🤖 AI Analysis

Final verdict: SAFE

The package has low risks associated with network calls, shell execution, and obfuscation. However, the metadata quality and maintainer activity are concerning, which slightly increases the risk score.

Low risk in network calls, shell execution, and obfuscation.
Poor metadata quality and low maintainer activity.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package relies on external services.
Shell: No shell execution detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.

🔬 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: phper.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentgraph-api

Create a social networking application called 'AgentConnect' using Python's 'agentgraph-api' package. This application will serve as a platform where users can connect based on shared interests, join groups, and share content. The app should be built with a focus on user engagement and privacy, offering features such as personalized news feeds, direct messaging, and group discussions. Here's a detailed plan for building this application:

1. **Setup Environment**: Start by setting up your development environment. Install the necessary packages including 'agentgraph-api'. Ensure you have Python installed and create a virtual environment for your project.

2. **Define User Model**: Define a user model that includes fields like username, email, password, bio, and profile picture. Use 'agentgraph-api' to manage these models efficiently, leveraging its FastAPI service layer capabilities for handling database operations.

3. **User Authentication**: Implement user authentication functionalities such as sign-up, login, and logout. Utilize 'agentgraph-api' to securely store and retrieve user credentials.

4. **Social Connections**: Allow users to follow other users and join interest-based groups. Use 'agentgraph-api' to manage connections between users and groups, ensuring efficient data retrieval and updates.

5. **Content Sharing**: Enable users to post updates, photos, and videos. Integrate 'agentgraph-api' to handle content storage and retrieval, making sure each piece of content is associated with the correct user and group(s).

6. **News Feed**: Develop a personalized news feed for each user, displaying posts from followed users and joined groups. Utilize 'agentgraph-api' to fetch and sort relevant content based on user preferences and activity.

7. **Direct Messaging**: Implement a direct messaging system allowing private conversations between users. Use 'agentgraph-api' to manage message threads and ensure secure communication.

8. **Group Discussions**: Allow users to create and participate in group discussions. Use 'agentgraph-api' to facilitate group management and discussion threading.

9. **Privacy Settings**: Provide users with options to control their privacy settings, such as who can view their profile and posts. Leverage 'agentgraph-api' to enforce these settings effectively.

10. **Testing & Deployment**: Thoroughly test all functionalities to ensure they work as expected. Once ready, deploy your application using a cloud provider of your choice, making sure to configure 'agentgraph-api' properly for production use.

By following these steps, you'll create a robust and engaging social networking application using 'agentgraph-api', showcasing its versatility and power in managing complex relationships and data in a web application.