parallel-cli

Execute web research and data enrichment tasks via the Parallel API.

Updated Jun 25, 2026

Search Tags:#automation #research #cli #web-search #enrichment #data-discovery

Ready to run

npx skills add https://github.com/Rheasilvia/hermes-desktop --skill parallel-cli-rheasilvia

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of performing complex, multi-step web research and entity discovery that exceeds the capabilities of standard, one-shot search tools.

Core Features & Use Cases

Deep Research: Executes long-running, asynchronous research tasks with support for context chaining and structured output.
Data Enrichment: Processes tabular data (CSV/JSON) by inferring and adding information via web research.
Entity Discovery: Performs web-scale entity discovery and monitoring to track changes or gather datasets.
Use Case: Use this to research a list of startups, enrich them with CEO and revenue data, and monitor their websites for future funding announcements.

Quick Start

Use the parallel-cli skill to perform a deep research run on the current state of AI coding agents and save the results to a file.

Frequently Asked Questions about parallel-cli

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

FAQPage Schema

How do I automate deep web research and data enrichment for a large list of entities?▼

Automated deep web research and data enrichment for large entity lists requires executing asynchronous jobs that chain context and process tabular data. This approach infers missing information via web search and outputs structured JSON for complex information gathering.

What is asynchronous job management for web search and why use it?▼

Asynchronous job management for web search allows long-running research tasks to execute without blocking your terminal environment. You use it to perform multi-step entity discovery and context chaining that exceeds standard one-shot search capabilities.

Can I enrich CSV data with web research using a command-line interface?▼

Yes, you can enrich CSV data with web research via a command-line interface. The process processes tabular CSV or JSON inputs, infers missing entity attributes through automated web search, and returns the enriched dataset as structured JSON output.

Do I need an authenticated Parallel API key to run entity discovery workflows?▼

Yes, running entity discovery workflows requires authenticated access to the Parallel API. You must also integrate the API with your local terminal environment to execute web-scale entity monitoring and data discovery tasks successfully.

What is the best way to monitor websites for future funding announcements?▼

The best way to monitor websites for funding announcements is using web-scale entity discovery and monitoring workflows. This technique gathers datasets, tracks changes over time, and performs asynchronous research to capture new information automatically.

How do I save structured JSON output from deep research tasks to a local file?▼

To save structured JSON output from deep research tasks, execute the research run through your command-line interface and redirect the asynchronous job results into a local file. This ensures the gathered information is stored for later analysis.