What problem does it solve? When a Cloud Composer (Apache Airflow) pipeline or 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 severity, environment, and DAG ID filters to locate failures and stack traces. - Remote Code Verification: Downloads the actual DAG code from the environment's GCS bucket to ensure analysis targets the code that really ran, not stale local copies. - Structured RCA Reports: Correlates logs with code, pinpoints the failing line or configuration, proposes a fix, and generates a saved RCA report. - Declarative Pipeline Templates: Provides compliant deployment.yaml and orchestration-pipeline.yaml structures for DBT, PySpark, and BigQuery actions. - Use Case: A user reports that the daily_sales_agg DAG failed yesterday at 2pm. The Skill fetches error logs, downloads the remote DAG, identifies a KeyError on a missing 'region' field at line 45, and produces an RCA report with a recommended fix. ## Quick Start Ask the assistant to troubleshoot why your Cloud Composer DAG failed and generate a root cause analysis report, providing the DAG ID and approximate failure time.
