Supercharge your Cursor AI with codebase-specific intelligence. Cursorify transforms your GitHub repositories into custom rules that teach Cursor AI how your project works, resulting in more accurate code suggestions, better refactoring, and contextually aware assistance. Now supports multiple LLM providers including Anthropic Claude, OpenAI, and local models like Ollama.
- ✅ Instant Expertise: Your AI assistant immediately understands your project conventions and architecture
- ✅ Better Suggestions: Get code completions that match your project's style and patterns
- ✅ Fewer Hallucinations: Rules constrain the AI to work within your codebase's patterns and dependencies
- ✅ Zero Learning Curve: One command to analyze your repo and generate optimized rules
- 🚀 Analyze GitHub repositories or local codebases with a single command
- 🧩 Intelligently extract project structure, conventions, and patterns
- 📚 Handles large codebases that exceed the token limit for LLMs
- 🤖 Generate tailored Cursor rules using multiple LLM providers
- 🔌 Multi-Provider Support: Anthropic Claude, OpenAI, Ollama, LM Studio, and more
- 🏠 Local AI Support: Use your own models with Ollama or LM Studio
- 📝 Create production-ready .rules.mdc files for immediate use
- 🔧 Customize analysis with flexible configuration options
# Install globally
npm install -g cursorify
# Or use with npx (no installation required)
npx cursorify- Node.js 18 or higher
- API key for your chosen provider (see Provider Setup below)
export ANTHROPIC_API_KEY='your-anthropic-api-key'export OPENAI_API_KEY='your-openai-api-key'No API key required! Just make sure your local service is running.
The basic command structure is:
cursorify <repo-path>Examples:
# Inside a repository (uses Anthropic Claude by default)
cursorify
# Using OpenAI
cursorify --provider openai --model gpt-4o
# Using local Ollama model
cursorify --provider local --model llama3.1 --base-url http://localhost:11434/v1
# Using local LM Studio
cursorify --provider local --model llama3.1 --base-url http://localhost:1234/v1
# Using local repository path
cursorify ./my-local-project/subdir
# Using GitHub URL
cursorify --remote https://github.com/fastapi/fastapi
# Cursorify with specific description as an option
cursorify --description "guidelines for extending the component using the base interface"
# Specify Cursor AI rule type
cursorify --rule-type "agent" --description "coding standards for React components"
# List available providers and models
cursorify --list-providers
# Use existing repomix output file
cursorify --repomix-file ./my-repo-output.txt
# Use repomix file with specific provider
cursorify --repomix-file ./output.txt --provider openai --model gpt-4oThis will:
- Fetch the repository content
- Based on the content, generate rules for Cursor AI
- Save the output to a
<repo-name>.rules.mdcfile
Options:
--provider <provider> LLM provider to use (anthropic, openai, local) (default: anthropic)
--model <model> Specific model to use (overrides provider default)
--api-key <key> API key for the provider (or set environment variable)
--base-url <url> Base URL for the API (useful for local providers)
--max-tokens <tokens> Maximum tokens for response (default: 8000)
--temperature <temp> Temperature for generation (0-2, default: 0.7)
--description <text> Description of what should be rulefied
--rule-type <type> Type of rule to generate (auto, manual, agent, always)
--chunk-size <size> Chunk size for processing (default: 100000)
--repomix-file <path> Path to existing repomix output file (skips repomix execution)
--list-providers List available providers and their models
-h, --help display help for command
- Models: claude-3-5-sonnet, claude-3-5-haiku, claude-3-opus, claude-3-sonnet, claude-3-haiku
- API Key:
ANTHROPIC_API_KEY - Best for: High-quality code analysis and rule generation
- Models: gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo
- API Key:
OPENAI_API_KEY - Best for: Fast processing and cost-effective analysis
- Models: llama3.1, codellama, mistral, mixtral, phi3, gemma, qwen, and more
- API Key: Optional (
LOCAL_API_KEY) - Best for: Privacy, offline usage, and custom models
# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh
# Pull a model
ollama pull llama3.1
# Run cursorify with Ollama
cursorify --provider local --model llama3.1- Download and install LM Studio
- Load a model in LM Studio
- Start the local server
- Run cursorify with the LM Studio URL:
cursorify --provider local --model llama3.1 --base-url http://localhost:1234/v1If you already have a repomix output file, you can use it directly without running repomix again:
# Use existing repomix file
cursorify --repomix-file ./my-repo-output.txt
# Use with specific provider and model
cursorify --repomix-file ./output.txt --provider local --model llama3.1
# Use with custom description
cursorify --repomix-file ./output.txt --description "React component guidelines"This is useful when:
- You want to reuse previously generated repomix output
- You have a custom repomix configuration
- You want to process the same repository with different LLM providers
- You want to avoid running repomix multiple times
cursorify supports all options supported by repomix. For example, select specific files:
cursorify --include "src/**/*.ts" --compressAfter generating your rules file, you'll need to install it in Cursor:
- Open Cursor editor
- Go to Settings > AI > Rules
- Click "Add Rules File" and select your generated
<filename>.rules.mdcfile - Restart Cursor to apply the new rules
For more detailed instructions, see the official Cursor documentation.
The generated .rules.mdc file will contain structured guidelines for Cursor AI when working with your codebase:
# .cursorrules for my-project
## Project Overview
This project is a TypeScript application that...
## Coding Standards
- Follow TypeScript best practices with strict typing
- Use async/await for asynchronous operations
...
## File Structure Guidelines
- Place core logic in the `src/` directory
...If you encounter rate limits or ihigh costs, try to minimize the context length using the following ways:
- Use
rulefy --compressto compress the context length - Use
rulefy --includeto include only the files you need with globs - Use
rulefy --excludeto exclude the files you don't need with globs - Control the size of the chunk being processed by one go by using the
--chunk-sizeoption. (default is--chunk-size 100000)
Cursorify is a fork of the original rulefy project by niklub, enhanced with additional features:
- 🔌 Multi-Provider LLM Support: Anthropic Claude, OpenAI, and local models (Ollama, LM Studio)
- 🏠 Local AI Integration: Use your own models with Ollama or LM Studio
- 📁 Repomix File Support: Use existing repomix output files
- ⚙️ Enhanced Configuration: More granular control over LLM parameters
- 🎯 Better Error Handling: Improved error messages and validation
This project is based on the excellent work of niklub and their original rulefy project. We're grateful for their foundational work that made this enhanced version possible.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is inspired by and builds upon the work of:
- rulefy - Original project by niklub (forked and enhanced)
- repomix - A tool for converting repositories into textual representations
- awesome-cursorrules - A curated list of cursor rules for different projects and languages
- cursor-custom-agents-rules-generator - best practices for Cursor custom agents and rules generator
We're grateful to these projects for their contributions to the developer tooling ecosystem.