ari-core

v0.1.0b4 suspicious
4.0
Medium Risk

Core workflow engine and OSS runtime for Ari

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

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.

πŸ“¦ Package Quality Overall: Low (2.0/10)

β—‹ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
β—ˆ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6183 chars)
β—‹ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
β—‹ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
β—‹ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked β€” contributor count unavailable

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

βœ“ Code Obfuscation

No obfuscation patterns detected

βœ“ Shell / Subprocess Execution

No shell execution patterns detected

βœ“ Credential Harvesting

No credential harvesting patterns detected

βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: ari.engineer>

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ 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 short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with ari-core
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. Here’s 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.

πŸ’¬ Discussion Feed

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