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parallel-web

Automate web research with search, URL extraction, and dataset enrichment.

4|Updated Mar 2, 2026
Search Tags:#citations#web research#data enrichment#deep research#url extraction#parallel-cli#academic sources
One-click install
npx skills add https://github.com/shushuzn/Rairos --skill parallel-web-shushuzn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parallel-web
Source: https://github.com/shushuzn/Rairos/tree/main/skills/parallel-web
Command: npx skills add https://github.com/shushuzn/Rairos --skill parallel-web-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It turns web-driven research work—finding sources, fetching pages, extracting content, and producing grounded reports—into a single streamlined workflow so you don’t have to manually browse and stitch information together.

Core Features & Use Cases

  • Web Search with academic-first strategy: Finds current, technical, and scholarly information with a bias toward peer-reviewed literature, preprints, and institutional sources.
  • URL content extraction: Fetches and extracts structured content from webpages, articles, and academic material (including PDFs), optionally focusing on the most valuable sections for research.
  • Web data enrichment at scale: Enriches CSV or inline lists by adding consistent web-sourced fields across multiple entities without running slow manual lookups.
  • Deep, exhaustive research reporting: Produces multi-source, literature-grounded reports when the user explicitly requests comprehensive coverage.
  • Operational support: Handles setup/authentication for parallel-cli, task status checks, and retrieving completed results.

Use case examples:

  • Build a cited overview of a scientific topic using academic sources prioritized for evidence quality.
  • Extract key sections from a set of URLs or a single academic paper and save the extracted output for later use.
  • Enrich a dataset of companies with web-sourced fields (e.g., leadership and founding year) in one batch pipeline.

Quick Start

Ask the AI to run a web search for your question, then extract and synthesize the most relevant academic sources into a grounded answer with citations.

Frequently Asked Questions about parallel-web

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

FAQPage Schema
How do I extract content and key sections from academic PDFs and webpages?▼

Web content extraction fetches structured text from webpages and academic PDFs, optionally focusing on the most valuable sections. It returns organized output so you can save and use the extracted research material immediately without manual browsing.

Can I enrich a CSV dataset with web-sourced fields for multiple companies at once?▼

Batch web data enrichment adds consistent web-sourced fields across multiple entities in a list or CSV. It automates lookups to populate fields like leadership or founding year without slow manual browsing for each entity.

What is the best way to build a cited overview using academic sources?▼

Web research with an academic-first strategy searches for peer-reviewed literature, preprints, and institutional sources. It synthesizes multiple relevant relevant sources into a grounded answer with citations for evidence quality.

Do I need parallel-cli and internet access to run deep research tasks?▼

Parallel-cli with internet access is required to automate web research, search sources, and extract content. The Skill uses parallel-cli to execute tasks and returns structured JSON outputs with polling for long-running operations.

Does parallel web research handle long-running task status checks?▼

Operational support handles setup and authentication for parallel-cli, checks task status, and retrieves completed results. For long-running tasks, it uses polling and status updates to return structured JSON outputs.

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