parallel-web

Name: parallel-web
Availability: InStock
Author: K-Dense

Search the web, extract URLs, enrich datasets, and generate scholarly reports.

33.0k|3.2k|Updated Oct 19, 2025

Search Tags:#research #academic #extraction #web #enrichment #literature

Ready to run

npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill parallel-web-k-dense-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: parallel-web
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/parallel-web
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill parallel-web-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill unifies web research tasks into a single workflow: fast web search, URL extraction, data enrichment from web sources, and deep scholarly reporting, enabling researchers to gather and synthesize information efficiently.

Core Features & Use Cases

Web Search: surface peer-reviewed papers, preprints, and scholarly sources; collect summaries and metadata.
Web Extract: fetch content from URLs, articles, and PDFs for structured data capture.
Data Enrichment: append web-derived fields to datasets (authors, affiliations, publication venues).
Deep Research: produce exhaustive multi-source reports grounded in academic literature.
Setup & Status: manage parallel-cli setup, authentication, and task monitoring.

Quick Start

Ask the skill to perform a web search, fetch pages, and generate a multi-source scholarly report.

Frequently Asked Questions about parallel-web

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

FAQPage Schema

How do I extract web content and metadata from academic papers and PDFs?▼

Web content extraction fetches articles, PDFs, and URLs to capture structured data including authors, affiliations, and publication venues. You can extract page content directly to append web-derived fields to your research datasets.

Can I enrich my existing datasets with web-sourced fields like author affiliations?▼

Data enrichment appends web-derived fields such as authors, affiliations, and publication venues to your existing datasets. The toolkit performs web searches and extracts structured metadata to populate missing fields across scientific domains.

What is the best way to generate multi-source research reports from academic literature?▼

Multi-source research reports are generated by combining fast web search, URL extraction, and data enrichment to synthesize information across scientific domains. The workflow surfaces peer-reviewed papers and preprints to produce exhaustive scholarly reports.

Do I need to set up parallel-cli before performing web searches for scholarly sources?▼

Yes, parallel-cli setup is required before performing web searches. The toolkit implements setup, status checks, and result retrieval via parallel-cli to manage authentication, task monitoring, and ensure reproducible research results.

How does literature discovery work across scientific domains?▼

Literature discovery works by performing fast web searches to surface peer-reviewed papers, preprints, and scholarly sources. It collects summaries and metadata across scientific domains, enabling cross-source reporting and academic paper collection.

Can I monitor task status and retrieve results for reproducible web research workflows?▼

Task monitoring and result retrieval are handled via parallel-cli status checks. The workflow ensures reproducible results by managing authentication, tracking task progress, and retrieving extracted web content and research reports systematically.