aqequil

v1.3.0 suspicious
4.0
Medium Risk

Python tools for aqueous chemical speciation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse with shell execution patterns observed during build and test phases, though these could be legitimate. Additionally, the author's metadata is incomplete, raising concerns about the authenticity of the package.

  • Shell risk due to observed shell execution patterns
  • Incomplete author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal for most packages.
  • Shell: Shell execution patterns observed are likely for build and test purposes, but warrant further investigation to ensure no unintended actions are being performed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's name is missing and the author has only one package, suggesting a potentially less experienced or suspicious account.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_integration.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3132 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in worm-portal/aqequil
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • # Run make clean subprocess.run( [make_cmd, 'clean'], cwd=source_dir
  • utables...") result = subprocess.run( [make_cmd, 'all'], cwd=source_dir,
  • as argument result = subprocess.run( [eqpt_path, str(data0_path)], cwd=w
  • try: result = subprocess.run( [sys.executable, compile_script],
✓ 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/aqequil 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 aqequil 
Create a Python-based mini-application named 'AqueousSpeciationAnalyzer' that leverages the 'aqequil' package to analyze and predict the chemical speciation of dissolved substances in aqueous solutions. This application will serve as an educational and practical tool for environmental scientists, chemists, and students interested in understanding the behavior of ions in water under different conditions. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Start by setting up a virtual environment for your project. Ensure you have Python installed and then create a new virtual environment. Install the 'aqequil' package along with other necessary Python libraries such as numpy and matplotlib.

2. **Application Structure**: Organize your project into modules. Create a main module that will handle user interaction and call functions from other modules dedicated to calculations, plotting, and data handling.

3. **User Input Handling**: Design a function within the application that prompts the user to input details about the aqueous solution they wish to analyze. This includes the type of substance, concentration, pH, and temperature. Use 'aqequil' functions to process these inputs and calculate the speciation of the substance under given conditions.

4. **Calculation Module**: Develop a module that uses 'aqequil' to perform speciation calculations based on the input parameters. This involves determining the distribution of various ion species in the solution at equilibrium.

5. **Visualization Module**: Implement a feature to visualize the results of the speciation analysis. Use matplotlib to plot graphs showing the distribution of each ion species across different pH levels or temperatures. This helps users understand how changes in conditions affect the speciation.

6. **Report Generation**: Add functionality to generate a report summarizing the analysis results. The report should include key findings, charts, and tables, providing a comprehensive overview of the aqueous speciation.

7. **Testing and Validation**: Test the application thoroughly using known datasets to validate the accuracy of the speciation predictions made by 'aqequil'.

8. **Documentation**: Finally, write documentation explaining how to install and use the application, including examples and tutorials.

By following these steps, you'll create a powerful yet user-friendly tool that not only demonstrates the capabilities of the 'aqequil' package but also serves as an invaluable resource for those studying or working with aqueous chemical systems.

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