aljabr

v0.5.2 safe
3.0
Low Risk

Linear operators interfaces

🤖 AI Analysis

Final verdict: SAFE

The package aljabr v0.5.2 presents minimal risks based on the analysis notes provided. It does not engage in network calls, shell executions, or obfuscations that could pose immediate threats.

  • No network calls
  • No shell execution
  • No obfuscation
  • No credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution patterns detected, indicating it does not appear to execute commands on the system.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, which may indicate a new or less active user.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://aljabr.readthedocs.io/
  • Detailed PyPI description (2990 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 138 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in forieux/aljabr
  • Two distinct contributors found

🔬 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: universite-paris-saclay.fr>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository forieux/aljabr appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "François Orieux" 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 aljabr 
Develop a Python-based mini-application that serves as a Linear Algebra Problem Solver. This application will utilize the 'aljabr' package, which provides interfaces for linear operators, to perform various operations on matrices and vectors. The application should allow users to input matrices and vectors in a user-friendly interface and then perform operations such as matrix multiplication, vector addition, finding eigenvalues and eigenvectors, and solving systems of linear equations. Here are the key steps and features for building this application:

1. **Setup Environment**: Install Python along with necessary libraries including 'aljabr'. Ensure you have a virtual environment set up for dependency management.
2. **User Interface**: Design a simple command-line interface (CLI) for user interaction. Alternatively, if you prefer, you could create a basic web interface using Flask or Django.
3. **Matrix/Vector Input**: Implement functionality to accept matrix and vector inputs from the user. Ensure the input format is validated to avoid errors.
4. **Operation Selection**: Provide options for different linear algebra operations like matrix multiplication, vector addition, etc. Each operation should be clearly documented and easy to select.
5. **Utilize 'aljabr' Package**: Use 'aljabr' to perform the selected operations. This involves creating instances of linear operators for the matrices/vectors and applying the appropriate methods provided by 'aljabr' for computation.
6. **Result Display**: After performing the chosen operation, display the result back to the user in a readable format.
7. **Error Handling**: Implement robust error handling to manage incorrect inputs or unsupported operations gracefully.
8. **Documentation**: Write comprehensive documentation for your application, explaining how to use it and detailing each feature.
9. **Testing**: Conduct thorough testing of all functionalities to ensure reliability and accuracy of results.
10. **Deployment**: Once developed and tested, deploy your application so others can access it easily.

By following these steps and utilizing the 'aljabr' package effectively, you'll create a powerful yet accessible tool for anyone needing to solve linear algebra problems.