AI Analysis
The package has low direct risks but raises concerns due to incomplete metadata and appears to be low-effort, which may indicate potential issues or abandonment.
- Incomplete maintainer information
- Seems low-effort
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package lacks essential maintainer information and seems to be low-effort, which could indicate potential risk.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (6183 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
Email domain looks legitimate: ari.engineer>
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 task management mini-app using the 'ari-core' package. This app will allow users to create, manage, and track workflows for their tasks. Hereβs a step-by-step guide on how to develop this application: 1. **Project Setup**: Start by setting up your Python environment and installing the 'ari-core' package. 2. **Workflow Definition**: Define workflows using 'ari-core'. Each workflow represents a series of steps or tasks that need to be completed. For example, you could define a simple 'Task Completion Workflow' that includes steps like 'Task Creation', 'Assign Task', 'Mark as In Progress', 'Complete Task', and 'Review Task'. 3. **User Interface**: Develop a simple UI where users can log in and view their workflows. Use Flask or Django to build the web interface. 4. **Task Management Features**: Implement features such as adding new tasks, assigning tasks to team members, marking tasks as in progress or complete, and reviewing completed tasks. Utilize 'ari-core' to handle the state transitions of these tasks within the defined workflows. 5. **Notifications**: Add functionality to notify users when a task is assigned to them or when a task they are waiting on has been completed. Notifications can be via email or in-app messages. 6. **Reporting**: Create reporting features that allow users to generate reports on the status of their workflows. These reports can include details like total tasks completed, time taken for completion, and more. 7. **Integration**: Optionally, integrate the app with other services such as calendars or project management tools to enhance its functionality. By utilizing 'ari-core', you will be leveraging its core workflow engine capabilities to ensure that tasks move through their predefined stages smoothly and efficiently.
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