🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low signs of potential risk, but the metadata quality and maintainer activity are questionable, raising concerns about its reliability and authenticity.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malintent.

📦 Package Quality Overall: Low (2.0/10)

○ 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 (1645 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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

No author email provided

✓ 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 alpha-omega-gravity

Create a mini-application called 'GravityExplorer' that leverages the Python package 'alpha-omega-gravity', which has successfully unified gravity and electromagnetism. This application will allow users to visualize and explore the effects of these forces on various objects and scenarios.

Step 1: Define the Core Functionality
- Users should be able to input the properties of objects such as mass, charge, and position.
- Calculate the combined gravitational and electromagnetic forces acting on these objects using the 'alpha-omega-gravity' package.

Step 2: Implement Visualization Features
- Use matplotlib or a similar library to create dynamic visualizations of the force fields around the objects.
- Allow users to manipulate the position and properties of objects in real-time to see changes in the force fields.

Step 3: Add Simulation Capabilities
- Integrate physics engines like Pygame or PyBullet to simulate the motion of objects under the influence of the calculated forces.
- Provide options for different environments (e.g., vacuum, atmosphere) that affect the strength and behavior of the forces.

Step 4: Enhance User Interaction
- Develop a user-friendly GUI using Tkinter or PyQt that allows easy input of object properties and viewing of simulations.
- Include educational content explaining the unification of gravity and electromagnetism, and how the simulation works.

Utilization of 'alpha-omega-gravity':
- Import the unified force calculation functions from the package to compute the resultant force on each object based on its mass and charge.
- Use the package's visualization tools to display the combined gravitational and electromagnetic field lines around the objects.
- Apply the package's simulation capabilities to accurately model the interaction between objects over time.