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parallel-web

Execute end-to-end web research with academic-prioritized search and URL or PDF extraction.

Updated Aug 23, 2026
Search Tags:#web search#literature review#academic research#data enrichment#URL extraction#parallel workflows#PDF content
One-click install
npx skills add https://github.com/gabrielvuksani/wotann --skill parallel-web-gabrielvuksani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parallel-web
Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/parallel-web
Command: npx skills add https://github.com/gabrielvuksani/wotann --skill parallel-web-gabrielvuksani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires parallel-cli, and includes references (resource) components.

What problem does it solve?

It eliminates slow, manual web research by automatically searching the internet, extracting page/PDF content, enriching structured data, and producing evidence-grounded academic-style reports.

Core Features & Use Cases

  • Web Search (academic-first): Finds relevant current information and prioritizes peer-reviewed papers, preprints, and scholarly databases for scientific/technical questions.
  • URL Extraction: Fetches and extracts full content from webpages and academic PDFs, using an optional objective to focus on the most valuable sections.
  • Data Enrichment (batch): Adds web-sourced fields to many entities at once (e.g., enriching company/person datasets) without repeatedly doing single lookups.
  • Deep Research (exhaustive): Produces multi-source, comprehensive literature-style reports when the user explicitly requests deep/exhaustive research.
  • Task lifecycle support: Handles setup/authentication, running tasks asynchronously, checking status, and retrieving completed results.

Quick Start

Use parallel-web to perform an academic-prioritized web search for the topic “effects of sleep deprivation on cognition” and return a cited summary.

Frequently Asked Questions about parallel-web

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

FAQPage Schema
How do I automate a literature review with web search and PDF content extraction?▼

Automated literature review uses web search to find peer-reviewed sources and extracts PDF content to produce cited summaries. This eliminates manual lookup by fetching academic documents, extracting relevant sections, and compiling evidence-grounded reports automatically.

Can I enrich a structured CSV dataset with web-sourced fields in bulk?▼

Yes, you can enrich structured CSV datasets by performing bulk web lookups across lists of entities. This batch data enrichment adds web-sourced fields to many records at once, automatically searching the internet to populate missing information without repeated single queries.

Do I need parallel-cli to run asynchronous web research tasks?▼

Yes, parallel-cli availability with internet access is required to execute web research tasks. The tool handles setup, authentication, running asynchronous tasks, polling for status updates, and retrieving completed results for web search and URL extraction operations.

What is the best way to extract full content from academic webpages and PDFs?▼

The best way to extract academic content is using URL extraction that fetches webpages and PDFs while focusing on valuable sections. This approach prioritizes scholarly and peer-reviewed institutional sources, ensuring all extracted findings include proper citations for research integrity.

How does deep research differ from standard web search for academic sourcing?▼

Deep research produces multi-source, comprehensive literature-style reports when explicitly requested, whereas standard web search returns relevant current information. Deep research aggregates findings from multiple scholarly databases and peer-reviewed papers to generate exhaustive, evidence-grounded academic answers.

What types of sources are prioritized for scientific and technical web searches?▼

Academic web search prioritizes peer-reviewed papers, preprints, and scholarly databases for scientific and technical questions. This ensures institutional sources are favored over general web content, maintaining research quality and providing reliable citations for extracted findings.

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