🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network calls, shell execution, and obfuscation, suggesting it operates within expected boundaries. However, its low maintenance and metadata quality warrant further scrutiny to ensure long-term reliability and security.

Low risk scores across all critical areas
Metadata quality concerns

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions detected, indicating the package does not execute system commands that could pose a risk.
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 package shows low maintenance and metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

Test runner config found: pyproject.toml
2 test file(s) detected (e.g. test_operator.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3457 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

17 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 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 airflow-turbine

Create a mini-application that leverages the 'airflow-turbine' package to perform automated data quality checks on a dataset using Apache Airflow. This application will serve as a tool for data engineers and analysts to ensure the integrity and reliability of their datasets before further processing or analysis.

Steps to Build the Application:
1. Set up an Apache Airflow environment.
2. Install the 'airflow-turbine' package and configure it within your Airflow setup.
3. Design a simple ETL pipeline where data is ingested from a source (e.g., CSV file).
4. Implement data quality checks using the 'airflow-turbine' operators, such as checking for null values, data type consistency, and value ranges.
5. Integrate alerts or notifications to inform users when data quality issues are detected.
6. Schedule the pipeline to run periodically (e.g., daily) to continuously monitor the dataset.
7. Provide a user-friendly interface or report to display the results of the data quality checks.

Suggested Features:
- Customizable data quality rules allowing users to define specific checks for their datasets.
- Historical tracking of data quality metrics over time to identify trends or anomalies.
- Integration with popular notification services like Slack or email for immediate alerting.
- Support for multiple data sources and formats, including CSV, JSON, and databases.
- Detailed logging and error handling for troubleshooting and auditing purposes.

How to Utilize 'airflow-turbine':
- Use the 'airflow-turbine' package to create custom operators that perform data quality checks as part of the ETL tasks.
- Leverage the package's capabilities to execute these checks efficiently and integrate them seamlessly into your Airflow DAGs.
- Ensure that the operators provide meaningful output and error messages that can be used to improve the data quality process.