The AI Paradox: When Detection Becomes Emulation
In a fascinating twist of technological irony, a new open-source plug-in is leveraging a Wikipedia-curated guide for detecting AI writing to achieve the exact opposite: making AI models sound more human. Tech entrepreneur Siqi Chen recently unveiled “Humanizer,” a simple yet ingenious prompt plug-in for Anthropic’s Claude Code AI assistant. Its mission? To instruct the AI to shed its tell-tale robotic linguistic patterns and adopt a more natural, human-like voice.
The Humanizer, which has rapidly garnered over 1,600 stars on GitHub since its release, feeds Claude a comprehensive list of 24 language and formatting patterns identified by Wikipedia editors as definitive signs of chatbot-generated text. As Chen succinctly put it on X, “It’s really handy that Wikipedia went and collated a detailed list of ‘signs of AI writing.’ So much so that you can just tell your LLM to … not do that.”
The Wikipedia Blueprint for AI Detection
The foundation of Humanizer lies in the diligent work of WikiProject AI Cleanup, a dedicated group of Wikipedia editors who have been on the front lines of identifying AI-generated articles since late 2023. Founded by French Wikipedia editor Ilyas Lebleu, this volunteer collective has flagged more than 500 articles for review, culminating in the formal publication of their observed AI writing patterns in August 2025.
Spotting the Patterns: What AI Writing Looks Like
The Wikipedia guide offers specific examples of common AI linguistic quirks. Chatbots, for instance, frequently employ inflated, promotional language, using phrases such as “marking a pivotal moment” or “stands as a testament to.” They often adopt a tourism brochure-like tone, describing views as “breathtaking” or towns as “nestled within” scenic locales. Another common tell is the overuse of “-ing” phrases tacked onto the end of sentences to feign analytical depth, like “symbolizing the region’s commitment to innovation.”
The Humanizer directly counters these habits. For example, it instructs Claude to transform verbose statements into plain facts:
- 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.”
By internalizing these rules, Claude, as a sophisticated pattern-matching machine, endeavors to produce output that aligns with a more human conversational context.
The ‘Humanizer’ in Action: A Double-Edged Sword
Chen’s Humanizer operates as a “skill file” for Claude Code, Anthropic’s terminal-based coding assistant. This Markdown-formatted file appends a list of written instructions to the prompt fed into the large language model, allowing Claude to interpret them with enhanced precision compared to a standard system prompt. (It’s worth noting that custom skills require a paid Claude subscription with code execution enabled.)
While early testing suggests the Humanizer can indeed make AI output sound less precise and more casual, its application isn’t without potential drawbacks. It’s crucial to understand that this tool won’t inherently improve the factuality of AI-generated content and could even compromise coding ability in certain scenarios.
Some of Humanizer’s instructions, while aiming for human-like imperfection, might lead users astray depending on the task. For instance, the skill file includes the directive: “Have opinions. Don’t just report facts—react to them. ‘I genuinely don’t know how to feel about this’ is more human than neutrally listing pros and cons.” While this advice fosters a more relatable tone, it would be counterproductive for tasks requiring objective, technical documentation.
The Elusive Quest for AI Writing Detection
The very existence of Humanizer underscores a fundamental challenge in the realm of AI: the inherent difficulty in reliably distinguishing human writing from that of large language models. As previous analyses have shown, there is no intrinsically unique quality in human writing that definitively separates it from sophisticated LLM output.
One key reason is that while AI models often gravitate towards certain linguistic patterns, they can also be explicitly prompted to avoid them, as demonstrated by the Humanizer. (OpenAI’s prolonged struggle with the em dash, for example, highlights how deeply ingrained some patterns can become.)
Furthermore, humans themselves can inadvertently adopt chatbot-like writing styles. An article penned by a professional writer might still trigger AI detectors due to stylistic choices or common phrases, especially since LLMs are trained on vast corpora of human-written professional texts. The Wikipedia guide itself acknowledges this, noting that its list comprises observations rather than infallible rules.
A 2025 preprint cited on the guide’s page indicates that heavy LLM users can correctly identify AI-generated articles about 90 percent of the time. While impressive, a 10 percent false positive rate is significant enough to potentially dismiss high-quality human writing in the pursuit of filtering out AI-generated “slop.” This ongoing digital cat-and-mouse game highlights the complex and evolving relationship between AI generation, detection, and the increasingly blurred lines of digital authorship.
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