🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in network, shell, and obfuscation activities, but the metadata risk score due to the missing maintainer's author name and potential inactivity raises some concerns.

Missing maintainer's author name
Potential inactivity of the maintainer

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of code being intentionally obscured.
Credentials: No credential harvesting patterns detected, indicating no suspicious activity related to secret or credential handling.
Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some suspicion but does not conclusively indicate 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yzhaoinuw/agent_collab_treaty 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 agent-collab-treaty

Create a collaborative coding environment where multiple developers and AI agents can work together on a shared Python project. Your task is to build a web-based application that leverages the 'agent-collab-treaty' package to facilitate real-time collaboration, documentation, and conflict resolution within the team. Here’s a detailed breakdown of the application's functionalities and how 'agent-collab-treaty' will be utilized:

1. **Real-Time Collaboration**: Developers and AI agents should be able to edit the same Python file simultaneously. Changes made by one participant should reflect in real-time for others.
2. **Documentation Integration**: Use 'agent-collab-treaty' to automatically generate and maintain up-to-date documentation based on the current state of the codebase. This includes adding comments, docstrings, and any other relevant documentation.
3. **Conflict Resolution**: Implement a feature that identifies conflicting changes made by different participants and suggests a resolution using 'agent-collab-treaty'. This could involve merging changes, asking for manual intervention, or utilizing AI to suggest the best course of action.
4. **User Interface**: Develop a clean and intuitive user interface that allows users to view, edit, and review the codebase. Ensure that the UI also displays the documentation generated by 'agent-collab-treaty'.
5. **Authentication & Authorization**: Set up basic authentication to allow only registered users and authorized AI agents to access the shared codebase.
6. **Testing & Validation**: Include automated testing mechanisms to validate the integrity of the codebase after each change. Use 'agent-collab-treaty' to ensure that all changes adhere to the agreed-upon documentation standards.

Your application should demonstrate the seamless integration of human and AI collaboration, leveraging 'agent-collab-treaty' to streamline the development process.