AI Analysis
Final verdict: SAFE
The package aestetik v0.3.1 exhibits minimal risk across all assessed categories. While the metadata suggests a potential new maintainer, there is no concrete evidence of malicious activity.
- Low risk scores across network, shell, obfuscation, and credential checks.
- Single package from maintainer increases suspicion but lacks evidence of malicious behavior.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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
Repository ratschlab/aestetik appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Kalin Nonchev" 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 aestetik
Create a mini-application that leverages the 'aestetik' package to analyze and visualize spatial transcriptomics data alongside morphological information from tissue samples. This application should allow users to upload their own spatial transcriptomics data and corresponding morphological images. Upon uploading, the app will preprocess the data using convolutional autoencoders to extract meaningful spot representations. Users should be able to explore these representations through interactive visualizations such as heatmaps and scatter plots. Additionally, implement a feature where users can input specific gene expressions of interest, and the app highlights these spots on both the heatmap and scatter plot. To enhance usability, include options for adjusting parameters such as the number of latent dimensions and the type of clustering algorithm applied to the spot representations. Ensure the application is user-friendly with clear instructions and intuitive interfaces.