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

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:

• Always review output before sharing

• Use .repomixignore for sensitive files

• Enable security checks for unknown codebases

• Avoid packaging .env files

• Check for hardcoded credentials

Disable security checks if needed:

repomix --no-security-check

Implementation Workflow

When user requests repository packaging:

Assess Requirements

• Identify target repository (local/remote)

• Determine output format needed

• Check for sensitive data concerns

Configure Filters

• Set include patterns for relevant files

• Add ignore patterns for unnecessary files

• Enable/disable comment removal

Execute Packaging

• Run repomix with appropriate options

• Monitor token counts

• Verify security checks

Validate Output

• Review generated file

• Confirm no sensitive data

• Check token limits for target LLM

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