AI Analysis
The package has a low risk score due to minimal concerns identified during analysis. It is designed for enterprise user management and authentication, with only a moderate network risk that requires further understanding of its purpose.
- Moderate network risk due to potential API interactions
- No evidence of shell execution, obfuscation, or credential harvesting
Per-check LLM notes
- Network: The network call pattern suggests the package may be making HTTP POST requests, possibly for API interactions or data submission, which is not inherently malicious but should be reviewed for its purpose.
- Shell: No shell execution patterns were detected, indicating no direct command execution risks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Medium (6.2/10)
Test suite present — 18 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml18 test file(s) detected (e.g. conftest.py)
Some documentation present
Documentation URL: "Documentation" -> https://github.com/jenreh/appkit/tree/main/docsDetailed PyPI description (11157 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
346 type-annotated function signatures detected in source
Active multi-contributor project
3 unique contributor(s) across 100 commits in jenreh/appkitSmall but multi-author team (3–4 contributors)
Heuristic Checks
Found 1 network call pattern(s)
} async with httpx.AsyncClient() as client: response = await client.post(
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
Repository jenreh/appkit appears legitimate
1 maintainer concern(s) found
Author "Jens Rehpöhler" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a desktop application using Python that helps users manage their daily tasks and reminders efficiently. This application will be built utilizing the 'appkit-user' package, which simplifies the process of creating user-friendly interfaces and integrating system functionalities. Your task is to develop a Task Manager that allows users to add, edit, delete, and mark tasks as completed. Additionally, the application should have a feature to set reminders for specific tasks, notify the user when a task is due, and provide a calendar view to visualize the week's tasks at a glance. Step 1: Set up your development environment with Python and install the 'appkit-user' package. Step 2: Design the user interface for adding new tasks, including fields for task name, description, priority level, and due date. Step 3: Implement functionality to save tasks to a local database or file and retrieve them for display. Step 4: Develop editing and deleting capabilities for tasks, ensuring data integrity and user convenience. Step 5: Integrate a reminder system that alerts users via notifications when tasks are approaching their due dates. Step 6: Create a calendar view that shows all tasks scheduled for the current week, highlighting completed and upcoming tasks. The 'appkit-user' package will be crucial for streamlining the development process, particularly in handling user interactions and system integrations such as notifications and calendar access. Use its features to enhance the usability and efficiency of your Task Manager application.