🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks in terms of network, shell, and obfuscation activities. However, there are some concerns regarding the metadata due to sparse author details and potential inactivity.

No network calls or shell executions detected.
Author details are limited and maintainer activity is questionable.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's details are sparse and the maintainer seems new or inactive, raising some concerns.

🔬 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: activegraf.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 activegraf-python

Create a Python-based dashboard application named 'WhatIfAnalyzer' that leverages the 'activegraf-python' package to perform comprehensive what-if analysis on datasets. This application should allow users to upload their own datasets and then run various scenarios to understand potential outcomes based on different conditions or variables. Here’s a detailed breakdown of the application's requirements:

1. **User Interface**: Design a simple yet intuitive GUI using a library like PyQt or Tkinter. The interface should have sections for data input, scenario setup, and result visualization.

2. **Data Handling**: Implement functionality to import CSV files directly into the application. Ensure that the application supports basic data cleaning operations such as handling missing values and outliers.

3. **Scenario Setup**: Users should be able to define different scenarios based on their dataset. For example, they could adjust certain parameters to see how changes affect outcomes.

4. **Analysis Execution**: Utilize the 'activegraf-python' package to execute these what-if analyses. This involves setting up the analysis models, running simulations, and interpreting results.

5. **Visualization**: Results from the analyses should be displayed in a visually appealing manner. Use matplotlib or seaborn to create graphs and charts that summarize findings.

6. **Exporting Results**: Allow users to export the results of their analyses into a new CSV file for further review or sharing.

7. **Documentation**: Provide clear documentation on how to use the application, including setup instructions and examples of different types of analyses that can be performed.

The goal is to create a tool that makes complex what-if analysis accessible to users without requiring deep technical knowledge about data science or programming.