AI Analysis
Final verdict: SAFE
The package abvelocity v0.3.0 is in early development and not recommended for production use. However, the analysis shows low risks across all categories except for a moderate obfuscation risk, which does not strongly suggest malicious intent.
- moderate obfuscation risk
- package is in alpha stage
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 direct system command risks.
- Obfuscation: The obfuscation patterns may indicate an attempt to hide code or dependencies, but without more context, it could also be part of legitimate functionality like data encoding.
- Credentials: No suspicious patterns for credential harvesting were detected.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, but there are no other suspicious flags.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
reykite forecasting suite, ts/eval (re-added; was dropped in ev15) patsy = "^0.5.6" #
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 2.0
1 maintainer concern(s) found
Author "Reza Hosseini" 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 abvelocity
Create a time-series forecasting mini-app using the 'abvelocity' Python package. Your app should allow users to upload time-series data, select from different forecasting models available within 'abvelocity', and visualize the forecasted results alongside historical data. Additionally, include a feature that automatically selects the best performing model based on backtesting accuracy. Here are the steps to develop your mini-app: 1. Set up a basic user interface where users can upload their CSV files containing time-series data. 2. Implement a dropdown menu allowing users to choose from various forecasting models supported by 'abvelocity'. 3. Use 'abvelocity' to preprocess the uploaded data, fit the selected model(s), and generate forecasts. 4. Display the forecast results visually using plots, comparing both the historical data and the predicted future values. 5. Integrate an automated model selection feature that runs backtests on different models provided by 'abvelocity' and suggests the one with the highest accuracy. 6. Ensure the app provides clear explanations of each model's performance metrics and visualizations. 7. Add error handling to manage potential issues such as incorrect file formats or missing data. 8. Document your code thoroughly, explaining how 'abvelocity' functions are utilized throughout the app. This project will not only showcase 'abvelocity's capabilities but also provide a practical tool for anyone interested in time-series analysis and forecasting.