🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all assessed categories with no indications of malicious activity. The primary concern lies in the new maintainer and lower metadata quality, but these do not suggest any malicious intent.

Low risk scores across all categories.
New maintainer with lower metadata quality.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API interactions.
Shell: No shell execution detected, reducing the likelihood of malicious activities.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
Metadata: The maintainer seems new and there's low metadata quality, but no clear malicious indicators.

🔬 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: live.com

✓ 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 "Menooker" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with KunQuant

Create a fully-functional mini-application that serves as a financial expression optimizer and executor tool using the 'KunQuant' Python package. Your task is to develop a user-friendly interface where users can input complex financial expressions and factors, which will then be optimized and executed in real-time to provide accurate results. Here’s a detailed step-by-step guide on how to build this application:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment. Install the 'KunQuant' package along with any necessary dependencies.
2. **User Interface Design**: Design a simple yet effective user interface. This could be a command-line interface (CLI) or a graphical user interface (GUI). The UI should allow users to input their financial expressions and see the optimized and executed results.
3. **Expression Input Handling**: Implement functionality within your application to accept financial expressions from the user. These expressions might include various financial metrics such as moving averages, relative strength index (RSI), etc.
4. **Optimization with KunQuant**: Use the 'KunQuant' package to optimize these expressions. The goal is to enhance performance while maintaining accuracy. Ensure that the optimization process is transparent to the user, showing how the original expression was transformed.
5. **Execution and Result Display**: Once the expressions are optimized, execute them using the 'KunQuant' package. Display the results in a clear and understandable format. If applicable, provide visual representations like charts or graphs.
6. **Error Handling and Validation**: Implement robust error handling to manage incorrect inputs or issues during execution. Validate user inputs to ensure they are valid financial expressions before processing.
7. **Documentation and User Guide**: Provide comprehensive documentation for both developers and end-users. Include a user guide that explains how to use the application effectively.
8. **Testing and Optimization**: Thoroughly test your application with a variety of financial expressions to ensure reliability and efficiency. Optimize the code and UI based on feedback and testing results.
9. **Deployment**: Finally, prepare your application for deployment. Decide whether it will be a desktop application or accessible via a web interface, and deploy accordingly.

Suggested Features:
- Support for a wide range of financial expressions and factors.
- Real-time optimization feedback.
- Execution results displayed in multiple formats (text, charts).
- Detailed documentation and user guides.
- Robust error handling and validation mechanisms.