AI Analysis
Final verdict: SUSPICIOUS
The package has a low risk profile for direct malicious activities such as network or shell exploitation, and it does not appear to be obfuscated. However, the metadata risk score suggests potential issues with maintenance and author activity, which could indicate a lack of support or updates, raising suspicions about its safety.
- Low metadata maintenance
- Limited author activity
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package is expected to interact with external services like databases.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for most Python packages.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to stealing credentials.
- Metadata: The package shows low maintenance effort and an author with limited activity, which raises some suspicion but does not definitively indicate malicious intent.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 12 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml12 test file(s) detected (e.g. conftest.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (7314 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
184 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with astrocyte-postgres
Create a mini-application called 'DocumentExplorer' that leverages the 'astrocyte-postgres' Python package to manage, search, and analyze a collection of documents. This application will serve as a powerful tool for researchers, writers, and anyone who needs to work with large sets of textual data. Here’s a detailed plan on how to build it: 1. **Setup Environment**: Start by setting up a Python virtual environment and installing necessary packages including 'astrocyte-postgres', 'flask' for the web interface, and 'pandas' for data manipulation. 2. **Database Initialization**: Use 'astrocyte-postgres' to set up a PostgreSQL database that includes vector stores for efficient similarity searches and document stores for storing metadata about each document. 3. **Document Upload**: Implement a feature where users can upload multiple documents at once. These documents could be in various formats like PDF, DOCX, or TXT. Use 'astrocyte-postgres' to store both the raw content and processed versions of these documents in the database. 4. **Search Functionality**: Develop a robust search engine that allows users to query the database using keywords, phrases, or even more complex queries. Utilize 'astrocyte-postgres'’s vector capabilities to provide recommendations based on semantic similarity. 5. **Analysis Tools**: Integrate analysis tools such as word frequency counters, sentiment analyzers, and topic modeling algorithms to help users gain deeper insights into their document collections. 6. **Visualization**: Create visual representations of the data analysis results, such as bar charts showing word frequencies or pie charts depicting topic distributions. 7. **User Interface**: Design a clean, intuitive user interface using Flask. Ensure that all functionalities are easily accessible and that the UI provides real-time feedback during operations like uploading and searching. 8. **Security Measures**: Implement basic security measures such as user authentication to protect user data. 9. **Testing and Documentation**: Thoroughly test the application to ensure reliability and performance. Document the setup process, usage instructions, and any troubleshooting tips. By following these steps, you'll create a versatile tool that not only helps in managing document collections but also enhances understanding through advanced analytical and visualization features.
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