assyst

v0.15.0 suspicious
4.0
Medium Risk

Reference implentation of the Automated Small Symmetric Structure Training method.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity, but the incomplete metadata and the potential use of pickle for obfuscation raise concerns about its legitimacy and intentions.

  • Incomplete author details
  • Potential obfuscation through pickle usage
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: The use of pickle for serialization and deserialization could indicate obfuscation but might also be legitimate for state management or object persistence.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The author's details are incomplete and they seem to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • 23 test file(s) detected (e.g. test_aspect.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (2204 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

  • 65 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in eisenforschung/assyst
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

Outbound Network Calls

No suspicious network call patterns found

Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • p = pickle.dumps(m) m2 = pickle.loads(p) assert m2 == m @pytest.mark.skipif(not HAS_TENSORPOT
  • p = pickle.dumps(g) g2 = pickle.loads(p) assert g2 == g import pickle from hypothesis import
  • p = pickle.dumps(f) f2 = pickle.loads(p) assert f2 == f import pickle from assyst.filters imp
  • pickle.dumps(f) f2 = pickle.loads(p) assert type(f2) is type(f) assert f2 == f
  • p = pickle.dumps(r) r2 = pickle.loads(p) # Should produce same next perturbation at1 = r(
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: posteo.de>

Suspicious Page Links

All external links appear legitimate

Git Repository History

Repository eisenforschung/assyst 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 assyst
Develop a small Python application named 'SymmetryCraft' that leverages the 'assyst' package to automate the training of small symmetric structures, such as those found in crystallography or molecular modeling. This application will serve as a user-friendly interface for scientists and researchers who need to quickly prototype and analyze symmetrical structures without delving into complex computational setups.

### Application Features:
- **User Input:** Allow users to input basic parameters for their desired symmetric structure, including symmetry type, initial dimensions, and other relevant physical properties.
- **Visualization Tool:** Implement a visualization feature using libraries like Matplotlib or Plotly to display the trained symmetric structure in both 2D and 3D formats.
- **Training Interface:** Utilize the 'assyst' package to handle the automated training process behind the scenes, ensuring the application focuses on usability rather than complexity.
- **Export Functionality:** Provide options to export the trained model and visualizations in common file formats such as .png, .pdf, and .json for further analysis or publication.
- **Interactive Adjustment:** Offer real-time adjustments during the training phase so users can tweak parameters and see immediate effects on the structure being modeled.
- **Documentation & Help:** Include comprehensive documentation and a help section within the application to guide new users through its functionalities.

### Steps to Develop 'SymmetryCraft':
1. **Setup Environment:** Begin by setting up your Python environment and installing necessary packages, including 'assyst'. Ensure all dependencies are clearly documented.
2. **Design User Interface:** Create a simple yet effective graphical user interface (GUI) using frameworks like Tkinter or PyQt. The GUI should facilitate easy interaction with the application's core functionalities.
3. **Integrate 'assyst':** Use the 'assyst' package to implement the automated training logic. Focus on leveraging its capabilities to streamline the training process for various types of symmetric structures.
4. **Implement Visualization:** Develop the visualization component that integrates seamlessly with the training process. This should allow users to visualize the evolution of the structure over time.
5. **Add Export Options:** Enable users to save their work easily by providing multiple export options for both the model and its visual representations.
6. **Test Thoroughly:** Conduct thorough testing to ensure all components work as expected. Pay special attention to the integration between the GUI and the backend processing handled by 'assyst'.
7. **Final Documentation:** Complete the project by writing detailed documentation that explains how to use 'SymmetryCraft', including setup instructions, usage examples, and troubleshooting tips.

By following these steps, you'll create a powerful yet accessible tool that democratizes the process of training and analyzing symmetric structures, making advanced computational methods more approachable for a broader audience.

💬 Discussion Feed

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