AI Analysis
The package shows low individual risks across various categories but the metadata risk score of 3 out of 10 due to missing author information and a potentially inactive account warrants further scrutiny.
- Low individual risk scores in network, shell, obfuscation, and credential risks.
- Metadata risk due to missing author details and possibly inactive account.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the library requires network interaction for its functionality.
- Shell: No shell execution detected, indicating no immediate risk of command injection or privilege escalation.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's name is missing and the account seems new or inactive, which raises some concern but not enough to conclude malice.
Package Quality Overall: Low (3.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (5067 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
Active multi-contributor project
18 unique contributor(s) across 100 commits in intel/auto-roundActive community — 5 or more distinct contributors
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: intel.com
All external links appear legitimate
Repository intel/auto-round 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
Create a Python-based financial calculator application named 'SmartRounder' that leverages the 'auto-round-lib' package to automatically round off monetary values based on specific rounding rules. This application will serve as a versatile tool for users who need to handle financial calculations where precision and proper rounding are crucial. ### Core Features: - **Input Interface**: Users can input monetary values and select from various rounding modes such as standard rounding, bankers' rounding, floor rounding, and ceiling rounding. - **Rounding Engine**: Utilize the 'auto-round-lib' package to perform the actual rounding operations. Ensure the engine can dynamically switch between different rounding methods based on user selection. - **Output Display**: Clearly show the rounded result along with the original value for comparison. - **Customization Options**: Allow users to set custom rounding thresholds or decimal places for more precise control over the rounding process. - **Error Handling**: Implement robust error handling to manage invalid inputs gracefully, providing clear feedback to the user. ### Additional Enhancements (Optional): - **History Feature**: Keep a log of previous calculations for reference. - **Graphical Representation**: Provide a graphical representation of the rounding effect using simple charts. - **Export Functionality**: Enable users to export their calculation history to a CSV file for record-keeping purposes. ### How to Use 'auto-round-lib': - Import the necessary functions from 'auto-round-lib'. For example, `from auto_round_lib import round_standard, round_bankers, round_floor, round_ceiling`. - Integrate these functions into your application logic so they can be called based on user input. - Pass the monetary value and any additional parameters required by the rounding functions to achieve the desired rounding effect. Your task is to design and implement this application ensuring it is user-friendly, efficient, and fully leverages the capabilities of 'auto-round-lib'.
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