Wikipedia's AI detection guide becomes blueprint for tool that makes chatbots sound more human

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A tool called Humanizer flips Wikipedia's AI detection guide on its head, using the same 24 patterns volunteer editors identified to spot AI-generated content to instead teach Anthropic's Claude how to avoid them. The ironic twist highlights the cat-and-mouse game between AI detection and evasion.

Wikipedia's Detection Manual Becomes an Evasion Tool

Tech entrepreneur Siqi Chen released an open source plug-in on Saturday that transforms Wikipedia's carefully crafted AI detection guide into instructions for Anthropic's Claude Code AI assistant to avoid sounding robotic

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. The Humanizer plug-in feeds Claude a list of 24 language and formatting patterns that Wikipedia editors compiled as chatbot giveaways, effectively teaching the language models to sidestep detection

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. Published on GitHub, the tool garnered over 1,600 stars by Monday, revealing strong demand for ways to make AI writing sound more human

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Source: Wired

Source: Wired

The source material comes from WikiProject AI Cleanup, a volunteer initiative founded by French Wikipedia editor Ilyas Lebleu in late 2023 to combat AI-generated content

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. These volunteers have tagged over 500 articles for review and published a formal list of patterns in AI writing in August 2025, documenting telltale signs like promotional language describing views as "breathtaking" or towns "nestled within" scenic regions

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. The guide also flags phrases like "marking a pivotal moment" and "stands as a testament to" as common chatbot giveaways

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How the Humanizer Plug-In Works

The Humanizer operates as a "skill file" for Claude Code, Anthropic's terminal-based coding assistant

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. Unlike standard AI prompts, this Markdown-formatted file contains structured instructions that Claude models are fine-tuned to interpret with greater precision. The skill file requires a paid Claude subscription with code execution enabled

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. Chen designed the tool to automatically push updates whenever Wikipedia's AI-spotting guide receives modifications, creating a dynamic system that evolves alongside detection methods

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Source: The Verge

Source: The Verge

The transformation targets specific linguistic patterns to detect AI writing. For instance, the Humanizer instructs Claude to replace inflated language with plain facts, changing "The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain" to simply "The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics"

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. Similarly, vague attributions like "Experts believe it plays a crucial role" become more specific: "according to a 2019 survey by..."

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Testing Results and Limitations

Limited testing shows the Humanizer made Claude's output sound less precise and more casual, but the approach carries notable drawbacks

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. The tool won't improve factuality and might harm coding ability. Some instructions could mislead users depending on the task. One directive tells the LLM to "have opinions" and "react" to facts rather than neutrally listing information, advice that would likely damage technical documentation quality. Since language models don't always perfectly follow skill files, the Humanizer's effectiveness varies across different use cases.

The Broader Implications for AI Detection

This development underscores why AI detection remains unreliable: there's nothing inherently unique about human writing that consistently differentiates it from AI-generated text

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. Even though most language models gravitate toward certain linguistic patterns, they can be prompted to avoid them, as the Humanizer demonstrates. OpenAI already addressed ChatGPT's overuse of the em dash after it became an indicator of AI-generated content, suggesting major AI companies will likely adjust their models against these tells

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. The irony is stark: one of the web's most referenced resources for spotting AI-assisted writing now helps people subvert it, creating an escalating cycle where detection methods inadvertently fuel more sophisticated evasion techniques.

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