atom-hifi

v0.6.0 suspicious
4.0
Medium Risk

Atom-HiFi: atomistic high-fidelity representative-set selection framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risk factors such as no network calls or shell executions, but the incomplete maintainer information and lack of a GitHub repository raise concerns about its provenance and maintainability.

  • Metadata risk due to incomplete maintainer information
  • No associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package has no associated GitHub repository and the maintainer's information is incomplete, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7332 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

  • 23 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

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 atom-hifi 
Create a mini-application named 'HiFiSelector' using Python that leverages the 'atom-hifi' package to demonstrate atomistic high-fidelity representative-set selection. This tool will be useful for researchers and scientists working with large datasets who need to select a subset of data points that accurately represent the entire dataset. The application should include the following functionalities:

1. Data Input: Allow users to upload their dataset in CSV format.
2. Preprocessing: Implement basic data preprocessing steps such as handling missing values, normalization, and feature scaling.
3. Representative Set Selection: Use the 'atom-hifi' package to select a representative subset from the uploaded dataset. This subset should be chosen based on its ability to accurately represent the overall distribution and characteristics of the full dataset.
4. Visualization: Provide visualizations of both the original dataset and the selected representative set, highlighting similarities and differences.
5. Output: Enable users to download the selected representative set as a new CSV file.
6. Documentation: Include comprehensive documentation explaining each step of the process and how the 'atom-hifi' package was utilized.
7. User Interface: Develop a simple yet intuitive web-based interface using Flask or Django to make the application accessible and user-friendly.

The goal is to create a practical, easy-to-use tool that showcases the capabilities of the 'atom-hifi' package while providing real value to end-users dealing with large datasets.

💬 Discussion Feed

Leave a comment

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