🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of network activity, which is concerning as it may indicate external communication that could be exploited for unauthorized data transfer. However, other risks such as shell execution and credential harvesting are minimal.

moderate network risk due to HTTP POST requests
low risk for shell execution, obfuscation, and credential harvesting

Per-check LLM notes

Network: The presence of HTTP POST requests suggests the package communicates externally, which could be for legitimate purposes like API calls, but warrants further investigation to ensure it's not being used for unauthorized data transfer.
Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

) ] try: with httpx.Client() as client: r = client.post( f"
try: with httpx.Client() as client: r = client.post(

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 8.0

4 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)
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 agentlens-kk

Create a comprehensive fleet management application using the 'agentlens-kk' Python package. This application will serve as a centralized dashboard to govern and monitor multiple agents across different frameworks. Your task is to design and implement a fully functional mini-application that showcases the core functionalities of the 'agentlens-kk' package. Here’s a step-by-step guide on what your application should achieve:

1. **Setup and Initialization**: Begin by setting up your Python environment and installing the 'agentlens-kk' package. Ensure you have the necessary dependencies installed.
2. **Agent Registration**: Implement functionality that allows users to register new agents within the system. Each agent should have unique identifiers and belong to specific frameworks (e.g., Kubernetes, Docker).
3. **Monitoring Dashboard**: Develop a monitoring dashboard where users can view real-time status updates of all registered agents. This dashboard should display key performance indicators such as CPU usage, memory consumption, and network activity.
4. **Governance Features**: Integrate governance features that allow administrators to manage policies and configurations across the fleet of agents. This includes setting quotas, enforcing security policies, and managing access controls.
5. **Alerting System**: Build an alerting system that notifies administrators when certain thresholds are breached or when critical issues arise with any of the agents.
6. **Reporting Module**: Include a reporting module that generates periodic reports summarizing the health and performance of the entire agent fleet over time.
7. **User Interface**: Design a user-friendly interface that allows both technical and non-technical users to interact with the application easily.

Throughout the development process, make sure to leverage the 'agentlens-kk' package effectively to streamline operations and enhance the overall functionality of your fleet management application.