What problem does it solve? When a Cloud Composer (Apache Airflow) DAG fails, finding the root cause requires digging through scattered logs and verifying which code version actually ran. This Skill guides an evidence-based Root Cause Analysis (RCA) workflow so failures are explained with concrete log entries and code references instead of guesswork. ## Core Features & Use Cases - Log-Based Evidence Gathering: Uses gcloud logging read with filters on severity, environment, and DAG ID to locate failures and stack traces. - Remote Code Verification: Downloads the actual DAG code from the environment's GCS bucket and checks consistency against local files before analysis. - RCA Report Generation: Correlates logs with code, pinpoints the failing line, proposes a fix, and produces a saved RCA report. - Pipeline Template Validation: Provides compliant templates for declarative deployment.yaml and orchestration-pipeline.yaml files covering dbt, PySpark, and BigQuery actions. - Use Case: A user reports that the daily_sales_agg DAG failed yesterday; the Skill fetches error logs, downloads the remote DAG, identifies a KeyError at a specific line, and generates an RCA report with a recommended fix. ## Quick Start Ask the assistant to troubleshoot a failed Composer DAG by providing the DAG ID, environment name, and approximate failure time, and request a root cause analysis report.
