Repomix

Repomix Skill

Repomix packs entire repositories into single, AI-friendly files. Perfect for feeding codebases to LLMs like Codex, 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:

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

Token Count Optimization

Understanding your codebase's token distribution is crucial for optimizing AI interactions. Use the --token-count-tree option to visualize token usage across your project:

repomix --token-count-tree

This displays a hierarchical view of your codebase with token counts:

🔢 Token Count Tree:
────────────────────
└── src/ (70,925 tokens)
    ├── cli/ (12,714 tokens)
    │   ├── actions/ (7,546 tokens)
    │   └── reporters/ (990 tokens)
    └── core/ (41,600 tokens)
        ├── file/ (10,098 tokens)
        └── output/ (5,808 tokens)

You can also set a minimum token threshold to focus on larger files:

repomix --token-count-tree 1000  # Only show files/directories with 1000+ tokens

This helps you:

• Identify token-heavy files that might exceed AI context limits
• Optimize file selection using --include and --ignore patterns
• Plan compression strategies by targeting the largest contributors
• Balance content vs. context when preparing code for AI analysis

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