rules4 1.1.1

A universal CLI utility to configure AI rules files for any project.

rules4

A universal CLI utility to configure AI rules files (e.g., .roo/rules, CLAUDE.md, .cursor/rules) for any project, based on the latest industry best practices via live Perplexity research.

Features

• Supports any language or framework via --lang and --tags options
• Configures rules for tools like Cursor, Roo, Claude, and more
• Flexible model selection: Use OpenAI or Anthropic models for both generation and review
• Mix and match models: e.g., Claude for generation, GPT-4 for review
• Uses live Perplexity API for up-to-date best practices
• Built-in --list-models command to see all available models
• Dry-run mode to preview changes
• Prompts before overwriting existing files
• Simple one-command install (packaged for PyPI)
• Designed for future MCP integration

Installation

pip install rules4

Quick Start

1. Initialize rules4 in your project:

rules4 init

2. Generate rules for your favorite AI coding assistant:

# For Cursor
rules4 cursor --lang python --tags "testing,security"

# For Claude with research
rules4 claude --research --lang javascript --tags "react,typescript"

# For all configured tools
rules4 generate

API Keys and Environment Variables

rules4 interacts with various AI models and research services. To use these features, you need to set up the corresponding API keys as environment variables:

• OPENAI_API_KEY: Required for generating rules using OpenAI models (e.g., gpt-4-turbo, gpt-4o).
• ANTHROPIC_API_KEY: Required for generating rules using Anthropic models (e.g., claude-3-5-sonnet-20241022, claude-3-opus-20240229).
• PERPLEXITY_API_KEY: Required if you use the --research flag to perform research with Perplexity AI.

Example (add to your shell profile, e.g., ~/.bashrc or ~/.zshrc):

export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export PERPLEXITY_API_KEY="your_perplexity_api_key"

Usage

Basic Rule Generation

To generate rules for a specific tool (e.g., copilot) for a given language and tags:

rules4 copilot --lang python --tags "pytest,langgraph"

This command will:

• Use gpt-4-turbo as the primary model (default).
• Generate rules for Python projects, focusing on pytest and langgraph.
• Save the rules to .github/copilot-python-pytest,langgraph.md (or similar, depending on the tool).

Advanced Usage

You can specify a primary model, a review model, and enable research. Both --primary and --review flags support OpenAI and Anthropic models:

# Use Claude as primary, GPT-4 as reviewer
rules4 copilot --primary claude-3-5-sonnet-20241022 --review gpt-4o --research --lang javascript --tags "react,typescript"

# Use GPT-4 for both generation and review
rules4 cursor --primary gpt-4-turbo --review gpt-4o --lang python --tags "async,testing"

# Use Claude for both generation and review
rules4 claude --primary claude-3-opus-20240229 --review claude-3-5-sonnet-20241022 --lang go --tags "concurrency"

These commands demonstrate the flexibility:

• You can use any combination of OpenAI and Anthropic models
• The same model can be used for both primary generation and review
• Research always uses Perplexity's sonar-pro model

Generating Rules for All Configured Tools

If you have a .rules4rc file configured, you can generate rules for all specified tools:

rules4 generate --lang go --tags "code style"

This command will:

• Read the list of tools from your .rules4rc file.
• Generate rules for each tool, focusing on code style for Go projects.

Command-Line Options

• --primary <model_name>: Specify the primary AI model for rule generation. Supports both OpenAI and Anthropic models (e.g., gpt-4-turbo, gpt-4o, claude-3-5-sonnet-20241022).
• --review <model_name>: Specify an AI model for reviewing and refining the generated rules. Also supports both OpenAI and Anthropic models.
• --research: Enable research using Perplexity AI before rule generation.
• --lang <language>: Specify the programming language for rule generation (e.g., python, javascript, go).
• --tags <tag1,tag2,...>: Comma-separated list of tags or topics for rule generation (e.g., pytest,langgraph, react,typescript, code style).
• --dry-run: Preview the changes without actually writing any files.
• --yes, -y: Overwrite existing files without prompting for confirmation.
• --project-path <path>: (Optional) Specify the target project directory. Defaults to the current directory.

Listing Available Models

To see all available models for use with --primary and --review:

rules4 list-models

This will display models grouped by provider (OpenAI, Anthropic, and Perplexity).

---

This project is in early development. For contributions, see CONTRIBUTING.md.

Publishing

For maintainers, this project includes a comprehensive publishing system:

Prerequisites

# Install publishing dependencies
pip install build twine

# Set up API tokens
export PYPI_API_TOKEN="your-pypi-token"           # For PyPI
export TEST_PYPI_API_TOKEN="your-test-pypi-token" # For TestPyPI

Publishing Commands

# Test publish (recommended first)
./publish.sh --test --dry-run    # Preview what would be published to TestPyPI
./publish.sh --test              # Publish to TestPyPI

# Production publish
./publish.sh --dry-run           # Preview what would be published to PyPI
./publish.sh                     # Publish to PyPI

# With version update
./publish.sh --version 1.2.3     # Update version and publish

Make Commands

make publish-test    # Publish to TestPyPI
make publish         # Publish to PyPI

Publishing Features

The enhanced publish.sh script includes:

• ✅ Pre-flight checks: Virtual environment, dependencies, API tokens
• ✅ Quality assurance: Runs all tests and linting before publishing
• ✅ Version management: Automatic version updates in both pyproject.toml and CLI
• ✅ Dual repositories: Support for both PyPI and TestPyPI
• ✅ Safety features: Dry-run mode, build validation, error handling
• ✅ User experience: Colored output, progress indicators, helpful messages

Development

Setup

# Clone and setup
git clone https://github.com/dimitritholen/airules.git
cd airules
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Quality Assurance

make test         # Run tests
make lint         # Run all linting checks
make lint-fix     # Auto-fix formatting issues
make format       # Format code with black
make type-check   # Run mypy type checking

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run make test lint to ensure quality
5. Submit a pull request

Support

• 📖 Documentation
• 🐛 Bug Reports
• 💡 Feature Requests