To work around those rules, the Humanizer skill tells Claude to replace inflated language with plain facts and offers this example transformation:
Before: “The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain.”
After: “The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics.”
Claude will read that and do its best as a pattern-matching machine to create an output that matches the context of the conversation or task at hand.
An example of why AI writing detection fails
Even with such a confident set of rules crafted by Wikipedia editors, we’ve previously written about why AI writing detectors don’t work reliably: There is nothing inherently unique about human writing that reliably differentiates it from LLM writing.
One reason is that even though most AI language models tend toward certain types of language, they can also be prompted to avoid them, as with the Humanizer skill. (Although sometimes it’s very difficult, as OpenAI found in its yearslong struggle against the em dash.)
Also, humans can write in chatbot-like ways. For example, this article likely contains some “AI-written traits” that trigger AI detectors even though it was written by a professional writer—especially if we use even a single em dash—because most LLMs picked up writing techniques from examples of professional writing scraped from the web.
Along those lines, the Wikipedia guide has a caveat worth noting: While the list points out some obvious tells of, say, unaltered ChatGPT usage, it’s still composed of observations, not ironclad rules. A 2025 preprint cited on the page found that heavy users of large language models correctly spot AI-generated articles about 90 percent of the time. That sounds great until you realize that 10 percent are false positives, which is enough to potentially throw out some quality writing in pursuit of detecting AI slop.
Taking a step back, that probably means AI detection work might need to go deeper than flagging particular phrasing and delve (see what I did there?) more into the substantive factual content of the work itself.
