AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk indicators such as no network calls, shell executions, obfuscation, or credential risks. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of detailed information, which warrants further scrutiny.
- Elevated metadata risk
- Maintainer account status is questionable
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating secure handling of secrets and credentials.
- Metadata: The maintainer has a new or inactive account and lacks a full author name, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (5.6/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.tomlClassifier: Framework :: Pytest
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (14156 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
66 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 97 commits in andy-takker/asynclySmall 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
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: mail.ru>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository andy-takker/asyncly appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 asyncly
Create a real-time weather monitoring application using the 'asyncly' package. This application will fetch live weather data from an API and display it in a user-friendly manner. The goal is to demonstrate the asynchronous capabilities of 'asyncly' by efficiently handling multiple concurrent requests and updating the UI in real-time without blocking the main thread.
### Project Overview:
- **Application Name:** Real-Time Weather Watcher
- **Primary Functionality:** Fetch and display current weather conditions for multiple cities.
- **Features:**
- User Interface: A simple web interface showing weather conditions (temperature, humidity, wind speed) for selected cities.
- Asynchronous Requests: Use 'asyncly' to handle multiple concurrent API calls for fetching weather data.
- Real-Time Updates: Automatically refresh weather data at regular intervals.
- Error Handling: Gracefully handle any errors that occur during API calls or data processing.
### Steps to Implement:
1. **Setup Environment:** Ensure Python and the necessary packages ('asyncly', 'aiohttp', 'flask') are installed.
2. **API Integration:** Obtain an API key from a weather service provider (e.g., OpenWeatherMap).
3. **Define Data Models:** Create models for storing weather data.
4. **Implement Async Functions:** Use 'asyncly' to define asynchronous functions for fetching weather data from the API.
5. **Develop Web Interface:** Build a Flask-based web application to display weather data.
6. **Integrate Async Functions with UI:** Ensure the UI updates automatically when new data is fetched.
7. **Test Application:** Thoroughly test the application for accuracy, performance, and error handling.
8. **Deployment:** Deploy the application to a web server or cloud platform.
### Utilization of 'asyncly':
- **HTTP Client:** Use 'asyncly' to make asynchronous HTTP requests to the weather API.
- **Concurrency Management:** Leverage 'asyncly' to manage multiple concurrent requests efficiently, ensuring smooth operation even under heavy load.
- **Data Processing:** Process incoming weather data asynchronously to update the application state without blocking the UI.
This project will not only showcase the power of asynchronous programming but also provide practical experience in building real-world applications using Python and modern web technologies.
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