aeolus-aq

v0.4.5.2 suspicious
4.0
Medium Risk

This module provides functions for downloading metadata and data files from

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks but has a concerning metadata risk due to the maintainer's new or inactive account with minimal package history and missing author information.

  • Low network, shell, obfuscation, and credential risks
  • High metadata risk due to maintainer's profile
Per-check LLM notes
  • Network: The network call patterns are typical for fetching external resources and do not inherently suggest malicious activity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The maintainer has a new or inactive account with minimal package history and missing author information, which could indicate potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ): ... response = requests.get(url) ... response.raise_for_status() ...
  • args): ... return requests.get(url, **kwargs) Note: This only adds the timeout
  • try: response = requests.get(url, params=params, timeout=timeout) if response.st
  • }/{endpoint}" response = requests.get(url, params=params, headers=headers, timeout=60) # Hand
  • }/{endpoint}" response = requests.get(url, params=params, headers=headers, timeout=30) respons
  • t/html``). """ resp = requests.get(_METADATA_CSV_URL, timeout=60) resp.raise_for_status()
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository southlondonscientific/aeolus 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 aeolus-aq 
Develop a Python-based mini-application named 'AirQualityVisualizer' that integrates with the 'aeolus-aq' package to provide real-time air quality monitoring and visualization capabilities. The application should allow users to input geographic coordinates or select predefined locations to fetch air quality data from the Aeolus satellite. Utilize the 'aeolus-aq' package to download relevant metadata and data files necessary for processing and analysis.

Key Features:
1. User Interface: Create a simple yet intuitive command-line interface (CLI) for users to interact with the application. Include options to specify location by latitude and longitude or choose from a list of predefined cities.
2. Data Fetching: Implement functionality within the application to utilize 'aeolus-aq' to download the latest air quality data for the specified location(s). Ensure the application handles any errors gracefully, such as invalid coordinates or missing data.
3. Data Processing: Process the downloaded data to extract key air quality metrics, such as particulate matter (PM), nitrogen dioxide (NO2), and ozone (O3) levels. Use standard units for these measurements.
4. Visualization: Develop a feature that visualizes the extracted air quality metrics using matplotlib or seaborn libraries in Python. Provide different visualization options, like line charts for time-series data or bar graphs for comparisons between different pollutants.
5. Reporting: Enable the application to generate a summary report of the air quality conditions at the specified location(s). The report should include average values over a certain period, peak pollution levels, and any trends observed.
6. Documentation: Write clear documentation for the application, explaining how to install dependencies, run the application, and interpret the output. Also, include details on how the 'aeolus-aq' package is integrated into the application.

Your task is to design and implement this mini-application following best practices in Python development, ensuring it is modular, well-documented, and easy to extend or modify in the future.