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Stonelukas
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repomix

Package local or remote repositories into XML, Markdown, JSON, or plain text files.

Updated Jun 16, 2025
Search Tags:#context#security#codebase#llm#packaging#repository#remote
One-click install
npx skills add https://github.com/Stonelukas/Mouse-on-Numpad --skill repomix-stonelukas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: repomix
Source: https://github.com/Stonelukas/Mouse-on-Numpad/tree/main/.opencode/skill/repomix
Command: npx skills add https://github.com/Stonelukas/Mouse-on-Numpad --skill repomix-stonelukas

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Repomix consolidates entire repositories into AI-friendly single files, simplifying large-scale code understanding and context packaging.

Core Features & Use Cases

  • AI-friendly repository packaging in XML, Markdown, JSON, and plain text formats for efficient ingestion by large language models.
  • Git-aware processing that respects .gitignore and supports remote repositories without cloning, enabling security-aware audits and architecture reviews.
  • Token counting, customizable output, and security checks to facilitate context management and risk assessment across codebases.

Quick Start

Run repomix at project root to generate a compact AI-ready snapshot for your codebase.

Frequently Asked Questions about repomix

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

FAQPage Schema
How do I package an entire codebase into a single file for LLM context?▼

To package a codebase for LLM context, run the tool at your project root. It consolidates your repository into a single AI-friendly file, respecting .gitignore rules to exclude unnecessary items.

What is the best way to provide repository context for a security audit?▼

Providing repository context for a security audit is done by packaging the codebase into a consolidated snapshot. This AI-friendly output includes security checks and token counting to facilitate risk assessment.

Can I package remote repositories for AI consumption without cloning them first?▼

Yes, you can package remote repositories for AI consumption without cloning. The tool supports remote repository packaging directly, allowing you to generate codebase snapshots for analysis without local copies.

What output formats are supported when packaging codebases for large language models?▼

Supported output formats for packaging codebases include XML, Markdown, JSON, and plain text. These formats ensure efficient ingestion and customizable context generation for large language models.

Does the repository packaging process respect Git ignore configurations?▼

Yes, the repository packaging process respects .gitignore configurations. This Git-aware processing ensures that only relevant code is included in the AI-ready snapshot, keeping the output compact.

How do I track token counts when preparing documentation context for an LLM?▼

You can track token counts when preparing documentation context by using the built-in token counting feature. This helps manage context limits and ensures the generated codebase snapshot fits your model's capacity.

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