AI Analysis
Final verdict: SAFE
The package arbiter3 v0.0.13 shows minimal signs of risk with all checks except network risk and metadata risk scoring low. There's no strong evidence suggesting a supply-chain attack.
- Network risk is moderate due to potential unknown URLs accessed.
- Maintainer has only one package, indicating possible new or less active status.
Per-check LLM notes
- Network: The use of aiohttp for network requests is common and suggests legitimate network interactions, but further investigation into the URLs accessed is recommended.
- Shell: No shell execution patterns were detected, which is normal and indicates no immediate risk from this aspect.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags were detected.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 2 test file(s) found
Test runner config found: pyproject.toml2 test file(s) detected (e.g. test_email.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (828 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
54 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
st[Limits]]): async with aiohttp.ClientSession() as session, asyncio.TaskGroup() as tg: tasks = []e, value): async with aiohttp.ClientSession() as session: return await set_property(target,
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: utah.edu
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Kai Forrest" 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 arbiter3
Create a mini-application named 'TaskManager' using the Python package 'arbiter3'. This application will serve as a simple yet powerful task scheduling tool designed for personal or small team use. The goal is to demonstrate the capabilities of 'arbiter3' in managing tasks efficiently. ### Application Features: - **Task Creation:** Users should be able to add new tasks with a title, description, start date, end date, and priority level (high, medium, low). - **Task Scheduling:** Use 'arbiter3' to schedule these tasks based on their start and end dates. The scheduler should be capable of handling periodic tasks and one-time tasks. - **Task Prioritization:** Implement a feature where tasks can be marked as high, medium, or low priority. 'arbiter3' should prioritize high-priority tasks over others when scheduling. - **Task Status Tracking:** Allow users to view the status of their tasks (scheduled, ongoing, completed). - **Task Modification:** Provide functionality to update task details including start/end dates, priority, and descriptions. - **Task Deletion:** Users should have the ability to delete tasks that are no longer needed. ### Utilizing 'arbiter3': - **Task Scheduling Logic:** Use 'arbiter3' to manage the scheduling logic behind adding, modifying, and deleting tasks. Specifically, leverage 'arbiter3' for its robust scheduling capabilities to ensure tasks are scheduled accurately and efficiently. - **Priority Handling:** Implement a custom priority handler within your application that integrates with 'arbiter3'. This handler should instruct 'arbiter3' to prioritize higher-priority tasks when scheduling. - **Status Updates:** Use 'arbiter3' to trigger updates on task statuses automatically as tasks progress from scheduled to ongoing and finally to completed based on predefined rules. ### Expected Outcome: By the end of this project, you should have a fully functional 'TaskManager' application that showcases the power of 'arbiter3' in managing complex task scheduling scenarios. The application should be user-friendly, efficient, and capable of handling multiple tasks simultaneously while prioritizing them effectively.
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