")
print(f"Public IP: {instance.public_ip_address}")
> OS package manager note:
> - Amazon Linux 2: use amazon-linux-extras install nginx1 -y — yum install nginx fails because nginx is not in the default AL2 repos
> - Amazon Linux 2023: use dnf install -y nginx
> - Ubuntu: use apt-get install -y nginx
> - Amazon Linux 2 / RHEL: httpd (Apache) is always available via yum install -y httpd
# Amazon Linux 2 — nginx via amazon-linux-extras
aws ec2 run-instances \
--image-id ami-0123456789abcdef0 \
--instance-type t3.micro \
--key-name my-key \
--security-group-ids sg-12345678 \
--subnet-id subnet-12345678 \
--user-data '#!/bin/bash
amazon-linux-extras install nginx1 -y
systemctl start nginx
systemctl enable nginx
'
# Amazon Linux 2 — httpd (Apache, simpler alternative)
# --user-data '#!/bin/bash
# yum install -y httpd
# systemctl start httpd
# systemctl enable httpd
# echo "<h1>Hello from $(hostname -f)</h1>" > /var/www/html/index.html
# '
# Create instance profile
aws iam create-instance-profile \
--instance-profile-name web-server-profile
aws iam add-role-to-instance-profile \
--instance-profile-name web-server-profile \
--role-name web-server-role
# Launch with profile
aws ec2 run-instances \
--image-id ami-0123456789abcdef0 \
--instance-type t3.micro \
--iam-instance-profile Name=web-server-profile \
...
aws ec2 create-image \
--instance-id i-1234567890abcdef0 \
--name "my-custom-ami-$(date +%Y%m%d)" \
--description "Custom AMI with web server" \
--no-reboot
The recommended way to use Spot Instances at scale is via Auto Scaling Groups with a mixed-instances policy — not the legacy request-spot-instances API. This supports instance diversification to minimize interruptions.
See instance-management.md for the full setup. Quick example:
# 1. Create launch template with IMDSv2
aws ec2 create-launch-template \
--launch-template-name my-lt \
--launch-template-data '{
"ImageId": "ami-0123456789abcdef0",
"SecurityGroupIds": ["sg-12345678"],
"IamInstanceProfile": {"Name": "my-profile"},
"MetadataOptions": {"HttpTokens": "required", "HttpEndpoint": "enabled"}
}'
# 2. Create ASG with mixed-instances (Spot + On-Demand diversification)
aws autoscaling create-auto-scaling-group \
--auto-scaling-group-name my-asg \
--min-size 0 --max-size 20 --desired-capacity 2 \
--vpc-zone-identifier "subnet-111,subnet-222" \
--mixed-instances-policy '{
"LaunchTemplate": {
"LaunchTemplateSpecification": {"LaunchTemplateName": "my-lt", "Version": "$Latest"},
"Overrides": [
{"InstanceType": "c5.xlarge"},
{"InstanceType": "c5.2xlarge"},
{"InstanceType": "c5a.xlarge"}
]
},
"InstancesDistribution": {
"OnDemandBaseCapacity": 0,
"OnDemandPercentageAboveBaseCapacity": 0,
"SpotAllocationStrategy": "capacity-optimized"
}
}'
# Create volume
aws ec2 create-volume \
--availability-zone us-east-1a \
--size 100 \
--volume-type gp3 \
--iops 3000 \
--throughput 125 \
--encrypted
# Attach to instance
aws ec2 attach-volume \
--volume-id vol-12345678 \
--instance-id i-1234567890abcdef0 \
--device /dev/sdf
# Create snapshot
aws ec2 create-snapshot \
--volume-id vol-12345678 \
--description "Daily backup"
| Command | Description |
|---------|-------------|
| aws ec2 run-instances | Launch instances |
| aws ec2 describe-instances | List instances |
| aws ec2 start-instances | Start stopped instances |
| aws ec2 stop-instances | Stop running instances |
| aws ec2 reboot-instances | Reboot instances |
| aws ec2 terminate-instances | Terminate instances |
| aws ec2 modify-instance-attribute | Modify instance settings |
| Command | Description |
|---------|-------------|
| aws ec2 create-security-group | Create security group |
| aws ec2 describe-security-groups | List security groups |
| aws ec2 authorize-security-group-ingress | Add inbound rule |
| aws ec2 revoke-security-group-ingress | Remove inbound rule |
| aws ec2 authorize-security-group-egress | Add outbound rule |
| Command | Description |
|---------|-------------|
| aws ec2 describe-images | List AMIs |
