🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators and does not suggest any malicious activities. The network, shell, obfuscation, and credential risks are all low.

Low network, shell, obfuscation, and credential risks.
Maintainer's metadata indicates they have only one package.

Per-check LLM notes

Network: Network calls are expected for packages that interact with external services or APIs.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

=120) async with aiohttp.ClientSession(headers=self.headers, timeout=timeout, trust_env=True) as se
l=120) async with aiohttp.ClientSession(headers=self.headers, timeout=timeout, trust_env=True) as se
=120) async with aiohttp.ClientSession(headers=self.gemini_headers, timeout=timeout, trust_env=True
async with aiohttp.ClientSession(headers=self.gemini_headers, timeout=timeout, trust_env=True
l=120) async with aiohttp.ClientSession(headers=self.gemini_headers, timeout=timeout, trust_env=True
) async with aiohttp.ClientSession(headers=self.headers, timeout=timeout, trust_env=True) as se

✓ 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

Repository webdkt/agentmatrix appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Agent-Matrix Contributors" 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 agentmatrix-core

Create a small but powerful AI-driven task management app using the 'agentmatrix-core' package. This app will allow users to create tasks, assign them to different agents, monitor progress, and receive notifications upon completion. Each task will be handled by an AI agent that follows a set of predefined protocols and skills provided by 'agentmatrix-core'. Here’s a detailed breakdown of the project requirements and steps to implement it:

1. **Project Setup**: Start by setting up your Python environment and installing the 'agentmatrix-core' package. Ensure you have all necessary dependencies installed.
2. **Task Creation Interface**: Develop a user-friendly interface where users can input new tasks. These tasks should include details such as title, description, deadline, and priority level.
3. **Agent Assignment**: Utilize 'agentmatrix-core' to define and assign different types of agents based on the nature of the task. For example, one type of agent could handle time-sensitive tasks while another handles research-intensive tasks.
4. **Progress Monitoring**: Implement a feature that allows users to track the progress of each task. This can be achieved through real-time updates from the agents using the MicroAgent protocol provided by 'agentmatrix-core'.
5. **Notifications System**: Set up a notification system that alerts users when a task is completed or if there are any issues encountered during the task execution. Use the skill system within 'agentmatrix-core' to customize these notifications according to user preferences.
6. **Data Visualization**: Integrate data visualization tools to provide graphical representations of task statuses and completion rates over time. This can help users better understand the efficiency of their task management process.
7. **User Management**: Include basic user management features like account creation, login, and password reset functionalities.
8. **Testing & Optimization**: Thoroughly test the application to ensure smooth operation and performance. Optimize the code for efficiency and scalability.
9. **Deployment**: Prepare the application for deployment, ensuring it can be easily accessed by multiple users simultaneously.

By following these steps and leveraging the capabilities of 'agentmatrix-core', you'll develop a robust AI-driven task management tool that enhances productivity and streamlines workflow processes.