🤖 AI Analysis

Final verdict: SAFE

The package exhibits no signs of malicious activity based on the provided analysis notes. It has minimal risk across all categories checked.

No network calls detected
No shell execution patterns
No obfuscation or credential harvesting

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository chinmaypandya/aegon appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 aegon-rs

Create a real-time monitoring tool for developers using the 'aegon-rs' Python package, which provides live TUI (Text User Interface) dashboards for observing Claude Code agentic runs. This tool will allow developers to monitor their code execution in real-time, gaining insights into performance metrics, errors, and other critical information as their code runs.

Step-by-Step Instructions:
1. Set up your development environment with Python and the 'aegon-rs' package installed.
2. Design a simple API that interacts with Claude Code to fetch run data.
3. Utilize the 'aegon-rs' package to create a live TUI dashboard that displays key metrics such as CPU usage, memory consumption, and runtime duration.
4. Implement error tracking within the dashboard to highlight any exceptions or warnings that occur during the execution of the code.
5. Add functionality to log these metrics and errors to a file or database for later analysis.
6. Ensure the dashboard updates in real-time as the code executes, providing immediate feedback on performance.
7. Consider adding additional features such as configurable alerts for specific conditions (e.g., high memory usage), and the ability to pause or stop the code execution from within the dashboard.
8. Test the application thoroughly with various types of code executions to ensure it captures all necessary data accurately.
9. Document the setup process, usage instructions, and any limitations of the tool.

Features:
- Live TUI dashboard displaying real-time performance metrics.
- Error tracking with visual alerts.
- Real-time updates reflecting current state of code execution.
- Logging capabilities for post-execution analysis.
- Configurable alerts based on performance criteria.
- Ability to control code execution (pause, stop) directly from the dashboard.