Repoyank: A Secure and Efficient Code Snippet Extraction Tool Built for Preparing Context for LLMs

With the widespread application of large language models (LLMs) in software development, developers need to provide code context to AI assistants. However, traditional methods have security issues (exposure of sensitive data) and efficiency problems (inefficient manual copying, automatic tools mixing in irrelevant code). Repoyank is a CLI tool that allows developers to safely and accurately prepare context for LLMs through local interactive selection and structured output, maintaining full control over their data.

Modern developers need to provide relevant context when using LLMs for tasks like code review and bug fixing. Traditional methods include manual copy-pasting (low efficiency, easy to miss key dependencies), uploading entire files (risk of sensitive information exposure), and IDE plugins for automatic extraction (too much irrelevant code). Repoyank aims to solve these pain points and give developers full control over the context.

The core feature of Repoyank is its terminal-based interactive selection interface. Developers can browse the codebase and select multi-granularity content such as files, functions, and custom code blocks. Real-time display of line count and character count statistics helps control the scope, making it especially suitable for large codebases and avoiding irrelevant code mixing.

Selected code is automatically formatted, including adding file path comments, preserving indentation, and handling multi-file organization. It supports multiple output formats such as plain text and Markdown code blocks. Structured output helps LLMs understand multi-file dependencies and optimize prompt effectiveness.

Repoyank adopts a local-first architecture where all processing is done locally with no automatic upload to remote services. Developers have full control over the scope of code sharing, making it suitable for enterprise-sensitive codebases. They can filter safe code to share while keeping sensitive parts processed locally.

Repoyank is suitable for various scenarios: extracting functions to review and their dependencies during code review; extracting error-related code during debugging; extracting key modules when learning new libraries; extracting minimal reproducible code for open-source contributors; extracting example code for technical writing, etc.

Compared to manual copying, it provides a structured and repeatable process; compared to IDE plugins, it is lighter and does not depend on specific environments; compared to automatic tools, it gives users full control. It is suitable for scenarios where security is valued and precise context control is needed.

In the future, Repoyank can be extended to support more output formats and LLM platforms, integrate semantic analysis to automatically suggest relevant code, add code compression to adapt to context limits, and support team collaboration for shared configurations. It represents the direction of AI-assisted development tools that leverage LLMs while maintaining developer control.