🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators, with no network calls, shell executions, or obfuscation techniques detected. The slight increase in metadata risk suggests some disengagement from the maintainer but does not indicate any malicious activity.

Low network and shell risk
No obfuscation or credential harvesting detected
Moderate metadata risk

Per-check LLM notes

Network: No network calls detected, which is normal for a package focused on economic modeling and simulation.
Shell: No shell executions detected, aligning with the expected behavior of a non-system utility package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk to secrets or credentials.
Metadata: Low risk but indicates low maintainer engagement and possibly low-quality metadata.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository GongJr0/SymbolicDSGE appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

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 SymbolicDSGE

Your task is to develop a Python-based mini-application that leverages the 'SymbolicDSGE' package to analyze and simulate linear Dynamic Stochastic General Equilibrium (DSGE) models. This application will serve as a tool for economists and researchers to better understand economic dynamics under uncertainty. Here are the key steps and features you should implement:

1. **Model Specification**: Allow users to input their DSGE model equations symbolically using SymPy-like syntax supported by SymbolicDSGE. The application should validate the input to ensure it conforms to the expected structure of DSGE models.

2. **Parameter Estimation**: Implement a feature where the user can specify initial parameters and ranges for those parameters. The application should then estimate the model parameters based on provided data using maximum likelihood estimation techniques, utilizing the symbolic capabilities of SymbolicDSGE to handle complex algebraic manipulations efficiently.

3. **Simulation Module**: Develop a module that simulates the behavior of the DSGE model over time given a set of shocks and initial conditions. Users should be able to visualize the simulation results through graphs and tables.

4. **Sensitivity Analysis**: Include functionality for conducting sensitivity analysis. Users should be able to vary specific parameters within a range and observe how these changes affect the model outcomes.

5. **Documentation and User Interface**: Ensure the application has clear documentation explaining how to use each feature. Consider developing a simple web interface using Flask or Django to make the application more accessible to non-technical users.

6. **Custom Backend Utilization**: Throughout the development process, utilize SymbolicDSGE’s custom backend for symbolic manipulation, which is crucial for handling the complexity of DSGE models without resorting to numerical approximations prematurely.

The goal is to create a robust, user-friendly tool that showcases the power and flexibility of SymbolicDSGE in economic modeling.