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ruiyangruiyi
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parallel-cli

Run agent-native web research and extraction tasks via Parallel CLI.

Updated May 26, 2026
Search Tags:#monitoring#web search#data enrichment#deep research#entity discovery#web extraction#parallel-cli
One-click install
npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill parallel-cli-ruiyangruiyi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parallel-cli
Source: https://github.com/ruiyangruiyi/hermes-agent/tree/main/optional-skills/research/parallel-cli
Command: npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill parallel-cli-ruiyangruiyi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you get high-quality web research outputs—search results, extracted content, deep multi-step findings, and enriched structured data—without manually juggling separate browsing, scraping, and formatting steps.

Core Features & Use Cases

  • Agent-native web research: Run deep research workflows that synthesize findings across sources with structured outputs suitable for downstream reasoning.
  • Web extraction & enrichment: Extract clean content from URLs and enrich entities/rows with additional attributes from the web.
  • FindAll & monitoring workflows: Discover large sets of entities and track changes over time with monitor-style workflows.

Quick Start

Ask the agent to run parallel-cli to research your question in JSON format and return a structured report with citations to the Parallel CLI output URLs.

Frequently Asked Questions about parallel-cli

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

FAQPage Schema
How do I automate web research and data extraction in a structured format?▼

You can automate web research and data extraction by running CLI commands that return JSON-first structured results, supporting both one-shot and asynchronous long-running tasks for reliable outputs without manual browsing.

What is the best way to enrich entities with additional attributes from the web?▼

The best way to enrich entities is using web extraction workflows that augment your rows with additional attributes from the web, returning JSON-first structured data outputs suitable for immediate downstream analysis.

Can I run async long-running web monitoring tasks and poll for results later?▼

Yes, async long-running web monitoring tasks are supported. You can use options like --no-wait and status/poll to track changes over time, discovering large sets of entities and retrieving results when ready.

Does web research support context chaining for follow-up iterations?▼

Yes, web research supports context chaining for follow-up iterations using the --previous-interaction-id option. This allows you to build upon previous queries while applying domain and date constraints for reliable structured results.

How do I discover large sets of entities and track changes over time?▼

You can discover large sets of entities and track changes over time using FindAll and monitoring workflows. These monitor-style workflows run via CLI to ensure reliable structured results with domain and date constraints.

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