---
title: Use Cases
description: Explore practical Repomix workflows for AI code review, bug investigation, refactoring, documentation, onboarding, security audits, and architecture analysis.
---

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# Use Cases

Repomix's strength lies in its ability to work with any subscription service like ChatGPT, Claude, Gemini, Grok without worrying about costs, while providing complete codebase context that eliminates the need for file exploration—making analysis faster and often more accurate.

With the entire codebase available as context, Repomix enables a wide range of applications including implementation planning, bug investigation, third-party library security checks, documentation generation, and much more.


## Real-World Use Cases

### Using Repomix with AI Assistants (Grok Example)
This video shows how to convert GitHub repositories into AI-readable formats using Repomix's web interface, then upload to AI assistants like Grok for strategic planning and code analysis.

**Use Case**: Quick repository conversion for AI tools
- Pack public GitHub repos via web interface
- Choose format: XML, Markdown, or Plain text
- Upload to AI assistants for codebase understanding

<YouTubeVideo video-id="XTifjfeMp4M" :start="488" />

### Using Repomix with Simon Willison's LLM CLI Tool
Learn how to combine Repomix with [Simon Willison's llm CLI tool](https://github.com/simonw/llm) to analyze entire codebases. This video shows how to package repositories into XML format and feed them to various LLMs for Q&A, documentation generation, and implementation planning.

**Use Case**: Enhanced codebase analysis with LLM CLI
- Package repositories with `repomix` command
- Use `--remote` flag to pack directly from GitHub
- Attach output to LLM prompts with `-f repo-output.xml`

<YouTubeVideo video-id="UZ-9U1W0e4o" :start="592" />

### LLM Code Generation Workflow
Learn how a developer uses Repomix to feed entire codebase context into tools like Claude and Aider. This enables AI-driven incremental development, smarter code reviews, and automated documentation, all while maintaining project-wide consistency.

**Use Case**: Streamlined development workflow with AI assistance
- Extract complete codebase context
- Provide context to LLMs for better code generation
- Maintain consistency across the entire project

[Read the full workflow →](https://harper.blog/2025/02/16/my-llm-codegen-workflow-atm/)

### Creating Knowledge Datapacks for LLMs
Authors are using Repomix to package their written content—blogs, documentation, and books—into LLM-compatible formats, enabling readers to interact with their expertise through AI-powered Q&A systems.

**Use Case**: Knowledge sharing and interactive documentation
- Package documentation into AI-friendly formats
- Enable interactive Q&A with content
- Create comprehensive knowledge bases

[Learn more about knowledge datapacks →](https://lethain.com/competitive-advantage-author-llms/)


## Other Examples

### Code Understanding & Quality

#### Bug Investigation
Share your entire codebase with AI to identify the root cause of issues across multiple files and dependencies.

```
This codebase has a memory leak issue in the server. The application crashes after running for several hours. Please analyze the entire codebase and identify potential causes.
```

#### Implementation Planning
Get comprehensive implementation advice that considers your entire codebase architecture and existing patterns.

```
I want to add user authentication to this application. Please review the current codebase structure and suggest the best approach that fits with the existing architecture.
```

#### Refactoring Assistance
Get refactoring suggestions that maintain consistency across your entire codebase.

```
This codebase needs refactoring to improve maintainability. Please suggest improvements while keeping the existing functionality intact.
```

#### Code Review
Comprehensive code review that considers the entire project context.

```
Please review this codebase as if you were doing a thorough code review. Focus on code quality, potential issues, and improvement suggestions.
```

#### Documentation Generation
Generate comprehensive documentation that covers your entire codebase.

```
Generate comprehensive documentation for this codebase, including API documentation, setup instructions, and developer guides.
```

#### Knowledge Extraction
Extract technical knowledge and patterns from your codebase.

```
Extract and document the key architectural patterns, design decisions, and best practices used in this codebase.
```

#### Codebase Onboarding
Help new team members quickly understand your codebase structure and key concepts.

```
You are helping a new developer understand this codebase. Please provide an overview of the architecture, explain the main components and their interactions, and highlight the most important files to review first.
```

### Security & Dependencies

#### Dependency Security Audit
Analyze third-party libraries and dependencies for security issues.

```
Please analyze all third-party dependencies in this codebase for potential security vulnerabilities and suggest safer alternatives where needed.
```

#### Library Integration Analysis
Understand how external libraries are integrated into your codebase.

```
Analyze how this codebase integrates with external libraries and suggest improvements for better maintainability.
```

#### Comprehensive Security Scanning
Analyze your entire codebase for potential security vulnerabilities and get actionable recommendations.

```
Perform a comprehensive security audit of this codebase. Check for common vulnerabilities like SQL injection, XSS, authentication issues, and insecure data handling. Provide specific recommendations for each finding.
```

### Architecture & Performance

#### API Design Review
Review your API design for consistency, best practices, and potential improvements.

```
Review all REST API endpoints in this codebase. Check for consistency in naming conventions, HTTP methods usage, response formats, and error handling. Suggest improvements following REST best practices.
```

#### Framework Migration Planning
Get detailed migration plans for updating to modern frameworks or languages.

```
Create a step-by-step migration plan to convert this codebase from [current framework] to [target framework]. Include risk assessment, estimated effort, and recommended migration order.
```

#### Performance Optimization
Identify performance bottlenecks and receive optimization recommendations.

```
Analyze this codebase for performance bottlenecks. Look for inefficient algorithms, unnecessary database queries, memory leaks, and areas that could benefit from caching or optimization.
```

## Related Resources

- [Prompt Examples](/guide/prompt-examples) - More prompt templates for AI analysis
- [Output Formats](/guide/output) - Choose the best format for your AI model
- [Custom Instructions](/guide/custom-instructions) - Add context to guide AI analysis
- [GitHub Repository Processing](/guide/remote-repository-processing) - Analyze remote repositories
- [FAQ and Troubleshooting](/guide/faq) - Solve common setup, privacy, and token usage questions
