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repomix

Packages code repositories into XML, Markdown, or JSON files for LLM context preparation.

Updated Feb 14, 2026
Search Tags:#security audit#codebase analysis#ai context#code packaging#repository snapshot#llm preparation
One-click install
npx skills add https://github.com/toanalien/ezdevsecops --skill repomix-toanalien
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: repomix
Source: https://github.com/toanalien/ezdevsecops/tree/main/.opencode/skills/repomix
Command: npx skills add https://github.com/toanalien/ezdevsecops --skill repomix-toanalien

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of preparing entire code repositories or specific parts of them into a single, AI-friendly file format, making them easily digestible for Large Language Models (LLMs).

Core Features & Use Cases

  • Repository Packaging: Converts codebases into AI-optimized formats (XML, Markdown, JSON, Plain text).
  • LLM Context Preparation: Ideal for feeding codebases to LLMs for analysis, review, or documentation.
  • Security Audits: Helps in packaging code for security analysis by identifying sensitive data.
  • Use Case: You need to provide a large codebase to an AI assistant for a security audit. Use Repomix to package the entire repository into a single XML file, ensuring sensitive information is flagged and comments are removed to reduce token count.

Quick Start

Use repomix to package the current directory into a markdown file.

Frequently Asked Questions about repomix

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I package a code repository for AI analysis?▼

Packaging a code repository for AI analysis involves converting the codebase into XML, Markdown, JSON, or plain text formats. This process creates a single AI-friendly file, making it easily digestible for LLMs during context preparation.

What is the best way to prepare a codebase snapshot for LLM context?▼

Preparing a codebase snapshot for LLM context is best achieved by packaging the repository into a single AI-optimized file. This approach ensures the entire codebase is structured effectively for large language model ingestion and review.

Can I run a security audit on a codebase by packaging it for LLMs?▼

You can perform a security audit by packaging the codebase for LLMs. The packaging process identifies sensitive data and can remove comments, ensuring the AI assistant receives a secure, token-optimized repository snapshot for analysis.

Does repository packaging support remote repositories and comment removal?▼

Repository packaging supports remote repository processing and comment removal. These features facilitate third-party library analysis and reduce token count by stripping unnecessary comments from the generated codebase snapshot.

What file formats are supported when packaging code for LLM preparation?▼

Packaging code for LLM preparation supports XML, Markdown, JSON, and plain text file formats. Converting the codebase into these AI-friendly formats allows flexible integration depending on your specific AI assistant requirements.

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