AI Analysis
Final verdict: SAFE
The package exhibits low risks across network, shell, obfuscation, and credential checks. The metadata risk, while present, does not strongly indicate malicious activity.
- Low network risk
- No shell or obfuscation risks detected
Per-check LLM notes
- Network: The presence of network calls is expected for packages that require internet access for functionalities like downloading resources or communicating with servers.
- Shell: No shell execution patterns detected, indicating low risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account with limited package history and missing author details, raising some concerns but not definitive evidence of malicious intent.
Package Quality Overall: Medium (6.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
✦ High
Documentation
9.0
Well-documented package
Documentation URL: "Documentation" -> https://simai.docs.pyansys.com13 documentation file(s) (e.g. conf.py)Detailed PyPI description (4983 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
293 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
8 unique contributor(s) across 100 commits in ansys/pysimaiActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
rects self._session = httpx.Client( mounts=mounts, headers=self._get_user_agent(),
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/pysimai appears legitimate
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-simai-core
Create a machine learning-based predictive maintenance tool using the 'ansys-simai-core' Python package. This tool will help engineers predict potential failures in mechanical components based on historical simulation data, thereby reducing downtime and maintenance costs. Step 1: Setup - Install the necessary packages including 'ansys-simai-core'. - Import the required modules from 'ansys-simai-core' to load and preprocess the data. Step 2: Data Loading - Use 'ansys-simai-core' to load simulation datasets containing historical performance metrics of various mechanical components. - Preprocess the data to ensure it is clean and ready for analysis. Step 3: Feature Engineering - Utilize 'ansys-simai-core' functions to extract meaningful features from the simulation data that could indicate impending failure. - Consider features such as stress levels, temperature variations, and wear patterns. Step 4: Model Training - Split the dataset into training and testing sets. - Train a machine learning model using 'ansys-simai-core' capabilities to predict component failure based on the extracted features. - Experiment with different algorithms provided by 'ansys-simai-core' to find the best performing one. Step 5: Evaluation - Evaluate the trained model's performance using the testing set. - Calculate key metrics like accuracy, precision, recall, and F1 score. Step 6: Deployment - Develop a user-friendly interface where engineers can input real-time data from their systems. - The tool should then use the trained model to predict if a component is likely to fail within a certain timeframe. - Provide recommendations for preventive maintenance actions based on the prediction results. Suggested Features: - Real-time data ingestion from IoT devices. - Historical data visualization to track component health over time. - Automated alerts for high-risk scenarios. - Integration with existing maintenance scheduling systems. How 'ansys-simai-core' is Utilized: - For loading and preprocessing simulation data. - To perform advanced feature engineering specific to mechanical simulations. - In selecting and training machine learning models tailored for predictive maintenance tasks. - For evaluating model performance using specialized metrics.