🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to incomplete maintainer information and potential shell execution, which could indicate either benign setup procedures or more nefarious actions.

Incomplete maintainer's author information
Potential shell execution during installation

Per-check LLM notes

Network: No network calls detected.
Shell: Detected shell execution may be for package installation or CLI operations, but context is needed to confirm benign use.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author information is incomplete and the account seems new or inactive, raising some concerns but not definitive indicators of malice.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1266 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

17 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

7 unique contributor(s) across 100 commits in aiidalab/aiidalab-qe-vibroscopy
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

un to run the command subprocess.run(command, check=True) else: print("Code phonopy@l
un to run the command subprocess.run(command, check=True) command = [ "pip",
un to run the command subprocess.run(command, check=True) if __name__ == "__main__": cli()

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: empa.ch>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aiidalab/aiidalab-qe-vibroscopy 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 aiidalab-qe-vibroscopy

Create a fully-functional mini-app that leverages the 'aiidalab-qe-vibroscopy' package to analyze and visualize vibrational spectra from quantum chemistry calculations. This app will serve as a tool for researchers and students to easily interpret their data without deep expertise in computational methods.

**Step-by-Step Requirements:**
1. **Setup Environment**: Ensure all necessary packages including 'aiidalab-qe-vibroscopy', 'numpy', 'matplotlib', and 'pandas' are installed in your Python environment.
2. **Data Input**: Design a user-friendly interface where users can upload their vibrational spectrum data files (e.g., CSV or JSON formats).
3. **Preprocessing Module**: Implement a module that preprocesses the uploaded data. This should include normalization, smoothing, and background subtraction functionalities.
4. **Analysis Engine**: Utilize 'aiidalab-qe-vibroscopy' to perform vibrational analysis on the preprocessed data. This includes identifying peaks, calculating peak intensities, and assigning possible molecular vibrations based on wavenumber ranges.
5. **Visualization Tools**: Develop interactive plots using matplotlib or similar libraries to display the raw data, processed data, identified peaks, and assigned vibrations.
6. **Report Generation**: Allow users to generate a comprehensive report of their analysis, which includes key findings, charts, and interpretations.
7. **User Interface**: Build a clean, intuitive GUI using a framework like PyQt or Streamlit to ensure ease of use.
8. **Documentation & Help**: Provide clear documentation and tooltips within the app to guide users through each step of the process.

**Suggested Features**:
- Real-time preview of data as it's being uploaded.
- Option to save and load previous analyses.
- Advanced settings for customization of preprocessing steps.
- Integration with cloud storage services for easy file management.
- Export options for reports in PDF and HTML formats.

The goal is to create an accessible tool that enhances the understanding and interpretation of vibrational spectroscopy data, making complex computations more approachable for a broader audience.