atompack-db

v0.4.0 suspicious
5.0
Medium Risk

Fast, compressed storage for atomic structures with properties.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and has a new maintainer account with limited history, raising concerns about its integrity.

  • High obfuscation risk
  • New maintainer with minimal package history
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Subprocess execution is observed but seems to be for internal package operations, not indicative of malicious activity.
  • Obfuscation: The observed patterns suggest potential obfuscation techniques that may hide malicious activities or complex logic, increasing suspicion of misuse.
  • Credentials: No clear evidence of credential harvesting is present based on the provided code snippets.
  • Metadata: The maintainer has a new or inactive account with minimal package history and lacks a proper author name.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 23 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://entalpic-atompack.readthedocs-hosted.com/en/latest/
  • Detailed PyPI description (1740 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

  • 528 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 28 commits in LeMaterial/atompack
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • alue(value) payload = pickle.loads(raw) if not isinstance(payload, dict): r
  • mpress else v d = pickle.loads(raw) _ = float(d["positions"][i % len(d["positio
  • lk_dec else v d = pickle.loads(raw) _ = float(d["positions"][i % len(d["positio
  • = reopened[0] restored = pickle.loads(pickle.dumps(fetched)) assert restored.energy == pytest
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • end("--populate") proc = subprocess.run( cmd, env=env, cwd=str(Path(__file__
  • mps(trials)) """ result = subprocess.run( [sys.executable, "-c", script], capture_out
  • str(python_src)]) res = subprocess.run( [sys.executable, "-c", "import atompack; assert has
  • str(python_src)]) res = subprocess.run( [sys.executable, "-c", "import atompack; atompack.h
  • = str(python_src) res = subprocess.run( [sys.executable, "-c", "import atompack"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: entalpic.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository LeMaterial/atompack 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 atompack-db 
Your task is to develop a Python-based mini-application that serves as a tool for researchers and scientists to efficiently manage and analyze atomic structure data. This application will leverage the 'atompack-db' package, which offers fast, compressed storage capabilities specifically designed for atomic structures along with their associated properties. Here’s a detailed plan on how to approach building this application:

1. **Project Setup**: Begin by setting up your development environment with Python installed. Use virtual environments to manage dependencies. Install 'atompack-db' and any other necessary packages like pandas for data manipulation and matplotlib for visualization.

2. **Database Initialization**: Create a database using 'atompack-db' to store atomic structures. Ensure that the database can handle different types of atomic structures and related properties such as atomic numbers, bonding information, and geometric configurations.

3. **Data Import**: Implement functionality to import atomic structure data from various sources, including files in formats commonly used in chemistry and materials science (e.g., CIF, XYZ). The application should validate imported data to ensure it meets the requirements for storage in the 'atompack-db'.

4. **Querying and Analysis**: Develop a user-friendly interface or command-line tool that allows users to query the database based on specific criteria such as atomic number, bond length, or crystal structure type. Additionally, provide analytical tools within the application to calculate properties like density, volume, or energy based on stored atomic structures.

5. **Visualization**: Integrate visualization capabilities into the application to display atomic structures in 3D. Users should be able to rotate, zoom, and pan through these visualizations. Consider exporting these visualizations as images or animations.

6. **Compression and Performance Optimization**: Utilize 'atompack-db's compression capabilities to optimize storage space while maintaining fast access times. Test the performance of your application with large datasets to ensure efficient querying and analysis.

7. **Documentation and User Guide**: Write comprehensive documentation for your application, including installation instructions, usage guides, and examples. Provide a user guide that explains how to use each feature of the application effectively.

8. **Testing and Validation**: Conduct thorough testing to ensure all functionalities work as expected. Validate the accuracy of calculations and the efficiency of queries.

9. **Deployment**: Prepare your application for deployment. This could involve packaging it as a standalone executable or deploying it as a web service accessible via HTTP requests.

Suggested Features:
- Support for multiple file formats for importing atomic structure data.
- Advanced querying options allowing complex searches over multiple attributes.
- Real-time visualization updates as users modify parameters.
- Export options for visualizations and analysis results.
- Integration with external analysis tools or APIs for extended functionality.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!