AI Analysis
Final verdict: SAFE
The package Allomorph v0.3.0 presents minimal risks as it lacks any signs of obfuscation or credential harvesting techniques.
- No obfuscation patterns detected
- No credential harvesting patterns detected
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Low (3.6/10)
✦ High
Test Suite
9.0
Test suite present — 6 test file(s) found
Test runner config found: pyproject.toml6 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4174 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
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
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 allomorph
Develop a Python-based application that leverages the 'allomorph' package to generate datasets of monometallic to trimetallic nanoparticle structures suitable for machine learning applications. Your task is to create a user-friendly interface where users can input parameters such as metal types, nanoparticle size, and desired output format (e.g., CSV, JSON). The application should also include visualization tools to display the generated nanoparticle structures in a 3D format, allowing users to rotate and zoom in/out to better understand the structure. Additionally, implement a feature that allows users to save their generated datasets and visualizations locally or upload them to a cloud storage service. Ensure your application includes error handling for invalid inputs and provides clear feedback messages to guide users through the process. Use the 'allomorph' package's core functionalities to handle the complex calculations and data generation required for creating these nanoparticle datasets.