🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all categories with only metadata indicating some uncertainty due to incomplete author information. Overall, it appears safe with no signs of malicious intent.

Low network and shell execution risks.
No obfuscation or credential harvesting detected.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author information is incomplete and may indicate a new or less active account, but no other suspicious elements are present.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. stress_test.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/nitin3150/agentsentinel#readme
Detailed PyPI description (11669 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

105 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 100 commits in nitin3150/agentsentinel
Small but multi-author team (3–4 contributors)

🔬 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

Email domain looks legitimate: northeastern.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository nitin3150/agentsentinel 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 agentsentinel-ai

Create a fully functional mini-application called 'AI Agent Monitor' using the Python package 'agentsentinel-ai'. This application will serve as a tool for developers to monitor and analyze the performance of their AI agents in real-time. It should include the following core functionalities:

1. **Agent Inspection**: The application should allow users to input details about their AI agent, such as its model type, version, and specific use case. The app then uses 'agentsentinel-ai' to perform a thorough inspection of the agent, providing insights into its current state, including any potential issues or areas for improvement.
2. **Performance Stress Testing**: Implement a feature that allows users to simulate various stress scenarios on their AI agents. This could involve testing the agent under heavy load conditions, with different types of data inputs, or even simulating network latency and failure scenarios. 'agentsentinel-ai' should be leveraged to automate these tests and provide detailed reports on the agent's performance under stress.
3. **Improvement Recommendations**: Based on the inspection and stress test results, the application should generate actionable recommendations for improving the agent's performance. These recommendations could range from optimizing code efficiency to suggesting changes in training data or model architecture.
4. **Real-Time Monitoring Dashboard**: Develop a user-friendly dashboard where users can view real-time metrics of their AI agent's performance, including response times, error rates, and resource usage. This dashboard should update dynamically as the agent operates and interact with 'agentsentinel-ai' to fetch live data.
5. **Report Generation**: Include functionality to generate comprehensive reports summarizing the agent's performance over time. Users should be able to customize these reports based on specific criteria, such as date ranges or performance metrics.

The application should be designed with a clean, intuitive interface, making it easy for both technical and non-technical users to utilize. Ensure that the integration of 'agentsentinel-ai' is seamless and that the application provides clear documentation on how to install and configure it.