AI Analysis
Final verdict: SAFE
The package exhibits very low risk indicators across all categories except metadata, where it shows signs of being newly created with limited activity. There are no clear malicious patterns observed.
- No network or shell execution risks detected
- Low obfuscation and credential risk
- New package with limited activity
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on unsupervised dataset generation.
- Shell: No shell execution patterns detected, consistent with the expected functionality of a data processing package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of being newly created with limited activity, but there are no clear red flags.
Package Quality Overall: Low (2.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (772 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
2.0
Single-author or unverifiable project
1 unique contributor(s) across 10 commits in prakHr/automate-unsupervised-dataset-generationSingle author with few commits — possibly a personal or throwaway project
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
Only one version has ever been released — brand new packageAuthor "Prakhar Gandhi" 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 automate-unsupervised-dataset-generation
Create a data science utility app named 'DataGenMaster' that leverages the 'automate-unsupervised-dataset-generation' package to generate synthetic datasets for unsupervised learning tasks. This app will serve as a powerful tool for researchers and data scientists who need to test algorithms on various types of data without the need for real-world data collection. The application should have the following key functionalities: 1. **User Interface**: Develop a simple yet intuitive GUI using Tkinter or a similar library, allowing users to interact with the dataset generation process easily. 2. **Dataset Parameters Configuration**: Users should be able to configure parameters such as the number of samples, dimensions, and distribution type (e.g., Gaussian, Uniform) for the datasets they want to generate. 3. **Visualization Tools**: Implement basic visualization tools to display the generated datasets in both 2D and 3D scatter plots, helping users understand the structure and distribution of the synthetic data. 4. **Save/Load Functionality**: Enable users to save the generated datasets to a file (CSV format) and load previously saved datasets for further analysis or testing. 5. **Algorithm Integration**: Allow users to apply common unsupervised learning algorithms (e.g., K-Means Clustering, DBSCAN) directly within the app on the generated datasets, showcasing the impact of different parameter settings on clustering outcomes. 6. **Documentation and Help Section**: Provide comprehensive documentation and a help section within the app explaining each feature and how to use them effectively. The 'automate-unsupervised-dataset-generation' package is crucial for automating the dataset generation process. It provides functions that simplify the creation of complex, multi-dimensional datasets based on user-defined specifications. By integrating this package, you ensure that the 'DataGenMaster' app can efficiently handle the computational demands of generating large, high-dimensional datasets suitable for advanced unsupervised learning research.
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