AI Analysis
The package exhibits moderate risk due to its potential for unauthorized network communication and signs of low maintainer effort. While there is no clear evidence of malicious activity, the lack of transparency and maintenance raises concerns.
- Moderate network risk
- Low maintainer effort
Per-check LLM notes
- Network: The network calls to an external API endpoint suggest the package might be communicating with a remote server, which could be legitimate but warrants further investigation to ensure it's not unauthorized data transmission.
- Shell: No shell execution patterns were detected, indicating no immediate risk from command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The package shows signs of low maintainer effort and may be new or inactive, raising some suspicion but not definitive evidence of malice.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
19 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 3 network call pattern(s)
lib.request req = urllib.request.Request( f"{PRO_URL}/api/v1/pro/cortex/inges) with urllib.request.urlopen(req, timeout=5) as resp: logger.debuDepends, Query _client = httpx.AsyncClient(timeout=10.0) logger.info("Pro SaaS proxy enabled → %s"
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a real-time weather monitoring system using the 'amfs-http-server' package. This system will fetch live weather data from an external API and stream it to users through a web interface, utilizing Server-Sent Events (SSE) for real-time updates. Your task is to develop a fully functional mini-app that includes the following features: 1. **User Interface**: A simple web page that displays current weather conditions such as temperature, humidity, wind speed, etc., for a user-selected city. 2. **Data Fetching**: Use an external weather API (such as OpenWeatherMap) to periodically fetch the latest weather data for the selected city. 3. **Real-Time Updates**: Implement SSE to push new weather data to the client-side every minute without requiring the user to refresh the page. 4. **Error Handling**: Ensure that the app gracefully handles errors such as network issues or invalid city names, displaying appropriate messages to the user. 5. **Configuration Management**: Allow users to set up their preferred cities and units of measurement (Celsius/Fahrenheit) via a configuration file or settings page. 6. **Logging**: Integrate logging to track important events like API calls, data fetching failures, and user interactions. Utilize the 'amfs-http-server' package to handle the HTTP requests and manage the SSE streams efficiently. Your solution should demonstrate a clear understanding of asynchronous programming and event-driven architecture. Additionally, provide documentation on how to install dependencies, run the server, and test the application.
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