Best static AI text humanizer. Two research-grounded LLM-agnostic skills that make AI writing sound human and relatable. Nine levers, 50+ peer-reviewed sources, 2024-2026 detection literature.
-
Updated
Jul 10, 2026 - HTML
Best static AI text humanizer. Two research-grounded LLM-agnostic skills that make AI writing sound human and relatable. Nine levers, 50+ peer-reviewed sources, 2024-2026 detection literature.
[AAAI 2025 oral] Official repository of Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection
Detect and eliminate AI writing patterns in your content. This Claude Code plugin performs multi-tier analysis of character patterns, language cues, structural issues, and voice authenticity. Auto-fix em dashes, smart quotes, and emojis. Keep documentation and prose sounding genuinely human.
This project aims to address this gap by conducting a systematic, controlled study of human versus LLM-generated text detectability using paired question–answer datasets. Rather than proposing a novel detection architecture, the focus is on analyzing detection robustness, failure modes, and the impact of adversarial humanization strategies.
AI detection on your hardware
Professional text refinement, AI detection, and style conversion services. 专业文本润色、AI检测和风格转换服务
한글 AI 글 윤문·탐지 Claude Code 플러그인 — AI가 쓴 한국어를 사람처럼 윤문하고 AI 작성 여부를 진단
6-class text authorship detection pipeline for human and LLM-generated text using TF-IDF, stylometric features, and stacked scikit-learn/LightGBM models for the MALTO Hackathon 2026 (F1: 0.9393).
Browser-based LLM stylometric fingerprinting
Quantifying the linguistic fingerprint of large language models
Proof of concept tool to bypass document replay technology (such as GPTZero).
🎲 Detect whether a GitHub repo's code was likely written by an LLM. Zero dependencies. Scores repos 0-100 using commit velocity, session analysis, burst detection, message patterns, and project-scale plausibility.
Catch AI code mistakes before they ship — 50+ checks for hallucinated APIs, stub functions, hardcoded secrets, and SQL injection. SARIF output for GitHub Code Scanning.
Projects concerning LLMs, prompting, NLP, webscraping, data aquisition and dataset analysis.
Python tool for simple comparison check on generated code vs suspected generated code.
The official repository for our ACL 2025 paper, "Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection"
A unified tool for testing and using LLM detectors
Detects AI-generated essays using an ensemble of LightGBM, CatBoost, Naive Bayes, SGD, and Random Forest. Custom BPE tokenizer built with Hugging Face + TF-IDF vectorization with 3-5 word n-grams. Weighted soft-voting classifier.
Block AI-slop PRs, crypto-airdrop spam, and drive-by farming accounts on your GitHub repo - automatically.
Add a description, image, and links to the llm-detection topic page so that developers can more easily learn about it.
To associate your repository with the llm-detection topic, visit your repo's landing page and select "manage topics."