RepoGuide — AI Developer Onboarding

Problem: Understanding an unfamiliar software repository can be difficult, especially for students, new developers, contributors, and developers joining an existing team. Before writing code, developers often need to manually explore folders, identify technologies, understand architecture, trace dependencies, and determine where different parts of the application fit together. This process is time-consuming and can make onboarding inconsistent and difficult. Solution: RepoGuide is a developer onboarding platform that transforms a public GitHub repository into a structured, interactive learning experience. A user connects a repository through its GitHub URL, after which RepoGuide securely performs a shallow repository analysis without executing the repository's code. The platform extracts structural information such as files, directories, technologies, extensions, and project organization. It then presents this information through a Project Overview and an Architecture Explorer with three complementary perspectives: Logical View, Data Flow, and Dependency Tree. The experience follows a guided workflow: Connect Repository → Analyze Repository → Project Overview → Architecture Explorer → Onboarding Journey → Task Verification → Completion Report Target Users: RepoGuide is designed for developers joining unfamiliar projects, students learning real-world codebases, open-source contributors, and teams looking for a more structured developer onboarding process. Interaction: Users simply provide a public GitHub repository URL and follow the generated onboarding experience. Instead of searching through a repository manually, they can progressively understand its structure and architecture through visual and contextual information. Creativity & Uniqueness: RepoGuide focuses on turning repository structure into an interactive onboarding journey, rather than being only a code search or documentation tool. Its architecture views provide different ways to understand the same codebase, while its planned AI layer uses local Ollama models to generate repository-specific onboarding tasks. The system also emphasizes security by analyzing repository structure without executing untrusted repository code and by using temporary storage for cloned repositories.