A high-performance utility designed to convert web pages into clean, developer-friendly markdown via the markdown.new API.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install markdown-extract
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 markdown-extract using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Markdown Extract is a specialized tool within the Openclaw Skills ecosystem that streamlines the process of fetching and cleaning web content. By integrating with the markdown.new API, it removes the clutter of ads, tracking scripts, and navigation elements, leaving only the core content in a structured markdown format. This makes it an invaluable asset for developers who need to feed high-quality data into Large Language Models (LLMs) or maintain technical documentation.
The skill provides flexibility by offering different extraction strategies depending on the source material. Whether you are dealing with a simple blog post or a complex single-page application (SPA), Markdown Extract ensures the resulting text is accurate and readable, enhancing the utility of your Openclaw Skills setup.
To start using this skill within your environment, ensure you have access to the Openclaw Skills framework and run the following command to verify connectivity:
!markdown-extract https://example.com
No additional API keys are required as it leverages the public markdown.new endpoint by default.
The skill processes inputs and generates outputs based on the following structure:
| Parameter | Type | Description |
|---|---|---|
| url | String | The target web address to extract content from. |
| method | String | Options: auto (default), ai (LLM-based), or browser (full JS rendering). |
| output | String | The processed markdown content or error details. |
Internal logic handles content negotiation using the Accept: text/markdown header for the fastest possible results.
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