apache-airflow-providers-pagerduty

v5.2.5 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no detected shell execution or credential harvesting activities. While there are some concerns about incomplete metadata and a non-HTTPS license link, these do not strongly indicate malicious intent.

  • Incomplete author information
  • Non-HTTPS license link
Per-check LLM notes
  • Network: The detected network call pattern is typical for making HTTP requests, likely to integrate with the PagerDuty API.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern is likely for extending module search path and not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author's information is incomplete and the license link is non-HTTPS, raising some concerns but not strong indicators of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • Test runner config found: conftest.py
  • 8 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-pag
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3664 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
  • 8 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 score 1.5

Found 1 network call pattern(s)

  • ) async with aiohttp.ClientSession() as session: res = await super().run(
⚠ 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-pagerduty 
Develop a small, fully-functional monitoring and alerting application using Apache Airflow and the 'apache-airflow-providers-pagerduty' package. Your application will monitor the health of a specific service (e.g., a web server) and send alerts via PagerDuty when the service is down or unhealthy.

Steps to follow:
1. Set up a basic Apache Airflow environment.
2. Install the 'apache-airflow-providers-pagerduty' package to integrate PagerDuty with your Airflow setup.
3. Define a DAG that periodically checks the health status of the target service (you can simulate this check with a simple HTTP request).
4. Configure the DAG to trigger an alert via PagerDuty if the service is not responding or returns an error status code.
5. Ensure that the application logs relevant information about each check and alert sent.
6. Implement a retry mechanism within the DAG to re-check the service before sending an alert.
7. Optionally, include a feature to acknowledge the alert and mark it as resolved once the service is back online.

Suggested Features:
- Use Airflow's sensors to perform the health check.
- Integrate with a real or mock API endpoint to simulate the service being monitored.
- Provide a configuration file to set thresholds for triggering alerts.
- Include a UI component to view the status of recent checks and alerts.

How 'apache-airflow-providers-pagerduty' is Utilized:
- Use the package to create a connection to PagerDuty within Airflow.
- Utilize the package's operators to send incidents to PagerDuty when the service check fails.
- Optionally, use the package to close incidents once the service is restored.

This project will serve as a practical example of integrating monitoring and alerting systems into an automated workflow using modern cloud tools.

💬 Discussion Feed

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