AI Analysis
The package has low immediate risks but shows signs of poor maintenance and recent creation, which raises concerns about potential malicious intent or supply-chain attacks.
- Metadata risk due to poor maintenance and newness
- No direct evidence of malicious activity
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interaction.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The package shows signs of being newly created and poorly maintained, raising concerns about potential malicious intent.
Package Quality Overall: Low (4.4/10)
Test suite present — 6 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml6 test file(s) detected (e.g. conftest.py)
Some documentation present
Detailed PyPI description (1051 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
17 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully functional web-based monitoring application for Thermia Genesis heat pumps using the 'aiothermiagenesis' Python package. This application will allow users to connect to their heat pump via Modbus TCP/IP and retrieve real-time data about its operation. Here are the steps and features you should include in your project: 1. **Setup Environment**: Ensure you have Python installed and create a virtual environment for the project. Install necessary packages including aiothermiagenesis, FastAPI, and Uvicorn. 2. **Connecting to Heat Pump**: Use aiothermiagenesis to establish a connection to the Thermia Genesis heat pump over Modbus TCP/IP. Implement error handling for potential connection issues. 3. **Data Retrieval**: Utilize aiothermiagenesis functions to fetch data from the heat pump such as temperature readings, operational status, energy consumption, etc. Ensure the data is retrieved asynchronously to maintain performance. 4. **Web Interface**: Develop a simple yet intuitive web interface using FastAPI to display the collected data. Include options for users to select which data points they want to monitor. 5. **Real-Time Updates**: Implement functionality to update the displayed data every minute without requiring a page refresh. Consider using WebSockets for efficient real-time updates. 6. **Alert System**: Add an alert system that notifies users via email or SMS if any critical parameters exceed predefined thresholds. 7. **User Authentication**: Integrate basic user authentication to ensure only authorized users can access the monitoring information. 8. **Documentation**: Provide clear documentation on how to set up and use the application, including details on installing aiothermiagenesis and configuring it with your specific heat pump settings.
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