🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, its novelty and lack of an associated GitHub repository raise concerns about its legitimacy and potential as a supply-chain attack vector.

Very new package
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal for a plotting library.
Shell: No shell execution detected, indicating no risk of command injection.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is very new and has no associated GitHub repository, raising some suspicion but not conclusive evidence of malice.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Package uploaded less than 24 hours ago (2026-06-04T19:29:13.000Z)
Author "FlowPlot contributors" 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 FlowPlotPy

Create a data visualization tool named 'DataVizFlow' using the Python package 'FlowPlotPy'. This tool will allow users to visualize various types of datasets in an interactive and visually appealing manner. The application should support importing CSV files and then generating dynamic plots such as line charts, bar charts, scatter plots, and histograms. Users should also be able to customize plot colors, labels, and legends through a simple graphical user interface (GUI). Additionally, the application should enable users to save their visualizations as image files (PNG, JPEG) or export them as PDF documents for reports.

To achieve these goals, follow these steps:
1. Set up a Python environment with the necessary packages including 'FlowPlotPy', 'pandas' for data manipulation, and 'tkinter' for the GUI.
2. Develop a file import function that reads CSV files into pandas DataFrames.
3. Implement a series of functions that use 'FlowPlotPy' to create different types of plots based on the selected DataFrame columns.
4. Design a GUI where users can select the type of plot, choose which columns to include, and customize plot elements like colors and titles.
5. Integrate functionality to save the generated plots either as images or PDFs.
6. Test the application with sample datasets to ensure all features work as expected.
7. Document the code thoroughly and provide instructions on how to install and run the application.

By completing this project, you'll gain experience in using 'FlowPlotPy' for complex data visualizations, working with pandas for data handling, and building GUI applications in Python.