humanizer — What is it?

The blader/humanizer project is an open-source skill designed to remove AI-generated text signatures, enhancing the naturalness and human-like quality of written text.

⭐ 14,413 Stars 🍴 1,270 Forks MIT Author: blader
Source: README View on GitHub →

Why it matters

The project is gaining attention due to its unique approach to addressing the challenge of discerning AI-generated text. It fills a gap in the market by providing a tool that can help improve the readability and authenticity of AI-generated content, which is particularly relevant in content creation and AI-assisted writing environments.

Source: README, project traits

Core Features

Text Humanization

The project's primary feature is the ability to analyze and rewrite text to remove signs of AI-generated writing, making it sound more natural and human-like. This is achieved by analyzing sentence rhythm, word choices, and quirks from a user's sample writing.

Source: README, Usage section
Pattern Detection

The skill detects 29 specific patterns indicative of AI-generated text, including content, language, and style anomalies, and rewrites the text accordingly.

Source: README, 29 Patterns Detected section
Voice Calibration

Users can provide a sample of their own writing to calibrate the skill to their personal style, enhancing the effectiveness of the humanization process.

Source: README, Voice Calibration section

Architecture

The architecture of the project is not explicitly detailed in the provided materials. However, based on the code tree, it appears to consist of a set of rules and algorithms for text analysis and rewriting, with a focus on pattern detection and style transformation.

Source: Code tree

Tech Stack

infra: Not enough information.  |  key_deps: Not enough information.  |  language: Not enough information.  |  framework: Not enough information.

Source: Dependency files + code tree

Quick Start

mkdir -p ~/.claude/skills/humanizer git clone https://github.com/blader/humanizer.git ~/.claude/skills/humanizer/ or mkdir -p ~/.config/opencode/skills git clone https://github.com/blader/humanizer.git ~/.config/opencode/skills/humanizer/ or cp SKILL.md ~/.claude/skills/humanizer/ or cp SKILL.md ~/.config/opencode/skills/humanizer/
Source: README Installation/Quick Start

Use Cases

The project is suitable for content creators, writers, and developers working with AI-generated text. It can be used to improve the readability and authenticity of AI-generated content, such as in AI-assisted writing, content moderation, and educational settings where the human-like quality of text is important.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Provides a unique solution for enhancing the human-like quality of AI-generated text.
  • Strength 2: Offers voice calibration to tailor the humanization process to individual writing styles.

Limitations

  • Limitation 1: The project's effectiveness is dependent on the quality and relevance of the user's sample writing for voice calibration.
  • Limitation 2: The project's architecture and technical details are not fully disclosed, which may limit its adoption by developers seeking to understand or modify its underlying algorithms.
Source: Synthesis of README, code structure and dependencies

Latest Release

2.5.1 - Added a passive-voice / subjectless-fragment rule, raising the total to 29 patterns.

Source: README Version History

Verdict

The blader/humanizer project is a valuable tool for anyone dealing with AI-generated text, offering a practical solution for improving the readability and authenticity of such content. Its focus on voice calibration and pattern detection makes it particularly useful for content creators and developers looking to enhance the human-like quality of AI-generated text. However, its limited technical disclosure may be a barrier for some developers seeking deeper integration or modification.

Source: Synthesis
Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-04-19 10:21. Quality score: 85/100.

Data sources: README, GitHub API, dependency files