🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package shows no signs of immediate harm with low risks across all categories except metadata, the limited activity and history from the maintainer raise concerns about its reliability and long-term support.

Low activity in the repository
Limited maintainer history

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The low activity in the repository and the maintainer's limited history suggest potential unreliability, but no clear indicators of malicious intent.

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Kamyar Hasanzadeh" 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 activity-space-tools

Create a web-based mini-application that allows users to model and visualize their personal activity space using the 'activity-space-tools' Python package. The application should enable users to upload location data (such as GPS coordinates from a CSV file) and select different models (distance-to-home, home range, and IREM) to analyze their movement patterns. Here’s a detailed plan for the application:

1. **User Interface**: Design a simple yet intuitive user interface where users can upload their location data via a CSV file input. Include options to select which model they want to apply.
2. **Data Processing**: Use 'activity-space-tools' to process the uploaded data. Ensure that the package is correctly installed and imported into your application.
3. **Model Selection**: Implement a feature that allows users to choose between the distance-to-home, home range, and IREM models. Each model should calculate specific metrics based on the uploaded location data.
4. **Visualization**: Create visual representations of the analysis results. For instance, use matplotlib or seaborn to plot the activity space according to the selected model. Visuals should clearly show areas of high activity density, travel distances, etc.
5. **Results Display**: Display the calculated metrics and visualizations directly within the application. Users should be able to see immediate results after uploading their data and selecting a model.
6. **Export Options**: Allow users to export the visualization and key metrics as PDF or PNG files for further analysis or reporting purposes.
7. **Error Handling**: Implement robust error handling to manage incorrect file formats, missing data, or other potential issues during the upload process.
8. **Documentation**: Provide clear instructions on how to use the application, including examples of typical usage scenarios and expected outcomes.

This project will not only demonstrate the practical application of 'activity-space-tools' but also offer a useful tool for anyone interested in analyzing their own activity spaces.