AI Analysis
The package has minimal direct risks but exhibits signs of low maintenance and suspicious metadata, which raises concerns about its origin and trustworthiness.
- metadata risk due to low maintenance and suspicious author details
- lack of clear purpose or functionality
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution detected, indicating no immediate risk of command injection or system compromise.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintenance and suspicious author details, indicating potential risks.
Package Quality Overall: Low (1.2/10)
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
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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
Your task is to develop a simple yet engaging weather forecast mini-application using Python, specifically leveraging the 'aisp-py' library. This application will serve as a user-friendly tool to fetch and display current weather conditions along with a short-term forecast for any given city. ### Core Functionality: - **Weather Data Retrieval**: Utilize 'aisp-py' to connect to a weather API and retrieve current temperature, humidity, wind speed, and weather description (e.g., sunny, rainy). - **Forecast Display**: Fetch and present a short-term weather forecast for the next three days, including high and low temperatures and weather descriptions. - **User Interface**: Design a simple command-line interface where users can input a city name and receive real-time weather updates and forecasts. ### Suggested Features: - **Interactive CLI**: Implement a loop that allows users to query multiple cities without restarting the program. - **Error Handling**: Include robust error handling to manage invalid city names or API request failures gracefully. - **Customizable Units**: Allow users to switch between Celsius and Fahrenheit for temperature readings. - **Visual Enhancements**: Add ASCII art or emojis to visually represent different weather conditions (e.g., ☀️ for sunny, 🌧️ for rainy). ### How 'aisp-py' Will Be Used: - **API Integration**: Use 'aisp-py' to integrate your application with a weather data provider's API. Ensure you understand how to make API calls and parse JSON responses effectively. - **Data Parsing**: Extract necessary weather information from the JSON response and format it appropriately for display in your CLI. - **Documentation & Testing**: Refer to 'aisp-py's documentation for examples and best practices. Write unit tests to verify your application works as expected under various scenarios. This project aims to provide a practical example of integrating third-party APIs into a Python application while offering a useful tool for everyday weather information.
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