AI Analysis
The package exhibits low risk in terms of network calls, shell execution, and obfuscation. However, the metadata quality is poor and there's low maintainer activity, which raises concerns about potential supply-chain risks.
- Low maintainer activity
- Poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
- Metadata: The package shows low maintainer activity and poor metadata quality, raising suspicion but not conclusive evidence of malice.
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
91 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β 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 mini-application called 'DataWatcher' that monitors changes in a PostgreSQL database and logs these changes into another table within the same database using the 'amfs-adapter-postgres' package. This tool will help users keep track of modifications made to their data over time, such as insertions, deletions, and updates. Hereβs a detailed breakdown of the requirements and features: 1. **Database Setup**: Set up a PostgreSQL database with at least two tables - one for the primary data and another for logging changes. 2. **Integration with 'amfs-adapter-postgres'**: Utilize the 'amfs-adapter-postgres' package to set up triggers that automatically log changes to the primary data table into the change log table whenever there is an insert, update, or delete operation. 3. **User Interface**: Develop a simple command-line interface (CLI) where users can interact with the application to view the current state of the primary data table, see recent changes logged in the change log table, and perform basic CRUD operations on the primary data table. 4. **Security Measures**: Implement basic security measures like user authentication before allowing any CRUD operations on the primary data table through the CLI. 5. **Real-time Monitoring**: Ensure that the application can monitor the database in real-time and immediately log any changes into the change log table without requiring manual intervention. 6. **Reporting Feature**: Include a feature that allows users to generate reports based on the change logs, such as identifying frequent data modifications or tracking changes over a specific period. 7. **Documentation**: Provide comprehensive documentation detailing how to install and use the 'DataWatcher' application, including setup instructions for the PostgreSQL database and the 'amfs-adapter-postgres' package. 8. **Testing**: Write unit tests to ensure that all functionalities work as expected and that the integration with 'amfs-adapter-postgres' is seamless.
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