🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk due to shell execution capabilities, although it does not appear to have any direct malicious intent. However, low maintainer activity and poor metadata quality raise concerns about its reliability and security over time.

Shell risk detected
Low maintainer activity

Per-check LLM notes

Network: No network calls detected, which is normal and not indicative of malicious activity.
Shell: Detection of shell execution may be legitimate if the package requires running Python code dynamically, but it could also indicate potential risks such as executing arbitrary code.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

try: completed = subprocess.run( [self._python, "-c", code],

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ 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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with abstractagent

Your task is to develop a simple yet powerful task management system named 'TaskMaster' using the Python package 'abstractagent'. This system will allow users to create, manage, and track tasks efficiently. Here’s a detailed breakdown of what your application should achieve and how you can utilize 'abstractagent' throughout the process:

1. **User Authentication**: Implement basic user authentication where users can sign up and log in. Use the 'abstractagent' package to manage user sessions and state transitions effectively.
2. **Task Creation**: Users should be able to create new tasks with descriptions, deadlines, priorities, and tags. Utilize 'abstractagent' to define task states such as 'Created', 'In Progress', 'Completed', and 'Cancelled'.
3. **Task Management**: Provide functionalities to update task details, change status, and delete tasks. Leverage 'abstractagent' to handle these state transitions smoothly and ensure data consistency.
4. **Task Filtering & Sorting**: Enable users to filter and sort their tasks based on various criteria like priority, deadline, and completion status. Use 'abstractagent' to dynamically generate and manage filters.
5. **Notifications**: Integrate notifications for upcoming deadlines and task completions. Use 'abstractagent' to schedule and dispatch notifications at appropriate times.
6. **Data Persistence**: Ensure that all task and user data is stored persistently across sessions. Use 'abstractagent' to abstract away the complexities of data storage and retrieval.
7. **UI/UX Design**: While the primary focus is on functionality, also consider a clean and intuitive user interface. 'Abstractagent' can help streamline backend processes, allowing you to focus more on frontend design.

Throughout the development process, utilize 'abstractagent' to define abstract classes and interfaces for runtime and core components, ensuring modularity and flexibility in your task management system. Your goal is to demonstrate how 'abstractagent' can enhance the development experience by simplifying complex state management and workflow processes.