🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual behavior, particularly with its network calls, despite having no clear signs of malicious intent. Given the low activity and limited history of the maintainer, further scrutiny is warranted.

Unusual network call to ipinfo.io
New package with limited maintainer history

Per-check LLM notes

Network: The network call to ipinfo.io is unusual and may indicate an attempt to gather system information, which could be benign but also potentially used for tracking or other unintended purposes.
Shell: No shell execution patterns were detected, suggesting that direct command execution risks are low.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package seems suspicious due to its newness and the maintainer's limited history, but lacks clear indicators of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

None, lambda: requests.get('https://ipinfo.io/json', timeout=6), )

✓ 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: uchicago.edu

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "ModCon BASE Core Agentic Frameworks Team" 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 academy-dashboard

Create a real-time observability dashboard for a simulated academy of AI agents using the 'academy-dashboard' Python package. Your task is to develop a mini-application that monitors and analyzes the performance of various AI agents participating in a simulated environment. This application will serve as a central hub for observing the training progress, identifying bottlenecks, and ensuring optimal performance of the AI agents.

Key Features:
1. **Agent Performance Monitoring**: Implement real-time graphs and charts that display the performance metrics of each agent over time. These metrics could include learning efficiency, accuracy, and response times.
2. **Environment Overview**: Provide a snapshot of the current state of the simulated environment, including any ongoing challenges or tasks that the agents are tackling.
3. **Alert System**: Set up an alert system that notifies users via email or SMS when certain thresholds are breached (e.g., if an agent's performance drops below a specified level).
4. **Customizable Dashboards**: Allow users to customize their dashboards by selecting which metrics they want to monitor and how these metrics are displayed (e.g., line graphs, bar charts).
5. **Historical Data Analysis**: Offer tools for analyzing historical data to identify trends and patterns in agent performance over extended periods.

Utilizing 'academy-dashboard':
- Use the 'academy-dashboard' package to handle the backend logic for collecting and processing data from the simulated environment and agents. This includes setting up the necessary APIs for communication between your application and the environment.
- Leverage the visualization capabilities provided by 'academy-dashboard' to create dynamic and interactive dashboards that update in real-time as new data comes in from the agents.
- Integrate the alert system with 'academy-dashboard' functionalities to ensure timely and accurate notifications based on predefined criteria.
- Customize the dashboard layout and content using the customization options available within the 'academy-dashboard' package, allowing users to tailor their monitoring experience according to their needs.