🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity or high-risk behavior. The metadata risk score is slightly elevated due to incomplete author details and a non-secure link, but this alone does not indicate a supply-chain attack.

No network or shell risks detected
Low obfuscation and credential risks
Metadata risk due to incomplete author details and non-secure link

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating the package does not perform system commands without explicit user action.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has a non-secure external link and an author with minimal information, suggesting potential low risk but requiring further investigation.

🔬 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: agenterprise.ai>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://www.agenterprise.ai

✓ Git Repository History

Repository agenterprise/agenterprise appears legitimate

⚠ 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 agenterprise

Your task is to create a fully-functional mini-application named 'AIEnvCraft' using the Python package 'agenterprise'. This application will serve as a versatile tool for generating customized AI environments tailored for various machine learning tasks such as reinforcement learning experiments, data visualization, and model training simulations.

Step-by-Step Instructions:
1. **Environment Setup**: Begin by setting up your development environment. Ensure you have Python installed along with 'agenterprise' and any other necessary packages.
2. **Define Core Features**: The application should allow users to specify parameters for their AI environment, including dimensions, initial conditions, and dynamic rules governing entity interactions.
3. **User Interface**: Develop a simple command-line interface (CLI) for user interaction. Users should be able to input parameters, view current settings, and generate new environments based on those inputs.
4. **Environment Generation**: Utilize 'agenterprise' to generate environments based on user specifications. Each environment should be unique and capable of supporting different types of AI tasks.
5. **Visualization**: Implement basic visualization capabilities to help users understand the generated environments. Consider integrating libraries like Matplotlib or Plotly for this purpose.
6. **Export Functionality**: Allow users to export their generated environments into files or formats suitable for further analysis or use in other applications.
7. **Documentation & Testing**: Write comprehensive documentation explaining how to use 'AIEnvCraft' and its functionalities. Conduct thorough testing to ensure reliability and robustness.

Suggested Features:
- Customizable parameters for environment generation.
- Interactive CLI for ease of use.
- Dynamic rule sets for varied AI task support.
- Visualization tools for better understanding of environments.
- Export options for generated environments.

How 'agenterprise' is Utilized:
'agenterprise' provides the foundational framework and utilities for generating these AI environments. Specifically, it offers methods to define and instantiate environments according to specified parameters. You'll need to explore the documentation of 'agenterprise' to understand how to integrate its functionalities into your application.