🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk factors with no network calls, shell executions, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to incomplete maintainer information.

No network calls or shell executions detected
Incomplete maintainer metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell executions detected, which is typical and indicates no immediate signs of malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author name is missing or very short, and the author seems to be new or inactive based on having only one package on PyPI.

🔬 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

Repository SINTEF/FEEMS appears legitimate

⚠ 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 MachSysS

Develop a machinery system monitoring app using the 'MachSysS' package in Python. This app will serve as a real-time monitoring tool for various machinery systems within an industrial setting. The goal is to provide operators with critical information about the status of different machines in their system, including but not limited to temperature, pressure, operational speed, and error codes. The app should also be capable of logging these data points over time for analysis and reporting purposes.

Key Features:
1. Real-time data acquisition from machinery systems through the 'MachSysS' package's interface.
2. User-friendly dashboard displaying key performance indicators (KPIs) of each machine.
3. Alert system that notifies users via email or SMS when any machine exceeds predefined thresholds.
4. Historical data storage and visualization for trend analysis.
5. Integration with external databases for long-term data archiving.

Steps to Implement:
1. Set up the development environment with Python and install the 'MachSysS' package.
2. Design the database schema for storing historical data, considering tables for machines, readings, alerts, and user settings.
3. Develop the backend logic using 'MachSysS' to interface with machinery systems, collect data at regular intervals, and store it in the database.
4. Create the frontend using a web framework like Flask or Django, designing a dashboard to display live KPIs and historical trends.
5. Implement the alert system, allowing users to set thresholds for different parameters and configuring notifications.
6. Test the entire system thoroughly, ensuring accurate data collection, reliable notifications, and efficient data storage.
7. Document the project, providing instructions on setup, configuration, and usage.