🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell execution, obfuscation, and credential handling, but the metadata shows some concerns with an incomplete author name and potential inactivity, raising suspicion.

Missing maintainer's author name
Potential inactivity of the maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows some red flags but no clear signs of malicious intent. The maintainer's author name is missing and they appear to be new or inactive.

🔬 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: anl.gov>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://www.arm.gov

✓ Git Repository History

Repository ARM-DOE/ACT 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 act-atmos

Create a weather analysis tool using the 'act-atmos' Python package that allows users to visualize and analyze atmospheric time series data. The tool should enable users to import their own dataset or use predefined datasets included in the 'act-atmos' package. Here are the key features your application should include:

1. **Data Importation**: Users should be able to upload CSV files containing atmospheric time series data. The application should validate the data format and handle common errors gracefully.
2. **Data Visualization**: Implement interactive plots using libraries like Matplotlib or Plotly to display temperature, pressure, humidity, and wind speed over time. Allow users to select which parameters they want to plot and adjust the time range dynamically.
3. **Statistical Analysis**: Provide basic statistical analysis tools such as mean, median, mode, standard deviation, and correlation coefficients between different parameters. Use 'act-atmos' functions to process the data efficiently and accurately.
4. **Forecasting Module**: Incorporate a simple forecasting model to predict future atmospheric conditions based on historical data. Utilize 'act-atmos' for preprocessing and feature extraction before feeding the data into a machine learning model.
5. **User Interface**: Develop a clean, user-friendly interface using frameworks like Flask or Django for web-based deployment, or Tkinter for desktop applications. Ensure the UI is responsive and intuitive.
6. **Documentation and Help**: Include comprehensive documentation and tooltips within the application to guide users through its functionalities.

Your task is to outline the steps needed to develop this application, from setting up the environment to deploying it. Make sure to utilize the 'act-atmos' package effectively throughout the development process.