AI Analysis
Final verdict: SAFE
The package LAMP-cortex v2026.5.18 appears to be legitimate with no clear indicators of malicious intent. The metadata risk is low, though the maintainer's account activity warrants monitoring.
- Single package from the maintainer, indicating a possibly new or less active account.
- No suspicious elements found in previous checks.
Per-check LLM notes
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious elements were found.
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: digitalpsych.org
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository BIDMCDigitalPsychiatry/LAMP-platform appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Division of Digital Psychiatry at Beth Israel Deaconess Medical Center" 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 LAMP-cortex
Your task is to develop a mini-application that leverages the 'LAMP-cortex' package to analyze time-series data from a simulated sensor network. This application will serve as a tool for monitoring environmental conditions, such as temperature and humidity, in real-time. The goal is to create a user-friendly interface where users can visualize data trends over time, set alerts for specific thresholds, and receive notifications when these thresholds are exceeded. Here’s a detailed breakdown of what your application should include: 1. **Data Collection**: Simulate data from multiple sensors placed in different locations. Each sensor should generate time-stamped readings for temperature and humidity. 2. **Data Analysis**: Use 'LAMP-cortex' to perform statistical analyses on the collected data. Implement features like moving averages, trend detection, and anomaly detection to highlight significant changes in the data. 3. **Visualization**: Provide a graphical user interface (GUI) using a library like PyQt or Tkinter, where users can see live updates of the sensor data and historical trends. Include charts and graphs to make the data more understandable. 4. **Alert System**: Allow users to define alert thresholds for temperature and humidity. When the actual readings exceed these predefined levels, trigger an alert via email or SMS. 5. **Notification Service**: Integrate with a simple notification service (e.g., sending emails using SMTP) to inform users about critical events. 6. **User Interface**: Design a clean and intuitive UI that allows users to easily navigate between different sections of the application, including settings for alert thresholds, viewing historical data, and real-time monitoring. The 'LAMP-cortex' package will be crucial for handling complex data analysis tasks efficiently. It provides advanced algorithms for processing large datasets, which are essential for accurate trend analysis and anomaly detection. By integrating 'LAMP-cortex', you ensure that your application not only collects but also intelligently interprets the sensor data, making it a powerful tool for environmental monitoring.