arlmet

v0.1.0a4 suspicious
4.0
Medium Risk

Read and write ARL meteorological files.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to incomplete metadata and shell execution, but lacks clear indicators of malicious activity.

  • Incomplete author information
  • Shell execution detected
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Shell execution detected may indicate the package runs external commands, which could be legitimate but should be reviewed for purpose and context.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious intent related to stealing secrets.
  • Metadata: The author's information is incomplete and the maintainer has only one package, which may indicate a less experienced or potentially suspicious account.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: pyproject.toml
  • 11 test file(s) detected (e.g. test_grid.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://jmineau.github.io/arl-met/
  • Detailed PyPI description (5214 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 180 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 93 commits in jmineau/arl-met
  • 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 score 2.0

Found 1 shell execution pattern(s)

  • () as workdir: proc = subprocess.run( [str(binary)], input=stdin,
✓ 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 jmineau/arl-met 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 arlmet 
Create a mini-application named 'Meteorologist' that leverages the Python package 'arlmet' to read and analyze meteorological data from ARL files. This application will serve as a tool for meteorologists and climate scientists to quickly access and interpret weather data stored in these specialized file formats. Here’s a detailed plan on how to proceed:

1. **Setup**: Begin by setting up your development environment with Python installed, along with the necessary packages including 'arlmet'. Ensure you have a basic understanding of Python and familiarity with reading and writing files.

2. **Data Input**: Utilize 'arlmet' to implement functionality that allows users to input the path to an ARL meteorological file. This step involves using 'arlmet' functions to open and load the data from the specified file into your application.

3. **Data Analysis**: Implement core analysis features such as temperature trends over time, wind speed variations, and precipitation levels. Use 'arlmet' to extract specific data points from the loaded file for these analyses.

4. **Visualization**: Incorporate a visualization module where users can view their data in graphical form. For example, plot temperature trends over time using libraries like matplotlib or seaborn. Ensure that the visualizations are clear and informative.

5. **Output Options**: Provide options for users to save their analyzed data or visualizations. They should be able to export the data back into an ARL file format or save the visualizations as images or PDFs.

6. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. Commands should include options to load files, select data for analysis, choose visualization types, and save outputs.

7. **Documentation**: Write comprehensive documentation explaining how to use each feature of 'Meteorologist', including examples and common use cases. Include details on installing and setting up the application.

8. **Testing**: Test the application thoroughly to ensure all functionalities work correctly. Include tests for edge cases and potential errors when dealing with ARL files.

By following these steps, you will create a valuable tool for anyone working with ARL meteorological data, making it easier to perform critical analyses and visualizations directly within the application.

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