promql-generator

Production-ready PromQL queries require proper label filtering, correct aggregations, and histogram_quantile usage with rate on bucketed metrics. Following these Prometheus best practices ensures reliable dashboard visualizations and accurate SLO monitoring across distributed systems.

Using histogram_quantile with rate for latency monitoring requires applying the rate function to bucketed metrics first. This Prometheus best-practice workflow ensures accurate percentile calculations and reliable SLO tracking across distributed systems.

Yes, generating PromQL alerts using RED and USE pattern templates is fully supported for distributed systems. These common patterns produce validated query expressions with documented references, ready for immediate deployment in alerting and SLO monitoring configurations.

To validate PromQL queries before adding them to alerts, check them against built-in references and Prometheus best practices. The skill validates generated expressions and produces explanations, ensuring proper aggregations and label filtering are present for reliable alerting rules.

To generate a PromQL expression for SLO monitoring, provide a request containing your monitoring goal, the specific metric, and the time window. The skill uses these inputs in an interactive planning process to align with your objectives before generating the validated query.