aqorg

v1.1.4 safe
3.0
Low Risk

Estimate thermodynamic properties of aqueous organic compounds

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no network calls, shell executions, or obfuscations. However, the incomplete author metadata slightly increases the risk.

  • No network calls detected
  • Incomplete author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The author information is incomplete, suggesting potential unreliability or lack of transparency.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (625 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 61 commits in worm-portal/aqorg
  • Small but multi-author team (3–4 contributors)

🔬 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: asu.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository worm-portal/aqorg 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 aqorg 
Create a desktop application named 'AqueousOrganicThermometer' using Python that leverages the 'aqorg' package to estimate thermodynamic properties of aqueous organic compounds. This application should provide a user-friendly interface where users can input details about their organic compound, such as chemical formula, concentration, and temperature range. The app will then use 'aqorg' to calculate and display key thermodynamic properties like enthalpy, entropy, and Gibbs free energy within the specified temperature range.

Key Features:
1. User Input Form: A form where users can enter the chemical formula of the organic compound, its concentration in solution, and the temperature range they're interested in.
2. Thermodynamic Property Calculator: Utilize 'aqorg' to calculate and display the enthalpy, entropy, and Gibbs free energy of the compound over the specified temperature range.
3. Graphical Representation: Display a graph showing how these properties change with temperature.
4. Save & Share Results: Allow users to save the results in a file format of their choice (CSV, PDF, etc.) and share them via email or download.
5. Help Section: Include a section explaining the significance of each thermodynamic property and how it affects the behavior of the organic compound in aqueous solutions.

Steps to Build the Application:
1. Set up your Python environment and install necessary packages including 'aqorg'.
2. Design the user interface using a library like Tkinter or PyQt.
3. Implement the backend logic to process user inputs and call functions from 'aqorg' to compute thermodynamic properties.
4. Integrate a plotting library like Matplotlib to visualize the data.
5. Add functionality to save and share the computed results.
6. Test the application thoroughly with different compounds and scenarios to ensure accuracy and usability.

💬 Discussion Feed

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