ansys-api-sherlock

v0.2.4 safe
3.0
Low Risk

Autogenerated python gRPC interface package for ansys-api-sherlock, built on 20:25:21 on 02 June 2026

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to limited package activity, but there's no evidence of malicious behavior.

  • Low network and shell risk
  • No obfuscation or credential harvesting
  • Elevated metadata risk due to limited package activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The low number of packages and lack of PyPI classifiers suggest a potentially new or less active maintainer, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

○ 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 (1808 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 15 unique contributor(s) across 100 commits in ansys/ansys-api-sherlock
  • Active community — 5 or more distinct contributors

🔬 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: ansys.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ansys/ansys-api-sherlock appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "ANSYS, Inc." 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 ansys-api-sherlock 
Your task is to develop a small but powerful utility called 'SherlockAnalyzer' using the Python package 'ansys-api-sherlock'. This tool aims to simplify the process of analyzing complex systems by leveraging advanced simulation capabilities provided by Ansys Sherlock. The application will allow users to upload system design files, configure simulation parameters, run simulations, and visualize results. Here’s a detailed breakdown of what your application should do and how it will utilize the 'ansys-api-sherlock' package:

1. **User Interface**: Design a simple yet intuitive GUI using Tkinter or PyQt that allows users to interact with the application easily. The UI should have sections for file upload, parameter configuration, and result visualization.

2. **File Upload**: Implement functionality for users to upload their system design files. These files could be in formats supported by Ansys Sherlock such as .xml or .json. Ensure that the application validates the uploaded files before processing them.

3. **Parameter Configuration**: Allow users to set various simulation parameters through the GUI. Parameters might include environmental conditions, material properties, and test conditions. Use the 'ansys-api-sherlock' package to define these parameters programmatically.

4. **Simulation Execution**: Utilize the 'ansys-api-sherlock' package to execute the configured simulations. This package provides a gRPC interface that you can use to send simulation requests and receive responses. Ensure that the application handles any errors or exceptions gracefully during the simulation process.

5. **Result Visualization**: After the simulation completes, parse the results and present them in a visual format within the application. This could include graphs, charts, and tables. Use libraries like Matplotlib or Plotly for data visualization.

6. **Report Generation**: Provide an option for users to generate comprehensive reports of the simulation results. These reports should include all relevant details about the input parameters, simulation process, and output data. Users should be able to save these reports in PDF or HTML format.

7. **Documentation**: Write clear and concise documentation for the application, explaining how each feature works and how users can benefit from it. Include examples and screenshots where applicable.

By completing this project, you will gain valuable experience in working with complex simulation tools and developing user-friendly applications. Good luck!

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