Automate Task Scheduling from Calendar Events with Python CLI

If you've ever found yourself manually adding tasks to your to-do list from calendar events, reminders, or deadlines, you're not alone. This process is time-consuming and error-prone. Fortunately, there's a way to automate this using a simple Python command-line tool that reads your calendar data and converts it into a structured to-do list. In this article, we'll walk through how to use a self-contained Python CLI tool that does just that, and how you can implement similar logic in your own scripts.

The tool, AutoTask Scheduler, is a lightweight Python 3 script that reads from a .json or .csv file and outputs a clean .json file containing your tasks. It has no dependencies on cloud services or API keys, making it ideal for local automation. Let's dive into how you can use it and how you can build similar functionality in your own projects.

How the Tool Works

At its core, the tool processes an input file, which could be a list of calendar events or reminders, and transforms it into a structured to-do list. The key CLI options are:

• --input: Path to your input file (.json or .csv)
• --output: Path to write the results (.json)
• --verbose: Print progress to stderr

These options allow you to control the input and output locations and enable detailed logging for debugging.

Here's a simple example of how the tool might process a .json file:

import json

def process_events(events):
    tasks = []
    for event in events:
        task = {
            "title": event.get("summary", ""),
            "due_date": event.get("end", {}).get("date", ""),
            "priority": "medium"
        }
        tasks.append(task)
    return tasks

if __name__ == "__main__":
    input_path = "events.json"
    output_path = "tasks.json"
    with open(input_path, "r") as f:
        events = json.load(f)
    tasks = process_events(events)
    with open(output_path, "w") as f:
        json.dump(tasks, f, indent=2)
    print(f"Tasks saved to {output_path}")

This script reads a JSON file of calendar events, processes them into a to-do list, and writes the output to a new file. You can extend this logic to handle different input formats, add more metadata, or integrate with other systems.

Implementing Similar Logic

Even if you're not using AutoTask Scheduler, understanding how to parse and transform data from various sources is a valuable skill. Let's look at how you might handle a .csv file with similar data.

import csv

def process_events_from_csv(csv_file):
    tasks = []
    with open(csv_file, "r") as f:
        reader = csv.DictReader(f)
        for row in reader:
            task = {
                "title": row.get("Summary", ""),
                "due_date": row.get("End Date", ""),
                "priority": row.get("Priority", "medium")
            }
            tasks.append(task)
    return tasks

if __name__ == "__main__":
    csv_file = "events.csv"
    tasks = process_events_from_csv(csv_file)
    print("Tasks:")
    for task in tasks:
        print(task)

This script reads a CSV file, extracts the relevant fields, and prints the resulting tasks. You can modify it to write to a file or integrate with a database.

Why This Matters

Automating task scheduling from calendar events is more than just a convenience—it's a way to reduce cognitive load and improve productivity. By using a tool like AutoTask Scheduler, you can focus on the tasks themselves rather than the mechanics of task creation.

If you're looking for a self-contained, no-frills solution that runs from the command line and handles malformed input gracefully, you'll find that AutoTask Scheduler meets the mark. It's designed to be used in environments where you don't want to rely on external services or APIs.

For more information and to download the tool, visit https://intellitools.gumroad.com/l/autotask-scheduler.