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dthuy62
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ck:repomix

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

Updated May 10, 2026
Search Tags:#security audit#codebase analysis#ai context#batch processing#repomix#repository packaging
One-click install
npx skills add https://github.com/dthuy62/kollet-app --skill ck-repomix-dthuy62
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:repomix
Source: https://github.com/dthuy62/kollet-app/tree/main/.opencode/skills/repomix
Command: npx skills add https://github.com/dthuy62/kollet-app --skill ck-repomix-dthuy62

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest, pytest-cov, pytest-mock, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Repomix removes the friction of turning a full repository into a compact, AI-friendly snapshot, so you can analyze large codebases without manually collecting files.

Core Features & Use Cases

  • Packages local or remote repositories into XML, Markdown, JSON, or plain text outputs.
  • Supports targeted inclusion and exclusion patterns, comment removal, and token-aware context preparation.
  • Helps with code review, bug investigation, security audits, documentation generation, and cross-repository comparisons.
  • A practical use case is preparing a clean repository snapshot before asking an AI to review architecture or investigate a defect.

Quick Start

Ask the skill to package the repository into an AI-ready snapshot with Repomix, choosing the output format and filters you need.

Frequently Asked Questions about ck:repomix

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

FAQPage Schema
How do I package a repository into an AI-ready snapshot for analysis?▼

You can package a repository into an AI-ready snapshot by using Repomix to aggregate local or remote codebases into XML, Markdown, JSON, or plain text outputs for analysis. This process supports configurable output formats, include and ignore filters, comment removal, and token-aware context preparation.

Can I run a security audit on a codebase using repository packaging?▼

Yes, repository packaging supports security audits by compiling the codebase into a compact snapshot with built-in security checks. This allows you to feed the complete codebase context into an AI for vulnerability investigation and review.

Does repository packaging work with both local and remote repositories?▼

Repository packaging works with both local and remote repositories. You can configure the packaging process with include and ignore filters to target specific files for code review, debugging, or documentation generation.

What is the best way to prepare a large codebase for AI context without losing structure?▼

The best way to prepare a large codebase for AI context is through token-aware packaging. This method removes comments and outputs the repository in XML or Markdown, preserving the structural hierarchy while compressing the content for AI analysis.

What output formats are supported when packaging a codebase for cross-repository comparison?▼

When packaging a codebase for cross-repository comparison, supported output formats include XML, Markdown, JSON, and plain text. These formats help structure the repository content for AI-driven comparative analysis.

Do I need to manually collect files for an AI code review?▼

No, you do not need to manually collect files for an AI code review. Repository packaging removes this friction by automatically aggregating the entire repository into an AI-friendly snapshot, allowing you to focus on architecture review and defect investigation.

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