🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network usage, shell execution, and obfuscation. However, the maintainer's lack of information and lower repository activity slightly increase its metadata risk, suggesting some uncertainty about its reliability.

Minimal network and shell execution risks.
No signs of code obfuscation or credential harvesting.
Unclear maintainer information and less active repository.

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 no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation for malicious purposes.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
Metadata: The maintainer's lack of information and repository activity suggest potential unreliability.

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 DiaModality

Develop a Python-based interactive mini-app called 'Modality Explorer' that leverages the 'DiaModality' package to visualize and analyze modality vectors from various datasets. This app will serve as an educational tool and a practical utility for researchers and data scientists who work with modalities in their projects.

The Modality Explorer should have the following core functionalities:
1. **Data Input**: Allow users to upload their own datasets in CSV format. These datasets should contain at least one column representing a modality vector.
2. **Visualization**: Utilize the 'DiaModality' package to generate modality vector diagrams based on user input. Users should be able to customize these diagrams by adjusting parameters such as color schemes, labels, and axis limits.
3. **Analysis Tools**: Implement basic statistical analysis tools that provide insights into the uploaded data, such as mean, median, mode, standard deviation, etc., specific to the modality vectors.
4. **Interactive Features**: Include interactive elements like tooltips for data points and zoom functionality to explore different parts of the diagram more closely.
5. **Export Options**: Enable users to export their visualizations and analysis results in common file formats such as PNG, PDF, and CSV.

To achieve these goals, the 'DiaModality' package will be used primarily for plotting modality vector diagrams. The application should demonstrate how to integrate 'DiaModality' into a larger, more complex system, showcasing its capabilities in handling real-world data and providing meaningful visual representations of modality vectors.