AI Analysis
The package shows low risks in terms of network usage, shell execution, and code obfuscation. However, the incomplete maintainer information and potential inactivity raise concerns about its origin and maintenance.
- Incomplete maintainer information
- Potential inactivity of the maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author information is incomplete and they appear to be new or inactive, which raises some suspicion but does not conclusively indicate malicious intent.
Package Quality Overall: Medium (5.0/10)
Test suite present — 6 test file(s) found
6 test file(s) detected (e.g. test_bes.py)
Some documentation present
Documentation URL: "Documentation" -> https://aleatory.readthedocs.io/en/latest/Detailed PyPI description (8279 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Limited contributor diversity
2 unique contributor(s) across 100 commits in quantgirluk/aleatoryTwo distinct contributors found
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: outlook.com>
All external links appear legitimate
Repository quantgirluk/aleatory appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a mini-application named 'StochSimVis' using Python that leverages the 'aleatory' package to simulate and visualize stochastic processes. This application will allow users to input parameters such as mean, variance, and time intervals to generate various types of stochastic processes, including Brownian motion, Poisson processes, and geometric Brownian motion. Users should be able to select different visualization options to better understand the behavior of these processes over time. Additionally, include a feature that allows users to save their simulations as images or CSV files for further analysis. Use 'aleatory' to handle the simulation logic, ensuring that the application provides accurate and visually appealing representations of stochastic processes.
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