🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks detected. However, the metadata quality and maintainer activity level raise some concerns but do not conclusively indicate a supply-chain attack.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions 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.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (5668 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

Type checker (mypy / pyright / pytype) referenced in project

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 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: schott.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amorphouspy

Create a mini-application named 'GlassExplorer' using the Python package 'amorphouspy'. This application will serve as a tool for researchers and students interested in exploring the properties of oxide glasses through atomistic modeling. Here are the steps and features you need to implement:

1. **Project Setup**: Initialize a new Python project and install 'amorphouspy' along with other necessary packages such as matplotlib for visualization.
2. **User Interface**: Develop a simple command-line interface (CLI) that allows users to input parameters for glass compositions and desired analysis types.
3. **Model Generation**: Utilize 'amorphouspy' to generate atomistic models of oxide glasses based on user inputs. Users should be able to specify the types of oxides (e.g., SiO2, B2O3) and their ratios.
4. **Analysis Tools**: Implement basic analysis tools within the application that allow users to calculate properties like density, coordination number, and radial distribution functions (RDFs) for the generated models.
5. **Visualization**: Integrate matplotlib to visualize the RDFs and other calculated properties graphically. Provide options for users to save these visualizations as image files.
6. **Documentation**: Write clear documentation for the CLI commands and functionalities provided by GlassExplorer.
7. **Testing and Validation**: Ensure that the application works correctly by testing it with known oxide glass compositions and comparing the results with expected values from literature or standard datasets.

The goal is to create an educational and research-oriented tool that simplifies the process of atomistic modeling for oxide glasses, making it accessible to those without extensive computational chemistry experience.