AI Analysis
The package exhibits minimal risks across all categories analyzed, with no indications of network or shell vulnerabilities, obfuscation, or credential theft. The metadata risk is noted as low-effort, but it does not suggest any malicious intent.
- No network or shell execution risks
- No signs of code obfuscation or credential harvesting
- Metadata quality could be improved but poses no significant threat
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell executions detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some low-effort signs but lacks clear malicious indicators.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (3651 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
64 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: gmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Beancount Financial Tracker Application using Python and the 'autobean-format' package. This application will serve as a personal finance management tool, allowing users to input their financial transactions in Beancount format, and then format and display them in a neat, readable manner. Steps to develop this application: 1. Setup the project environment by installing necessary packages including 'autobean-format'. 2. Design a user-friendly interface where users can input their financial transactions in Beancount format directly into the app. 3. Implement functionality within the app that utilizes 'autobean-format' to automatically format these entries for better readability and consistency. 4. Develop a feature that allows users to view their formatted financial data in a structured format, such as a table or graph. 5. Add a feature to export the formatted data into a PDF or Excel file for easy sharing and archiving. 6. Optionally, include a feature that analyzes the financial data to provide insights such as monthly spending trends or savings rates. Suggested Features: - Input validation for Beancount entries to ensure they follow the correct syntax. - Real-time formatting of entered data as the user types. - Option to save formatted data locally or in the cloud. - Ability to import existing Beancount files for quick setup. - Integration with common calendar APIs to highlight important financial dates. How 'autobean-format' is utilized: - For automatic formatting of Beancount entries as they are added to the system. - To ensure all entries are consistently styled, making it easier for users to read and understand their financial data. - As part of the export process to make sure the exported documents are well-formatted and professional looking.
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