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Found 50 Skills
Set up, configure, and troubleshoot Grafana Cloud integrations for AWS, Azure, and other cloud providers. Use when the user asks to connect AWS CloudWatch, set up Azure Monitor, configure Confluent Cloud observability, install a Grafana integration, set up hosted exporters, use AWS Firehose for CloudWatch logs, or troubleshoot a cloud integration. Triggers on phrases like "AWS CloudWatch", "Azure Monitor", "Confluent integration", "cloud integration", "hosted exporter", "AWS Firehose", "install integration", "cloud metrics", or "cloud logs".
Grafana Cloud infrastructure monitoring — Kubernetes monitoring, cloud provider integrations (AWS, Azure, GCP), host and container monitoring, infrastructure dashboards, and collector setup. Use when setting up Kubernetes monitoring, connecting cloud provider metrics, configuring node exporter or cAdvisor, setting up infrastructure dashboards, or using the k8s-monitoring Helm chart.
Expert evaluator for Prometheus label strategy. Audits, designs, and improves label schemas using cardinality scoring, access-pattern alignment, static vs. dynamic label rules, histogram bucket discipline, instrumentation hygiene, and source-side prevention via relabel_config / metric_relabel_configs. Use when the user asks to evaluate, audit, design, or improve Prometheus labels — or asks how to prevent high cardinality at the source. For post-ingest aggregation, see the adaptive-metrics skill. For "why is my Prometheus slow / expensive right now" triage, see prometheus-cardinality-troubleshooter.
Grafana Loki log aggregation and LogQL query language. Covers LogQL syntax (log queries, metric queries, label matchers, line filters, parsers: json/logfmt/pattern/regexp/unpack, label filters, line_format), Loki architecture, log ingestion via Alloy/Promtail/Fluent Bit, structured metadata, and Logs Drilldown. Use when writing LogQL queries, configuring Loki, troubleshooting log pipelines, or analyzing logs.
Grafana Cloud Application Observability (APM), Frontend Observability (RUM/Faro), and AI Observability. Covers RED metrics (Rate/Error/Duration), service maps, span metrics from traces, Faro JavaScript/React SDK for browser instrumentation, session replay, AI/LLM model monitoring, and integration with traces/logs/profiles for full-stack correlation. Use when setting up APM, configuring frontend monitoring, analyzing service performance, or monitoring AI/LLM applications.
Build Grafana plugin pages using the @grafana/scenes framework. Use this skill when creating new scene pages, adding panels/visualizations, setting up drilldown navigation, defining variables, configuring query runners, building table/timeseries/stat panels, or extending SceneObjectBase for custom scene objects. Triggers on any work involving SceneApp, SceneAppPage, EmbeddedScene, SceneQueryRunner, SceneDataTransformer, PanelBuilders, SceneFlexLayout, QueryVariable, or drilldown/tab configuration in Grafana plugins.
Grafana Cloud AI and ML features — Grafana Assistant (natural language queries, dashboard generation, incident investigations), Dynamic Alerting (ML forecasting and outlier detection), Sift (automated root cause analysis with 8 analysis types), Knowledge Graph (entity discovery and RCA Workbench), and the LLM Plugin (OpenAI/Anthropic/Azure integration). Use when setting up AI-powered alerting, using natural language to query metrics/logs, automating incident investigation, or integrating LLMs with Grafana panels and workflows.
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.
Grafana Alerting, Incident Response Management (IRM), and SLOs. Covers Grafana-managed and data source-managed alert rules, notification policies, contact points (Slack/PagerDuty/email/webhook), silences, muting, on-call scheduling, incident management workflows, and SLO configuration with burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, managing incidents, defining SLOs, or provisioning alerting via YAML/API.
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
Grafana Cloud private network connectivity — AWS PrivateLink, Azure Private Link, and GCP Private Service Connect. Send telemetry (metrics, logs, traces, profiles) to Grafana Cloud without traversing the public internet. Eliminates cloud egress costs, meets compliance requirements (PCI-DSS, HIPAA). Use when setting up secure private telemetry ingestion from AWS/Azure/GCP, reducing egress costs, or meeting data residency/compliance requirements.
Use when working with CUE kind definitions, schemas, or versioning in grafana-app-sdk projects (app platform apps). This skill should be used when the user asks to "define a kind", "add a CUE kind", "write a kind schema", "create a CUE schema", "model a resource", "add a new resource type", "edit kinds/", "what is a kind in grafana-app-sdk", "add a version to a kind", or asks about CUE kind structure, versioning, schema fields, validation constraints, or the codegen configuration section. Provides guidance on authoring CUE kind definitions for grafana-app-sdk projects.