apache-airflow-providers-opsgenie

v5.10.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, with only minor metadata concerns. There's no indication of malicious activity or supply-chain attack.

  • Low network and shell execution risks
  • Minor metadata issues but no clear malicious intent
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell executions detected, indicating no direct system command invocations within the package.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has some minor issues with maintainer history and an insecure link but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: conftest.py
  • 15 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-ops
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3516 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
  • 12 type-annotated function signatures detected in source
✦ 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-opsgenie 
Create a fully-functional mini-application using Apache Airflow and the 'apache-airflow-providers-opsgenie' package to automate incident management and alerting processes. Your application should be designed to monitor specific system health metrics (such as CPU usage, memory usage, disk space, etc.) of a set of servers and trigger alerts through Opsgenie when these metrics exceed predefined thresholds. The application should include the following steps and features:

1. **Setup Environment**: Ensure your development environment is ready for Apache Airflow. Install necessary packages including 'apache-airflow-providers-opsgenie', 'apache-airflow[celery]', and any other dependencies needed.
2. **Define Metrics and Thresholds**: Define which metrics you will monitor and set up threshold values for each metric. For example, if the CPU usage exceeds 90%, or if the disk space falls below 10% free, an alert should be triggered.
3. **Create Monitoring DAGs**: Develop Directed Acyclic Graphs (DAGs) in Apache Airflow that periodically check the defined metrics on the specified servers. Use operators such as 'BashOperator' or 'PythonOperator' to execute scripts or functions that fetch the current state of the metrics.
4. **Integrate Opsgenie Alerts**: Utilize the 'apache-airflow-providers-opsgenie' package to send alerts to Opsgenie when any of the metrics exceed their thresholds. Configure the connection details for Opsgenie within Airflow, and create tasks that use the 'OpsgenieAlertOperator' to notify the appropriate teams about incidents.
5. **Customize Alert Messages**: Tailor the alert messages sent to Opsgenie to provide useful information to the incident response team, including the nature of the problem, the affected server, and any immediate actions required.
6. **Testing and Validation**: Implement a testing phase where you simulate different scenarios to ensure that the alerts are correctly sent under various conditions. This includes both normal operations and critical incidents.
7. **Documentation and Deployment**: Document the setup process, configuration options, and operational procedures for the application. Plan for deployment in a production environment, considering security, scalability, and maintenance aspects.

This project aims to showcase how Apache Airflow can be leveraged along with the 'apache-airflow-providers-opsgenie' package to improve incident management and response times in a real-world scenario.

💬 Discussion Feed

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