How to Make a Twitter Bot That Writes and Posts Content for You

Let’s be honest. Twitter (sorry, X) is still the town square.

If you're building something, selling something, or just trying to stay relevant, posting smart content matters.

It keeps you visible. It builds trust. It pulls people in.

But doing it every single day? Nobody has the time or the will to keep sounding insightful every day.

That’s why I built an AI-powered Twitter bot to do it for me.

In this guide, I’ll show you exactly how I built it in 5 simple steps.

But first, let’s talk about what a Twitter bot actually is.

What’s a Twitter bot?

At its simplest, it’s software that posts on your behalf. But that’s baby bot stuff.

What’s special about this bot? It doesn’t just post. It thinks.

It scrapes viral content ideas, builds a content calendar, writes posts, schedules everything, and publishes without you lifting a finger.

It’s basically your unpaid social media intern who never sleeps and doesn’t ask for stipend money.

Why would you even need one?

A bot solves that. It saves time, saves money, and keeps you visible while you do literally anything else.

Cut the crap, you want the bot, you know why. Let’s build the damn thing.

How to make a Twitter bot that writes and posts content for you?

So what exactly are we building? A bot that does everything.

It finds content ideas from trends and top accounts → builds a content calendar → writes the posts → schedules and publishes them.

Here’s what you’ll need to pull this off:

1. Lobstr.io for scraping content from X/Twitter
2. ChatGPT for analyzing data, generating calendars, and writing tweets
3. Make.com for automating the whole workflow

Ready? Let’s start with how the bot finds content ideas in the first place.

Finding content that performs

Before your bot can write anything, it needs to know what’s already working in your niche.

That means studying content that performs, not just randomly tweeting into the void and hoping for likes from your mom and one crypto bot.

The first step is figuring out what kinds of posts get traction.

You can do it by:

1. Analyzing tweets from top accounts in your niche
2. Monitoring trends and topics people actually engage with
3. Spotting patterns in post formats, tone, timing, and topics

This is where people usually stop. Because collecting and analyzing that kind of data manually is a pain.

So first thing first, let’s automate it.

We basically have to do 2 things:

1. Collect Tweets data
2. Ask AI to analyze them
3. Automate the process

1. Collecting tweets data with Lobstr.io

To collect tweets, we’ll use Lobstr.io, a no-code scraper we built so you’d never have to manually copy a tweet into a spreadsheet again.

It runs in the cloud, exports data straight into Google Sheets, and once it’s live, it works for you 24/7 without maintenance.

You can use two of Lobstr’s scrapers to collect Tweets data:

1. Twitter User Tweets Scraper
2. Twitter Search Results Scraper

Twitter User Tweets Scraper

Use this to collect tweets from specific accounts like your competitors, niche leaders, or that one SEO guy who posts viral threads about indexing strategies like it’s performance art.

You just drop in their usernames or profile links, and Lobstr will pull:

1. Tweet content
2. Likes, views, replies, quotes
3. Publish date and time
4. And much more

You can schedule it to run hourly, daily, weekly or monthly, so you're always tracking the freshest content in your niche.

Perfect if you:

1. Want to model your content strategy after what’s already working
2. Need to keep tabs on what your audience is already liking
3. Are too lazy to do manual competitor research (like me)

Twitter Search Results Scraper

This one’s for hunting trends or tracking topics.

Let’s say you’re in the SEO space and want to monitor tweets around technical SEO, content optimization, or Google algorithm updates.

You can plug those keywords or hashtags into the scraper, and it’ll return top and/or latest tweets.

You’ll get:

1. Tweet content
2. Engagement metrics (likes, replies, quotes, views, bookmarks)
3. Rank/position in the search results
4. Post date
5. And much more

This gives you real insight into what’s trending, what kind of content format is performing, and what people are actually talking about right now.

For example, I used the Twitter Search Results Scraper to track what people were posting about lead generation.

Not the top tweets, but actual replies, questions, and opinions. That way, I wasn’t just scraping surface-level engagement, I was collecting conversations.

To make the search even more useful, I filtered it with Twitter Advanced Search to show only replies posted during last month.

Wondering how to do that? Yeah, I’ve got you covered.

Replies are gold if you’re trying to understand what your audience cares about, struggles with, or rants about at 2am.

They give context, unique angles, and content ideas that don’t sound like recycled guru advice.

But how do you collect those replies with Lobstr.io?

