data-collection

Automate parallel data collection from web sources, APIs, and documentation sites.

1|Updated Jun 9, 2026

Search Tags:#API #parallel processing #data collection #web scraping #multiprocessing

Ready to run

npx skills add https://github.com/aivos-xie/hermes-skills --skill data-collection-aivos-xie

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-collection
Source: https://github.com/aivos-xie/hermes-skills/tree/main/software-development/data-collection
Command: npx skills add https://github.com/aivos-xie/hermes-skills --skill data-collection-aivos-xie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of collecting data from multiple sources simultaneously, optimizing the process for speed and efficiency.

Core Features & Use Cases

Parallel Data Collection: Gather data from web sources, APIs, and documentation sites in parallel.
Multi-Process Architecture: Leverages multi-core servers for true parallelism, with each process maintaining its own state file.
Use Case: Ideal for tasks such as crawling GitHub repositories, scraping web pages, or collecting data from APIs, where speed and coverage are critical.

Quick Start

Execute the data-collection skill to initiate parallel data collection from specified sources.

Frequently Asked Questions about data-collection

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

FAQPage Schema

How do I collect data from multiple APIs in parallel?▼

You can collect data from multiple APIs in parallel by using a multi-process architecture that leverages multi-core servers. Each process handles its own requests and maintains a separate state file to optimize retrieval speed and efficiency.

What is the best way to scrape web pages using multiprocessing?▼

The best way to scrape web pages using multiprocessing is to run parallel collection processes optimized for multi-core servers. This approach allows simultaneous gathering from diverse web sources, ensuring fast and comprehensive data coverage.

Do I need Python to run parallel web scraping tasks?▼

Yes, you need Python to run parallel web scraping and data collection tasks. The process requires Python along with appropriate libraries for handling network requests and processing JSON data from APIs and web sources.

How does a multi-process architecture handle state during data collection?▼

A multi-process architecture handles state during data collection by assigning each parallel process its own individual state file. This ensures that processes gathering data from web sources and APIs do not conflict and can operate independently.

Can I use parallel processing to crawl GitHub repositories?▼

Yes, you can use parallel processing to crawl GitHub repositories. The multi-process data collection approach is specifically designed for tasks like GitHub crawling, web scraping, and API data retrieval where speed and coverage are critical.

Why does parallel API data retrieval fail on single-core machines?▼

Parallel API data retrieval is optimized for multi-core servers to achieve true parallelism. Running it on single-core machines limits the multiprocessing benefits, causing processes to share resources and significantly reducing overall collection speed.