AI Analysis
The package shows no signs of malicious behavior with minimal risks across all categories checked. The metadata risk is slightly elevated due to the maintainer's single package, but this alone does not indicate any malicious intent.
- No network or shell risks detected
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
- Metadata: The maintainer has only one package, indicating a new or less active account, but no other suspicious activities are flagged.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (2097 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
8 type-annotated function signatures (partial)
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "alisonsalmeida" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully functional microservice-based weather information system using the 'aiofase' Python package. This application will fetch real-time weather data from an API and serve it through a RESTful interface. Here are the steps and features you need to implement: 1. Set up your development environment with Python 3.x and install the required packages including 'aiofase', 'requests', and 'fastapi'. 2. Design your microservices architecture. At least two services are required: one for fetching weather data and another for serving the data via HTTP requests. 3. Use 'aiofase' to create asynchronous microservices. Ensure that the service responsible for fetching data can asynchronously request data from an external weather API like OpenWeatherMap. 4. Implement caching mechanisms within 'aiofase' to reduce redundant API calls and improve response times. 5. Develop a FastAPI endpoint that serves as the main entry point for clients to request weather data. This endpoint should communicate with the weather data fetching service asynchronously. 6. Include error handling and logging within your services to ensure robustness and ease of debugging. 7. Add documentation to your code explaining how each component interacts and how to run the application locally. 8. Finally, deploy your microservice architecture on a cloud platform of your choice, ensuring that it can scale based on demand. The goal is to showcase the capabilities of 'aiofase' in building scalable, efficient, and maintainable microservices systems.