🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk across all categories, with no indications of malicious activities or network/shell interactions. The metadata shows some low-effort signs, but there is no evidence to suggest a supply-chain attack.

Low network and shell risk
No obfuscation or credential risks detected
Metadata shows some low-effort signs

Per-check LLM notes

Network: No network calls detected, which is normal for a graphics library like PyQtChart-Qt5.
Shell: No shell execution detected, which aligns with the expected behavior of a graphics library.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The package shows some low-effort signs, but there's no clear indication of malicious intent.

🔬 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: riverbankcomputing.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 "Riverbank Computing Limited" 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 PyQtChart-Qt5

Your task is to develop a compact yet powerful data visualization tool using Python and the PyQtChart-Qt5 package. This tool will allow users to visualize complex data sets through various chart types, including line charts, bar charts, pie charts, and scatter plots. The application should be user-friendly, allowing users to upload their own CSV files containing data points for plotting. Additionally, it should provide options to customize chart styles, such as colors, labels, and legends, as well as zoom and pan functionalities for better data exploration.

Here are the key steps and features to implement:
1. **Data Import**: Implement a feature that allows users to import CSV files containing two columns: one for categories (e.g., dates, names) and another for values (e.g., sales figures, temperatures).
2. **Chart Selection**: Provide a dropdown menu where users can select the type of chart they want to generate from the imported data (line chart, bar chart, pie chart, scatter plot).
3. **Customization Options**: Offer customization options for the selected chart, such as setting background color, changing axis labels, adding legends, and choosing between different chart styles.
4. **Interactive Features**: Include interactive features like zooming and panning to explore data in more detail.
5. **Save Feature**: Allow users to save the generated chart as an image file (PNG format).
6. **Real-time Updates**: If the user modifies the data or changes chart settings, ensure that the chart updates in real-time.

To achieve these goals, you'll need to utilize the PyQtChart-Qt5 package for rendering charts and integrating them into a graphical user interface. Ensure your application is well-documented, with clear instructions on how to install dependencies and run the application. Also, include comments in your code to explain critical sections and decisions made during development.