https://github.com/scarowar/scars-of-leetcode
Automation-driven LeetCode workflow with solutions, spaced-repetition flashcards, and Anki integration.
https://github.com/scarowar/scars-of-leetcode
algorithms anki automation coding-interviews flashcards interview-preparation leetcode neetcode150 open-source python spaced-repetition
Last synced: 3 months ago
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Automation-driven LeetCode workflow with solutions, spaced-repetition flashcards, and Anki integration.
- Host: GitHub
- URL: https://github.com/scarowar/scars-of-leetcode
- Owner: scarowar
- License: gpl-3.0
- Created: 2025-06-08T18:39:08.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-21T16:25:23.000Z (4 months ago)
- Last Synced: 2025-07-26T15:45:29.774Z (3 months ago)
- Topics: algorithms, anki, automation, coding-interviews, flashcards, interview-preparation, leetcode, neetcode150, open-source, python, spaced-repetition
- Language: Python
- Homepage:
- Size: 539 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# Scars of LeetCode
[](LICENSE)
[](https://github.com/scarowar/scars-of-leetcode)
[](https://github.com/scarowar/scars-of-leetcode/actions/workflows/pre-commit.yml)
[](https://github.com/scarowar/scars-of-leetcode/actions/workflows/codeql.yml)
[](https://github.com/scarowar/scars-of-leetcode/pulls?q=is%3Apr+label%3Adependencies)
**Every Accepted was paid in blood, sweat, and runtime errors.**
> **Note:** This repository is a work in progress. Expect ongoing improvements and refinements!
## Table of Contents
* [Overview](#overview)
* [Features](#features)
* [Installation](#installation)
* [The Workflow: A Quick Start](#the-workflow-a-quick-start)
* [Command Usage](#command-usage)
* [Configuration](#configuration)
* [File & Folder Structure](#file--folder-structure)
* [Contributing & Support](#contributing--support)
* [License](#license)
* [OpenAI API Usage (Optional)](#openai-api-usage-optional)
## Overview
**Scars of LeetCode** is a repository powered by **Scarleet**, a command-line tool designed to automate and streamline the entire workflow for mastering LeetCode problems. It helps you:
* Organize solutions in a structured way.
* Automatically generate high-quality documentation for solved problems.
* Create evidence-based, spaced-repetition flashcards for deep learning.
* Track your progress with a dynamic summary table.
* Seamlessly integrate with Anki for efficient review.
This project is for anyone who wants to build a deep, recallable understanding of algorithms and data structures through a powerful, automated system.
## Features
* **CLI-Driven Workflow**: Manage your entire practice from the command line.
* **Automated Scaffolding**: Instantly create a structured directory for any LeetCode problem.
* **AI-Powered Documentation**: Use a local language model to automatically generate high-quality READMEs explaining your solution's intuition, approach, and complexity.
* **Automated Flashcard Generation**: Turn your solutions into effective Anki flashcards (both Q&A and Cloze deletion) with a single command.
* **Intelligent Anki Integration**: Automatically detects your environment (including WSL) to connect to Anki and sync cards without manual configuration.
* **Progress Tracking**: Generate a summary table of your progress at any time.
## Installation
**Prerequisites:**
* Python 3.8+
* [Ollama](https://ollama.com/) for running the local language model.
* [Anki](https://apps.ankiweb.net/) desktop application with the [AnkiConnect](https://ankiweb.net/shared/info/2055492159) add-on installed.
**Setup Steps:**
1. Clone the repository:
```sh
git clone [https://github.com/scarowar/scars-of-leetcode.git](https://github.com/scarowar/scars-of-leetcode.git)
cd scars-of-leetcode
```
2. Install the required Python packages:
```sh
pip install "typer[all]" rich toml requests
```
3. Run the interactive setup command. This will create your configuration file, check for Ollama, and download the recommended language model.
```sh
python main.py setup
```
## Installation (Local CLI)
You can install all dependencies with either:
```sh
pip install -r requirements.txt
```
or, if you prefer PEP 621/modern Python packaging:
```sh
pip install .
```
Both methods will install all required packages for Scarleet.
See [INSTALL.md](INSTALL.md) for more details.
## The Workflow: A Quick Start
The entire process is designed to be simple and flow naturally with your practice.
1. **Create a New Problem:**
```sh
scarleet new
# Example: scarleet new two-sum
```
2. **Solve It:** Navigate to the new `problems/[id]-[slug]` directory and write your solution in `solution.py`.
3. **Generate Docs:** After your solution is accepted on LeetCode, let Scarleet document it for you.
```sh
scarleet docgen
```
4. **Create Flashcards:** Turn your documented solution into study material.
```sh
scarleet flashcards
```
5. **Track Your Progress:** View a summary of all your solved problems and update the master `README.md`.
```sh
scarleet status --update-readme
```
## Command Usage
* **`setup`**: Initializes the project. Should be run once.
* **`new `**: Scaffolds a new problem directory.
* **`docgen `**: Generates a `README.md` for a problem using your solution and the local SLM.
* **`flashcards `**: Generates Anki flashcards from your `README.md` and solution.
* **`status`**: Displays a summary table of all problems. Use the `--update-readme` or `-u` flag to write this table to `problems/README.md`.
## Configuration
All configuration is managed in the `scarleet.toml` file, which is created by the `setup` command. Here you can change:
* The language model used (e.g., from `gemma:2b` to another Ollama model).
* The prompts used for generating documentation and flashcards.
* The name of your Anki deck.
## File & Folder Structure
```
.
├── scarleet.toml # Global configuration for the tool
├── main.py # The CLI entrypoint
├── commands/ # Logic for each CLI command
├── core/ # Core logic for Anki, SLM, and LeetCode API
├── problems/
│ ├── ... # One folder per problem, each with solution and docs
│ └── README.md # Auto-generated progress summary
└── .scarleet/
└── lists/
├── blind75.json # Blind 75 list
└── neetcode150.json # NeetCode 150 list
```
## OpenAI or Azure OpenAI Usage (Optional)
Scarleet can use either OpenAI or Azure OpenAI models (like GPT-4, GPT-3.5, etc.) for generating flashcards and documentation. To enable this, set the following environment variables in a `.env` file (which should NOT be committed to version control):
### For OpenAI:
```
OPENAI_API_KEY=sk-...your-key...
OPENAI_API_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o
OPENAI_API_VERSION=2024-06-01
```
### For Azure OpenAI:
```
AZURE_API_KEY=your-azure-key
AZURE_API_BASE=https://your-resource.openai.azure.com
AZURE_DEPLOYMENT_NAME=your-deployment
AZURE_API_VERSION=2024-06-01
```
- Scarleet will auto-detect which provider to use based on your `.env`.
- If neither is set, Scarleet will use your local SLM endpoint as a fallback.
- Install dependencies for .env support:
```sh
pip install python-dotenv
```
- The `openai` Python package is required (see `requirements.txt`).
**Never commit your `.env` file or API keys to version control.**
---
## Contributing & Support
Contributions, suggestions, and improvements are welcome! Please open an issue or PR. By contributing, you agree that your contributions will be licensed under the GNU General Public License v3.0.
## License
This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.
**Happy grinding — and may your scars become your strength!**