repomix

To package a codebase for AI analysis, you can use a repository packaging tool to consolidate your repository into a single-file format like XML, Markdown, or plain text, preserving the directory structure and providing token counting for LLM context management.

Preparing a repository snapshot for a security audit involves packaging the codebase into an AI-friendly format with human-readable summaries and preserved structure, allowing language models to efficiently review and analyze the code.

Yes, when converting a repository for LLM context, you can apply include and exclude filtering to select specific files, while built-in token counting ensures the generated output fits within your model's context window limits.

Repomix supports multiple output formats for cross-project code comparisons, allowing you to package repositories into XML, Markdown, JSON, or plain text files to streamline analysis and context sharing.

Code reviews benefit from AI-friendly packaging because it consolidates large codebases into a single file with preserved directory structure, enabling AI assistants to efficiently analyze the complete context and identify issues.

You need to package a repository into a single file when providing context to AI models that have token limits, as it streamlines analysis by combining code with token counting and custom headers for efficient LLM ingestion.