🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its metadata indicating it's from a newly created and less historically active maintainer, coupled with undocumented subprocess execution.

Metadata risk due to a new and less established maintainer.
Potential shell risk from undocumented subprocess execution.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API access.
Shell: Subprocess execution might be used for legitimate purposes but could indicate potential risk if not documented. Further investigation is needed.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets.
Metadata: The package shows signs of being newly created and maintained by an account with limited history, which could indicate potential risk.

🔬 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)

ean sys.modules. result = subprocess.run( [ sys.executable, "-c",

✓ 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 score 2.5

Git history flags: Single contributor with only 3 commit(s) — possibly throwaway account

Single contributor with only 3 commit(s) — possibly throwaway account

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 agent-dashboard

Develop a real-time monitoring dashboard for a fictional tech support center using the Python package 'agent-dashboard'. This application will allow supervisors to monitor the status of various agents working on customer tickets in real-time. Each agent's status (e.g., available, on call, busy) and ticket details will be displayed on the dashboard.

Key Features:
1. User Authentication: Implement basic user authentication so only authorized personnel can access the dashboard.
2. Real-Time Updates: Use websockets or similar technology to ensure the dashboard updates in real-time as agents' statuses change.
3. Agent Status Tracking: Display each agent's current status (available, on call, busy) along with their name and photo.
4. Ticket Management: Show open tickets assigned to each agent, including ticket ID, customer name, and issue summary.
5. Customizable Dashboard Layout: Allow supervisors to customize the layout of the dashboard to suit their needs.
6. Notifications: Implement notifications to alert supervisors when an agent's status changes or if there's an urgent ticket.

How to Utilize 'agent-dashboard':
- Use 'agent-dashboard' to define the data models for agents and tickets, ensuring that the structure is optimized for real-time rendering.
- Leverage 'agent-dashboard' to create a visually appealing and interactive interface for displaying agent statuses and ticket information.
- Integrate 'agent-dashboard' functionalities to handle the dynamic updating of the dashboard based on real-time data from the backend.

This project aims to showcase the capabilities of 'agent-dashboard' in building efficient and responsive dashboards for managing complex systems.