🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except metadata, where it scores a moderate risk due to the maintainer having only one package. Overall, there are no indications of malicious activities or supply-chain attacks.

No network calls or shell executions detected.
Low obfuscation and credential risks.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository dantezhu/agentclub appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Dante Zhu" 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 agentclub

Develop a social media monitoring tool named 'AIWatch' using the Python package 'agentclub'. This tool will enable users to monitor and analyze conversations on various social media platforms through AI agents hosted on the 'agentclub' server. Users can interact with these agents to receive real-time updates, sentiment analysis, trending topics, and more.

Steps to develop AIWatch:
1. Set up an 'agentclub' server instance locally or remotely to host your AI agents.
2. Integrate the 'agentclub' package into your Python environment to communicate with the server.
3. Create a set of AI agents that can connect to different social media APIs (e.g., Twitter, Reddit) and gather data based on user-defined keywords and hashtags.
4. Implement a dashboard feature where users can log in and view the aggregated data from all connected agents in a user-friendly interface.
5. Develop a sentiment analysis module that processes the collected data and provides insights into public opinion regarding specific topics or brands.
6. Allow users to customize their dashboard by setting up personal alerts for new posts or comments that match their interests.
7. Ensure that the tool supports multiple languages and can handle large volumes of data efficiently.

Features of AIWatch:
- Real-time monitoring of social media trends and discussions.
- Sentiment analysis of collected data to gauge public opinion.
- Customizable alerts for specific keywords or hashtags.
- Multi-language support.
- Scalable architecture capable of handling high traffic and large datasets.
- User authentication and personalized dashboards.

How 'agentclub' is utilized:
- Use 'agentclub' to manage and deploy AI agents that interact with social media APIs.
- Leverage the built-in communication channels (OpenClaw, Hermes, Nanobot) to facilitate seamless data exchange between agents and the backend system.
- Utilize 'agentclub' to host and manage the AI agents' lifecycle, ensuring they operate smoothly and securely.