🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell execution, obfuscation, and credential harvesting. However, the missing repository and the maintainer's single package presence raise concerns about potential supply-chain risks.

Repository not found
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of unauthorized secret harvesting.
Metadata: The repository is not found and the maintainer has only one package, which raises some suspicion but does not definitively 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "The AgentForge Authors" 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 agentforge-memory-neo4j

Create a social network analysis tool using the 'agentforge-memory-neo4j' package. This tool will help users understand complex relationships within their social networks by visualizing connections between individuals based on shared activities, mutual friends, and communication patterns. Here’s a detailed plan for building this tool:

1. **Setup Environment**: Install Python and necessary libraries including 'agentforge-memory-neo4j'. Ensure Neo4j database is running locally or on a remote server.
2. **Data Collection**: Develop a feature where users can import data from various sources such as email clients, social media platforms, or manually input connections. This data will include user profiles, interactions, and relationship types.
3. **Data Storage**: Utilize 'agentforge-memory-neo4j' to store this information in a Neo4j graph database. Each user will be represented as a node, and their interactions as edges.
4. **Graph Analysis**: Implement algorithms to analyze the stored data. For example, calculate centrality measures to identify key influencers within the network.
5. **Visualization**: Create a user-friendly interface where users can visualize their social network. Highlight key nodes and edges, and allow filtering by different criteria like activity type or time period.
6. **Security & Privacy**: Ensure all data handling complies with privacy regulations. Allow users to control who can access their data and what level of detail is visible.
7. **Testing & Feedback**: Conduct thorough testing to ensure the app works smoothly and gather feedback from early users to refine the design and functionality.

By leveraging 'agentforge-memory-neo4j', you'll be able to efficiently manage and query large datasets, making it possible to provide insightful analytics about social networks.