Design And Development of an AI Powered Open-Source GitHub Repository Finder

The rapid progress in Artificial Intelligence (AI) and Natural Language Processing (NLP) has enabled the automation of intelligent decision-making processes across various domains, including software development. This research focuses on the design and implementation of an AI-driven Open Source GitHub Repository Finder (OSGRF), developed to assist developers in identifying open-source projects that align with their technical skills and experience. The proposed system utilizes NLP techniques to extract key skills from a user’s resume and employs Sentence-BERT embeddings for semantic matching with relevant GitHub repositories. By ranking repositories according to factors such as similarity, popularity, and difficulty level, and by providing personalized AI-based contribution recommendations, the system ensures a seamless onboarding experience for developers entering the open-source community. The overall architecture integrates multiple layers built using ReactJS, Node.js, MongoDB, and Python, forming a scalable and intelligent framework for efficient open-source exploration.