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safha

Scrape and clean Arabic web content into JSONL training datasets.

1|Updated Apr 19, 2026
Search Tags:#jsonl#training-data#web-scraping#data-cleaning#arabic#dialect-detection#nlp-prep
One-click install
npx skills add https://github.com/jackquelinunpredictable827/mkhlab --skill safha-jackquelinunpredictable827
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: safha
Source: https://github.com/jackquelinunpredictable827/mkhlab/tree/main/hermes-skills/safha
Command: npx skills add https://github.com/jackquelinunpredictable827/mkhlab --skill safha-jackquelinunpredictable827

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Collect, clean, and dialect-tag Arabic content from the web to create training data.

Core Features & Use Cases

  • Arabic content scraping from web pages and sitemaps.
  • Data cleaning and normalization to JSONL-ready format.
  • Dialect detection and categorization for diverse Arabic datasets.
  • Lightweight analytics to track data volume and quality.

Quick Start

Run safha scrape URL to collect Arabic content from the web and save it in JSONL format.

Frequently Asked Questions about safha

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

FAQPage Schema
How do I collect Arabic web content and format it as JSONL training data?▼

To collect Arabic web content and generate JSONL training data, you can use safha to scrape web pages and sitemaps, clean the extracted text, and output structured datasets ready for NLP model training.

Can I automatically detect Arabic dialects in scraped web text?▼

Yes, Arabic dialect detection and categorization is supported during data cleaning. This allows you to tag diverse Arabic datasets automatically as you scrape web content for your training pipelines.

What is the best way to clean raw Arabic text for NLP preparation?▼

The best way to clean raw Arabic text for NLP preparation is using a tool like safha that normalizes scraped web content and formats it into a JSONL-ready structure, ensuring data quality for model evaluation.

Do I need curl installed to scrape sitemaps and web pages for Arabic datasets?▼

Yes, you need curl and a compatible runtime installed to run safha commands. The tool relies on these dependencies to crawl sitemaps, scrape web pages, and output structured JSONL datasets.

How does sitemap crawling work for building Arabic NLP datasets?▼

Sitemap crawling systematically discovers and extracts Arabic content across entire websites. The scraped data then undergoes cleaning, normalization, and dialect detection before being exported as JSONL for NLP workflows.

Can I track data volume and quality metrics when scraping Arabic web pages?▼

Yes, safha includes lightweight analytics to track data volume and quality. This helps monitor the effectiveness of your Arabic content scraping and cleaning processes before generating the final JSONL output.

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