austinsonger

#!/bin/zsh
###################################################################################################
# Created by Noah Anderson | se@kandji.io | Kandji, Inc. | Systems Engineering
###################################################################################################
# Created on 07/22/2022
###################################################################################################
# Software Information
###################################################################################################
#
# Script downloads two .apps, one for Intel architecture, one for Apple silicon, and

austinsonger

austinsonger

import os
import yaml
import hcl2

# Constants
YAML_FILE_PATH = '<Path to YAML File>'
TERRAFORM_DIRECTORY_PATH = '<Path to Terraform Code>'

def parse_yaml(yaml_file_path):
    """Parse the YAML file containing the AWS Conformance Pack criteria."""

austinsonger

The Nmap NSE script "Authenticated AWS RDS Vulnerability Scanning" is a comprehensive security tool designed for conducting in-depth, authenticated vulnerability assessments on Amazon Web Services (AWS) Relational Database Service (RDS) instances. This script is an integral part of a security professional's toolkit, enabling the identification and evaluation of security vulnerabilities within AWS RDS databases, including popular engines like PostgreSQL and MySQL.

TO-DO

• Ensure the openssl library or any other cryptographic library you plan to use is accessible within the NSE environment. NSE does not natively support openssl, so you might need to adapt or find alternatives.
• External Script Integration: If NSE cannot directly execute the necessary cryptographic functions, consider implementing these in an external script (e.g., Python) and call it from the NSE script.
• Complete Signature Implementation: The functions provided for AWS Signature Version 4 are conceptual. Ensure each step correctly imple

austinsonger

#!/bin/bash

# Function to install Docker for Debian-based distributions
install_docker_debian() {
    # Add Docker’s official GPG key
    curl -fsSL https://download.docker.com/linux/debian/gpg | sudo apt-key add -

    # Set up the stable repository
    echo "deb [arch=amd64] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | \
        sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

austinsonger

#!/bin/bash

# Function to check if Homebrew is installed
check_and_install_brew() {
    if ! command -v brew &> /dev/null
    then
        echo "Homebrew not installed. Installing now..."
        /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
    else
        echo "Homebrew is already installed."

austinsonger

# not an exhaustive list

nsurlsessiond "icloud sync"
fseventsd "macos file system events"
WindowServer "macos windows"
DisplayLinkManager "macos driver"
configd "macos dynamic configuration"
displaypolicyd "macos process"
CommCenter "macos keychain"
kernel_task "macos kernel"

austinsonger

This Python script automates the process of creating draft podcast episodes in Transistor from a CSV file. The script reads episode data (titles and summaries) from a CSV file and uses the Transistor API to create each episode as a draft in your Transistor account.

How it Works

• CSV File Parsing: The script begins by reading a CSV file specified by csv_file_path. This file should contain the episode titles and summaries. It's expected that the first row of the CSV file is a header row, which the script skips.
• Creating Episode Objects: For each row in the CSV file, the script extracts the title and summary, ensuring that both are present. It then creates an episode object containing this data, along with the show ID (show_id).
• Interacting with the Transistor API: The script sends a POST request to the Transistor API for each episode object. The API endpoint (api_url) is set to "https://api.transistor.fm/v1/episodes".
• API Authentication: The script uses an API key (api_key) for authentication. This

austinsonger

"""
Keyword Analysis on specified columns in a spreadsheet

This script performs keyword analysis on specified columns of a spreadsheet.  
It adds a new column listing the top unique keywords for each row based on the content of the specified columns.
"""

import pandas as pd
from collections import Counter
import string

austinsonger

import requests
import json
import os
import time

CLIENT_ID = 'your_client_id'
CLIENT_SECRET = 'your_client_secret'
ACCESS_TOKEN = 'your_access_token'  # Implement OAuth for this
BROADCASTER_ID = 'broadcaster_id'  # Replace with actual broadcaster ID