Intelligent code review coach

Intelligent Code Review Coach addresses a common software development challenge: code is often merged with security risks, bugs, or weak tests because manual reviews are time-consuming and can miss important issues. Our solution provides an automated first-pass review of Git code changes before they are merged. It analyzes Git diffs, identifies potential security vulnerabilities, likely programming errors, sensitive data exposure, unsafe input handling, and weak testing practices. Each finding includes the affected file and line, a severity level, a clear explanation of the problem, and a practical recommendation for fixing it. The target users are developers, students, and software teams that want faster and more consistent code reviews. Users can interact with the project through a web interface, command-line tool, or GitHub integration. This allows the Coach to fit naturally into different development workflows, from local testing to pull request reviews. What makes the project unique is its focus on safety, simplicity, and actionable feedback. The submitted code is never executed, and the core analysis works locally using deterministic rules, so it does not depend on an external AI service to provide useful results. Optional LLM support can enhance the wording of summaries without replacing the rule-based detection engine. The project is also bilingual, supporting both English and Spanish, which makes the feedback more accessible to a wider range of developers. By combining automated analysis, clear explanations, concrete fixes, and multiple integration options, Intelligent Code Review Coach helps developers detect problems earlier, reduce review effort, and improve code quality before changes reach the main codebase.