apache-airflow-providers-jenkins

v4.2.5 safe
2.0
Low Risk

Provider package apache-airflow-providers-jenkins for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of network risks, shell risks, or credential risks. The low obfuscation risk is due to common practices rather than suspicious activity.

  • No network calls detected.
  • No shell execution patterns detected.
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 does not execute system commands.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and does not indicate malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-jen
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3482 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 5 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow 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 apache-airflow-providers-jenkins 
Create a mini-application that integrates Jenkins CI/CD pipelines with Apache Airflow using the 'apache-airflow-providers-jenkins' package. Your application should allow users to manage Jenkins jobs directly from their Airflow DAGs. Here’s a detailed breakdown of what your application should include:

1. **Project Setup**: Start by setting up a Python environment with all necessary dependencies, including Apache Airflow and the 'apache-airflow-providers-jenkins' package.
2. **Jenkins Connection Configuration**: Develop a mechanism within Airflow to securely configure connections to Jenkins servers. This includes storing Jenkins server URLs, usernames, and API tokens.
3. **DAG Creation**: Design a Directed Acyclic Graph (DAG) that represents the workflow for triggering Jenkins jobs. Each task in the DAG should correspond to a Jenkins job.
4. **Task Execution**: Implement tasks within the DAG that use the 'apache-airflow-providers-jenkins' package to trigger Jenkins jobs based on predefined schedules or events.
5. **Status Monitoring**: Add functionality to monitor the status of Jenkins jobs triggered by the DAG. This could involve checking if the job was successful, failed, or still running.
6. **Notifications**: Set up notifications within Airflow to alert users via email or Slack when a Jenkins job completes successfully or fails.
7. **Customization Options**: Allow for customization options such as specifying parameters to pass to Jenkins jobs during execution.
8. **Documentation**: Provide comprehensive documentation explaining how to install and use the application, including examples of DAG configurations and connection settings.

This project aims to streamline the process of integrating Jenkins into existing Airflow workflows, making it easier to manage CI/CD pipelines alongside other data processing tasks.

💬 Discussion Feed

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