Software developer Akash Saha has released RepoRecon, an AI-powered architectural analysis tool designed to simplify the process of understanding undocumented legacy codebases. The project, submitted for the GitHub Finish-Up-A-Thon Challenge, automatically ingests a GitHub repository URL to provide high-level architecture summaries, which are visualized through Mermaid sequence diagrams (a text-based tool for defining software architecture diagrams).

Beyond architectural mapping, the tool calculates a data-driven repository health score evaluating security, performance, maintainability, and documentation. It features a context-aware codebase Q&A system for natural language queries and identifies prioritized issues with specific remediation steps, allowing users to export reports in PDF or PNG formats. RepoRecon is currently available on the Puter App Center, enabling users to perform analyses without requiring individual account management or infrastructure setup.

The project originated as a concept during a Major League Hacking (MLH) hackathon in early 2026. After an initial prototype phase, the developer conducted a significant refactoring process that involved resetting the repository's git history, removing bloated dependencies, and integrating the Puter AI ecosystem to improve natural language processing capabilities.

During the development cycle, the author utilized GitHub Copilot to accelerate the creation of repetitive code, such as regex patterns for input sanitization and API integrations. The developer noted that this support allowed for deeper focus on refining the utility of the Mermaid diagram outputs. The tool is live and accessible via Vercel, with the source code hosted on GitHub.

Software developer Akash Saha has released RepoRecon, an AI-powered architectural analysis tool designed to simplify the process of understanding undocumented legacy codebases. The project, submitted for the GitHub Finish-Up-A-Thon Challenge, automatically ingests a GitHub repository URL to provide high-level architecture summaries, which are visualized through Mermaid sequence diagrams (a text-based tool for defining software architecture diagrams).

Beyond architectural mapping, the tool calculates a data-driven repository health score evaluating security, performance, maintainability, and documentation. It features a context-aware codebase Q&A system for natural language queries and identifies prioritized issues with specific remediation steps, allowing users to export reports in PDF or PNG formats. RepoRecon is currently available on the Puter App Center, enabling users to perform analyses without requiring individual account management or infrastructure setup.

The project originated as a concept during a Major League Hacking (MLH) hackathon in early 2026. After an initial prototype phase, the developer conducted a significant refactoring process that involved resetting the repository's git history, removing bloated dependencies, and integrating the Puter AI ecosystem to improve natural language processing capabilities.

During the development cycle, the author utilized GitHub Copilot to accelerate the creation of repetitive code, such as regex patterns for input sanitization and API integrations. The developer noted that this support allowed for deeper focus on refining the utility of the Mermaid diagram outputs. The tool is live and accessible via Vercel, with the source code hosted on GitHub.