🤖 AI Analysis

Final verdict: SAFE

The package exhibits typical behaviors for a scientific computing tool with no signs of malicious activities. However, it has some minor concerns like low maintainer activity and potential insecure links.

No network or shell risks detected that indicate malicious behavior.
Low maintainer activity and potentially insecure links increase the metadata risk slightly.

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: Detected shell execution is likely for installing dependencies and checking compiler versions, which is typical for packages involving scientific computing.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags such as low maintainer activity and an insecure link, but no clear signs of malicious intent.

📦 Package Quality Overall: Low (4.6/10)

○ 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 (3032 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

10 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

5 unique contributor(s) across 100 commits in AndresOrtegaGuerrero/aiidalab-qe-pp
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 10.0

Found 6 shell execution pattern(s)

un to run the command subprocess.run(command, check=True) else: print("Code python3@l
fortran...") try: subprocess.run(["gfortran", "--version"], stdout=subprocess.PIPE, check=Tru
ing fortran via mamba subprocess.run(["mamba", "install", "gfortran", "-y"], check=True) else
p cmake...") try: subprocess.run(["cmake", "--version"], stdout=subprocess.PIPE, check=True)
lling cmake via mamba subprocess.run(["mamba", "install", "cmake", "-y"], check=True) else:
git" try: subprocess.run( ["git", "clone", repo_url, str(CRITIC_PATH_

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://nccr-marvel.ch/

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Andres Ortega-Guerrero" 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-pp

Create a mini-application named 'QuantumESPRESSOPPAnalyzer' that leverages the 'aiidalab-qe-pp' package to streamline the post-processing of Quantum ESPRESSO calculations. This application should enable users to easily upload their Quantum ESPRESSO output files, perform various analyses on these files, and visualize the results interactively. Here’s a detailed breakdown of the application’s requirements:

1. **User Interface**: Design a user-friendly interface using Streamlit or a similar web framework to allow users to upload Quantum ESPRESSO output files.
2. **File Upload**: Implement functionality to accept .out files from Quantum ESPRESSO calculations.
3. **Data Extraction**: Use the 'aiidalab-qe-pp' package to extract relevant data from the uploaded files such as total energy, band structure, density of states, etc.
4. **Analysis Tools**: Include tools within the application to perform basic analyses on the extracted data, like plotting the band structure, calculating the energy difference between states, and visualizing the density of states.
5. **Visualization**: Integrate visualization capabilities to display the analyzed data in an interactive manner. For instance, use matplotlib or Plotly for generating plots.
6. **Report Generation**: Allow users to generate a report summarizing the analysis performed, which includes key metrics and visualizations.
7. **Integration with AiiDA**: If possible, integrate the application with the AiiDA database to store and retrieve calculation metadata and results.

The application should be designed to be modular and extensible, allowing for easy addition of new analysis tools and visualizations in the future.