A sophisticated text transformation tool that strips AI-generated signatures and robotic patterns to produce natural, human-sounding content.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install humanizer-plus
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install humanizer-plus using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Claw0x Humanizer is a high-performance utility within the Openclaw Skills library designed to bridge the gap between machine-generated drafts and authentic human writing. By targeting 24 specific linguistic signatures—such as sycophantic tones, filler phrases, and formulaic structures—this skill ensures your content resonates with readers rather than sounding like a predictable algorithm.
The tool utilizes a dual-layer approach, combining advanced LLM rewriting with a deterministic regex fallback engine. This ensures that whether you are refining a blog post or cleaning up automated documentation, the output remains high-quality, nuanced, and capable of bypassing common AI detection markers while preserving the original intent of your message.
To integrate this component of Openclaw Skills, you need a valid API key from the Claw0x platform.
export CLAW0X_API_KEY='your_api_key_here'
curl -s -X POST https://claw0x.com/v1/call \
-H "Authorization: Bearer $CLAW0X_API_KEY" \
-H "Content-Type: application/json" \
-d '{"skill": "humanizer", "input": {"text": "Your AI text here"}}'
The skill processes structured JSON inputs and provides detailed metrics on the transformation.
| Field | Type | Description |
|---|---|---|
humanized_text |
string | The final output with AI patterns removed. |
original_length |
number | Character count of the input text. |
humanized_length |
number | Character count of the humanized output. |
method |
string | Indicates if llm or regex was used for processing. |
text, content, or body) for seamless integration with existing pipelines.Loading
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