repomix

To package a code repository for AI context, you can use tools that aggregate local files into a single representation. This process generates consolidated outputs with optional headers and summaries, making the codebase ready to feed directly into LLMs for analysis.

Markdown, XML, JSON, and plain-text formats are effective for sharing codebases with LLMs. These formats structure the repository content into a single file, enabling AI models to parse the code structure and documentation context efficiently.

Yes, you can run security audits by generating contextual snapshots of your repository to feed into LLMs. This approach packages the code with security checks, allowing AI models to analyze the entire codebase structure for potential vulnerabilities.

You can include or exclude specific files during repository packaging by applying pattern matching rules. This allows you to filter out unnecessary files and optimize the AI context, ensuring only relevant code is packaged into the final output.

Repository packaging supports both local and remote repositories. You can aggregate code from a local directory or fetch it directly from a remote source, generating a unified, token-aware context file for AI workflows.

Token counting is necessary when preparing codebases for LLM workflows to ensure the packaged context fits within the model's token limits. It provides a token-aware summary of the repository, helping you manage context window constraints effectively.