🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks as it does not engage in network calls, obfuscation, or credential harvesting. The only notable risk is from the use of os.system for shell commands, but this is common practice in similar packages and does not indicate malicious intent.

Low network risk
Moderate shell risk due to os.system usage
No signs of obfuscation or credential harvesting

Per-check LLM notes

Network: No network calls were detected, which is normal unless the package is expected to communicate with external services.
Shell: The use of os.system for executing shell commands related to packaging and deploying the software is common but may pose some risk if not properly sanitized or controlled.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_dbt_hook.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6328 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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 score 10.0

Found 5 shell execution pattern(s)

rsal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.exec
to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git tag
('Pushing git tags…') os.system('git tag v{0}'.format(about['__version__'])) os.syst
bout['__version__'])) os.system('git push --tags') sys.exit() setup( name='ai
env: {sub_env}") sp = subprocess.Popen( dbt_cmd, env=sub_env, s

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://opensource.org/licenses/MIT

✓ 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-dbt-winwin

Your task is to create a data orchestration tool that integrates Apache Airflow with dbt (data build tool) using the 'airflow-dbt-winwin' package. This tool will streamline the process of scheduling and executing dbt tasks within an Airflow environment, ensuring seamless data transformations and analytics pipeline management. Your goal is to develop a mini-application that allows users to define dbt projects, schedule their execution through Airflow DAGs, monitor job statuses, and retrieve logs for troubleshooting.

Key Features:
- User-friendly interface for defining dbt projects and tasks
- Integration with existing Airflow environments for scheduling
- Real-time monitoring of dbt jobs within Airflow UI
- Log retrieval and error reporting for each executed task
- Support for incremental data processing and model dependencies

Steps to Build the Application:
1. Set up a development environment with Python, Apache Airflow, and dbt installed.
2. Install the 'airflow-dbt-winwin' package to facilitate the integration between Airflow and dbt.
3. Design a user interface where users can input details of their dbt projects and specify which models or operations they wish to execute.
4. Implement functionality to automatically generate Airflow DAGs based on the provided dbt project information.
5. Ensure that the application can handle dependencies between different dbt models and manage them appropriately during execution.
6. Develop a feature that allows users to monitor the status of their dbt jobs in real-time from within the Airflow UI.
7. Integrate logging capabilities so that users can access detailed logs for any executed dbt tasks for debugging purposes.
8. Test the application thoroughly under various scenarios to ensure reliability and efficiency.
9. Document the setup process and usage instructions for other developers who may want to use or contribute to the project.