AI Analysis
Final verdict: SUSPICIOUS
The package has moderate risks due to potential unsanitized shell executions and signs of low maintainer activity, though no direct evidence of malicious intent was found.
- Shell risk due to potential unsanitized shell executions
- Low maintainer activity and poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal and expected.
- Shell: The use of shell execution to run 'Allrun' scripts might be part of the package's functionality, but it could also pose a risk if not properly sanitized or controlled.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malicious intent.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 6.0
Found 3 shell execution pattern(s)
subdomains)) result = subprocess.run( ["bash", "Allrun"], cwd=out(n_subdomains)) result = subprocess.run(["bash", "Allrun"], cwd=output_path, capture_output=True, te, "w") as log: proc = subprocess.Popen( cmd, cwd=str(case_dir),
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 airfoilranssolver
Develop a mini-application called 'AirfoilSim' that leverages the 'airfoilranssolver' Python package to simulate airflow over different 2D airfoil shapes. This application should allow users to input various airfoil geometries, select simulation parameters such as Reynolds number and Mach number, and visualize the resulting pressure distribution and velocity vectors around the airfoil. Key Features: 1. User Interface: Create a simple GUI using Tkinter or PyQt5 where users can upload their custom airfoil geometry files (.dat format) or choose from a predefined list of common airfoil shapes. 2. Parameter Input: Allow users to set simulation parameters including Reynolds number, Mach number, and angle of attack. 3. Simulation Execution: Utilize the 'airfoilranssolver' package to run CFD simulations based on user inputs. Ensure the package is properly installed and configured within your application. 4. Visualization: Implement visualization capabilities using Matplotlib or similar libraries to display the pressure distribution and velocity vectors around the airfoil post-simulation. 5. Report Generation: Automatically generate a PDF report summarizing the simulation results, including key performance indicators like lift coefficient and drag coefficient. The goal is to create a user-friendly tool that simplifies the process of running complex CFD simulations for educational and research purposes. Make sure to include error handling and informative messages to guide users through the process.