AI Analysis
The package exhibits low risks across all categories except for metadata quality, where it shows signs of low maintainer activity and poor metadata. However, there are no clear indications of malicious intent.
- Low network and shell execution risk
- No obfuscation or credential harvesting patterns detected
- Poor metadata quality and low maintainer activity
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution patterns detected, indicating no direct command-line interface interactions that could pose a risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.
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 (823 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
250 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 personal finance management tool using the Python package 'autobean-refactor'. This tool will allow users to manage their financial transactions more efficiently by providing features such as transaction categorization, budget tracking, and report generation. Here are the steps and features to include in your application: 1. **Setup and Configuration**: Initialize the project with necessary dependencies including 'autobean-refactor'. Users should be able to input their Beancount file paths which contain their financial data. 2. **Transaction Categorization**: Implement a feature where users can categorize their transactions into predefined categories like 'Groceries', 'Utilities', 'Salary', etc. Use 'autobean-refactor' to manipulate the Beancount files and apply these categorizations. 3. **Budget Tracking**: Allow users to set monthly budgets for different categories. The tool should then track the spending against these budgets and alert users when they are nearing or exceeding their limits. 4. **Report Generation**: Provide the ability to generate various types of reports from the financial data. Reports could include monthly summaries, category breakdowns, and comparison charts between months. Utilize 'autobean-refactor' to extract relevant information for report generation. 5. **User Interface**: Develop a simple yet intuitive command-line interface (CLI) for users to interact with the tool easily. Consider adding a graphical user interface (GUI) if you have experience with frameworks like PyQt or Tkinter. 6. **Data Visualization**: Integrate a visualization library like Matplotlib or Plotly to display budget tracking and report data visually. 7. **Testing and Documentation**: Ensure thorough testing of all features and provide comprehensive documentation for both users and developers. This project aims to streamline personal finance management by leveraging the powerful capabilities of 'autobean-refactor' for Beancount file manipulation.
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