🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious intent with low risks across network, shell, obfuscation, and credential checks. However, incomplete author information and potential inactivity of the maintainer slightly elevate the metadata risk.

Low risk scores across all technical checks
Incomplete author information and potential inactivity of maintainer

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 of malicious activity.
Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
Metadata: The author information is incomplete and the maintainer may be new or inactive, which raises some concern but not enough to conclusively determine malintent.

🔬 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-airflow-plugin

Create a fully-functional mini-application that integrates the 'acryl-datahub-airflow-plugin' into an Apache Airflow DAG (Directed Acyclic Graph). This application will serve as a data lineage tracking system, allowing users to monitor and visualize the flow of data through various tasks within their workflows. Here’s a step-by-step guide on how to develop this mini-application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python, pip, and virtualenv installed. Create a new virtual environment and activate it.

2. **Install Dependencies**: Install necessary Python packages including `apache-airflow`, `acryl-datahub-airflow-plugin`, and any other dependencies required for your DAGs.

3. **Configure DataHub**: Set up a DataHub instance if you don't already have one. Configure your Airflow environment to connect to this DataHub instance using the `acryl-datahub-airflow-plugin`.

4. **Develop a Basic DAG**: Write a simple DAG that includes at least three tasks representing different steps in a data processing pipeline (e.g., data ingestion, transformation, and export).

5. **Integrate DataHub Plugin**: Utilize the `acryl-datahub-airflow-plugin` to automatically capture and send execution metadata of each task in the DAG to DataHub. Ensure that this metadata includes task names, start/end times, and success/failure statuses.

6. **Enhance with Additional Features**: Add features such as dynamic task creation based on input parameters, error handling, and logging improvements. Consider adding support for different types of data sources and sinks.

7. **Visualization**: Integrate a visualization component that allows users to view the lineage of data flow through the DAG on the DataHub UI.

8. **Testing**: Thoroughly test your application under different scenarios to ensure robustness and reliability. Document any issues encountered and solutions implemented.

9. **Documentation**: Provide comprehensive documentation for your mini-application, detailing setup instructions, usage guidelines, and best practices.

This mini-application not only showcases the capabilities of the 'acryl-datahub-airflow-plugin' but also serves as a practical tool for managing and understanding data lineage in complex workflows.