| agent: | Auto Exec |
What is an "Expert"? How do we create our own expert?
Add credentials for various integrations
Managing workspaces and access control
DagKnows Architecture Overview
Setting up SSO via Azure AD for Dagknows
Enable "Auto Exec" and "Send Execution Result to LLM" in "Adjust Settings" if desired
(Optionally) Add ubuntu user to docker group and refresh group membership
Deployment of an EKS Cluster with Worker Nodes in AWS
Adding, Deleting, Listing DagKnows Proxy credentials or key-value pairs
Comprehensive AWS Security and Compliance Evaluation Workflow (SOC2 Super Runbook)
AWS EKS Version Update 1.29 to 1.30 via terraform
Instruction to allow WinRM connection
MSP Usecase: User Onboarding Azure + M365
Post a message to a Slack channel
How to debug a kafka cluster and kafka topics?
Open VPN Troubleshooting (Powershell)
Execute a simple task on the proxy
Assign the proxy role to a user
Create roles to access credentials in proxy
Install OpenVPN client on Windows laptop
Setup Kubernetes kubectl and Minikube on Ubuntu 22.04 LTS
Install Prometheus and Grafana on the minikube cluster on EC2 instance in the monitoring namespace
update the EKS versions in different clusters
AI agent session 2024-09-12T09:36:14-07:00 by Sarang Dharmapurikar
Parse EDN content and give a JSON out
Check whether a user is there on Azure AD and if the user account status is enabled
Get the input parameters of a Jenkins pipeline
Filter out all unused/unattached AWS Elastic IP addresses
This step involves searching through regions to identify Elastic IPs that are not currently associated with any instances. By iteratively querying each region's EC2 service, the script collects details about unattached Elastic IPs, including their public IP addresses, allocation IDs, and regions.
- 1c6CFzJzAaTihnk9SROZcRelease unused/unattached AWS Elastic IP addresses
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Release unused/unattached AWS Elastic IP addresses
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.Once the unattached Elastic IPs are identified, this task releases them from the AWS environment. By using the collected allocation IDs and regions, the script communicates with the EC2 service to release each unattached Elastic IP. This process ensures that these IPs are no longer reserved, contributing to cost savings and resource optimization.
inputsoutputsimport boto3 import botocore.exceptions creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] # Function to release an Elastic IP def release_elastic_ip(ec2_client, allocation_id, region): ec2 = boto3.client('ec2', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) try: # Attempt to release the Elastic IP response = ec2.release_address(AllocationId=allocation_id) return f"Released Elastic IP {allocation_id} in region {region}" except botocore.exceptions.BotoCoreError as e: return f"Error releasing Elastic IP {allocation_id} in region {region}: {e}" # List of regions to search for unattached Elastic IPs #regions = ["us-east-1"] # Add your desired regions here if len(unattached_ips) == 0: print("No unattached Elastic IPs found.") else: print("Unattached Elastic IPs:") # Print details for each unattached Elastic IP for ip_info in unattached_ips: print(f"Public IP: {ip_info['public_ip']}, Allocation ID: {ip_info['allocation_id']}, Region: {ip_info['region']}") # Release unattached Elastic IPs for ip_info in unattached_ips: response = release_elastic_ip(boto3.client('ec2'), ip_info['allocation_id'], ip_info['region']) print(response) context.proceed = Falsecopied1 - 2oqRPhDo262rV0rQ4Q0BLList and release all Elastic IPs in all regions
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List and release all Elastic IPs in all regions
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This task is a combination of listing all Elastic IPs and releasing them if they are not associated with any instance all the while looping through all the regions.
Note:- Even though the script is a one time stop for finding unattached Elastic IPs in all regions it takes relatively higher time to complete the process than the script focused on specified regions as it loops through all the regions.
inputsoutputsimport boto3 creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] # Create an EC2 client instance ec2 = boto3.client("ec2",aws_access_key_id=access_key,aws_secret_access_key=secret_key, region_name='us-east-1') # Dictionary to store unused Elastic IPs with their allocation IDs and regions unused_ips = {} #Loop through all regions for region in ec2.describe_regions()["Regions"]: region_name = region["RegionName"] try: # Create an EC2 client for the specific region ec2conn = boto3.client("ec2",aws_access_key_id=access_key,aws_secret_access_key=secret_key, region_name=region_name) # Retrieve all addresses (Elastic IPs) addresses = ec2conn.describe_addresses( Filters=[{"Name": "domain", "Values": ["vpc"]}] )["Addresses"] # Iterate through each address for address in addresses: # Check if the address is not associated with any instance if ( "AssociationId" not in address and address["AllocationId"] not in unused_ips ): # Store the unused Elastic IP's allocation ID and region unused_ips[address["AllocationId"]] = region_name # Release the unused Elastic IP ec2conn.release_address(AllocationId=address["AllocationId"]) print( f"Deleted unused Elastic IP {address['PublicIp']} in region {region_name}" ) except Exception as e: # Handle cases where there's no access to a specific region print(f"No access to region {region_name}: {e}") # Print the summary of deleted unused Elastic IPs print(f"Found and deleted {len(unused_ips)} unused Elastic IPs across all regions:") print(unused_ips)copied2