parallel-web

Name: parallel-web
Availability: InStock
Author: vini

Search the web for academic sources and extract content from pages and PDFs.

Updated Apr 2, 2026

Search Tags:#academic #search #extraction #web #enrichment #literature #parallel-cli

Ready to run

npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill parallel-web-viniruggeri

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: parallel-web
Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/parallel-web
Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill parallel-web-viniruggeri

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill consolidates web search, extraction, enrichment, and deep research into a single, consistent toolkit, helping users quickly discover, capture, and organize web-sourced information with an academic-first focus.

Core Features & Use Cases

Web Search: fast lookups and literature discovery prioritizing peer-reviewed papers, preprints, and scholarly databases.
Web Extract: fetch and parse content from pages, articles, and PDFs, extracting relevant text and metadata.
Data Enrichment: append web-derived fields to datasets (CSV/JSON) to build richer records.
Deep Research: generate exhaustive multi-source reports grounded in academic sources.
Setup & Retrieval: scaffold the environment, check status, and retrieve results for ongoing tasks.

Quick Start

Use this skill to search a topic, fetch results from multiple sources, and enrich a dataset with key web-derived fields.

Frequently Asked Questions about parallel-web

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

FAQPage Schema

How do I search for academic literature and peer-reviewed papers on the web?▼

Academic literature search prioritizes peer-reviewed papers, preprints, and scholarly databases to retrieve high-quality sources, consolidating web search and extraction into a consistent toolkit for research and data enrichment.

What's the best way to extract text and metadata from web pages and PDFs for research?▼

Web extraction fetches and parses content from pages, articles, and PDFs to isolate relevant text and metadata, enabling literature lookups and multi-source reporting within a single research workflow.

Can I enrich a dataset with web-derived fields from academic sources?▼

Data enrichment appends web-derived fields to CSV or JSON datasets, building richer records by fetching supplementary information from prioritized academic sources and standard web pages.

Do I need parallel-cli and internet access to perform deep web research?▼

Yes, parallel-cli and active internet access are required to perform deep web research, generate exhaustive multi-source reports, and manage environment setup, status checks, and result retrieval.

How does multi-source reporting work for academic deep research?▼

Multi-source reporting generates exhaustive documents grounded in academic sources by consolidating retrieved web content, extracted metadata, and enriched datasets into a unified research output.

Are there limitations when using web search for scholarly literature discovery?▼

Web search for scholarly literature discovery is limited by internet access availability and source prioritization, focusing on academic databases and preprints rather than exhaustive coverage of all public web content.