🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some suspicious signs such as non-secure links and a rapid commit history, which might indicate potential risks. However, there are no clear indications of malicious activities.

Suspicious metadata with non-secure links and rapid commit history
No detected shell execution, obfuscation, or credential harvesting patterns

Per-check LLM notes

Network: The network call is likely intended for legitimate communication with a server, but could be a concern if the server's behavior is unknown or untrusted.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
Metadata: Suspicious activity includes non-secure links and rapid commit history, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

} try: resp = httpx.post(HUB_URL, json=payload, headers={"A2A-Version": "1.0"}, timeo

✓ 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 score 10.0

Found 7 suspicious link(s) on the package page

Non-HTTPS external link: http://0.0.0.0:8000
Non-HTTPS external link: http://your-server:8000/join/xK9mP2
Non-HTTPS external link: http://your-server:8000/mcp
Non-HTTPS external link: http://your-server:8000/.well-known/agent-card.json
Non-HTTPS external link: http://127.0.0.1:8000/mcp
Non-HTTPS external link: http://your-server:8000/

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
All 71 commits happened within 24 hours

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "ClydeShen" 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 agentcouncil-hub

Create a versatile mini-application named 'AgentCouncilConnector' using the Python package 'agentcouncil-hub'. This application will serve as a bridge between different AI agents, enabling them to communicate and collaborate seamlessly. The goal is to demonstrate the package's capability to integrate various AI agents through a unified interface.

### Application Overview:
- **Name:** AgentCouncilConnector
- **Purpose:** To facilitate communication and task collaboration among different AI agents.
- **Features:**
- **Agent Registration:** Allow users to register new AI agents to the system.
- **Task Assignment:** Assign tasks to registered agents based on their capabilities.
- **Message Relay:** Enable real-time message passing between agents.
- **Status Updates:** Provide status updates of ongoing tasks and completed tasks.
- **Analytics Dashboard:** Display analytics about the performance of each agent and overall system efficiency.

### Step-by-Step Development Guide:
1. **Setup Environment:** Begin by setting up a Python environment with all necessary dependencies including 'agentcouncil-hub'. Ensure the environment is ready for development.
2. **Register Agents:** Implement a feature within the application where users can register new AI agents. This registration process should include specifying the agent's unique identifier and its capabilities.
3. **Task Management System:** Develop a task management system that allows assigning tasks to agents based on their capabilities. Tasks should be defined with specific requirements and deadlines.
4. **Real-Time Communication:** Utilize 'agentcouncil-hub' to set up a real-time communication channel between agents. Messages should be routed correctly to ensure seamless interaction.
5. **Monitoring and Analytics:** Implement a monitoring system to track the progress of tasks and provide analytics about the performance of each agent. Visualize these metrics on an analytics dashboard.
6. **Testing and Deployment:** Conduct thorough testing to ensure all functionalities work as expected. Once tested, deploy the application to a server or cloud platform for public access.

### Utilizing 'agentcouncil-hub':
- **Integration:** Use 'agentcouncil-hub' to integrate different AI agents into your application. This involves setting up the hub to recognize and manage connections from various agents.
- **Communication Protocol:** Leverage 'agentcouncil-hub' to define and enforce a communication protocol that ensures reliable and efficient data exchange between agents.
- **Scalability:** Explore the scalability features of 'agentcouncil-hub' to ensure your application can handle an increasing number of agents and tasks without degradation in performance.