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repomix

VoDaiLocz/kilo-kit-mcp/skills/engineering/repomix/SKILL.md

Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.

Skill26 starsChanged 44 days ago
  • Reads credentials
  • Installs packages

What's in it

  1. Repomix Skill
  2. When to Use
  3. Quick Start
  4. Check Installation
  5. Install
  6. Basic Usage
  7. Core Capabilities
  8. Repository Packaging
  9. Remote Repository Support
  10. Comment Removal
  11. Common Use Cases
  12. Code Review Preparation
  13. Security Audit
  14. Documentation Generation
  15. Bug Investigation
  16. Implementation Planning
  17. Command Line Reference
  18. File Selection
  19. Output Options
  20. Configuration
  21. Token Management
  22. Security Considerations
  23. Implementation Workflow
  24. Reference Documentation
  25. Additional Resources
---
name: repomix
description: Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
---

# 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
```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
```

## 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:
```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
```

## Common Use Cases

### 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
```

## Command Line Reference

### 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
```

## 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:
```bash
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](./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

## Additional Resources

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

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