🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks across various categories, but the metadata risk due to an unverified maintainer account warrants further investigation.

New or inactive maintainer account
Lack of proper author name

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external communications.
Shell: No shell executions 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 maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion but does not strongly indicate malice.

🔬 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 adrq/agentbeacon 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 agentbeacon

Create a mini-application named 'CodeBot' that leverages the 'agentbeacon' package to manage and orchestrate various AI coding tools. CodeBot should serve as an intelligent coding assistant that can perform tasks such as code completion, debugging, and code review. Here’s a detailed plan on how to develop this application:

1. **Project Setup**: Start by installing the 'agentbeacon' package along with other necessary libraries such as 'requests' for making HTTP requests and 'pyyaml' for handling configuration files.
2. **Agent Configuration**: Use 'agentbeacon' to define agents for different coding tasks. For instance, create agents for code completion, debugging, and code review. Each agent should have its own configuration file specifying the API endpoints, authentication details, and parameters needed to communicate with external AI coding services.
3. **User Interface**: Develop a simple command-line interface (CLI) where users can interact with CodeBot. The CLI should allow users to select which task they want to perform (e.g., code completion, debugging, code review).
4. **Task Execution**: Implement logic within CodeBot to dispatch user requests to the appropriate agents using 'agentbeacon'. For example, when a user requests code completion, CodeBot should use 'agentbeacon' to send the request to the code completion agent, which then communicates with the relevant AI service to generate suggestions.
5. **Feedback Loop**: Integrate a feedback mechanism into CodeBot so that users can rate the quality of responses from the AI coding tools. This feedback can be used to improve the performance of the agents over time.
6. **Logging & Monitoring**: Set up logging for all interactions between CodeBot and the AI coding tools. Additionally, implement monitoring to track the performance of each agent and identify any issues that need addressing.
7. **Documentation**: Provide comprehensive documentation detailing how to set up and use CodeBot, including how to configure new agents for additional coding tasks if needed.

By following these steps, you will create a powerful and versatile tool that enhances the coding experience through the integration of multiple AI coding tools managed by 'agentbeacon'.