🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the incomplete author information and new/inactive account suggest potential issues that warrant further investigation.

Incomplete author information
New or inactive account

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 direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not enough to conclude malice.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 32 test file(s) found

Test runner config found: conftest.py
Test runner config found: conftest.py
Test runner config found: conftest.py
32 test file(s) detected (e.g. __init__.py)

✦ High Documentation 9.0

Well-documented package

Documentation URL: "Documentation" -> https://ideogenesis-ai.github.io/Alice
1 documentation file(s) (e.g. hooks.py)
Detailed PyPI description (6157 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 5.0

Partial type annotation coverage

161 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 100 commits in Ideogenesis-AI/Alice
Single author but highly active (100 commits)

🔬 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: lmu.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Ideogenesis-AI/Alice 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 alice-net

Develop a Python-based mini-application that leverages the 'alice-net' package to perform tensor network calculations, specifically focusing on 1D tensor networks. This application will serve as a tool for researchers and students interested in quantum physics and tensor network algorithms. The application should include the following features:

1. **User Interface**: Create a simple command-line interface (CLI) that allows users to input parameters such as tensor dimensions, network structure, and specific operations they wish to perform.
2. **Tensor Network Operations**: Implement basic tensor network operations like contraction, decomposition, and normalization using the 'alice-net' package functionalities.
3. **Visualization**: Integrate a visualization component that can graphically represent the tensor network structures and results of operations performed by the user.
4. **Documentation**: Provide comprehensive documentation explaining each function, its parameters, and how to use the CLI effectively.
5. **Examples and Tutorials**: Include a set of examples and tutorials that demonstrate various applications of the tensor network algorithms, such as simulating simple quantum systems or solving linear equations.
6. **Testing**: Ensure robust testing mechanisms are in place to validate the correctness of tensor network operations.

The 'alice-net' package is utilized throughout the application for all tensor network computations. It provides the necessary algorithms and utilities to handle complex tensor manipulations efficiently. Your task is to design and implement this application, ensuring it is user-friendly, efficient, and showcases the capabilities of the 'alice-net' package.