A powerful bilingual tool designed to detect AI writing patterns and humanize text through statistical analysis and natural vocabulary replacement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bilingual-humanizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bilingual-humanizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Bilingual Humanizer?
Bilingual Humanizer is a sophisticated editing skill designed to bridge the gap between mechanical AI output and authentic human expression. By leveraging 49 specific pattern detectors and a massive database of over 500 inflated terms, it identifies the subtle markers that distinguish AI-generated content from human writing. Whether you are working in Spanish or English, this skill provides the technical framework to refine prose, ensuring it resonates with a genuine voice rather than a repetitive algorithm.
As a versatile addition to the Openclaw Skills ecosystem, it functions across multiple environments including CLI for power users, MCP servers for desktop integration, and an autonomous mode for web-based workflows. It goes beyond simple synonym swapping by analyzing deep linguistic metrics such as burstiness, Type-Token Ratio (TTR), and readability indices to transform robotic text into engaging, professional content.
Bilingual Humanizer Use Cases
- Refining AI-generated blog posts and marketing copy to ensure a natural, human tone.
- Auditing large document repositories for repetitive linguistic patterns using the scan feature.
- Comparing draft versions of content to track improvements in readability and humanization scores.
- Normalizing bilingual content (English/Spanish) to maintain a consistent brand voice across languages.
- Improving SEO performance by reducing robotic footprints and increasing content engagement metrics.
How Bilingual Humanizer Works
- The user initiates a score or analyze command to identify the presence of AI patterns and statistical anomalies.
- The skill cross-references detected IDs against the bilingual knowledge base (patterns-es.md or patterns-en.md).
- Statistical metrics like burstiness and TTR are calculated to provide a quantitative baseline of the text's mechanical nature.
- Specific suggestions or automatic fixes are applied to replace inflated vocabulary and vary sentence structure.
- A final verification step re-scores the text to ensure it meets the target naturalness thresholds (Burstiness > 0.6).
Bilingual Humanizer Setup
To get started with this skill in your environment, follow these steps:
# Install the humanizer skill
openclaw install bilingual-humanizer
# Analyze a specific file for AI patterns
humanizer analyze -f document.txt
# Run an automatic humanization fix
humanizer humanize --autofix -f document.txt
# Check statistical metrics of your content
humanizer stats -f document.txt
Bilingual Humanizer Data Schema & Taxonomy
The skill organizes its intelligence through a modular directory structure and provides structured outputs for integration:
| Component | File Path | Purpose |
|---|---|---|
| Pattern Registry | knowledge/patterns-*.md |
Definitions of the 49 detectable AI markers |
| Vocabulary DB | knowledge/vocabulary-*.md |
500+ inflated terms and their natural alternatives |
| CLI Output | JSON / Markdown |
Structured reports for automation or human review |
| Metrics | Stats Object |
Data on Burstiness, TTR, HLR, and IFSZ (Readability) |
Bilingual Humanizer Advanced Features
- Multi-mode operation: Seamlessly switch between CLI, MCP (Model Context Protocol), and Autonomous modes.
- Bulk Scanning: Recursively analyze entire directories and rank files by their humanization priority.
- Statistical Benchmarking: Technical tracking of Hapax Legomena Ratio (HLR) and Paragraph Readability (IFSZ).
- Automated Refactoring: Use the --autofix flag for rapid, safe linguistic corrections based on high-confidence patterns.
- Bilingual Support: Dedicated logic and knowledge silos for both Spanish and English linguistic structures.
SKILL.md
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