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ck:repomix

Package entire code repositories into XML, Markdown, JSON, or plain text.

Updated Jan 11, 2026
Search Tags:#code analysis#codebase#llm#packaging#ai context#repository snapshot
One-click install
npx skills add https://github.com/felix-howard/ella --skill ck-repomix
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:repomix
Source: https://github.com/felix-howard/ella/tree/main/.opencode/skills/repomix
Command: npx skills add https://github.com/felix-howard/ella --skill ck-repomix

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 directories 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 XML, Markdown, JSON, or plain text formats.
  • LLM Context Preparation: Ideal for feeding code to AI models for analysis, review, or documentation.
  • Use Case: You need to provide a large codebase to an AI for a security audit. Use Repomix to package the entire repository into an XML file, ensuring the AI has all necessary context without manual file selection.

Quick Start

Package the current directory into an AI-friendly Markdown file.

Frequently Asked Questions about ck:repomix

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

FAQPage Schema
How do I package an entire code repository for LLM context?▼

Package a code repository for LLM context by converting the entire codebase into a single AI-friendly file format such as XML, Markdown, JSON, or plain text. This creates a comprehensive snapshot for models to analyze.

What is the best way to prepare a codebase snapshot for a security audit?▼

Prepare a codebase snapshot for a security audit by packaging the entire repository into an AI-friendly format like XML. This ensures the AI receives all necessary contextual code without requiring manual file selection.

Can I process remote repositories when creating AI-friendly codebase files?▼

Yes, you can process remote repositories when creating AI-friendly codebase files. The packaging tool supports remote repository processing directly, allowing you to generate snapshots without cloning locally first.

Does packaging code for AI models support token counting for context management?▼

Yes, packaging code for AI models supports token counting for LLM context management. This helps you track and manage the size of the generated codebase snapshot against model context limits.

How do I remove comments when packaging a repository for AI analysis?▼

Remove comments when packaging a repository for AI analysis using the tool's comment removal feature during the conversion process. This strips out non-essential text to reduce token usage in the final output.

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