Project Code Namer

A powerful tool to analyze and improve variable and function names in JavaScript/TypeScript projects using AI-powered suggestions and predefined naming rules.

🚀 Quick Start

1. Install the package:

   # With npm
   npm install --save-dev project-code-namer
   
   # With yarn
   yarn add --dev project-code-namer

2. Set up AI (recommended):

   # Install Ollama (free local AI)
   brew install ollama && brew services start ollama
   
   # Run setup script
   yarn setup-ollama  # or npm run setup-ollama

3. Start analyzing:

   yarn project-code-namer  # or npx project-code-namer

That's it! The tool will guide you through the rest.

🚀 Features

• 🧠 Smart Analysis: Analyzes code context to suggest more descriptive names
• 🤖 AI Integration: Support for multiple AI providers (OpenAI, Anthropic, Google Gemini, Ollama, GitHub Copilot)
• 📋 Predefined Rules: Rule-based system following naming best practices
• ⚛️ React Support: Specific suggestions for React components, hooks, and events
• 🧪 Context Detection: Recognizes different file types (testing, API, components, etc.)
• 📊 Detailed Statistics: Complete analysis reports
• 🎯 Easy to Use: Interactive and user-friendly CLI interface
• 🏗️ Clean Architecture: Built with SOLID principles and clean code practices

📦 Installation

Global Installation

With npm:

npm install -g project-code-namer

With yarn:

yarn global add project-code-namer

Project-specific Installation

With npm:

npm install --save-dev project-code-namer

With yarn:

yarn add --dev project-code-namer

🤖 AI Setup (Optional but Recommended)

Option 1: Ollama - Free Local AI (Recommended)

Ollama allows you to run AI models locally for free, ensuring privacy and no API costs.

Step 1: Install Ollama

macOS:

# Install Ollama using Homebrew
brew install ollama

# Start the Ollama service
brew services start ollama

Windows:

1. Download from https://ollama.ai/download/windows
2. Run the installer
3. Or use winget:

   winget install Ollama.Ollama

Linux:

# Install using the official script
curl -fsSL https://ollama.ai/install.sh | sh

# Start the service
sudo systemctl start ollama
sudo systemctl enable ollama

Step 2: Download a Model

After installing Ollama, download a lightweight model:

# Download a small, fast model (1.3GB) - Recommended
ollama pull llama3.2:1b

# Or a more capable model (4.7GB) - Better quality
ollama pull llama3.2:3b

# Verify downloaded models
ollama list

Step 3: Verify Installation

Test that everything is working:

# Test the model
ollama run llama3.2:1b "Hello, can you suggest better names for a variable called 'data'?"

Step 4: Create Configuration File

In your project root, create a file called .ai-config.json:

{
  "provider": "ollama",
  "ollama": {
    "endpoint": "http://localhost:11434/api/generate",
    "model": "llama3.2:1b"
  }
}

Option 2: OpenAI (Paid)

If you prefer OpenAI's models:

1. Get your API key from OpenAI Platform
2. Create a .ai-config.json file in your project root:

   {
     "provider": "openai",
     "openai": {
       "apiKey": "your_actual_api_key_here",
       "model": "gpt-3.5-turbo"
     }
   }

3. Or use environment variables by creating a .env file:

   OPENAI_API_KEY=your_actual_api_key_here

Auto-Setup for Ollama

Run the automatic setup script to configure Ollama:

With npm:

npm run setup-ollama

With yarn:

yarn setup-ollama

🎯 Usage

Command Line

With npm:

npx project-code-namer

With yarn:

yarn project-code-namer

If installed globally:

project-code-namer

Programmatic Usage

import { NamerSuggesterApp, CodeAnalyzer, SuggestionService } from 'project-code-namer';

// Use the complete application
const app = new NamerSuggesterApp();
await app.run();

// Or use individual components
const result = CodeAnalyzer.analyzeFile('./src/example.ts');

const suggestionService = new SuggestionService(config);
const suggestions = await suggestionService.getSuggestions(
  'data',
  'variable',
  '',
  fileContext
);

⚙️ Configuration

Quick Start Configuration

After installation, you need to configure an AI provider. The easiest way is:

1. Run the setup script (automatically creates .ai-config.json):

   # With npm
   npm run setup-ollama
   
   # With yarn
   yarn setup-ollama

2. Or manually create .ai-config.json in your project root:

AI Configuration Options

Create a .ai-config.json file in your project root with one of these configurations:

Option 1: Ollama (Free Local AI - Recommended)

{
  "provider": "ollama",
  "ollama": {
    "endpoint": "http://localhost:11434/api/generate",
    "model": "llama3.2:1b"
  }
}

Option 2: OpenAI (Paid)

{
  "provider": "openai",
  "openai": {
    "apiKey": "your-api-key-here",
    "model": "gpt-3.5-turbo"
  }
}

Option 3: Mixed Setup (Auto-fallback)

{
  "provider": "auto",
  "ollama": {
    "endpoint": "http://localhost:11434/api/generate",
    "model": "llama3.2:1b"
  },
  "openai": {
    "apiKey": "your-api-key-here",
    "model": "gpt-3.5-turbo"
  }
}

Option 4: Rules Only (No AI)

{
  "provider": "rules"
}

💡 Tip: The setup script (yarn setup-ollama) automatically creates the Ollama configuration for you!

