aodkit

v0.3.0 suspicious
4.0
Medium Risk

imutum's packages for aerosol optical depth retrieval

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, and obfuscation. However, the lack of a GitHub repository and sparse maintainer information raises concerns about its reliability and potential maintenance.

  • No network calls detected
  • Sparse maintainer information
  • No associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell executions detected, indicating the package does not attempt to execute system commands without user intervention.
  • 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 package has no associated GitHub repository and the maintainer's information is sparse, suggesting potential unreliability.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (7593 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
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 aodkit 
Create a Python-based mini-application that leverages the 'aodkit' package to analyze satellite data for retrieving Aerosol Optical Depth (AOD). This application will serve as a tool for environmental scientists and researchers interested in studying atmospheric conditions and pollution levels. Here are the steps and features your application should include:

1. **Setup Environment**: Ensure you have Python installed along with necessary libraries such as 'numpy', 'pandas', and 'matplotlib'. Install 'aodkit' via pip.
2. **Data Acquisition**: Implement functionality to fetch satellite imagery data from public sources like NASA's MODIS or VIIRS datasets. The data should be in a format compatible with 'aodkit'.
3. **Preprocessing**: Develop preprocessing steps using 'aodkit' functions to clean and prepare the satellite data for AOD retrieval. This includes correcting for atmospheric conditions and calibrating the sensor readings.
4. **AOD Retrieval**: Use 'aodkit' to process the preprocessed data and retrieve AOD values. Ensure your application can handle different geographic regions and time periods.
5. **Visualization**: Create visual representations of the AOD data using 'matplotlib'. Visualizations should include maps highlighting areas with high AOD levels, graphs showing trends over time, and scatter plots correlating AOD with other atmospheric parameters if available.
6. **Reporting**: Implement a feature where users can generate reports summarizing their findings. Reports should include key metrics, visualizations, and any insights derived from the AOD analysis.
7. **User Interface**: Although not mandatory, consider adding a simple command-line interface or even a basic web interface using Flask to allow non-technical users to interact with your application.
8. **Documentation**: Provide comprehensive documentation detailing how to install, use, and extend your application. Include examples of how to run the app with sample datasets.

Your application should be modular, allowing for easy updates and extensions. Emphasize clarity and efficiency in your code, making sure it's maintainable and scalable for future enhancements.

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