Exploring Repomix and Learning from Its Feature

This time, I studied an open-source project called Repomix to better understand how professional open-source tools organize their code and implement advanced features. Repomix is a CLI tool that packages repository context for LLMs — similar to my own project Repository Context Packager, but much more feature-complete. To analyze Repomix, I used a tool called DeepWiki. DeepWiki automatically indexes the source code of open-source repositories and presents it in a structured, documentation-style website. It breaks the project down into categories such as Command Line Interface, Configuration System, and Core Packager, and provides direct links to the original GitHub files. This made the process much faster — instead of opening dozens of files manually, I could see how Repomix’s architecture fits together at a high level and then jump straight to the parts I cared about.

The feature I chose to explore was: Include Patterns. By tracing through the DeepWiki documentation and the linked source files, I found that Repomix implements file filtering in three main stages:

1. Parsing the CLI Arguments Repomix uses Commander to parse options like --include "*.js,*.py". The argument string is split into an array of patterns (for example: [".js", ".py"]).
2. Merging Configuration Sources In defaultAction.ts, the CLI arguments are merged with configuration file values (repomix.toml or .json) so that defaults are respected and CLI options override them when provided.
3. Applying the Filters with globby During the repository scanning phase, Repomix uses globby to list all files, passing the include/exclude patterns directly to it. This allows the user to precisely control what files are read and included in the output. What I liked most was how modular this logic is, instead of writing custom filtering code, Repomix delegates the complex part to globby, which supports glob patterns, .gitignore, and cross-platform path normalization.

From this learning process, I do learned that instead of reinventing pattern matching, using globby simplifies the entire filtering process. Good naming helps understanding and searching. It’s easier to learn when you have a concrete feature to trace instead of reading everything blindly.