GitHub Data Scraper

The tool collects GitHub repository metadata from public sources to support analysis, benchmarking, and content strategy. It targets teams that need objective signals about a codebase's popularity, activity, and tech profile. Users include developers evaluating libraries, data scientists building repo datasets, and SEO teams mapping topic trends. It enables cross-repo comparisons and historical trend tracking without manual scraping.
Conceptually, the tool takes a configuration of targets, selects the fields to extract, and retrieves data via GitHub API or HTML scraping. It normalizes each repo to a common schema and handles pagination automatically. The system attaches timestamps, manages rate limits, and can cache results for consistency across runs.
Core features include: field selectors, optional topics and license extraction, output formats (CSV/JSON), and optional scheduling. It also supports delta updates, which fetch only changed repos since the last run, and export-ready schemas for BI tools. The value is speed, consistency, and scalability for large repos sets.
Use cases span competitive benchmarking, content strategy planning, and research projects that require structured repo data. The tool distinguishes itself by combining API-first extraction with robust fallback scraping, normalization, and a compact data model designed for SEO analysis and data science workflows.