Glad you asked.

I’ve already written a detailed walkthrough on scraping Twitter search results—no coding required:

All you need to do is:

1. Sync your Twitter account
2. Copy the search URL from Twitter
3. Feed it to the Twitter Search Results Scraper

The bot takes care of the rest.

If you want to stay on top of the same topic over time, you can use Lobstr’s schedule feature to collect the latest tweets on a recurring basis.

Set it once, and it’ll keep pulling fresh results without you lifting a finger.

But what if you want to filter by date instead of tweet count?

Lobstr doesn’t support that directly (yet), but I figured out a solid workaround. I asked Claude to write a small script that generates Twitter search URLs with custom date ranges.

Yep, turns out AI is good for something after all.

How do you use that script with Lobstr?

But how do I use the API?

You already know what I’m going to say. I have written a detailed, python guide for it.

Can’t code? That’s adorable. It’s 2025.

It will write the code for you.

In short, whatever method you choose, this is one time setup and you won’t be doing any of this ever again (unless you want to tweak something).

Next, let’s find the actual content ideas from this data.

2. Using AI to analyze data and find content ideas

Now that we’ve scraped the tweets and dumped them into a lovely Google Sheet, it’s time to bring in AI to do what humans hate most i.e. analysis.

In my experience, they handle structured analysis more accurately and give cleaner outputs without going off the rails.

First thing first, we need a prompt!

You can scrape all the data you want, but if your prompt sucks, so will your content ideas.

A solid prompt has four core parts:

1. Role — Who should the AI pretend to be?
2. Task — What do you actually want it to do?
3. Process — How should it approach the task?
4. Output — What should the final output look like?

Here’s the prompt I used to get content ideas out of raw tweet data.

You are a Twitter analytics strategist and social media manager.

You're analyzing a batch of tweets with engagement metrics. Your job is to extract the dominant themes and turn them into fresh content ideas for the company's account.

Look at the full set of tweets as a whole. Spot what worked. Find the angles that drove engagement.

Then generate 3 to 5 new content ideas based on your analysis.
For each one, include the best format, tone, and style it should be written in.

Only output the ideas. One line per idea. No extra commentary.
Each line should follow this format:
<Idea> — Format: <format> | Tone: <tone> | Style: <style>
f

Still unsure if your prompt is any good? Ask the AI to refine it.

Literally just say, “Improve this prompt to get better analysis from you” and paste what you have.

For example, since I refined the prompt for my bot like this:

But where’s automation?

3. Automate the process

I’m not about to manually upload tweet data into an AI tool every week.

This is where workflow automation comes in.

You can use tools like n8n or Make.com to connect Google Sheets, and your AI model so the whole thing runs on autopilot.

And yes, because I clearly have no concept of boundaries or chill, I wrote an entire blog ranking workflow automation tools.

For this tutorial, I’m using Make.com.

Originally, I built the first version of this bot with n8n, and honestly, I love it.

But it has the learning curve of a brick wall.

So for now, I’m going with something a bit more beginner-friendly.

Make.com has a clean interface, a solid visual builder, and doesn’t make you feel like you’re debugging the Matrix just to run a workflow.

The workflow building is easy.

1. Create a new scenario
2. Add a Google Sheet module
3. Choose Watch New Rows
4. Sync your Google account
5. Choose the Google Sheet synced to Lobstr.io
6. Select the Sheet name and save it.

Choose the AI model and add the prompt you’ve built in the message section.

The prompt we generated was a system prompt. It acts as the rule book for our AI agent. We’ll supply column names to look at in the user prompt.

1. Under messages, select Model as role.
2. Choose message type `Text
3. Add your prompt in the text box.

This sheet will store the output from our AI model. You can either create a new Spreadsheet or add a new Sheet inside your existing Spreadsheet.

And we’re ready. Let’s run it and test if our bot works till this step.

Bravo! The workflow works!

Now let’s move to the next stage i.e. writing tweets on auto-pilot.

Writing and scheduling Tweets with AI

Once the content calendar is ready, it’s time to write the actual tweets and get them posted, without touching Twitter yourself.

This part is fully automated.

AI will write the tweets, and Make.com will publish them for you on schedule. All you need to do is connect the dots once.

Let’s do the writing part first.

4. Writing Tweets with AI

Inside Make, just add a new OpenAI (ChatGPT) module to your scenario.

Then we have to add a system and user prompt.

