https://github.com/sanjeed5/awesome-cursor-rules-mdc
Curated list of awesome Cursor Rules .mdc files
https://github.com/sanjeed5/awesome-cursor-rules-mdc
List: awesome-cursor-rules-mdc
Last synced: 8 months ago
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Curated list of awesome Cursor Rules .mdc files
- Host: GitHub
- URL: https://github.com/sanjeed5/awesome-cursor-rules-mdc
- Owner: sanjeed5
- License: cc0-1.0
- Created: 2025-02-17T11:23:04.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-17T12:50:27.000Z (8 months ago)
- Last Synced: 2025-02-17T13:27:19.222Z (8 months ago)
- Language: Python
- Homepage:
- Size: 322 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Awesome Cursor Rules .mdc [](https://github.com/sindresorhus/awesome)
All thanks to the awesome content on https://github.com/PatrickJS/awesome-cursorrules which was processed using LLMs to create this. Waiting for this to be integrated into the awesome https://cursor.directory/ ([PR raised](https://github.com/PatrickJS/awesome-cursorrules/pull/60))
# Cursor Rules MDC Converter
This script converts Cursor rules from various formats to the `.mdc` format, using LLM to intelligently process and structure the rules.
## Setup
1. Install dependencies using `uv`:
```bash
uv venv
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv sync
```
2. Set up your environment variables:
Create a `.env` file with your LLM API key:
```
GEMINI_API_KEY=your_api_key_here
```
## Usage
The script processes files from the `awesome-cursorrules/rules` directory and:
1. Creates `.mdc` versions in the same folder as the original files.
2. Creates a new folder structure in `awesome-cursor-mdc-rules` with the converted files.
3. Generates a conversion report in `conversion_report.json` after processing.
### Test Mode
By default, the script runs in test mode, which only processes 2 folders. To process all folders:
1. Open `convert_to_mdc.py`
2. Find the line `test_mode = True`
3. Change it to `test_mode = False`
### Running the Script
```bash
uv run convert_to_mdc.py
```
### Analyzing Conversion Status
To analyze the conversion status and generate a report, run the following command:
```bash
uv run test_conversion.py
```
This will compare the source and output folders, providing a summary of the conversion process and saving the report as `conversion_report.json`.
## Features
- Retries on API failures with exponential backoff
- Processes files in both original location and separate directory
- Maintains folder structure
- Detailed logging
- Test mode for initial validation
## Error Handling
The script includes comprehensive error handling:
- Logs errors for individual file processing
- Continues processing even if some files fail
- Retries LLM API calls on failure