AI Analysis
Final verdict: SUSPICIOUS
The package has low risks associated with network and shell activities, but the metadata suggests potential issues with the author's credibility due to incomplete information and account status.
- Incomplete author information
- Possibly inactive or new author account
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
- Metadata: The author's information is incomplete and the account seems new or inactive, which may indicate a lower level of trustworthiness.
Package Quality Overall: Low (3.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3194 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
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 29 commits in jeffreyutley/aomodel_publicSmall but multi-author team (3–4 contributors)
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.con>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository jeffreyutley/aomodel_public appears legitimate
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 aomodel
Create a financial market simulation tool using the Python package 'aomodel'. This tool will generate synthetic time-series data representing stock prices and other financial indicators for various companies over a specified period. Users should be able to customize parameters such as initial price, volatility, trend direction, and seasonality. Additionally, include functionality to visualize the generated data using matplotlib or seaborn. Implement features like saving the generated data to a CSV file and loading pre-existing datasets for analysis. Utilize 'aomodel' to handle the core generation of time-series data, ensuring the tool can simulate realistic market conditions for educational or testing purposes.
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