🤖 AI Analysis

Final verdict: SAFE

The package poses minimal direct risks due to its limited functionality and lack of network, shell, or obfuscation activities. However, the low maintainer activity and poor metadata quality slightly increase the overall risk score.

Low network and shell execution risks
No signs of obfuscation or credential harvesting
Poor metadata quality and low maintainer activity

Per-check LLM notes

Network: No network calls suggest the package is not designed to communicate externally, which is typical for many utility packages.
Shell: No shell execution patterns indicate that the package does not execute external commands, reducing the risk of it being used for malicious purposes.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, which may indicate a lower level of trustworthiness.

🔬 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: example.com>

✓ 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 aceiot-cim-rdf

Create a Python-based mini-application called 'PowerModelConverter' which leverages the 'aceiot-cim-rdf' package to facilitate the conversion of power system models between IEEE RAW format and CIM RDF (Turtle). This application will serve as a bridge for users who need to work with different standards for power system modeling.

The application should have the following core functionalities:
1. **File Input/Output:** Allow users to input IEEE RAW files and output CIM RDF Turtle files, and vice versa.
2. **Conversion Process:** Utilize 'aceiot-cim-rdf' to handle the conversion logic seamlessly. Ensure the application provides feedback on the conversion process and any errors encountered.
3. **User Interface:** Develop a simple command-line interface (CLI) for ease of use. The CLI should guide users through the file selection and conversion process.
4. **Validation:** Implement basic validation checks to ensure that the input files adhere to the expected formats before initiating the conversion process.
5. **Help Documentation:** Include comprehensive help documentation within the application to assist users in understanding the usage, limitations, and potential issues they might encounter.

Suggested Additional Features:
- **Batch Processing:** Extend the application to support batch processing of multiple files at once.
- **Progress Tracking:** Add a progress bar or status updates during the conversion process for large files.
- **Configuration Settings:** Allow users to configure certain settings related to the conversion process, such as verbosity levels or specific model elements to focus on.
- **Integration Testing:** Write tests to verify the correctness of the conversions using known sample data sets.

Your task is to design and implement this application from scratch, ensuring it is well-documented and user-friendly. Emphasize the integration and utilization of 'aceiot-cim-rdf' throughout your development process.