🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with low risks across all categories assessed. It is safe to use given its current analysis.

No network calls detected.
No shell execution patterns found.

Per-check LLM notes

Network: No network calls detected, which is normal for most Python packages unless they require external API access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands that could pose a risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities were detected.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://aiidalab-qe.readthedocs.io/
Detailed PyPI description (4354 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

57 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

8 unique contributor(s) across 100 commits in aiidalab/aiidalab-qe
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

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: materialscloud.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aiidalab/aiidalab-qe appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AiiDAlab team" 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

Create a fully-functional mini-app using the 'aiidalab-qe' package that simplifies Quantum ESPRESSO (QE) calculations for materials science researchers. Your app should enable users to easily input material structures, select appropriate calculation types, and run simulations without needing deep knowledge of QE. Here’s a step-by-step guide on what your application should achieve:

1. **User Interface Design**: Develop a clean, user-friendly interface where users can upload their material structure files (e.g., CIF, POSCAR). Ensure the interface allows for easy selection of different QE calculation types such as static, molecular dynamics, band structure, etc.
2. **Input Parameter Customization**: Implement a feature allowing users to customize input parameters for their chosen calculation type. This could include setting up k-point grids, pseudopotentials, and other simulation settings.
3. **Job Submission and Management**: Integrate functionality to submit these customized jobs to a computational resource via AiiDA workflows. Provide real-time feedback on job status, progress, and completion.
4. **Visualization Tools**: Include tools to visualize results post-calculation, such as density of states (DOS), band structure plots, and charge density maps.
5. **Documentation and Help Resources**: Offer comprehensive documentation and help resources within the app to assist users with common tasks and troubleshooting.

**Utilizing 'aiidalab-qe'**: This package provides the foundational setup for integrating Quantum ESPRESSO into a web-based application through AiiDA, a workflow management system. It includes pre-configured workflows and plugins necessary for interfacing with QE, handling input/output files, and managing computational resources efficiently. Your task is to leverage these capabilities to streamline the QE workflow for non-expert users, making advanced materials simulations more accessible.