| aws ec2 create-image | Create AMI from instance |
| aws ec2 copy-image | Copy AMI to another region |
| aws ec2 deregister-image | Delete AMI |
| Command | Description |
|---------|-------------|
| aws ec2 create-volume | Create EBS volume |
| aws ec2 attach-volume | Attach to instance |
| aws ec2 detach-volume | Detach from instance |
| aws ec2 create-snapshot | Create snapshot |
| aws ec2 modify-volume | Resize/modify volume |
# Require IMDSv2 on existing instance
aws ec2 modify-instance-metadata-options \
--instance-id i-1234567890abcdef0 \
--http-tokens required \
--http-endpoint enabled
First: identify the error type — it points to different root causes:
| Error | What it means | Primary suspects |
|-------|--------------|-----------------|
| Connection refused | Network is reachable, but SSH daemon is not listening | sshd crashed, sshd not installed, OS firewall (ufw/iptables) blocking, wrong port |
| Connection timed out | Packets never arrive | Security group blocks port 22, NACL blocks traffic, no public IP, wrong IP |
| Permission denied | Connected, but auth failed | Wrong key file, wrong username, key not authorized |
Common username by OS:
| OS | Default SSH user |
|----|-----------------|
| Amazon Linux 2 / 2023 | ec2-user |
| Ubuntu | ubuntu |
| Debian | admin |
| CentOS / RHEL | ec2-user or centos |
| Windows | Administrator |
Diagnostic commands:
# 1. Check instance state and public IP
aws ec2 describe-instances \
--instance-ids i-1234567890abcdef0 \
--query "Reservations[].Instances[].{State:State.Name,PublicIP:PublicIpAddress,StatusChecks:State.Name}"
# 2. Check instance status (system + instance checks)
aws ec2 describe-instance-status --instance-ids i-1234567890abcdef0
# 3. Check security group rules for port 22
aws ec2 describe-security-groups \
--group-ids sg-12345678 \
--query "SecurityGroups[].IpPermissions[?ToPort==\`22\`]"
# 4. Get console output to see boot logs, sshd errors, OOM events
aws ec2 get-console-output \
--instance-id i-1234567890abcdef0 \
--latest \
--query Output --output text
If connection refused — get inside via Session Manager to fix sshd:
# Requires SSM agent on instance + AmazonSSMManagedInstanceCore policy
aws ssm start-session --target i-1234567890abcdef0
# Once inside, diagnose:
systemctl status ssh # Ubuntu
systemctl status sshd # Amazon Linux
df -h # Check disk full
sudo sshd -t # Test sshd config for syntax errors
sudo journalctl -u ssh -n 50 # Recent sshd logs
Use Session Manager instead of SSH (no open ports, no key pair needed):
aws ssm start-session --target i-1234567890abcdef0
# Port forwarding via SSM
aws ssm start-session \
--target i-1234567890abcdef0 \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["22"],"localPortNumber":["2222"]}'
Causes:
# Check instance state reason
aws ec2 describe-instances \
--instance-ids i-1234567890abcdef0 \
--query "Reservations[].Instances[].StateReason"
# Check instance status
aws ec2 describe-instance-status \
--instance-ids i-1234567890abcdef0
# Get console output
aws ec2 get-console-output \
--instance-id i-1234567890abcdef0 \
--latest
# Get screenshot (for Windows/GUI issues)
aws ec2 get-console-screenshot \
--instance-id i-1234567890abcdef0
# Enable detailed monitoring
aws ec2 monitor-instances \
--instance-ids i-1234567890abcdef0
# Check CloudWatch metrics (cross-platform date command)
START=$(date -u -v-1H +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || date -u --date='1 hour ago' +%Y-%m-%dT%H:%M:%SZ)
aws cloudwatch get-metric-statistics \
--namespace AWS/EC2 \
--metric-name CPUUtilization \
--dimensions Name=InstanceId,Value=i-1234567890abcdef0 \
--start-time "$START" \
--end-time "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
--period 300 \
--statistics Average
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take itsmostafa/ec2 from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference apt.
Without those the skill loads but fails at the first command.