repomix

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

Check Installation

```bash

repomix --version

```

Install

```bash

# npm

npm install -g repomix

# Homebrew (macOS/Linux)

brew install repomix

```

Basic Usage

```bash

# 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

```

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:

```bash

# 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):

```bash

repomix --remove-comments

```

Code Review Preparation

```bash

# Package feature branch for AI review

repomix --include "src/*/.ts" --remove-comments -o review.md --style markdown

```

Security Audit

```bash

# Package third-party library

npx repomix --remote vendor/library --style xml -o audit.xml

```

Documentation Generation

```bash

# Package with docs and code

repomix --include "src/,docs/,*.md" --style markdown -o context.md

```

Bug Investigation

```bash

# Package specific modules

repomix --include "src/auth/,src/api/" -o debug-context.xml

```

Implementation Planning

```bash

# Full codebase context

repomix --remove-comments --copy

```

File Selection

```bash

# Include specific patterns

repomix --include "src/*/.ts,*.md"

# Ignore additional patterns

repomix -i "tests/*,.test.js"

# Disable .gitignore rules

repomix --no-gitignore

```

Output Options

```bash

# 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

```bash

# Use custom config file

repomix -c custom-config.json

# Initialize new config

repomix --init # creates repomix.config.json

```

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:

```bash

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:

```bash

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

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:

```bash

repomix --no-security-check

```

When user requests repository packaging:

1. Assess Requirements

- Identify target repository (local/remote)

- Determine output format needed

- Check for sensitive data concerns

1. Configure Filters

- Set include patterns for relevant files

- Add ignore patterns for unnecessary files

- Enable/disable comment removal

1. Execute Packaging

- Run repomix with appropriate options

- Monitor token counts

- Verify security checks

1. Validate Output

- Review generated file

- Confirm no sensitive data

- Check token limits for target LLM

1. Deliver Context

- Provide packaged file to user

- Include token count summary

- Note any warnings or issues

For detailed information, see:

• [Configuration Reference](./references/configuration.md) - Config files, include/exclude patterns, output formats, advanced options
• [Usage Patterns](./references/usage-patterns.md) - AI analysis workflows, security audit preparation, documentation generation, library evaluation

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