ranbot-ai/awesome-skills

aws-cost-cleanup

Automated cleanup of unused AWS resources to reduce costs

First seen Apr 21, 2026

Installation

$ npx skills add ranbot-ai/awesome-skills --skill aws-cost-cleanup

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Agent compatibility

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Repository health

Stars 6
License MIT
Default branch main
Open issues 3
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents antigravity

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,319 B
  • docs SUMMARY.md 81 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

AWS Cost Cleanup

Automate the identification and removal of unused AWS resources to eliminate waste.

When to Use This Skill

Use this skill when you need to automatically clean up unused AWS resources to reduce costs and eliminate waste.

Automated Cleanup Targets

Storage

  • Unattached EBS volumes
  • Old EBS snapshots (>90 days)
  • Incomplete multipart S3 uploads
  • Old S3 versions in versioned buckets

Compute

  • Stopped EC2 instances (>30 days)
  • Unused AMIs and associated snapshots
  • Unused Elastic IPs

Networking

  • Unused Elastic Load Balancers
  • Unused NAT Gateways
  • Orphaned ENIs

Cleanup Scripts

Safe Cleanup (Dry-Run First)

#!/bin/bash
# cleanup-unused-ebs.sh

echo "Finding unattached EBS volumes..."
VOLUMES=$(aws ec2 describe-volumes \
  --filters Name=status,Values=available \
  --query 'Volumes[*].VolumeId' \
  --output text)

for vol in $VOLUMES; do
  echo "Would delete: $vol"
  # Uncomment to actually delete:
  # aws ec2 delete-volume --volume-id $vol
done
#!/bin/bash
# cleanup-old-snapshots.sh

CUTOFF_DATE=$(date -d '90 days ago' --iso-8601)

aws ec2 describe-snapshots --owner-ids self \
  --query "Snapshots[?StartTime<='$CUTOFF_DATE'].[SnapshotId,StartTime,VolumeSize]" \
  --output text | while read snap_id start_time size; do
  
  echo "Snapshot: $snap_id (Created: $start_time, Size: ${size}GB)"
  # Uncomment to delete:
  # aws ec2 delete-snapshot --snapshot-id $snap_id
done
#!/bin/bash
# release-unused-eips.sh

aws ec2 describe-addresses \
  --query 'Addresses[?AssociationId==null].[AllocationId,PublicIp]' \
  --output text | while read alloc_id public_ip; do
  
  echo "Would release: $public_ip ($alloc_id)"
  # Uncomment to release:
  # aws ec2 release-address --allocation-id $alloc_id
done

S3 Lifecycle Automation

# Apply lifecycle policy to transition old objects to cheaper storage
cat > lifecycle-policy.json <<EOF
{
  "Rules": [
    {
      "Id": "Archive old objects",
      "Status": "Enabled",
      "Transitions": [
        {
          "Days": 90,
          "StorageClass": "STANDARD_IA"
        },
        {
          "Days": 180,
          "StorageClass": "GLACIER"
        }
      ],
      "NoncurrentVersionExpiration": {
        "NoncurrentDays": 30
      },
      "AbortIncompleteMultipartUpload": {
        "DaysAfterInitiation": 7
      }
    }
  ]
}
EOF

aws s3api put-bucket-lifecycle-configuration \
  --bucket my-bucket \
  --lifecycle-configuration file://lifecycle-policy.json

Cost Impact Calculator

#!/usr/bin/env python3
# calculate-savings.py

import boto3
from datetime import datetime, timedelta

ec2 = boto3.client('ec2')

# Calculate EBS volume savings
volumes = ec2.describe_volumes(
    Filters=[{'Name': 'status', 'Values': ['available']}]
)

total_size = sum(v['Size'] for v in volumes['Volumes'])
monthly_cost = total_size * 0.10  # $0.10/GB-month for gp3

print(f"Unattached EBS Volumes: {len(volumes['Volumes'])}")
print(f"Total Size: {total_size} GB")
print(f"Monthly Savings: ${monthly_cost:.2f}")

# Calculate Elastic IP savings
addresses = ec2.describe_addresses()
unused = [a for a in addresses['Addresses'] if 'AssociationId' not in a]

eip_cost = len(unused) * 3.65  # $0.005/hour * 730 hours
print(f"\nUnused Elastic IPs: {len(unused)}")
print(f"Monthly Savings: ${eip_cost:.2f}")

print(f"\nTotal Monthly Savings: ${monthly_cost + eip_cost:.2f}")
print(f"Annual Savings: ${(monthly_cost + eip_cost) * 12:.2f}")

Automated Cleanup Lambda

import boto3
from datetime import datetime, timedelta

def lambda_handler(event, context):
    ec2 = boto3.client('ec2')
    
    # Delete unattached volumes older than 7 days
    volumes = ec2.describe_volumes(
        Filters=[{'Name': 'status', 'Values': ['available']}]
    )
    
    cutoff = datetime.now() - timedelta(days=7)
    deleted = 0
    
    for vol in volumes['Volumes']:
        create_time = vol['CreateTime'].replace(tzinfo=None)
        if create_time < cutoff:
            try:
                ec2.delete_volume(VolumeId=vol['VolumeId'])
                deleted += 1
                print(f"Deleted volume: {vol['VolumeId']}")
            except Exception as e:
                print(f"Error deleting {vol['VolumeId']}: {e}")
    
    return {
        'statusCode': 200,
        'body': f'Deleted {deleted} volumes'
    }

Cleanup Workflow

  1. Discovery Phase (Read-only)

- Run all describe commands - Generate cost impact report - Review with team

  1. Validation Phase

- Verify resources are truly unused - Check for dependencies - Notify resource owners

  1. Execution Phase (Dry-run first)

- Run cleanup scripts with dry-run - Review proposed changes - Execute actual cleanup

  1. Verification Phase

- Confirm deletions - Monitor for issues - Document savings

Safety Checklist

  • Run in dry-run mode first
  • Verify resources have no dependencies
  • Check re