by anasu1 · Agent Tool · ★ 494
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text-humanizer English | 中文 Overview text-Humanizer is a completely free open-sourced project designed to humanize AI-generated text through a multilingual LLM-powered rewriting pipeline. By leveraging large language models for semantic-preserving text transformation, the system rewrites content with more natural sentence structures, varied linguistic patterns, and improved stylistic diversity while maintaining the original meaning across multiple languages. Features Preserves original meaning while improving text naturalness Bypasses most of AI-detectors Fully free and open-source Adaptable to different writing styles and tones 8 languages supported(en, ja, zh, ko, de, fr, es.) How it works Step 1 - LLM Rewrite (DeepSeek) Text is rewritten using the DeepSeek LLM to generate a semantically equivalent version with modified sentence structures, phrasing, and information flow. During this stage, the model also translates the content into Chinese as an intermediate representation to introduce structural variation and reduce original language patterns. Step 2 - Google Translation (EN → TR) The output from Step 1
| Stars | 494 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.5874489783599/100 |
| Last Updated | 2026-07-30 |
| Created | 2026-06-16 |
| Platforms | python |
| Est. Tokens | ~6k |
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text-humanizer is text-humanizer is an open-source project designed to convert text generated by AI to its "humanized" version. Bypasses the most of AI detectors such as Turnitin or GPTZero. It is categorized as a Agent Tool with 494 GitHub stars.
text-humanizer is primarily written in Python. It covers topics such as ai, ai-detector, ai-humanizer.
You can find installation instructions and usage details in the text-humanizer GitHub repository at github.com/anasu1/text-humanizer. The project has 494 stars and 10 forks, indicating an active community.
text-humanizer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to text-humanizer on Agent Skills Hub include StealthHumanizer, humanize-text, patina. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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