🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious intent based on the current checks but warrants further scrutiny due to incomplete author information and a single-package maintainer.

Incomplete author information
Single-package maintainer

Per-check LLM notes

Network: No network calls detected, which is normal and does not indicate any risk.
Shell: No shell execution patterns detected, indicating no immediate risk from this aspect.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author information is incomplete and the maintainer has a single package, which may indicate a less experienced or potentially suspicious user.

🔬 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 marciogameiro/DSGRN 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 DSGRN

Create a Python-based mini-application that leverages the DSGRN package to analyze and visualize the dynamics of regulatory networks. Your application should allow users to input parameters representing different regulatory interactions within a biological network and then generate dynamic signatures for these interactions using DSGRN. Here are the key steps and features your application should include:

1. **User Input Interface**: Develop a simple command-line interface where users can input parameters defining their regulatory network. Parameters could include gene-to-gene interactions, activation/repression states, and any initial conditions.
2. **DSGRN Integration**: Use DSGRN to compute the parameter space for the user-defined regulatory network. This involves generating all possible parameter sets that lead to distinct dynamical behaviors.
3. **Visualization Module**: Implement a feature that visualizes the computed parameter spaces. Users should be able to see which regions of the parameter space correspond to stable fixed points, periodic behavior, or chaotic dynamics.
4. **Analysis Tools**: Include tools for analyzing the results, such as identifying critical parameter values that cause transitions between different dynamical regimes.
5. **Report Generation**: Allow the application to output a report summarizing the findings, including graphical representations of the parameter spaces and textual descriptions of the identified dynamical behaviors.

Your task is to outline the core functionalities of this application, detail how each step integrates with DSGRN, and suggest any additional features that would enhance its usability or analytical capabilities.