web

Automate web search, content extraction, and LLM-based summarization.

56|12|Updated Apr 26, 2026
One-click install
npx skills add https://github.com/Neuron-Mr-White/UniPi --skill web-neuron-mr-white
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: web
Source: https://github.com/Neuron-Mr-White/UniPi/tree/main/packages/web-api/skills/web
Command: npx skills add https://github.com/Neuron-Mr-White/UniPi --skill web-neuron-mr-white

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires smart-fetch-engine, wreq-js, defuddle, linkedom, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of web search, content reading, and summarizing, saving time on manual web browsing and content analysis.

Core Features & Use Cases

  • Web Search: Perform quick or in-depth searches using a variety of providers.
  • Content Reading: Extract and read content from URLs with options for different providers and formats.
  • Content Summarization: Summarize the content of a URL with the option to provide custom prompts.
  • Use Case: When you need to quickly understand the main points of an article or research the latest developments in a specific field.

Quick Start

To search for information about TypeScript, use the command: web_search(query: "TypeScript generics tutorial")

Frequently Asked Questions about web

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

FAQPage Schema
How do I automate web content extraction and summarization for research?

Yes, you can summarize web content from specific URLs by providing custom prompts to the LLM provider. This allows you to extract and read content from URLs while tailoring the LLM summarization output to your specific research or content analysis needs.

What is the best way to perform batch web information retrieval and metadata extraction?

Perform batch web information retrieval and metadata extraction by utilizing an automated smart-fetch engine. This approach accesses multiple URLs simultaneously, extracting readable content and metadata for efficient research and information retrieval workflows.

Can I use custom prompts for LLM summarization of web search results?

Yes, you can use custom prompts for LLM summarization of web search results. By passing custom prompts to the external provider, you can control how the extracted web content is summarized to fit your specific analysis requirements.

Does this web search and content reading approach support in-depth research workflows?

Yes, this web search and content reading approach supports in-depth research workflows by offering quick or in-depth search options through various providers. It automates content access and metadata retrieval, making it ideal for researching the latest developments in specific fields.

How does smart-fetch engine handle web content reading and URL extraction?

The smart-fetch engine handles web content reading and URL extraction by automating the retrieval process and parsing the HTML content. It works with external providers to ensure clean content extraction for accurate LLM-based summarization.