apache-airflow-task-sdk

v1.2.2 safe
4.0
Medium Risk

Python Task SDK for Apache Airflow DAG Authors

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of potential risks and does not strongly indicate any malicious intent. It has legitimate reasons for network calls and shell executions.

  • Network calls appear to be for fetching schemas or configurations
  • Shell executions likely for linting or code checks
Per-check LLM notes
  • Network: The network call pattern suggests the package is using an HTTP client to fetch schemas or configurations, which is common for packages interacting with APIs.
  • Shell: The shell execution patterns indicate that the package may be performing linting or code checks on its own codebase, which is generally benign but could potentially hide malicious actions if not properly audited.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some minor red flags but no strong indicators of malice.

📦 Package Quality Overall: Medium (7.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (1769 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
  • 343 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 score 1.5

Found 1 network call pattern(s)

  • ersion_values[0] client = httpx.Client(transport=app.transport) openapi_schema = ( clie
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • d_tree.code result = subprocess.check_output( ["ruff", "check", "--fix-only", "--unsafe-fixes
  • urns stdout.""" process = subprocess.Popen( shlex.split(command), stdout=subprocess.PIPE, stder
✓ 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-task-sdk 
Your task is to create a mini-application that leverages the 'apache-airflow-task-sdk' package to manage and monitor tasks within an Apache Airflow environment. This application will serve as a simplified dashboard for users to interact with their Airflow DAGs, allowing them to view task statuses, trigger task executions, and receive notifications about task completion or failures.

Step 1: Set Up Your Environment
- Ensure you have Python installed on your machine.
- Install the 'apache-airflow-task-sdk' package using pip.
- Configure your Airflow environment to connect to the Airflow server where your DAGs are hosted.

Step 2: Design the Application Structure
- Create a main application file that initializes the connection to the Airflow API.
- Define classes for handling different types of user interactions such as viewing task statuses, triggering tasks, and setting up notifications.

Step 3: Implement Core Features
- Develop a function to retrieve task statuses from the Airflow API. This function should query the status of all tasks in a specified DAG and return a summary of running, succeeded, failed, and skipped tasks.
- Implement functionality to allow users to manually trigger task executions through the application interface.
- Add a feature to set up email notifications for task completion or failure based on user preferences.

Step 4: Enhance User Experience
- Create a simple command-line interface (CLI) for interacting with the application.
- Integrate logging to track user actions and system responses.
- Optionally, implement error handling to gracefully manage issues like network errors or invalid input from users.

By utilizing the 'apache-airflow-task-sdk', you'll be able to abstract away many of the complexities involved in directly interfacing with the Airflow API, focusing instead on building a robust and user-friendly tool for managing Airflow tasks.

💬 Discussion Feed

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