AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential risk due to shell execution with hidden output and incomplete maintainer information, suggesting possible attempts to conceal behavior or lack of maintenance oversight.
- Shell execution with stdout suppressed
- Incomplete author and maintainer information
Per-check LLM notes
- Network: No network calls detected.
- Shell: Shell execution with stdout suppressed may indicate an attempt to hide behavior, but without additional context, it's hard to determine if it's malicious.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author information is incomplete and the maintainer seems new or inactive, raising some suspicion.
Package Quality Overall: Low (3.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3496 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
60 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
uts has been ensured. subprocess.Popen(commandlist, cwd=fl_workingDir, stdout=subprocess.DEVNULL)uts has been ensured. subprocess.Popen(commandlist, cwd=workingDir, stdout=subprocess.DEVNULL) # n
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: ansys.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 ansys-ptw
Create a Python-based mini-application named 'TurbineSim' that leverages the Ansys PTW package to automate the setup and execution of turbomachinery simulations using Ansys Fluent. This application should enable users to input basic parameters such as inlet flow conditions, geometry specifications, and desired output metrics. The app will then generate a fluent case file, set up the necessary boundary conditions, and run the simulation. Key Features: 1. User Interface: Develop a simple command-line interface for ease of use. 2. Parameter Input: Allow users to specify parameters like inlet velocity, pressure, temperature, and geometry dimensions. 3. Case File Generation: Automatically create a .cas file based on the user inputs. 4. Simulation Execution: Run the Ansys Fluent simulation using the generated case file. 5. Result Visualization: Display key results such as efficiency, power, and performance curves. 6. Error Handling: Implement robust error handling to guide users through common issues. 7. Documentation: Provide comprehensive documentation on how to install dependencies and run the application. How 'ansys-ptw' is Utilized: - Use 'ansys-ptw' to define the physical model and meshing requirements. - Leverage the package's capabilities to set up boundary conditions efficiently. - Automate the process of running the Fluent solver and extracting results. - Employ the post-processing features of 'ansys-ptw' to visualize and interpret the simulation outcomes.
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