🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network activity, shell execution, and obfuscation. However, the metadata risk score suggests potential issues with maintainer effort and transparency, making it suspicious.

Low maintainer effort and lack of transparency
No direct evidence of malicious activities

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer effort and lack of transparency, raising some suspicion but not strong evidence of malice.

🔬 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

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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 Bloomerp

Create a mini-application called 'BusinessManager' using the Bloomerp package, which is designed to streamline business operations through a custom-built business management system. Your task is to develop a feature-rich application that allows users to manage various aspects of their business such as customer information, sales tracking, inventory management, and employee records. Utilize Bloomerp's capability to define Django database models to automatically generate the necessary components for these features.

Step 1: Define Models
- Create a Customer model with fields such as name, email, phone number, and address.
- Develop a Product model including SKU, name, price, stock quantity, and supplier details.
- Design a Sales model to record each sale transaction, linking it to the customer and product models.
- Implement an Employee model containing personal details like name, role, hire date, and contact information.

Step 2: Customize Views and Templates
- Use Bloomerp to auto-generate views and templates for CRUD operations on each defined model.
- Enhance these auto-generated views and templates to include custom functionalities such as filtering products based on stock levels, generating monthly sales reports, and displaying employee attendance records.

Step 3: Integrate Authentication
- Enable user authentication and authorization within the application, ensuring that only authorized personnel have access to specific features.
- Consider implementing role-based access control where different roles (e.g., manager, salesperson, accountant) have varying levels of access to the application's features.

Step 4: Testing and Deployment
- Thoroughly test all functionalities of the application, ensuring data integrity and security.
- Prepare the application for deployment by configuring settings for production environments and setting up necessary server configurations.

By following these steps, you will utilize Bloomerp's core features to create a robust, scalable, and secure mini-application tailored for business management needs.