asgt-engine

v5.4.0 suspicious
5.0
Medium Risk

ASGT Engine: zero-float algebraic tensor vision with native archive loading

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in direct threats like network calls or shell execution, but the unavailability of the repository and the maintainer's single-package profile raise concerns about potential supply-chain risks.

  • Repository not found
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The repository is not found, and the maintainer has a single package, which raises some suspicion but does not conclusively indicate malicious intent.

📦 Package Quality Overall: Low (2.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2739 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alhdrawi" 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 asgt-engine 
Develop a mini-application called 'TensorVisionExplorer' that leverages the 'asgt-engine' Python package to showcase its capabilities in zero-float algebraic tensor operations and native archive loading. This application will allow users to upload tensor data archives, perform various algebraic operations on tensors, and visualize the results. Here are the steps and features you should include:

1. **Setup Environment**: Ensure that the environment includes all necessary dependencies, including 'asgt-engine'.
2. **User Interface**: Design a simple, user-friendly interface where users can select and upload their tensor data archives.
3. **Data Loading**: Utilize 'asgt-engine' to load the uploaded tensor data from the archive directly into your application without converting it to float representations, thanks to its zero-float capability.
4. **Operation Selection**: Provide options for common algebraic operations such as addition, subtraction, multiplication, and more advanced operations like tensor dot product.
5. **Visualization**: Implement visualization tools to display the original tensor data and the result of the applied operations. This could include heatmaps, 3D plots, etc., depending on the tensor dimensions.
6. **Result Saving**: Allow users to save the modified tensor data back into an archive format, again using 'asgt-engine' to ensure compatibility and efficiency.
7. **Documentation**: Include comprehensive documentation on how to use the application and what each feature does, highlighting the benefits of using 'asgt-engine'.

This project aims to demonstrate the power and flexibility of 'asgt-engine' in handling complex tensor data efficiently and visually.

💬 Discussion Feed

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