repomix

To package a codebase for AI context, run a command in your project directory like `repomix --style markdown -o context.md`. This consolidates repository contents into structured formats like XML, Markdown, JSON, or Plain Text while preserving file structure for efficient LLM consumption.

Yes, you can package remote repositories for AI analysis without cloning by using the `--remote owner/repo` command. This fetches the remote codebase and outputs a consolidated, AI-friendly single file in your chosen format, such as XML.

The best way to prepare a large codebase for an LLM security audit is to package it into a token-conscious single file. This approach counts tokens to optimize AI input and fits context windows, ensuring the entire repository structure is available for comprehensive analysis.

XML, Markdown, JSON, and Plain Text are the supported output formats for feeding repository snapshots to AI models. These formats preserve file structure and context while allowing you to choose the syntax best suited for your specific LLM's parsing capabilities.

Yes, packaging a repository for AI context includes token counting to optimize AI input and support context windows. This ensures the generated repository snapshot stays within the token limits required by large language models for efficient processing.