web-reader

Extract and clean web page content into Markdown format.

15|2|Updated May 1, 2026
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill web-reader-opensucker
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: web-reader
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/web-reader
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill web-reader-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the process of retrieving and cleaning web content, enabling users to read articles, documentation, and online reports without intrusive ads or clutter.

Core Features & Use Cases

  • Content Retrieval: Convert URLs into Markdown-formatted text while removing ads, navigation menus, and styling.
  • Research and Documentation: Efficiently gather technical articles, API documentation, and GitHub project pages for review and analysis.
  • Use Case: For example, fetch a GitHub README page to extract and study project details without distractions.

Quick Start

Use the web-reader skill to fetch and clean the content from a specified URL for easy reading and analysis.

Frequently Asked Questions about web-reader

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert web pages into clean Markdown for offline reading?

To convert web pages into clean Markdown, this Skill extracts and filters URL content, stripping ads and styling to produce standardized text for offline or AI-assisted review.

Can I extract technical articles and GitHub documentation from dynamically rendered pages?

Yes, extracting technical articles and GitHub documentation is fully supported, including handling dynamically rendered pages and Chinese content for efficient research analysis.

Do I need Python scripts to fetch and clean URL content?

Yes, you need Python scripts for URL fetching and content filtering, which execute the underlying logic to retrieve web pages and standardize the extracted content into Markdown.

What is the best way to remove ads and navigation menus from online reports?

The best way to remove ads and navigation menus from online reports is using this content extraction process, which cleans web page clutter into a distraction-free Markdown format.

Does web scraping with this approach work for API documentation and GitHub project pages?

Web scraping with this approach works effectively for API documentation and GitHub project pages, retrieving and cleaning the content for streamlined technical review and analysis.

Are there limitations when processing complex web content into Markdown format?

Processing complex web content into Markdown format requires Python scripts for URL fetching, and while it handles dynamic pages, highly interactive non-article elements may not convert cleanly.