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Humanize AI text and audit detector risk with evidence-based analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "humanize" yet — see the docs or source repo.
Please rewrite the following AI-generated copy so it sounds more natural and human while preserving the original meaning and key information, and reduce the chance of being flagged by perplexity-based detectors: {{paste text}}A revised version that reads more naturally and human-like.
Please perform an ai-check on the following text, provide a forensic-style score, and flag suspicious passages; include quoted evidence from the text and a brief explanation for each finding: {{paste text}}A detection report with scoring, flagged passages, and quoted evidence.
Using the humanize approach, rewrite this passage with multiple style-adjustment levers so it feels more natural without becoming too casual; explain the key changes you made: {{paste text}}An improved text version plus a summary of the main rewrite choices.
Writers or marketers can draft with an LLM first, then use this skill to make the text sound more natural while preserving the original message. It fits blogs, emails, landing pages, and similar content.
Students, editors, or researchers can use ai-check for forensic-style analysis with scoring, flagged passages, and quoted evidence. It is useful for reviewing detector risk and suspicious patterns before submission.
It provides two research-grounded capabilities: humanize rewrites AI text to sound more natural, and ai-check gives forensic-style scoring with evidence-quoted flags.
The description says both skills work in any LLM, specifically naming Claude, ChatGPT, Gemini, and Codex.
The provided info emphasizes that humanize is designed against perplexity-based detectors, while ai-check does more than score by providing quoted evidence for its flags. For implementation details, see the source repository.
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