🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of low maintainer activity and poor metadata quality, raising concerns about its long-term maintenance and reliability. However, there are no indications of malicious activities.

Metadata risk due to low maintainer activity and poor metadata quality
No evidence of malicious code or network 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 immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but there's no clear indication of malicious intent.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ 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 TopoStateGrid

Create a mini-application named 'PowerGridAnalyzer' using the Python package 'TopoStateGrid'. This application should analyze a given power grid network by converting its physical topology, component attributes, and operational states into a machine learning ready graph dataset. Here are the steps and features you should include:

1. **Setup**: Start by setting up a virtual environment and installing the necessary packages including TopoStateGrid.
2. **Data Input**: Design a user-friendly interface where users can upload or input details about their power grid network such as node connections, line capacities, transformer ratings, and current operational states.
3. **Graph Conversion**: Utilize TopoStateGrid to convert the uploaded data into a graph dataset. Ensure that both static (topology and component attributes) and dynamic (operational states) data are accurately represented.
4. **Analysis Module**: Implement an analysis module within PowerGridAnalyzer that leverages the graph dataset to perform various analyses such as identifying critical nodes, assessing the impact of potential failures, and optimizing load distribution.
5. **Visualization**: Integrate a visualization tool that displays the analyzed results graphically. Users should be able to see visual representations of their network, highlighted critical areas, and optimized layouts.
6. **Report Generation**: Finally, allow users to generate comprehensive reports summarizing the analysis findings. These reports should include visual charts, graphs, and key performance indicators derived from the analysis.

Throughout the development process, ensure that TopoStateGrid is utilized effectively to transform raw power grid data into actionable insights through advanced graph-based analysis techniques.