AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to the maintainer having only one package.
- No network calls detected
- Single package by maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, suggesting a new or less active account which may warrant further investigation.
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: brown.edu
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository xr-cc/ace-of-clust appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Xiran Liu" 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 ace-of-clust
Develop a mini-application named 'OmicsClusterExplorer' that leverages the 'ace-of-clust' package to perform advanced omics data clustering analysis. This application will allow researchers to upload their omics datasets, apply various clustering algorithms, and visualize the results in an intuitive manner. Here’s a detailed breakdown of the functionalities and steps to create this application: 1. **User Interface**: Design a clean, user-friendly interface using a web framework like Flask or Django. The UI should include options for uploading datasets, selecting clustering methods, and viewing results. 2. **Data Processing**: Implement functionality to preprocess uploaded datasets, handling common issues such as missing values and normalization. 3. **Clustering Algorithms**: Utilize 'ace-of-clust' to apply different clustering techniques (e.g., K-means, hierarchical clustering) on the processed data. Ensure that the package’s alignment, comparison, and evaluation features are integrated to enhance the clustering process. 4. **Visualization Tools**: Develop interactive visualizations for the clustering results, including dendrograms, heatmaps, and scatter plots. These should allow users to explore clusters at different levels of detail. 5. **Result Export**: Provide an option for users to export their clustering results and visualizations in formats such as CSV, PNG, or PDF. 6. **Documentation & Support**: Include comprehensive documentation within the application to guide users through each step of the process, and offer support via an FAQ section or direct contact form. By following these steps, you will create a powerful yet accessible tool for researchers working with complex omics data.