PyThermoLinkDB

v1.4.16 suspicious
4.0
Medium Risk

PyThermoLinkDB is a Python package providing a robust and efficient interface between `PyThermoDB` and other applications.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks in terms of network, shell, and obfuscation activities, but the metadata risk is elevated due to incomplete author information and a potentially inactive or new account.

  • Incomplete author information
  • Potentially inactive or new account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, reducing the risk of command injection or system compromise.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The author's information is incomplete and the account seems new or inactive, which raises some suspicion but not enough to conclusively indicate malice.

🔬 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 sinagilassi/PyThermoLinkDB 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 PyThermoLinkDB 
Develop a comprehensive mini-application that leverages the PyThermoLinkDB package to facilitate interactive exploration of thermodynamic data from the PyThermoDB database. This application will allow users to query and visualize thermodynamic properties of various substances under different conditions. Here’s a detailed breakdown of the project requirements and steps:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary libraries including PyThermoLinkDB.
2. **Data Querying Interface**: Implement a user-friendly command-line interface (CLI) where users can input queries about specific substances and their thermodynamic properties. Users should be able to specify conditions such as temperature and pressure.
3. **Query Processing**: Utilize PyThermoLinkDB to process these queries efficiently. The package should handle the connection to the PyThermoDB database and retrieve relevant thermodynamic data based on user inputs.
4. **Data Visualization**: Integrate a simple plotting library like matplotlib or seaborn to visually represent the retrieved thermodynamic data. Users should be able to see graphical representations of properties such as enthalpy, entropy, and Gibbs free energy as functions of temperature and pressure.
5. **Interactive Features**: Enhance the application with interactive elements allowing users to adjust parameters in real-time and observe changes in the graphical outputs instantly.
6. **Documentation and Help**: Provide comprehensive documentation within the application itself, guiding users on how to use the CLI effectively. Include a help function that lists all available commands and their usage.
7. **Testing and Validation**: Test the application thoroughly to ensure it handles various types of input correctly and provides accurate outputs. Validate the results against known thermodynamic data sources to ensure reliability.
8. **Deployment**: Package the application into a standalone executable that can be easily distributed and run on different machines without requiring installation of additional dependencies.

By following these steps, you'll create a powerful tool for anyone interested in exploring thermodynamic data in an accessible and interactive way.

💬 Discussion Feed

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