🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential issues due to incomplete metadata and lack of a GitHub repository, which raises concerns about its origin and maintenance.

Incomplete author details and missing GitHub repository.
Metadata risk score of 4 out of 10.

Per-check LLM notes

Network: The use of httpx for network requests is common and expected in packages that need to communicate with external services.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has no associated GitHub repository and the author details are incomplete, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1015 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

16 type-annotated function signatures detected in source

○ 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 score 7.5

Found 5 network call pattern(s)

ct[str, Any]]: async with httpx.AsyncClient(timeout=self._timeout_s) as client: r = await client.g
builtin_tools async with httpx.AsyncClient(timeout=self._timeout_s) as client: r = await client.p
ig"] = config async with httpx.AsyncClient(timeout=self._timeout_s) as client: r = await client.p
etadata or {}} async with httpx.AsyncClient(timeout=self._timeout_s) as client: r = await client.p
tool_results async with httpx.AsyncClient(timeout=None) as client: async with client.stream("POS

✓ 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

No author email provided

✓ 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 ai-layer-client

Create a Python-based mini-application called 'AI-QueryBot' that leverages the 'ai-layer-client' package to interact with Nexus AI's infrastructure API. This application will serve as a command-line tool for querying AI models hosted on Nexus AI, allowing users to input their queries and receive responses directly from the AI models. Here’s a step-by-step guide on how to build this application:

1. **Setup**: Begin by setting up your development environment. Install Python and ensure you have pip installed. Then, install the 'ai-layer-client' package using pip.
2. **Application Structure**: Define the basic structure of your application. It should include modules for handling user inputs, interacting with the API via 'ai-layer-client', and displaying outputs.
3. **User Input Module**: Develop a module that prompts the user to enter their query and select an AI model from a predefined list of available models. Ensure this module validates the input to prevent errors.
4. **API Interaction Module**: Use the 'ai-layer-client' package to create an asynchronous function that sends the user's query to the selected AI model. This function should handle authentication, API calls, and error handling gracefully.
5. **Output Display Module**: Create a module to format and display the response received from the AI model back to the user. Consider adding features like text-to-speech for auditory feedback.
6. **Enhancements**: To make the application more user-friendly, consider adding features such as history logging of queries and responses, the ability to save favorite models, and support for multiple languages.
7. **Testing and Deployment**: Test the application thoroughly with different inputs and scenarios to ensure reliability. Once satisfied, prepare the application for deployment by packaging it into a distributable format such as a .exe or .app file for Windows and macOS respectively.
8. **Documentation**: Write comprehensive documentation explaining how to install, use, and extend the functionality of 'AI-QueryBot'. Include examples and best practices.

This project will not only showcase the capabilities of the 'ai-layer-client' package but also provide a practical tool for exploring AI models in a simple and accessible way.