NuCS

v11.2.0 safe
4.0
Medium Risk

A Numpy and Numba based Python library for solving Constraint Satisfaction Problems over finite domains

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity based on the analysis checks performed. However, the incomplete metadata raises some concern about the maintainer's experience level.

  • No network calls detected
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive information being stolen.
  • Metadata: The maintainer's author information is incomplete and may indicate a less experienced or potentially inactive user.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yangeorget/nucs appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with NuCS 
Your task is to create a simple yet powerful application using the 'NuCS' package, which leverages Numpy and Numba for efficient Constraint Satisfaction Problem (CSP) solving over finite domains. This application will be a Sudoku solver, designed to solve standard 9x9 Sudoku puzzles. Here’s a detailed breakdown of the steps and features your application should include:

1. **Introduction**: Begin by introducing the concept of Sudoku and its importance in recreational mathematics and logic puzzles. Mention how CSPs can be effectively applied to solve Sudoku puzzles.
2. **Setup Environment**: Ensure you have Python installed along with the necessary packages like 'NuCS', 'numpy', and 'numba'. Provide installation instructions if needed.
3. **Understanding NuCS**: Briefly explain what NuCS does and how it can be used to solve CSPs efficiently. Highlight its use of Numpy arrays and Numba for optimization.
4. **Application Design**: Design the application such that it can accept a partially filled Sudoku grid as input and output a solved Sudoku grid. The input grid should be a 9x9 matrix where empty cells are represented by zeros.
5. **Implementation Steps**:
   - Initialize the Sudoku grid and define the constraints of the puzzle (each row, column, and 3x3 subgrid must contain unique numbers from 1 to 9).
   - Use NuCS to encode these constraints into a CSP model.
   - Implement a function that initializes the CSP solver with the given Sudoku grid and solves the puzzle.
   - Incorporate a user-friendly interface that allows users to input their Sudoku puzzle and view the solution.
6. **Advanced Features**:
   - Include a feature to check if the provided Sudoku puzzle has a unique solution or multiple solutions.
   - Add a timer to measure how long it takes to solve the puzzle.
   - Implement a difficulty rating system for the puzzles based on the number of initial clues given.
7. **Testing**: Provide several test cases including easy, medium, and hard level Sudoku puzzles to demonstrate the application's functionality.
8. **Documentation**: Write clear documentation explaining each part of the code, especially how NuCS is utilized in solving the CSP.
9. **Conclusion**: Summarize the project, highlighting the efficiency of using NuCS for solving Sudoku puzzles and the potential for similar applications in other fields involving CSPs.

This project not only showcases the power of NuCS but also provides a practical tool for Sudoku enthusiasts.