AI Analysis
The package shows minimal signs of potential malicious activity based on the provided analysis notes. The primary concern lies with the metadata, which requires further investigation.
- Low risk scores across all categories except metadata.
- Incomplete maintainer information and lack of GitHub repository warrant additional scrutiny.
Per-check LLM notes
- Network: No network calls suggest normal operation if the package does not require external services.
- Shell: No shell executions indicate that the package is likely not executing system commands, which is typical for a non-malicious package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no risk of malicious credential theft.
- Metadata: Low risk but requires further investigation due to incomplete maintainer information and lack of GitHub repository.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
402 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
No author email provided
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 financial planning mini-application named 'Asset Allocator' using Python and the 'asset-allocation-contracts' package. This application will serve as a tool for users to simulate different asset allocation strategies based on their risk tolerance and investment goals. The application should include the following core functionalities: 1. User Registration and Login: Allow users to create accounts and log in securely. 2. Risk Tolerance Assessment: Implement a questionnaire to assess the user's risk tolerance level (e.g., conservative, moderate, aggressive). 3. Investment Goal Setting: Enable users to define their financial goals, such as retirement savings, buying a house, etc. 4. Portfolio Simulation: Based on the user's risk tolerance and investment goals, the application should generate a diversified portfolio of assets (stocks, bonds, real estate, etc.). Use the 'asset-allocation-contracts' package to handle the underlying contracts and rules for asset allocation. 5. Performance Tracking: Provide a feature to track the simulated performance of the portfolios over time, showing potential returns and risks. 6. Educational Resources: Include articles, videos, and infographics about different investment strategies and asset classes to educate users. 7. Customization Options: Allow advanced users to tweak the asset allocation parameters according to their specific needs. 8. Data Visualization: Use charts and graphs to visually represent the portfolio compositions and performance. The 'asset-allocation-contracts' package is crucial for ensuring that the asset allocation logic adheres to industry best practices and standards. It provides shared contracts that enforce consistency and correctness across different components of the application. Your task is to design and implement these features, ensuring that the application is user-friendly, informative, and educational.
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