atst-tools

v2.1.0 suspicious
5.0
Medium Risk

Advanced ASE Transition State Tools for ABACUS and Deep-Potential

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has legitimate use cases as it does not involve network calls or credential harvesting. However, the shell execution and low maintainer effort raise concerns about its legitimacy and potential for misuse.

  • Shell execution detected
  • Low maintainer effort and transparency
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not indicate any risk.
  • Shell: Shell execution detected may indicate the package performs system tasks, but requires further investigation to confirm legitimate use.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The package shows signs of low maintainer effort and lack of transparency, which raises some concerns but does not definitively indicate malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. band.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11297 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

  • 188 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • "" import os os.system(f'ase gui {traj_file}@-{self.n_images}:') """Reactive-mode
  • try: process = subprocess.Popen( self._command(), cwd=str(se
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with atst-tools 
Create a Python-based mini-application named 'TransitionStateAnalyzer' that leverages the 'atst-tools' package to analyze and visualize transition states in materials science simulations. This application should serve as a user-friendly tool for researchers working with ABACUS and Deep-Potential models. Here are the key steps and features for the project:

1. **Setup**: Begin by setting up your Python environment with necessary packages including 'atst-tools'. Ensure you have the latest version of 'atst-tools' installed.
2. **Input Handling**: Develop a feature that allows users to input their simulation data in a structured format compatible with 'atst-tools'. This could include files generated from ABACUS or Deep-Potential simulations.
3. **Analysis Module**: Utilize 'atst-tools' to perform advanced analysis on the transition states identified in the simulation data. This includes calculating energy barriers, identifying stable and unstable states, and analyzing structural changes during transitions.
4. **Visualization**: Implement a visualization component using libraries like Matplotlib or Plotly to graphically represent the transition states and their properties. Users should be able to interact with these visualizations to explore different aspects of the transition state analysis.
5. **Report Generation**: Integrate functionality to generate detailed reports summarizing the findings from the analysis. These reports should include graphs, tables, and textual descriptions of the analysis results.
6. **User Interface**: Optionally, develop a simple command-line interface or a graphical user interface (using PyQt or Tkinter) to make the application more accessible to non-programmers.
7. **Documentation**: Provide comprehensive documentation detailing how to use the application, interpret the outputs, and integrate it into existing workflows.

This project aims to streamline the process of analyzing transition states for researchers, making it easier to understand complex material transformations.

💬 Discussion Feed

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