Loading...
Loading...
Found 87 Skills
Grafana Mimir scalable long-term metrics storage. Covers architecture (distributor/ingester/compactor/querier/ query-frontend/store-gateway/ruler), deployment modes (monolithic/microservices), configuration, Prometheus remote write, PromQL querying, multi-tenancy, compaction, and operations. Use when working with Mimir for metrics storage, scaling Prometheus, configuring Mimir clusters, writing PromQL, or debugging Mimir.
Reduce Grafana Cloud Metrics costs by managing cardinality with Adaptive Metrics aggregation rules. Use when the user asks to reduce metrics costs, manage cardinality, create aggregation rules, apply label dropping, analyse unused metrics, understand Active Series, or optimise Prometheus storage. Triggers on phrases like "adaptive metrics", "reduce cardinality", "aggregation rules", "metrics cost", "too many series", "Active Series", "label dropping", "unused metrics", "cardinality reduction", or "metrics spend".
Grafana Professional Services tool for identifying which Prometheus metrics drive high Data Points per Minute (DPM). Analyzes metric-level DPM with per-label breakdown to help optimize Grafana Cloud costs. Use when the user asks about DPM analysis, high-cardinality metrics, metric cost optimization, finding noisy metrics, or running dpm-finder against a Grafana Cloud Prometheus endpoint.
Monitoring and observability strategy, implementation, and troubleshooting. Use for designing metrics/logs/traces systems, setting up Prometheus/Grafana/Loki, creating alerts and dashboards, calculating SLOs and error budgets, analyzing performance issues, and comparing monitoring tools (Datadog, ELK, CloudWatch). Covers the Four Golden Signals, RED/USE methods, OpenTelemetry instrumentation, log aggregation patterns, and distributed tracing.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.
Integrates OpenTelemetry tracing, metrics, and logging into iii workers. Use when setting up distributed tracing, Prometheus metrics, custom spans, or connecting to observability backends.
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
Prometheus, Grafana, CloudWatch, Azure Monitor, Stackdriver, logging, alerting, and SRE practices
Write, validate, and optimise PromQL queries for Prometheus and Grafana Cloud Metrics. Use when the user asks to query metrics, write a PromQL expression, calculate rates, aggregate across labels, build histogram quantiles, create recording rules, debug query performance, or understand metric cardinality. Triggers on phrases like "PromQL", "Prometheus query", "write a metric query", "calculate rate", "histogram_quantile", "recording rule", "metric cardinality", "sum by", "rate vs irate", "absent()", or "query is slow".
Guide for implementing Grafana Mimir - a horizontally scalable, highly available, multi-tenant TSDB for long-term storage of Prometheus metrics. Use when configuring Mimir on Kubernetes, setting up Azure/S3/GCS storage backends, troubleshooting authentication issues, or optimizing performance.