arrakis-schema

v0.4.0 suspicious
4.0
Medium Risk

Schemas for the Arrakis API

🤖 AI Analysis

Final verdict: SUSPICIOUS

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)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.ligo.org/ngdd/arrakis-schema
  • Detailed PyPI description (2424 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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: ligo.org>

Suspicious Page Links

All external links appear legitimate

Git Repository History

No GitHub repository linked

  • No GitHub repository link found
Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 arrakis-schema
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.

💬 Discussion Feed

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