🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks identified. It does not engage in network calls, shell executions, or any form of obfuscation or credential harvesting.

Low metadata risk
No network or shell activity detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package is expected to perform network operations.
Shell: No shell execution detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some low-effort indicators but lacks clear malicious signals.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Agent Control Team" 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 agent-control-models

Create a fully-functional mini-application that allows users to manage and control agents through a simple web interface. This application will utilize the 'agent-control-models' Python package, which provides shared data models for both the Agent Control server and SDK, enabling seamless communication between the two. Your task is to develop a system where users can register, authenticate, create agents, assign tasks to these agents, monitor their status, and receive notifications about their performance.

Step 1: Set up your development environment by installing necessary packages including 'agent-control-models'. Ensure you have Flask or Django installed for backend services and React or Vue.js for the frontend.

Step 2: Design the database schema using SQLAlchemy or another ORM of your choice, leveraging the models provided by 'agent-control-models' to ensure consistency and compatibility with the Agent Control server.

Step 3: Implement user authentication and authorization mechanisms. Users should be able to sign up, log in, and manage their profiles. Use JWT tokens for secure session management.

Step 4: Develop the backend API endpoints that allow users to interact with the agent control models. These endpoints should enable CRUD operations on agents and tasks, as well as provide real-time updates about agent statuses.

Step 5: Create the frontend interface using HTML, CSS, and JavaScript frameworks like React or Vue.js. The UI should be intuitive and responsive, allowing users to easily navigate through different functionalities such as viewing a list of agents, assigning tasks, and monitoring their progress.

Step 6: Integrate real-time notifications into the application. Whenever an agent completes a task or encounters an error, users should receive immediate alerts via email or push notifications.

Step 7: Test the application thoroughly to ensure all components work seamlessly together. Pay special attention to security aspects and user experience.

Features:
- User registration and login
- Agent creation, modification, and deletion
- Task assignment and tracking
- Real-time status updates for agents
- Notification system for critical events

By following these steps and utilizing the 'agent-control-models' package effectively, you'll build a robust and scalable mini-application that demonstrates the power of integrating custom-built systems with standardized data models.