AI Analysis
The package shows low risks in terms of network, shell, obfuscation, and credential handling. However, the absence of author information and lack of a linked GitHub repository raise concerns about its provenance and maintainability.
- missing author information
- no linked GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has some red flags including missing author information and no linked GitHub repository, but there are no clear signs of typosquatting or other malicious intent.
Package Quality Overall: Low (2.4/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Documentation URL: "Documentation" -> https://docs.ligo.org/ngdd/arrakis-schemaDetailed PyPI description (2424 chars)
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
Email domain looks legitimate: ligo.org>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 Python-based weather forecasting mini-app that integrates with the Arrakis API using the 'arrakis-schema' package. Your app should allow users to input their location and retrieve detailed weather forecasts. Here are the steps and features you need to implement: 1. **Setup**: Install necessary packages including 'arrakis-schema', 'requests', and any other dependencies required for handling JSON data and making HTTP requests. 2. **Schema Utilization**: Use 'arrakis-schema' to validate and structure incoming and outgoing data from the Arrakis API. Ensure that your app strictly adheres to the schemas provided by 'arrakis-schema' for both request and response formats. 3. **User Interface**: Develop a simple command-line interface (CLI) where users can enter their city name or zip code to fetch weather information. 4. **Data Fetching**: Implement a function that takes user input, constructs a proper API request according to the schema, sends it to the Arrakis API, and retrieves the forecast data. 5. **Data Parsing & Validation**: Parse the received JSON data, ensuring it conforms to the 'arrakis-schema' specifications. Handle exceptions gracefully if the data doesn't match expected formats. 6. **Output Display**: Display the parsed weather data in an easily readable format. Include key details such as temperature, humidity, wind speed, and a summary of the weather conditions. 7. **Error Handling**: Implement robust error handling to manage issues like invalid user inputs, network errors, and incorrect responses from the API. 8. **Testing**: Write unit tests to ensure each component works as expected, especially focusing on how well the app handles various edge cases and unexpected inputs. 9. **Documentation**: Provide clear documentation on how to install and run the app, along with examples of valid inputs and expected outputs. This project will showcase your ability to work with APIs, handle data validation through schemas, and create a functional CLI application.
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