🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct security risks, but concerns arise from low maintenance activity and poor metadata quality, suggesting potential issues with long-term support and reliability.

Low maintenance activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution patterns detected, indicating no direct command execution risks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The package shows low maintenance activity and poor metadata quality, raising suspicion but not conclusive evidence of 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: teradata.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

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 TeradataGE

Create a social network analysis tool using the Python package 'TeradataGE'. This tool will help users analyze complex relationships within a social network dataset stored in a Teradata database. Your application should be able to perform the following tasks:

1. **Graph Creation**: Allow users to input a dataset containing nodes (individuals) and edges (relationships between individuals). Use TeradataGE to create a graph representation of this data directly within the Teradata database.
2. **Centrality Measures**: Implement functionality to calculate centrality measures such as Degree Centrality, Betweenness Centrality, and Closeness Centrality using TeradataGE functions. These measures should provide insights into which nodes are most influential or central within the network.
3. **Community Detection**: Utilize TeradataGE to detect communities or clusters within the graph. This could involve implementing algorithms like Louvain or Label Propagation.
4. **Visualization**: Although the main computations happen in the database, provide a simple visualization feature that allows users to visualize the graph and its communities using a Python plotting library like NetworkX or Matplotlib. This visualization should be interactive, allowing users to highlight specific nodes or communities.
5. **Query Interface**: Develop an intuitive query interface where users can input SQL-like queries to retrieve specific information from the graph, such as the shortest path between two nodes or the density of a community.
6. **User-Friendly Dashboard**: Design a web-based dashboard using Flask or Django where users can upload their datasets, view real-time analytics, and interact with the graph data.

The goal is to leverage TeradataGE's powerful in-database graph analytics capabilities while providing a user-friendly interface for non-technical users to explore and understand complex network structures.