ai-user-activity-monitor

v1.0.5 safe
4.0
Medium Risk

AI User Activity Monitor — open-source Django user activity dashboard by Nitesh Kumar Singh (nkscoder)

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks across all categories except for metadata, where concerns arise from an author with limited information and a newly created account. However, these factors alone do not conclusively indicate malicious intent.

  • Minimal network, shell, and obfuscation risks
  • No clear evidence of credential harvesting
  • Metadata raises some concerns due to author details and account age
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell executions detected, indicating no direct system command invocations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The package shows some red flags such as an author with no details and a new account, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/nkscoder/ai-user-activity-monitor#readme
  • Detailed PyPI description (6255 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 15 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in nkscoder/ai-user-activity-monitor
  • Single 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: nkscoder.in>

✓ 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

  • Author name is missing or very short
  • Author "" 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 ai-user-activity-monitor 
Create a comprehensive user engagement analytics tool using the 'ai-user-activity-monitor' package. This tool will serve as a dashboard for monitoring user activities within a Django web application, providing insights into user behavior patterns. The goal is to build a fully-functional mini-app that not only integrates the 'ai-user-activity-monitor' package but also extends its functionalities with additional features such as real-time activity tracking, user session analysis, and interactive visualization of data.

Steps to follow:
1. Set up a new Django project and install the 'ai-user-activity-monitor' package along with other necessary dependencies like Django Rest Framework for API endpoints and Matplotlib for data visualization.
2. Integrate the 'ai-user-activity-monitor' package into your Django project according to the documentation provided by the author. Ensure that the package is correctly configured to track user activities such as logins, page views, and form submissions.
3. Develop custom models and views to extend the basic functionalities of the package. For instance, create a model to store session data and a view to display detailed session information.
4. Implement real-time activity tracking using Django Channels or similar technology. This feature will allow users to see live updates on user activities happening on the site.
5. Design and implement interactive visualizations using libraries like Plotly or Bokeh to represent the collected data in meaningful ways. These visualizations should include graphs showing user activity trends over time and heatmaps indicating peak usage hours.
6. Create a secure authentication system to restrict access to the dashboard to authorized personnel only. Use Django's built-in authentication framework to manage user permissions and roles.
7. Test the application thoroughly to ensure all components work as expected. Pay special attention to data privacy and security measures.
8. Deploy the application to a production environment using a cloud service provider such as AWS or Heroku. Make sure to configure proper scaling and load balancing settings.

Suggested Features:
- Real-time activity notifications via WebSocket connections
- Detailed session reports including session duration, IP addresses, and device types
- Customizable dashboards allowing users to select which metrics they want to monitor
- Export functionality for data export in CSV or Excel format
- Integration with third-party tools like Slack or Email for sending alerts about unusual activity

The 'ai-user-activity-monitor' package will be utilized primarily for its ability to track various user activities across different parts of the website. By leveraging this package, you'll be able to focus more on building out the extended features mentioned above rather than implementing the core activity tracking functionality from scratch.