🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to sparse author information, but there are no clear signs of malicious intent.

No network calls or shell executions detected.
Sparse author information raises minor concerns.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command risks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The author's information is sparse, suggesting a potentially new or less active maintainer, but no 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

Repository datahub-project/datahub 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 acryl-datahub-gx-plugin

Your task is to develop a small but powerful utility called 'DataHub Monitor' using Python, which leverages the 'acryl-datahub-gx-plugin' package. This utility will serve as a bridge between various data execution systems and the DataHub platform, ensuring real-time monitoring and reporting of data operations. Here’s a detailed breakdown of what your utility should achieve:

1. **Setup**: Begin by installing Python and setting up a virtual environment. Next, install the 'acryl-datahub-gx-plugin' package along with any other necessary dependencies such as requests and logging.
2. **Configuration**: Design a configuration file where users can specify their DataHub endpoint, API keys, and other relevant details for authentication and communication.
3. **Execution Monitoring**: Implement functionality within your utility to monitor specific data execution processes. This could include SQL queries, ETL jobs, or any other form of data processing tasks. Ensure that the utility captures key metrics such as start time, end time, duration, success/failure status, and any error messages.
4. **Data Reporting**: Utilize the 'acryl-datahub-gx-plugin' to send these captured metrics to the DataHub platform in real-time. The plugin should handle the serialization of data and the actual transmission to the DataHub server.
5. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with your utility. This CLI should allow users to start/stop monitoring, view logs, and check the status of monitored executions.
6. **Error Handling and Logging**: Incorporate robust error handling and logging mechanisms to ensure that all errors and critical information are logged and can be reviewed later if needed.
7. **Security Considerations**: Ensure that sensitive information like API keys and credentials are securely stored and handled. Avoid hardcoding these values directly into the code.
8. **Documentation**: Provide comprehensive documentation on how to set up, configure, and use your utility effectively.

Suggested Features:
- Support for multiple data sources (e.g., PostgreSQL, MySQL)
- Ability to schedule monitoring sessions at regular intervals
- Detailed reporting capabilities, including graphical representations of execution performance over time
- Alerting system that notifies users via email or SMS in case of critical issues

By following these steps and incorporating the suggested features, you'll create a versatile and valuable tool that enhances the visibility and manageability of data operations within organizations.