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Found 99 Skills
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.
Configure Prometheus Alertmanager with routing trees, receivers (Slack, PagerDuty, email), inhibition rules, silences, and notification templates for actionable incident alerting. Use when implementing proactive monitoring with automated incident detection, routing alerts to the appropriate team by severity, reducing alert fatigue through grouping and deduplication, integrating with on-call systems like PagerDuty, or migrating from legacy alerting to Prometheus-based alerting.
Discover optimal autoscaling parameters for a Deco site by analyzing Prometheus metrics. Correlates CPU, concurrency, and latency to find the right scaling target and method.
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
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".
Integrates OpenTelemetry tracing, metrics, and logging into iii workers. Use when setting up distributed tracing, Prometheus metrics, custom spans, or connecting to observability backends.
Perses datasource lifecycle management: create, update, delete datasources at global, project, or dashboard scope. Supports Prometheus, Tempo, Loki, Pyroscope, ClickHouse, and VictoriaLogs. Uses MCP tools when available, percli CLI as fallback. Use for "perses datasource", "add datasource", "configure prometheus perses", "perses data source". Do NOT use for dashboard creation (use perses-dashboard-create).
Bridge between OMO Prometheus and TKT ticket lifecycle. Generates structured review context for Prometheus after bundle close, converts Prometheus plans into TKT bundle commands, writes review feedback into the Review Agent Inbox, and provides a structured question protocol for gathering information efficiently. Load this skill when you need to: (1) generate a review prompt for a completed ticket/bundle, (2) convert a Prometheus plan.md into TKT worker tickets, (3) write review feedback back into the ticket system, (4) ask structured questions using the question tool across all scenarios (requirements, decisions, review, planning).
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.
Prometheus, Grafana, CloudWatch, Azure Monitor, Stackdriver, logging, alerting, and SRE practices
Sending telemetry data to Grafana Cloud — metrics via Prometheus remote write or OTLP, logs via Loki push or Alloy, traces via OTLP to Tempo, profiles via Pyroscope. Covers Alloy-based pipelines, direct SDK/agent integrations, cloud integrations catalog, and credentials management. Use when connecting an application or infrastructure to Grafana Cloud, setting up data ingestion, configuring remote write, or choosing between ingestion methods.
Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.