AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity such as network calls, shell execution, or credential harvesting. However, the incomplete metadata raises some concern about the maintainer's status.
- No network calls detected.
- Incomplete maintainer metadata.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, reducing risk of command injection or similar attacks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has incomplete information and appears to be new or inactive, which could indicate potential risk.
Package Quality Overall: Medium (5.4/10)
✦ High
Test Suite
9.0
Test suite present — 35 test file(s) found
Test runner config found: pyproject.toml35 test file(s) detected (e.g. test_backoff_logic.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3553 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: Typed120 type-annotated function signatures detected in source
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 17 commits in avtomatika-ai/avtomatika-workerSingle author with few commits — possibly a personal or throwaway project
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository avtomatika-ai/avtomatika-worker appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with avtomatika-worker
Create a mini-application named 'TaskMaster' using Python that leverages the 'avtomatika-worker' package to manage tasks efficiently across multiple workers. TaskMaster will serve as a simple task management system where users can create tasks, assign them to different workers, and track their progress. The application should include the following features: 1. User Authentication: Implement basic user authentication to allow registered users to log in and out. 2. Task Creation: Users should be able to create new tasks, specifying details such as task name, description, due date, and priority level. 3. Worker Assignment: Tasks should be assigned to specific workers. Users should be able to see a list of available workers and select one or more for each task. 4. Progress Tracking: Each task should have a status indicating its current state (e.g., pending, in progress, completed). Users should be able to update the status of a task. 5. Notifications: Implement a notification system that alerts users when a task has been marked as completed or if it's overdue. 6. Reporting: Provide a reporting feature that generates summaries of completed tasks over a specified period. To achieve these features, utilize the 'avtomatika-worker' package to handle the backend processing. Specifically, use it to distribute task updates among workers and manage task synchronization. Your application should demonstrate a practical use case for the 'avtomatika-worker' package, showcasing its capabilities in task orchestration and worker management.
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