🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential aspects. However, the metadata risk raises some concern due to the author having only one package, suggesting potential novelty or lesser activity, which warrants closer scrutiny.

Metadata risk score of 3 out of 10 due to the author's limited package history.
Shell risk score of 3 out of 10 due to subprocess calls needing further investigation.

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Subprocess calls may be legitimate if the package uses DVC commands for version control or similar purposes, but requires further investigation into the specific use case.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package, which may 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 score 6.0

Found 3 shell execution pattern(s)

json target`.""" result = subprocess.run( _dvc_cmd("status", "--json", target), cwd=c
try: subprocess.run( _dvc_cmd(*cmd), cwd
continue subprocess.run( _dvc_cmd(*cmd), cwd=countries_dir,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: berkeley.edu

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ligon/LSMS_Library appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Ethan Ligon" 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 LSMS_Library

Create a mini-application that leverages the 'LSMS_Library' package to analyze and visualize Living Standards Measurement Survey (LSMS) data. This application should allow users to explore various socio-economic indicators such as income levels, education attainment, and household amenities across different regions or countries. Here are the steps and features you should include in your application:

1. **Data Loading**: Utilize 'LSMS_Library' to load LSMS datasets from a specified location. Ensure the application supports multiple dataset formats supported by the library.
2. **Data Exploration**: Implement functionality to display summary statistics of the loaded data, including mean, median, mode, and standard deviation for key variables like income and education level.
3. **Interactive Filtering**: Allow users to filter data based on specific criteria such as age group, gender, or geographic region. Users should be able to select multiple filters at once.
4. **Visualization**: Integrate visual elements using Python's Matplotlib or Seaborn libraries to plot histograms, bar charts, and line graphs showing trends over time or comparisons between groups. Ensure these visualizations are interactive where possible.
5. **Export Functionality**: Enable users to export their filtered and analyzed data into CSV or Excel format for further analysis outside the application.
6. **User Interface**: Design a simple yet intuitive graphical user interface (GUI) using Tkinter or Streamlit to make the application accessible to non-technical users.
7. **Documentation**: Provide clear documentation explaining how to use the application, including setup instructions and examples of analyses that can be performed.

This application will serve as a powerful tool for researchers, policymakers, and students interested in understanding global socio-economic conditions.