🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all checked categories, with no indications of malicious activity. The metadata suggests potential issues with maintenance effort, but this alone is insufficient to classify it as suspicious or malicious.

No network calls detected
No shell execution patterns found

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 likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low effort and possibly new/inactive maintainer, but lacks clear indicators of malicious intent.

🔬 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 6.0

3 maintainer concern(s) found

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 agentium-tracer

Create a mini-application that monitors the performance of various tasks in real-time using the 'agentium-tracer' Python package. This application will serve as a simple yet powerful tool for developers to understand the execution flow and performance bottlenecks within their code. Here are the key requirements and steps for building this application:

1. **Setup**: Begin by installing the necessary packages, including 'agentium-tracer'. Ensure your environment supports Python 3.x.
2. **Application Design**: Your application should simulate multiple tasks running concurrently, such as fetching data from different APIs, processing images, or generating reports. Each task should have its own unique identifier and should be traceable through the 'agentium-tracer' package.
3. **Task Execution**: Implement a mechanism to execute these tasks asynchronously. Use Python's asyncio library to manage concurrent operations efficiently.
4. **Tracing Operations**: Utilize 'agentium-tracer' to track each task's start, end, and any errors encountered during execution. Record metrics like execution time, number of retries, and success/failure status.
5. **Visualization**: Develop a simple UI or console output to display the status of all ongoing and completed tasks. This should include visual indicators for task progress and any alerts for failed tasks.
6. **Reporting**: Add functionality to generate periodic reports summarizing the overall performance of tasks executed over a given period. Reports should highlight slow-running tasks, frequent failures, and other critical insights.
7. **Customization**: Allow users to customize which tasks are monitored and the level of detail recorded for each task. This could involve setting thresholds for acceptable performance levels or choosing specific events to track.
8. **Testing & Documentation**: Write comprehensive tests to ensure the application functions correctly under various conditions. Provide clear documentation explaining how to install, configure, and use the application.

By following these steps, you'll create a versatile monitoring tool that leverages the capabilities of 'agentium-tracer' to provide valuable insights into the performance of complex applications.