Here's the system prompt I used:

You are a Twitter content strategist with expertise in writing high-performing tweets for B2B and startup audiences.
Your job is to take a content idea with format, tone, and style already included, and write one strong tweet based on it. Keep the tweet under 280 characters total, including any hashtags.
Do not include explanations, hashtags spam, or variations. Just write one clean, post-ready tweet that aligns with the instructions.
f

Let’s run it to confirm…

And… It works!

Now comes the most interesting part i.e. publishing content to X/Twitter.

5. Publishing and scheduling Tweets

So, our tweets are written. Now it's time to actually publish them, automatically, of course.

There are a couple ways to post content to X inside a Make.com workflow.

1. Use the official Twitter API
2. Use a Twitter post scheduling tool

You can use the official Twitter/X API.

If you enjoy begging for API keys, reading complicated API docs, handling tokens, and debugging random 403 errors, knock yourself out.

The better route? Use a social media scheduler.

Tools like Buffer, Hypefury, Typefully, and friends let you schedule and manage posts across platforms without writing a single line of code.

My personal pick is Buffer. Why?

1. It supports multiple social media platforms (X, LinkedIn, Insta, etc.)
2. It’s free
3. It integrates directly with Make.com

And most importantly, I didn’t feel like writing custom API calls just to post a tweet

But how to use Make.com with Buffer?

It’s simple!

First, connect your X account to Buffer. It only takes a couple of clicks and you’ll never have to touch the Twitter interface again.

Then, inside Make.com, search for Buffer in the list of apps and connect it using OAuth. No API keys, no access token secret, just log in and you’re done.

After that, add a Buffer module to your scenario. You can choose how you want the tweet to be published; either post it instantly or add it to your queue.

Map the tweet text from your AI module as the content input, save the scenario, and it’s ready to fire.

And that’s it.

But how do I schedule my posts?

You can use Buffer’s built-in schedule if you want to manage posting frequency inside Buffer.

But I prefer Make.com’s built-in schedule trigger to control the entire pipeline from scraping to publishing.

Just go to the Make scenario settings, pick your preferred interval (daily, weekly, hourly), and let it roll

If you're using Lobstr.io’s scheduling feature, time your Make workflow to run shortly after the tweet data gets scraped and exported to Sheets.

That way, everything stays in sync.

If you’re on n8n instead of Make, you can even trigger your workflow from a webhook tied to Lobstr’s email notifications.

Let’s test the final step of our workflow.

And it works!

Once done, simply save the workflow and you’re all set.

We’ve now got a bot that finds content, writes tweets, and publishes them without you lifting a finger.

What’s next?

Test the workflow before going live.

Use dummy ideas, see how the AI performs, and tweak prompts as needed.

Monitor your content.

If the tweets start sounding weird, refine your prompt or add a review step.

Scale it up.

Add more sources, diversify prompts, or generate content for multiple accounts.

Want threads, images, or LinkedIn posts? You can expand the bot to do that too. Same structure, just more modules.

You can also add functionalities like send direct messages, retweet tweets, and even add a AI chatbot.

But how much does this whole setup cost?

Is making a Twitter aka X bot free?

Not entirely. But the price you pay is way cheaper than hiring someone to write tweets and forget to post them.

Here’s what this setup actually costs:

If you're looking to save a bit more, here are your options:

Use Gemini API instead of OpenAI. It’s currently free and works well for basic analysis and tweet generation. (But it occasionally hallucinates and doesn’t follow instructions).

Switch from Make.com to n8n, a self-hosted automation tool. It’s more complex but works fine on a $4/month VPS.

So no, it’s not 100% free. But for less than $100/month, you get a full content pipeline that writes, schedules, and posts tweets like it’s getting paid.

Now before wrapping up, let me answer some FAQs.

FAQs

Are Twitter bots illegal?

No Twitter automation is fully legal.

Automating tweets or collecting public data is allowed, as long as you’re not spamming, impersonating, or doing anything shady.

This setup just analyzes public tweets and generates original content.

How do you create Twitter Bots with Python?

All services/tools used in this setup offer APIs and documentations in multiple programming languages.

You can also use Twitter's own API with Tweepy to create a custom Twitter bot.

But you'll need a Twitter Developers account and inform Twitter about your use case. Plus it's super costly at scale.

Conclusion

That’s a wrap on my step-by-step guide on: How to make a Twitter bot that writes and posts content for you.