🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minor concerns regarding network activity and maintainer engagement. There are no indications of malicious behavior or supply-chain attacks.

Low risk scores across all categories except network risk.
No evidence of obfuscation, shell execution, or credential harvesting.

Per-check LLM notes

Network: The observed network calls suggest legitimate data retrieval, but further investigation is needed to ensure the destination and purpose are benign.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk, but the maintainer's profile and package details suggest low engagement or possibly a new account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: with requests.get(https_data_path, stream=True, timeout=timeout) as r:
ogin...') transport = paramiko.Transport((hostname, port)) transport.connect(username=self.sf

✓ 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: email.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jerryjerry9/satellite_tools appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ael-satellite-tools

Create a Python-based mini-application named 'SatelliteDataExplorer' that leverages the 'ael-satellite-tools' package to download and process satellite imagery data. The app should allow users to select specific types of satellite products (e.g., MODIS, Landsat) and regions of interest to analyze. Here’s a detailed plan for building this application:

1. **Setup Environment**: Begin by setting up a virtual environment and installing necessary packages including 'ael-satellite-tools'. Ensure you have the required dependencies installed such as numpy, pandas, matplotlib for data processing and visualization.

2. **User Interface**: Design a simple command-line interface (CLI) where users can input their preferences regarding the type of satellite product they wish to download and the geographic area they are interested in. Consider adding options for specifying dates and time periods.

3. **Data Downloading**: Implement functionality within the application that uses 'ael-satellite-tools' to download the selected satellite imagery data from public repositories or APIs. Make sure the application handles errors gracefully, providing meaningful feedback to the user if something goes wrong during the download process.

4. **Data Processing**: Once the data is downloaded, the application should use 'ael-satellite-tools' to preprocess the satellite images. This could include tasks like image cropping based on the specified region, adjusting brightness and contrast, or converting the format to a more usable one for analysis.

5. **Visualization**: After preprocessing, provide the user with the ability to visualize the processed satellite imagery. Utilize matplotlib to display these images directly in the CLI, allowing for zooming, panning, and basic image manipulation.

6. **Output Options**: Finally, give users the option to save the processed images locally or upload them to cloud storage services like AWS S3 or Google Cloud Storage. Ensure the application also logs all actions taken and provides a summary report at the end of each session.

7. **Testing and Documentation**: Thoroughly test your application with various inputs to ensure robustness and reliability. Write clear documentation explaining how to install and use the application, along with examples and best practices for working with satellite imagery data.

By following these steps, you will create a powerful yet easy-to-use tool for exploring satellite imagery data using the 'ael-satellite-tools' package.