airflow-dag-patterns
Pass
Audited by Gen Agent Trust Hub on Feb 27, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: No security threats detected. The skill contains educational content and code templates for Apache Airflow.
- [DATA_EXPOSURE]: The implementation playbook includes network operations via
requests.getand cloud storage operations using placeholder domains (api.example.com) and S3 buckets. These are standard demonstrations for data engineering workflows. - [DYNAMIC_EXECUTION]: Pattern 2 demonstrates dynamic DAG registration using the Python
globals()dictionary. This is a common and accepted pattern within the Airflow ecosystem for generating DAGs from configuration data. - [INDIRECT_PROMPT_INJECTION]: The skill defines a surface for processing untrusted data.
- Ingestion points: CSV files read from S3 (
resources/implementation-playbook.md) and JSON responses from external APIs. - Boundary markers: None present in the code templates.
- Capability inventory: Writing parquet files to S3, network requests, and console logging.
- Sanitization: Not demonstrated in the templates, which is typical for boilerplate code. Users should implement validation when adopting these patterns.
Audit Metadata