🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories assessed, with no indications of malicious behavior. The metadata risk is slightly elevated due to the maintainer's single package history.

Low risk scores across all technical categories.
Metadata risk moderately elevated due to maintainer's limited package history.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
Metadata: The maintainer has only one package, indicating a potentially new or less active account.

📦 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 (30218 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

38 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

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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "David Onuh" 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 ahnlich-client-py

Create a Python-based mini-application called 'AhnlichSearch' that leverages the 'ahnlich-client-py' package to interact with Ahnlich DB and AI. This application will serve as a simple yet powerful tool for users to query and retrieve information from the Ahnlich database using natural language queries. Here’s a detailed step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python development environment. Ensure you have Python installed and create a virtual environment for your project. Install the 'ahnlich-client-py' package along with any other necessary dependencies.

2. **Project Structure**: Organize your project into directories such as 'src', 'tests', and 'docs'. Within 'src', create modules for handling user input, communicating with the Ahnlich API, and displaying results.

3. **User Interface**: Design a simple command-line interface (CLI) for interacting with your application. The CLI should allow users to input queries in natural language and receive structured responses.

4. **Query Processing**: Implement a module that processes user inputs, sanitizes them, and translates them into requests that can be understood by the 'ahnlich-client-py' package. This module should also handle any errors gracefully and provide meaningful feedback to the user.

5. **API Interaction**: Utilize the 'ahnlich-client-py' package to establish a connection to the Ahnlich DB and AI service. Develop functions within this module to send queries, manage sessions, and handle responses efficiently.

6. **Result Display**: Create a module responsible for formatting and displaying the results retrieved from the Ahnlich service. Results should be presented in a clean, readable format that highlights key information.

7. **Testing**: Write tests for each component of your application to ensure reliability and functionality. Use tools like pytest to automate testing and maintain high-quality code.

8. **Documentation**: Document your project thoroughly, including setup instructions, usage examples, and API documentation for the 'ahnlich-client-py' package integration.

Suggested Features:
- Support for multiple query types (e.g., search, compare).
- Integration with external services for additional data retrieval.
- User authentication and session management.
- Logging and error reporting mechanisms.

By following these steps and incorporating the suggested features, you'll create a robust and user-friendly application that showcases the capabilities of the 'ahnlich-client-py' package.