🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no network or shell risks and no suspicious maintainer activities. The only concern is the presence of a non-HTTPS link, which slightly elevates metadata risk.

Low network and shell risk
Non-HTTPS link present
No suspicious maintainer activity

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Metadata: The presence of a non-HTTPS link is concerning but the maintainer has no suspicious activity flagged.

🔬 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: humlab.lu.se

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://cranlogs.r-pkg.org/badges/grand-total/ETDQualitizer?color=green

✓ Git Repository History

Repository dcnieho/ETDQualitizer appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Diederick Niehorster" 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 ETDQualitizer

Create a mini-application named 'EyeTrackAnalyzer' using Python that leverages the 'ETDQualitizer' package to analyze the quality of eye-tracking data collected from screen-based eye trackers. This application will serve as a tool for researchers and developers who need to assess the reliability and accuracy of their eye-tracking datasets before proceeding with further analysis or experiments. Here’s a step-by-step guide on what your application should include:

1. **Data Import**: Allow users to import eye-tracking data files (CSV format) into the application. Ensure that the imported data includes standard columns such as timestamps, gaze coordinates (x,y), pupil size, etc.
2. **Quality Assessment**: Utilize 'ETDQualitizer' to perform automated quality checks on the imported data. This should include identifying outliers, detecting saccades, blinks, fixations, and other key metrics relevant to eye movement analysis.
3. **Visualization**: Implement a feature where the user can visualize the analyzed data. This could include plotting gaze paths over time, heatmaps of fixation points, and graphs showing changes in pupil size over time.
4. **Report Generation**: Enable the generation of comprehensive reports summarizing the quality assessment results. These reports should include statistical summaries, visualizations, and any anomalies detected during the analysis.
5. **User Interface**: Develop a simple and intuitive graphical user interface (GUI) using libraries like Tkinter or PyQt, making it easy for users to interact with the application without needing extensive technical knowledge.
6. **Customization Options**: Offer customization options within the GUI allowing users to adjust parameters for quality assessment according to specific research needs or standards.
7. **Integration with External Tools**: Provide functionality to export the processed data and reports in formats compatible with common data analysis tools like Excel or SPSS.

By following these steps, you'll create a robust and user-friendly tool that not only utilizes 'ETDQualitizer' but also enhances its capabilities through additional functionalities tailored towards usability and accessibility.