5 AI Automation Scripts So Good, I Stopped Using ClickUp

I used to rely on ClickUp for everything, until these five Python scripts started doing a better job. No SaaS, just raw automation. Here’s how I built them (and why they might just replace your favorite tools too).

Photo by Igor Omilaev on Unsplash

I Didn’t Plan to Replace ClickUp It Just Happened If you’d told me a few months ago that I’d ditch ClickUp for a bunch of Python scripts, I’d have laughed. ClickUp was my digital command center task tracking, deadlines, reminders, notes, workflows. All in one place. But here’s the thing: I started noticing I was spending more time managing my productivity tools than actually doing the work.

That’s when I did what any stubborn automation nerd would do: I built five AI-powered Python scripts to handle the essentials. They don’t just save me time, they think for me.

This post isn’t just about what I built. It’s about why building these tools made me more productive than any fancy SaaS ever did. Let’s get into it.

1. Smart Daily Planner Bot

Problem: ClickUp’s “daily agenda” is fine, but it lacks real context. I wanted a script that understands what I’m working on, not just when.

Solution: A smart scheduler that reviews your tasks, context-switches based on mental fatigue, and reorganizes your plan accordingly. Libraries used:

pandas
datetime
nltk (for task context analysis)
tkinter (for a lightweight UI)

How It Works:

You feed in tasks with estimated durations and rough descriptions.
It scores each task based on complexity using NLP.
Then it auto-generates a daily plan starting with easier tasks and ramps up focus-heavy ones.

import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
from datetime import datetime, timedelta

nltk.download('punkt')

tasks = pd.DataFrame({
    "task": ["Write Medium article", "Fix Python bug", "Research new AI libraries"],
    "duration_min": [60, 30, 45]
})

# Complexity scoring based on word count
def complexity_score(task_desc):
    words = word_tokenize(task_desc)
    return len(words)

tasks["score"] = tasks["task"].apply(complexity_score)
tasks = tasks.sort_values("score")

start_time = datetime.now()
for _, row in tasks.iterrows():
    print(f"{start_time.time()} - {row['task']} ({row['duration_min']} mins)")
    start_time += timedelta(minutes=row["duration_min"])

This script doesn’t just schedule it understands the workload.

2. Meeting Summarizer That Actually Works

Problem: I was manually summarizing my own Zoom meetings. That’s right copying notes from a transcript into ClickUp like a caveman.

Solution: An offline script that processes .txt transcripts and summarizes in bullet format. No OpenAI API needed.

Libraries used:

sumy (for extractive summarization)
argparse (for CLI use)
textwrap

from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer
import textwrap

def summarize_text(file_path, num_sentences=5):
    parser = PlaintextParser.from_file(file_path, Tokenizer("english"))
    summarizer = LsaSummarizer()
    summary = summarizer(parser.document, num_sentences)
    for sentence in summary:
        print("•", textwrap.fill(str(sentence), width=80))

# usage
summarize_text("meeting_transcript.txt")

Want to store the summaries in a markdown file instead? Append them using Python’s built-in file methods. You don’t need another app for that.

3. Focus Tracker With Real-Time Alerts

Problem: ClickUp tells me I worked 4 hours. Cool. But how much of that was actual focus time?

Solution: A desktop-based focus timer that monitors keyboard and mouse activity, logs idle time, and sends you a notification when you’ve been AFK for too long.

Libraries used:

pynput
time
plyer (for cross-platform notifications)

from pynput import mouse, keyboard
from plyer import notification
import time

last_active = time.time()

def on_event(x):
    global last_active
    last_active = time.time()

mouse.Listener(on_click=on_event).start()
keyboard.Listener(on_press=on_event).start()

while True:
    if time.time() - last_active > 600:  # 10 minutes of inactivity
        notification.notify(
            title="Focus Alert",
            message="You've been inactive for a while. Time to get back?",
            timeout=10
        )
    time.sleep(60)

This script actually knows when you’re zoning out. ClickUp doesn’t.

4. Weekly Task Digest Emailer

Problem: ClickUp sends weekly summaries, but they feel robotic and miss key tasks.

Solution: A Python script that pulls from your local task database and generates a human-like summary sent to your inbox every Sunday. Libraries used:

smtplib
email.message
jinja2 (for email templating)
sqlite3 (task store)

from email.message import EmailMessage
import smtplib
import sqlite3

conn = sqlite3.connect("tasks.db")
cursor = conn.cursor()
cursor.execute("SELECT title, status FROM tasks WHERE week = 'current'")
tasks = cursor.fetchall()

body = "\n".join([f"- {t[0]} ({t[1]})" for t in tasks])

msg = EmailMessage()
msg.set_content(f"Here’s what you did this week:\n\n{body}")
msg["Subject"] = "Weekly Digest"
msg["From"] = "[email protected]"
msg["To"] = "[email protected]"

with smtplib.SMTP("smtp.gmail.com", 587) as server:
    server.starttls()
    server.login("[email protected]", "yourpassword")
    server.send_message(msg)

ClickUp sends data. This script sends a narrative.

5. Dynamic Priority Shuffler

Problem: My priorities change every day, but ClickUp makes reordering them manually a drag.

Solution: A script that uses task metadata (urgency, due date, status) to reshuffle tasks every morning and save the list as a sorted CSV or display it in terminal. Libraries used:

* 		csv
* 		tabulate
* 		datetime
import csv
from datetime import datetime
from tabulate import tabulate

with open("tasks.csv") as f:
    reader = csv.DictReader(f)
    tasks = sorted(reader, key=lambda x: (x["urgency"], x["due_date"]))

print(tabulate(tasks, headers="keys", tablefmt="fancy_grid"))

You could easily wire this into your terminal startup script. That’s right your CLI becomes your ClickUp. Final Thoughts: Why Build When ClickUp Already Exists? Because productivity software isn’t designed around you. It’s designed around one-size-fits-all workflows.

These five scripts:

Let me move faster
Reduce friction
Fit my unique working style

And let’s be honest: it’s just more fun. If you want to level up your automation skills, don’t start by learning tools. Start by solving annoyances.

Read the full article here: https://python.plainenglish.io/5-ai-automation-scripts-so-good-i-stopped-using-clickup-3ab78e8b9ff4