What is the primary purpose of the Repomix skill?

The Repomix skill bundles an entire repository into a single, AI-optimized file so you can provide full codebase context to Large Language Models like Claude for better analysis.

How does Repomix ensure my security keys aren't leaked?

Repomix integrates Secretlint, which automatically scans for API keys, passwords, and credentials, warning you or blocking the packaging process if sensitive data is found.

Does it respect .gitignore settings?

Yes, Repomix is Git-aware and will respect your .gitignore rules by default, ensuring that build artifacts and dependency folders aren't included in the package.

Which output formats does Repomix support?

Repomix can generate output in XML, Markdown, JSON, and Plain Text formats, allowing you to choose the structure that best fits the LLM you are using.

Can I use Repomix with remote repositories?

Yes, Repomix supports remote repository processing. You can package a codebase by providing its GitHub or GitLab URL without needing to clone it locally first.

Repomix Repository Packager

Name: Repomix Repository Packager
Author: Ven0m0

byVen0m0

데이터 과학 및 ML

Packages entire codebases into single, AI-optimized files to provide comprehensive context for LLMs like Claude, ChatGPT, and Gemini.

소개

Repomix is a specialized utility designed to streamline the process of feeding entire repositories into Large Language Models. It intelligently compresses codebases into single, token-aware files using formats like XML, Markdown, or JSON, while respecting .gitignore rules and stripping unnecessary comments. By including built-in security auditing via Secretlint and remote repository support, it ensures that developers can safely and efficiently provide high-fidelity context for complex tasks such as deep code reviews, security audits, and bug investigations across large projects.

주요 기능

Automated token counting for precise LLM context window management
Multi-format packaging including XML, Markdown, JSON, and Plain Text
Integrated security auditing to detect sensitive data leaks before packaging
0 GitHub stars
Remote repository processing directly from GitHub/GitLab URLs
Git-aware file selection and language-specific comment removal

사용 사례

Preparing comprehensive codebase context for AI-driven code reviews and refactoring
Generating snapshots of remote repositories for architectural analysis without cloning
Creating secure, audit-ready files for third-party library evaluations and bug hunting

소개

주요 기능

Automated token counting for precise LLM context window management
Multi-format packaging including XML, Markdown, JSON, and Plain Text
Integrated security auditing to detect sensitive data leaks before packaging
0 GitHub stars
Remote repository processing directly from GitHub/GitLab URLs
Git-aware file selection and language-specific comment removal

사용 사례

Preparing comprehensive codebase context for AI-driven code reviews and refactoring
Generating snapshots of remote repositories for architectural analysis without cloning
Creating secure, audit-ready files for third-party library evaluations and bug hunting