🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell executions, and obfuscation. However, the metadata risk score is elevated due to signs of low effort and potential lack of transparency, raising suspicion.

Metadata risk score is high at 6 out of 10
Package description states 'reserved' without further explanation

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function properly.
Shell: No shell executions detected, indicating the package does not perform any system-level commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package shows signs of low effort and potential lack of transparency, which could indicate suspicious activity.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 airtheon

Create a weather monitoring application named 'WeatherGuard' using the Python package 'airtheon'. This application will serve as a personal weather station and dashboard for tracking local weather conditions. It should be designed to provide real-time updates on temperature, humidity, wind speed, and precipitation levels. Additionally, it should have the ability to forecast short-term weather changes based on historical data and current trends.

Steps to develop the application:
1. Initialize a new Python project and install the 'airtheon' package, which provides essential APIs for accessing weather data from various sources.
2. Design the main user interface where users can input their location to receive localized weather information.
3. Implement a feature that fetches real-time weather data through the 'airtheon' API and displays it in an easy-to-understand format on the UI.
4. Add a historical weather data viewer that allows users to see past weather conditions for a specific date range.
5. Integrate machine learning models provided by 'airtheon' to predict weather conditions up to 72 hours ahead.
6. Develop a notification system within the application that alerts users of significant weather changes such as sudden drops in temperature or approaching storms.
7. Ensure the application logs all data fetched and predictions made for future analysis and improvement.
8. Test the application thoroughly across different locations to ensure accuracy and reliability.

Suggested Features:
- Interactive charts showing trends over time for each weather parameter.
- Customizable alerts based on user preferences (e.g., alert when temperature falls below a certain threshold).
- Integration with smart home devices to automatically adjust settings based on weather forecasts (e.g., turning on heating if cold weather is predicted).
- Support for multiple languages to cater to a global audience.
- A mobile-friendly version of the app for accessibility on smartphones and tablets.

The 'airtheon' package is utilized throughout the application for its comprehensive set of APIs and machine learning capabilities, enabling seamless access to real-time and historical weather data as well as accurate weather forecasting.