🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell execution, and obfuscation but shows signs of low effort and potential lack of transparency in its metadata, raising suspicion.

Low effort and potential lack of transparency in metadata.
No significant risks detected in other areas.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command execution from the package.
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 effort and potential lack of transparency, raising suspicion.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 GBconfusion

Create a Python-based mini-application that estimates galactic confusion levels using the LISA (Laser Interferometer Space Antenna) data. This tool will serve as a valuable resource for astronomers and astrophysicists who need to analyze the noise floor caused by unresolved galactic sources in gravitational wave observations. Your task is to develop a user-friendly interface that allows users to input parameters related to their specific observation scenarios and receive detailed reports on expected galactic confusion levels.

Key Features:
1. User Input Form: Allow users to input necessary parameters such as observation duration, frequency range, and sky region of interest.
2. Data Visualization: Implement plots and charts to visually represent the galactic confusion levels across different frequencies and sky regions.
3. Report Generation: Automatically generate comprehensive reports summarizing the findings from the analysis.
4. Interactive Mode: Provide an interactive mode where users can adjust parameters in real-time and see the effects on the galactic confusion levels.

How to Utilize GBconfusion:
- Use the 'GBconfusion' package to perform the core calculations required for estimating galactic confusion levels based on the user inputs.
- Integrate the package's functions into your application flow to ensure accurate and efficient processing of the data.
- Explore additional functionalities provided by the package to enhance the analytical capabilities of your application.