web-scraper

Extract structured data from web pages into JSON, CSV, or Markdown.

Updated Dec 29, 2025

Search Tags:#automation #data extraction #browser automation #web scraping #data normalization #web fetch

Ready to run

npx skills add https://github.com/AmidVoshakul/chatorai --skill web-scraper-amidvoshakul

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: web-scraper
Source: https://github.com/AmidVoshakul/chatorai/tree/main/assets/skills/web-scraper
Command: npx skills add https://github.com/AmidVoshakul/chatorai --skill web-scraper-amidvoshakul

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the challenge of gathering structured data from complex, dynamic, or paginated websites without manual copy-pasting.

Core Features & Use Cases

Multi-strategy extraction: Automatically switches between static fetching, browser automation for JS-rendered content, and API calls.
Intelligent data handling: Supports pagination, data transformation, deduplication, and validation with confidence scoring.
Use Case: Extract pricing tiers from multiple SaaS competitor websites and compile them into a clean, normalized Markdown table for comparison.

Quick Start

Use the web-scraper skill to extract all product names and prices from the provided URL into a markdown table.

Frequently Asked Questions about web-scraper

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

FAQPage Schema

How do I extract structured data from dynamic, JavaScript-rendered web pages?▼

Web data extraction from dynamic pages requires browser automation to render JavaScript content. This skill automatically escalates from static fetching to browser automation, ensuring structured output captures fully rendered data for research or monitoring tasks.

What's the best way to scrape paginated product data and compile it into a normalized table?▼

Web scraping paginated product data is handled through intelligent data normalization, which automatically transforms, deduplicates, and validates extracted records. It compiles multiple pages into clean, structured JSON, CSV, or Markdown tables for cross-source comparison.

Can I automatically fetch data from multiple sources and compare pricing tiers?▼

Automatic web fetching across multiple sources supports cross-source comparison by extracting pricing tiers and applying data normalization. Confidence scoring validates the extracted data, ensuring high-confidence results for compiling competitor pricing into a structured Markdown table.

Does this web scraping approach work for API interaction and static fetching simultaneously?▼

Multi-strategy web scraping supports API interaction, static fetching, and browser automation within a single workflow. Automatic fallback escalation switches between these extraction methods based on target site behavior, maximizing the success rate of structured data extraction.

Why does web scraping fail on complex websites and how is data validation handled?▼

Web scraping fails when sites block static requests or require JavaScript execution. This skill applies automatic fallback escalation across multiple strategies and uses confidence scoring for data validation, ensuring high-confidence structured output even from complex sources.