parallel-cli

Name: parallel-cli
Availability: InStock
Author: plaidmustache

Orchestrate async vendor web research workflows with JSON output.

Updated May 4, 2026

Search Tags:#monitoring #cli #async #web-search #enrichment #deep-research #parallel-cli

Ready to run

npx skills add https://github.com/Plaidmustache/hermes-nulab --skill parallel-cli-plaidmustache

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: parallel-cli
Source: https://github.com/Plaidmustache/hermes-nulab/tree/main/optional-skills/research/parallel-cli
Command: npx skills add https://github.com/Plaidmustache/hermes-nulab --skill parallel-cli-plaidmustache

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Parallel-CLI vendor skill orchestrates async web search, extraction, and deep-research workflows from a single command-line interface, reducing context switching and enabling scalable investigations.

Core Features & Use Cases

Async research orchestration: launch, monitor, and aggregate results across sources.
Structured outputs: JSON-friendly results suitable for downstream analysis and summarization.
Enrichment and monitoring: supports enrichment tasks and recurring monitoring workflows for continuous updates.

Quick Start

Launch a parallel-cli research run and request JSON output to begin an asynchronous deep-research task.

Frequently Asked Questions about parallel-cli

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

FAQPage Schema

How do I orchestrate async deep research workflows from a command line interface?▼

Async deep research workflows are orchestrated by launching multi-step web searches, extraction, and monitoring tasks that run asynchronously and output structured JSON. This enables scalable investigations without context switching.

Can I monitor long-running web search tasks non-interactively?▼

Long-running web search tasks can be monitored non-interactively using asynchronous polling. The workflow supports context chaining via previous interaction IDs to track and aggregate results across continuous updates.

What is the best way to enrich vendor data with deep research workflows?▼

Vendor data enrichment is handled through deep research workflows that execute multi-step web searches and extraction. The process aggregates results across sources into a JSON-friendly format suitable for downstream analysis.

Does async web research support structured JSON output for downstream analysis?▼

Async web research supports structured JSON output designed specifically for downstream analysis and summarization. This format ensures results from extraction and enrichment tasks are immediately usable by other systems.

How do I chain context from previous asynchronous research interactions?▼

Context from previous asynchronous research interactions is chained using previous interaction IDs. This allows subsequent research runs to build upon prior extraction and monitoring results within the workflow.