aiida-bader

v0.1.12 safe
3.0
Low Risk

Collections of node-based workflow created using aiida-workgraph.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators, with no network calls, shell risks within expected norms for environment management, no signs of obfuscation or credential harvesting. The main concern is the potentially new or inactive maintainer.

  • No network calls detected.
  • Shell execution aligned with normal package behavior.
  • Maintainer metadata may indicate a new or inactive project.
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution is used to manage environments and run commands, which aligns with typical package behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing or very short and appears to be new or inactive, which raises some concern, but there are no other significant red flags.

📦 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 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aiida-bader.readthedocs.io
  • Detailed PyPI description (2063 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

  • 3 type-annotated function signatures (partial)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 53 commits in superstar54/aiida-bader
  • Small but multi-author team (3–4 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 5 shell execution pattern(s)

  • ation ] try: subprocess.run(command, check=True) print("Conda environment 'bader
  • """ try: result = subprocess.run( ["conda", "env", "list"], capture_output=True,
  • """ try: result = subprocess.run( ["conda", "run", "--name", "bader", "which", "b
  • args): return subprocess.run( *args, env=env, capture_output=True, ch
  • ader_path, ] subprocess.run(command, check=True) print(f"Code bader@{computer_la
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ 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

Repository superstar54/aiida-bader 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 aiida-bader 
Your task is to create a mini-application that leverages the 'aiida-bader' Python package to analyze electronic density from computational chemistry calculations. This application will serve as a tool for researchers in materials science to gain insights into the electronic structure of materials. Here’s a step-by-step guide on how to approach this project:

1. **Setup Environment**: Begin by setting up a virtual environment and installing necessary packages including 'aiida-core', 'aiida-bader', and any other dependencies required for running workflows.
2. **Data Input**: Design a user-friendly interface where users can input data from their computational chemistry calculations (e.g., output files from DFT simulations).
3. **Workflow Execution**: Utilize the 'aiida-bader' package to define a workflow that processes the input data to calculate the Bader charges and volumes. Ensure the workflow is flexible enough to handle different types of input data.
4. **Result Visualization**: Implement functionality to visualize the results of the Bader analysis, such as charge distribution and volume plots. Consider using libraries like matplotlib or seaborn for plotting.
5. **Report Generation**: Create a feature that automatically generates a report summarizing the key findings from the Bader analysis. The report should include visualizations and textual descriptions.
6. **Documentation and Testing**: Write comprehensive documentation for your application, explaining how to use it and what each part does. Also, implement unit tests to ensure the reliability of your code.

Suggested Features:
- Support for multiple file formats commonly used in computational chemistry.
- Real-time progress tracking during workflow execution.
- Advanced filtering options for result visualization based on user-defined criteria.
- Integration with cloud storage services for saving and sharing reports.

How 'aiida-bader' is Utilized:
- The 'aiida-bader' package provides nodes and workflows for performing Bader analysis within the AiiDA framework. You will utilize these nodes to process the input data and extract meaningful information about the electronic structure of materials. The package simplifies the integration of complex computational tasks into automated workflows, making it easier to scale up your analysis to larger datasets.