AI Analysis
The package shows minimal risk in terms of network activity, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to the maintainer's limited presence on PyPI, raising suspicion about the package's origin and purpose.
- Maintainer has only one package
- Lack of PyPI classifiers
Per-check LLM notes
- Network: No network calls detected, which is normal for a logging package.
- Shell: No shell execution patterns detected, aligning with expectations for a logging utility.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package and lacks PyPI classifiers, suggesting potential low effort or inexperience.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (4287 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
44 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Author "Sonia & Om" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a simple monitoring tool using Python that leverages the 'autourgos-agent-logger-core' package to log various system metrics such as CPU usage, memory usage, and disk space. This tool will periodically collect these metrics and log them to the console, providing real-time insights into the system's performance. Steps to complete this project: 1. Set up a Python environment with the necessary packages installed, including 'autourgos-agent-logger-core'. 2. Write a function to retrieve current CPU usage, memory usage, and available disk space on the local machine. 3. Use the 'autourgos-agent-logger-core' package to create a logger instance that outputs logs to the console in a human-readable format. 4. Implement a loop that periodically (e.g., every minute) calls the metric retrieval function and logs the results using the logger instance. 5. Add command-line arguments to allow users to specify the interval between metric checks and the duration of the monitoring session. 6. Ensure the tool gracefully handles interruptions (e.g., when the user presses Ctrl+C) by cleaning up any resources and logging a final message before exiting. Suggested Features: - Allow users to select which metrics they want to monitor (CPU, Memory, Disk). - Integrate a simple GUI using a library like Tkinter to display the logged data in real-time. - Implement email alerts if certain thresholds are exceeded. - Save the logged data to a file for later analysis. How 'autourgos-agent-logger-core' is Utilized: - The 'autourgos-agent-logger-core' package is used to handle all logging tasks within the application. It provides the ability to output logs in a structured and readable format directly to the console, making it easy to monitor the system's performance in real-time.
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