mdnew

Web & Frontend Development
v1.0.0
Benign

Fetch clean, agent-optimized Markdown from any URL using the markdown.new service.

458 downloads458 installsby @thendcn

Setup & Installation

Install command

clawhub install thendcn/mdnew

If the CLI is not installed:

Install command

npx clawhub@latest install thendcn/mdnew

Or install with OpenClaw CLI:

Install command

openclaw skills install thendcn/mdnew

or paste the repo link into your assistant's chat

Install command

https://github.com/openclaw/skills/tree/main/skills/thendcn/mdnew

What This Skill Does

Fetches clean, token-efficient Markdown from any URL using the markdown.new three-tier conversion pipeline. Strips boilerplate, ads, and navigation menus, leaving only core content. Handles JS-heavy pages via Cloudflare Browser Rendering as a fallback.

Reduces page size by up to 80% compared to raw HTML, making it practical for agents with tight context windows that would otherwise struggle with full-page content.

When to Use It

  • Extracting article text for LLM analysis
  • Scraping docs pages when web_fetch returns noisy HTML
  • Reading JS-rendered pages without a full browser setup
  • Reducing token usage when ingesting long web pages
  • Feeding clean web content into agent context windows
View original SKILL.md file
# mdnew

Fetch clean Markdown from any URL using the `markdown.new` three-tier conversion pipeline (Header Negotiation -> Workers AI -> Browser Rendering).

## Usage

Run the script with a target URL:

```bash
python3 scripts/mdnew.py <url>
```

## Why use mdnew?

1. **Token Efficiency**: Reduces content size by up to 80% compared to raw HTML.
2. **Clean Data**: Strips boilerplate, ads, and nav menus, leaving only core content.
3. **JS Execution**: Automatically handles JS-heavy pages via Cloudflare Browser Rendering fallback.
4. **Agent-First**: Includes `x-markdown-tokens` tracking to help manage context windows.

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Extracting article text for LLM analysis

AGENT
  1. 1Extracting article text for LLM analysis
  2. 2Scraping docs pages when web_fetch returns noisy HTML
  3. 3Reading JS-rendered pages without a full browser setup
  4. 4Reducing token usage when ingesting long web pages
  5. 5Feeding clean web content into agent context windows
OUTPUT
Fetch clean, agent-optimized Markdown from any URL using the markdown.new service.

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Details

LanguageMarkdown
Last updatedFeb 26, 2026