🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks such as network or shell execution vulnerabilities, but its recent creation and lack of a public git repository raise concerns about its origin and development history.

Metadata risk due to new package and missing git repo
Potential supply-chain attack indicators

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
Metadata: The package shows signs of being newly created and the git repository is not available, which raises some suspicion.

🔬 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Hussain Alkhatib" 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 agency-intelligence

Your task is to develop a sophisticated command-line utility called 'AgentHub' using the Python package 'agency-intelligence'. This utility aims to streamline the management and orchestration of multiple AI agents across various tasks and environments. Here's a detailed plan on how to proceed with the development of 'AgentHub':

1. **Project Setup**: Start by installing the 'agency-intelligence' package. Ensure you have a clean virtual environment set up for this project.

2. **Core Functionality**: Implement the core functionalities that allow users to:
- List all available AI agents registered within the system.
- Deploy new AI agents to perform specific tasks.
- Monitor the status and performance metrics of deployed agents.
- Scale up or down the number of agents handling a particular task based on demand.

3. **Advanced Features**: Enhance the utility by adding the following advanced features:
- Automated failover mechanisms to ensure high availability of services managed by the agents.
- Integration with popular logging and monitoring tools for real-time analytics.
- Support for different types of tasks (e.g., data processing, web scraping, natural language processing).

4. **User Interface**: Design a user-friendly CLI interface that allows for easy interaction with 'AgentHub'. Commands should be intuitive and well-documented.

5. **Security Measures**: Incorporate security best practices into your application to protect sensitive information and ensure secure communication between the CLI and the AI agents.

6. **Testing and Documentation**: Write comprehensive tests to validate the functionality of 'AgentHub'. Also, prepare detailed documentation that explains how to install, configure, and use the utility effectively.

Remember to leverage the 'agency-intelligence' package's capabilities to their fullest extent, ensuring that your application is robust, scalable, and efficient.