What problem does it solve? Tuning Managed Service for Apache Airflow (Cloud Composer) environments is hard without data: teams often blindly upsize machines or guess at configuration changes when facing workload restarts, slow DAG parsing, or resource saturation. This Skill gathers real telemetry from Cloud Monitoring and produces evidence-based recommendations for scaling, configuration, and reliability. ## Core Features & Use Cases - Telemetry-Driven Diagnostics: Runs Python scripts that query Cloud Monitoring via PromQL to collect CPU, memory, disk usage, restart counts, DAG parsing stats, and environment health percentages for each Airflow component (scheduler, workers, webserver, triggerer). - Configuration Inspection: Uses gcloud commands from the bundled reference to inspect environment size, scaling parameters, bucket contents, and component logs. - Structured Recommendations: Produces categorized advice covering infrastructure and scaling, airflow.cfg overrides, bucket hygiene, and production best practices such as high-resilience mode and deferrable operators. - Use Case: Your Cloud Composer scheduler keeps restarting and DAGs are delayed. Ask the Skill to analyze the environment; it pulls restart counts, memory usage, and DAG parsing times, then identifies an OOMing scheduler and recommends specific memory limits and parsing optimizations. ## Quick Start Analyze my Cloud Composer environment 'prod-airflow' in project my-gcp-project, region us-central1, over the past 24 hours and give me tuning recommendations.
