This is a follow-up for a skill I made based on the [breakdown I posted](https://www.reddit.com/r/ClaudeAI/comments/1ucpw87/i_pulled_90000_reddit_posts_about_what_makes/) of \~90,000 Reddit posts on what people actually flag as AI-written text. People asked for a tool they could use, and I had built it into a Claude skill, so this post is dedicated to that.

Unslop-text is built strictly on that data. The ranking is based on volume, where em dashes are at the top because they were the most cited tell in the corpus, well ahead of any specific buzzword. This ensures that the target is placed on the giveaways that trigger people most often.

The scanner is a plain Python script that catches surface stuff like em dashes, "as an AI language model," diction memes, and formatting tics. It runs in CI and gives you a slop score per file. But most of the strongest tells in the data are structural, like uniform sentence rhythm or a paragraph that sounds fluent and says nothing. No regex is going to catch those. So the skill flags them for a "read-aloud pass" so you can verify it yourself.

It is not a detector, and it has no "house style." It strips the tells and makes you commit to a voice (that way the onus is on *you* to come up with your desired style rather than having it inevitably default to the same AI-isms). It is the same data as the original post, just repurposed for something usable in your own work.

Let me know if you have any recommendations or questions!

Repo and scanner: [https://github.com/JCarterJohnson/vibecoded-design-tells/tree/main/unslop-ai-text](https://github.com/JCarterJohnson/vibecoded-design-tells/tree/main/unslop-ai-text) (under /unslop-ai-text)

unslop-text: a Claude skill that flags and removes the patterns that make writing read as AI-generated.