🚀 SKT-NRS / GIT-SCRAPED

This repository is dedicated to hosting structural, curated, and diverse datasets—including GitHub roadmaps,and Roadmaps.sh Sites system architectures, technical diagrams, and mass scraped assets.

Our ultimate mission is to fuel the development of next-generation Sovereign Indian Intelligence base models with high-fidelity, production-grade text-image structures.

---

📂 Repository Structure

All the raw and structured crawled data is systematically hosted inside the primary ingestion folder:

SKT-NRS/GIT-SCRAPED/
└── 📁 SCRAPED-DATA/           # Core production folder for incoming bulk assets
    ├── 📁 images   # Extracted markdown, charts, and sequence text files
    ├── 📁 texts    # Architecture flowcharts and structural diagrams
    └── 📁 Zip For Directly Download  # Added To Directly Download

Phase 1: Recovery & Synchronization (Current Phase)

• Status: In Progress ⚙️
• Re-extracting and consolidating the initial layout of 1200+ technical images and web roadmaps.
• Synchronizing local automated dumps directly from cloud compute grids straight into the SCRAPED-DATA perimeter using multi-threaded chunk uploads.

Phase 2: Mass Scaling

• Status: Planned 🎯
• Transitioning away from standard element-level frontend scraping to heavy backend API Sniffing and Open-Source Bulk Dump Data Ingestion.
• Integrating multi-threaded pipelines via img2dataset to pool millions of high-resolution aesthetic and architectural data pairs.
• Curating specialized technical infographics, programming flowcharts, and system layout pipelines for multi-modal training.

Phase 3: Public Sharing & Open Exchange

• Status: Continuous 🌐
• Opening the repository splits to the public and AI community.
• Actively facilitating a collaborative data ecosystem: developers can securely pull curated roadmaps from here while contributing back fresh token pairs to accelerate open-source AI growth.

🛠️ How to Load This Dataset

You can easily pull the data down to your local cluster or cloud server using the Hugging Face datasets library:

from datasets import load_dataset

# Load the entire scraped dataset pipeline
dataset = load_dataset("SKT-NRS/GIT-SCRAPED", data_files="SCRAPED-DATA/**")

# Inspect the structure
print(dataset)