AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activities such as network calls, shell executions, or credential theft. The metadata risk is slightly elevated due to low activity and poor metadata quality, but this alone does not indicate malicious intent.
- Low risk scores across all categories.
- Metadata suggests low activity and poor metadata quality.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- 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: Low activity and poor metadata suggest potential low quality, but insufficient evidence for malice.
Package Quality Overall: Low (3.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3062 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
111 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 63 commits in AlexDev505/DBCoreTwo distinct contributors found
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
Email domain looks legitimate: yandex.ru>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository AlexDev505/DBCore appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "AlexDev505" 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 aiodbcore
Create a simple task management application using Python's 'aiodbcore' package. This application will allow users to create, read, update, and delete tasks, as well as mark them as completed. The app should be built around a SQLite database for simplicity and ease of use during development. ### Key Features: 1. **User Authentication:** Implement basic user registration and login functionality. Users should be able to register with their email and password, and log in to manage their tasks. 2. **Task Management:** Users should be able to add new tasks with a title, description, due date, and priority level. They should also be able to view all their tasks, update existing tasks, and mark them as completed or deleted. 3. **Search Functionality:** Provide a search feature where users can find tasks based on keywords in the title or description. 4. **Sorting and Filtering:** Allow users to sort their tasks by priority, due date, and completion status. Additionally, they should be able to filter tasks by these criteria. 5. **Notifications:** Send notifications (email or in-app) when tasks are marked as completed or are approaching their due date. ### Utilizing 'aiodbcore': - Use 'aiodbcore' to define your models (users and tasks). Since 'aiodbcore' is an ORM that doesn't force you to change your coding style, you'll integrate it seamlessly into your application logic. - For each model, define fields such as id, title, description, due_date, priority, etc., for tasks; and id, email, password_hash, for users. - Implement CRUD operations (Create, Read, Update, Delete) for both users and tasks using 'aiodbcore'. Ensure that these operations are asynchronous to take advantage of 'aiodbcore's async capabilities. - Use 'aiodbcore' to handle relationships between users and tasks (e.g., one-to-many). - Explore 'aiodbcore's query-building capabilities to implement sorting, filtering, and searching functionalities efficiently. ### Additional Considerations: - Design a clean and intuitive UI/UX for the application, even if it's a command-line interface (CLI). - Ensure the application is secure, especially regarding user data and authentication. - Write comprehensive documentation and include comments in your code to explain your implementation choices and usage of 'aiodbcore'. - Test your application thoroughly to ensure all features work as expected.