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Repomix

  • 372 installs
  • 2.2k repo stars
  • Updated April 3, 2026
  • mrgoonie/claudekit-skills

repomix is a Claude Code skill that packs entire code repositories into single AI-friendly files using the Repomix CLI with git-aware ignore rules, token counting, and XML, Markdown, or JSON output formats.

About

repomix is a Claude Code skill from mrgoonie/claudekit-skills wrapping the Repomix CLI for packaging entire code repositories into single AI-friendly files. The skill supports customizable include and exclude patterns, git-aware processing that respects .gitignore, token counting for LLM context budgets, security checks for sensitive information, and four output formats: XML, Markdown, JSON, and plain text. Common workflows cover codebase analysis, third-party library evaluation, security audit preparation, documentation context generation, and bug investigation across large trees. Bundled references include configuration.md for patterns and output options and usage-patterns.md for audit and analysis workflows. Developers reach for repomix when an agent needs full repository context in one bounded artifact instead of ad-hoc file-by-file reads during review or refactor tasks.

  • Repo-to-context bundling
  • Ignore and include rules
  • Token-efficient packing
  • Large-codebase onboarding

Repomix by the numbers

  • 372 all-time installs (skills.sh)
  • +5 installs in the week ending Jul 26, 2026 (Skillselion tracking)
  • Ranked #2,036 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 3, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mrgoonie/claudekit-skills --skill repomix

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Listed on Skillselion
Installs372
repo stars2.2k
Last updatedApril 3, 2026
Repositorymrgoonie/claudekit-skills

How do you pack a repo for LLM analysis?

Pack entire repositories into LLM-friendly bundles with ignore rules so agents can review, refactor, or document large codebases efficiently.

Who is it for?

Developers preparing full codebase snapshots for LLM review, security audits, third-party library evaluation, or large-scale refactor planning.

Skip if: Developers who only need live official API documentation for an external library should use docs-seeker instead of packing an entire repository.

When should I use this skill?

User needs to package a codebase for AI analysis, create repository snapshots for LLM context, audit third-party code, or evaluate unfamiliar repos with token-aware filters.

What you get

Single AI-friendly repository bundle file with preserved directory structure, token count metadata, gitignore-respecting filters, and optional security redaction checks.

  • Packed repository file
  • Token count summary
  • Filtered codebase snapshot

By the numbers

  • Supports 4 output formats: XML, Markdown, JSON, and plain text
  • Bundles 2 reference guides: configuration.md and usage-patterns.md

Files

SKILL.mdMarkdownGitHub ↗

Repomix Skill

Repomix packs entire repositories into single, AI-friendly files. Perfect for feeding codebases to LLMs like Claude, ChatGPT, and Gemini.

When to Use

Use when:

  • Packaging codebases for AI analysis
  • Creating repository snapshots for LLM context
  • Analyzing third-party libraries
  • Preparing for security audits
  • Generating documentation context
  • Investigating bugs across large codebases
  • Creating AI-friendly code representations

Quick Start

Check Installation

repomix --version

Install

# npm
npm install -g repomix

# Homebrew (macOS/Linux)
brew install repomix

Basic Usage

# Package current directory (generates repomix-output.xml)
repomix

# Specify output format
repomix --style markdown
repomix --style json

# Package remote repository
npx repomix --remote owner/repo

# Custom output with filters
repomix --include "src/**/*.ts" --remove-comments -o output.md

Core Capabilities

Repository Packaging

  • AI-optimized formatting with clear separators
  • Multiple output formats: XML, Markdown, JSON, Plain text
  • Git-aware processing (respects .gitignore)
  • Token counting for LLM context management
  • Security checks for sensitive information

Remote Repository Support

Process remote repositories without cloning:

# Shorthand
npx repomix --remote yamadashy/repomix

# Full URL
npx repomix --remote https://github.com/owner/repo

# Specific commit
npx repomix --remote https://github.com/owner/repo/commit/hash

Comment Removal

Strip comments from supported languages (HTML, CSS, JavaScript, TypeScript, Vue, Svelte, Python, PHP, Ruby, C, C#, Java, Go, Rust, Swift, Kotlin, Dart, Shell, YAML):

repomix --remove-comments

Common Use Cases

Code Review Preparation

# Package feature branch for AI review
repomix --include "src/**/*.ts" --remove-comments -o review.md --style markdown

Security Audit

# Package third-party library
npx repomix --remote vendor/library --style xml -o audit.xml

Documentation Generation

# Package with docs and code
repomix --include "src/**,docs/**,*.md" --style markdown -o context.md

Bug Investigation

# Package specific modules
repomix --include "src/auth/**,src/api/**" -o debug-context.xml

Implementation Planning

# Full codebase context
repomix --remove-comments --copy

Command Line Reference

File Selection

# Include specific patterns
repomix --include "src/**/*.ts,*.md"

# Ignore additional patterns
repomix -i "tests/**,*.test.js"

# Disable .gitignore rules
repomix --no-gitignore

Output Options

# Output format
repomix --style markdown  # or xml, json, plain

# Output file path
repomix -o output.md

# Remove comments
repomix --remove-comments

# Copy to clipboard
repomix --copy

Configuration

# Use custom config file
repomix -c custom-config.json

# Initialize new config
repomix --init  # creates repomix.config.json

Token Management

Repomix automatically counts tokens for individual files, total repository, and per-format output.

Typical LLM context limits:

  • Claude Sonnet 4.5: ~200K tokens
  • GPT-4: ~128K tokens
  • GPT-3.5: ~16K tokens

Security Considerations

Repomix uses Secretlint to detect sensitive data (API keys, passwords, credentials, private keys, AWS secrets).

Best practices: 1. Always review output before sharing 2. Use .repomixignore for sensitive files 3. Enable security checks for unknown codebases 4. Avoid packaging .env files 5. Check for hardcoded credentials

Disable security checks if needed:

repomix --no-security-check

Implementation Workflow

When user requests repository packaging:

1. Assess Requirements

  • Identify target repository (local/remote)
  • Determine output format needed
  • Check for sensitive data concerns

2. Configure Filters

  • Set include patterns for relevant files
  • Add ignore patterns for unnecessary files
  • Enable/disable comment removal

3. Execute Packaging

  • Run repomix with appropriate options
  • Monitor token counts
  • Verify security checks

4. Validate Output

  • Review generated file
  • Confirm no sensitive data
  • Check token limits for target LLM

5. Deliver Context

  • Provide packaged file to user
  • Include token count summary
  • Note any warnings or issues

Reference Documentation

For detailed information, see:

  • Configuration Reference - Config files, include/exclude patterns, output formats, advanced options
  • Usage Patterns - AI analysis workflows, security audit preparation, documentation generation, library evaluation

Additional Resources

  • GitHub: https://github.com/yamadashy/repomix
  • Documentation: https://repomix.com/guide/
  • MCP Server: Available for AI assistant integration

Related skills

FAQ

Which output formats does the repomix skill support?

The repomix skill supports Repomix CLI output in XML, Markdown, JSON, and plain text formats, with git-aware include and exclude patterns, token counting, and security checks for sensitive data before feeding code to LLMs.

When should developers use repomix versus docs-seeker?

repomix packs an entire local or cloned repository into one AI-friendly file for review or audit. docs-seeker fetches live official documentation via llms.txt, Context7 MCP, or Repomix analysis of upstream GitHub repos.

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