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parallel-cli

Run vendor-specific parallel web research tasks with JSON-first outputs.

1|Updated Jan 31, 2026
Search Tags:#json#web-search#enrichment#deep-research#monitor#parallel-cli
One-click install
npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill parallel-cli-monjyu1101
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parallel-cli
Source: https://github.com/Monjyu1101/AiDiy2026/tree/main/backend_hermes/optional-skills/research/parallel-cli
Command: npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill parallel-cli-monjyu1101

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The parallel-cli skill provides a vendor-specific, orchestrated workflow for Web search, extraction, deep research, enrichment, and monitoring, enabling agents to perform complex tasks without building custom tooling.

Core Features & Use Cases

  • Non-interactive, JSON-first outputs: produce machine-readable results suitable for downstream automation and logging.
  • Async research and monitoring: launch long-running tasks with status polling and result delivery, ideal for enterprise-grade workflows.
  • Context-aware collaboration: chain interactions using previous task IDs to maintain continuity across steps like search, extract, and enrichment.

Quick Start

Install and authenticate parallel-cli, then initiate a long-running research task and poll for results.

Frequently Asked Questions about parallel-cli

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

FAQPage Schema
How do I run async web search and deep research tasks for automated agent workflows?▼

You can run async web search and deep research by initiating long-running tasks and polling for structured JSON results. This approach enables non-interactive execution and status monitoring across multiple sources suitable for downstream automation.

What is context chaining in parallel research and extraction workflows?▼

Context chaining in research workflows uses previous interaction IDs to maintain continuity across sequential steps like search, extraction, and enrichment, ensuring structured JSON results remain linked throughout the automated process.

Does this research CLI support non-interactive JSON output for automated pipelines?▼

Yes, the research CLI supports non-interactive execution and produces JSON-first outputs. This machine-readable format is specifically designed for logging and downstream automation in enterprise-grade research workflows.

How do I monitor long-running enrichment tasks across multiple web sources?▼

You monitor long-running enrichment tasks by launching them asynchronously and using status polling to track progress. This delivers structured JSON results continuously across multiple sources without blocking interactive sessions.

What's the best way to structure web search results for downstream agent processing?▼

The best way to structure web search results for agents is using a JSON-first output pipeline. This ensures machine-readable data delivery suitable for automated extraction, enrichment, and logging across complex research workflows.

Do I need a specific pipeline environment to use async research and monitoring tools?▼

Yes, you need a JSON-output capable pipeline that supports non-interactive execution and context chaining via interaction IDs to properly run async research, extraction, and monitoring tasks.

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