Supported Providers

• 🆓 ollama: Free local models (Recommended)
• 🆓 rules: Predefined naming rules only
• 💰 openai: OpenAI GPT models (Paid)
• 💰 anthropic: Anthropic Claude (Paid)
• 💰 gemini: Google Gemini (Paid)
• 🔄 auto: Tries all available providers
• 🐙 copilot: GitHub Copilot CLI

🏗️ Architecture

The project follows Clean Code and SOLID principles:

src/
├── analyzers/          # Code analysis and context extraction
├── cli/               # Command line interface
├── config/            # Configuration management
├── providers/         # AI providers
├── services/          # Main business logic
├── types/            # TypeScript type definitions
├── utils/            # General utilities
├── bin/              # Executables
└── app.ts            # Main application

Applied Principles

• SRP (Single Responsibility Principle): Each class has a specific responsibility
• DRY (Don't Repeat Yourself): Reusable code without duplication
• Clean Code: Descriptive names, small functions, clear structure
• Separation of Concerns: Clear separation between analysis, suggestions, CLI, and configuration

💡 Types of Suggestions

Functions

• Event handlers (handle*, on*)
• API functions (fetch*, load*, retrieve*)
• Validators (validate*, is*Valid)
• Initializers (initialize*, setup*, create*)

Variables

• States and flags (is*, has*, should*)
• Data and collections (items, collection, payload)
• Counters and indices (counter, index, position)

React Specific

• Components (*Component)
• Custom hooks (use*)
• Event handlers (handle*, on*)
• States (*State)

📊 Example

Before

function getData() {
  const d = fetch('/api/users');
  return d;
}

const flag = true;
const arr = [1, 2, 3];

After (suggestions)

function fetchUserData() {
  const userData = fetch('/api/users');
  return userData;
}

const isVisible = true;
const userItems = [1, 2, 3];

🎮 Interactive Demo

When you run project-code-namer, you'll see an interactive menu:

🔍 Namer Suggester - Name Analyzer
---------------------------------------
🧰 Project detected: TYPESCRIPT
🛠️ Framework: NEXTJS
🤖 Suggestion engine: Automatic (tries all available)

🔧 What would you like to do?
❯ 📂 Analyze files and get naming suggestions
  ⚙️ Configure AI providers
  ❓ View help
  ❌ Exit

📈 Benefits

• Improved Code Readability: More descriptive and meaningful names
• Consistency: Follows established naming conventions
• Team Alignment: Standardized naming across the team
• Learning Tool: Helps developers learn better naming practices
• Time Saving: Automated suggestions instead of manual thinking
• Context Awareness: Suggestions based on code context and purpose

🔧 Development

Prerequisites

• Node.js 16+
• TypeScript 4.5+

Build

With npm:

npm run build

With yarn:

yarn build

Project Structure

The codebase is organized into specialized modules:

• analyzers/: AST parsing and context extraction
• cli/: Interactive user interface
• config/: Configuration management
• providers/: AI service integrations
• services/: Core business logic
• types/: TypeScript definitions
• utils/: Shared utilities

🤝 Contributing

1. Fork the project
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Guidelines

• Follow TypeScript best practices
• Maintain test coverage
• Update documentation
• Follow existing code style

❓ FAQ

Which AI provider should I use?

For most users, we recommend Ollama:

• ✅ Free: No API costs
• ✅ Private: Your code never leaves your machine
• ✅ Fast: No network latency
• ✅ Reliable: No rate limits or quotas

Use OpenAI if:

• You already have credits
• You need the absolute best quality suggestions
• You don't mind the API costs

Does this work offline?

With Ollama: Yes! Once you download a model, everything works completely offline.

With OpenAI: No. Requires internet connection for API calls.

How much disk space does Ollama use?

• llama3.2:1b: ~1.3GB (recommended for fast suggestions)
• llama3.2:3b: ~4.7GB (better quality, slower)

Is my code sent to external servers?

With Ollama: No. Everything stays on your local machine.

With OpenAI: Yes. Code snippets are sent to OpenAI's servers for analysis.

Can I use this in CI/CD?

Yes! Use the rules provider for CI/CD environments:

{
  "provider": "rules"
}

This uses only predefined rules without requiring AI models.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by code naming best practices
• Uses Babel for AST analysis
• Integration with multiple AI providers for advanced suggestions
• Built with modern TypeScript and Node.js

📞 Support

• 🐛 Report Issues
• 💬 Discussions
• 📧 Contact

🔮 Roadmap

• Unit and integration tests
• VS Code extension
• More AI providers
• Custom rule configuration
• Batch processing mode
• CI/CD integration
• Support for more languages

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Made by Juan David Peña

Helping developers write better, more readable code, one name at a time.