🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate suspicion due to shell execution risks and a lack of maintainer information. However, there are no concrete indicators of malicious intent.

Shell execution detected
Missing maintainer's author name

Per-check LLM notes

Network: No network calls detected, which is normal and poses no immediate risk.
Shell: Shell execution detected may indicate legitimate functionality but could also be a potential risk if the commands are not well-documented and controlled.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author name is missing and appears to be new or inactive, which raises some suspicion but not enough to conclude malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

ls print("Preprocessing ...") os.system(f"python {__location__}/docs_preprocessing.py --docdir {__lo

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: icloud.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository KarlNaumann/MacroStat appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 MacroStat

Create a Python-based mini-application that utilizes the MacroStat package to analyze and visualize the outcomes of an agent-based macroeconomic model. This application will simulate a simplified economy with various agents (e.g., consumers, producers, banks) interacting according to predefined rules. The goal is to understand the emergent behaviors of the system under different policy interventions. Here are the steps and features you should include:

1. **Setup**: Begin by setting up your development environment with Python and installing the MacroStat package.
2. **Model Definition**: Define the economic model within MacroStat, specifying the types of agents, their behaviors, and the interactions between them.
3. **Simulation Engine**: Implement a simulation engine using MacroStat's tools to run multiple iterations of the model, each time applying a different set of policies (e.g., varying interest rates, tax levels).
4. **Data Analysis**: Use MacroStat's statistical analysis tools to process the output data from the simulations, focusing on key economic indicators such as GDP growth, inflation, employment rate, etc.
5. **Visualization**: Develop a user-friendly interface (using libraries like Matplotlib or Plotly) to visualize the results of the simulations. Users should be able to see how different policy settings affect economic outcomes over time.
6. **Policy Recommendation Tool**: Based on the analysis, create a feature that suggests optimal policy settings to achieve desired economic goals.
7. **Documentation & Testing**: Ensure comprehensive documentation of the application and its functionalities, along with thorough testing to validate the accuracy of the simulations and analyses.

By following these steps, you'll develop a powerful tool for understanding complex macroeconomic systems and exploring the impacts of policy decisions.