Claude Skills by grafana

Use when the user asks to "write a validator", "add validation", "implement admission control", "write a mutating webhook", "add a mutation handler", "validate incoming resources", "implement admission logic", "add admission webhooks", "write ingress validation", or asks how to validate or mutate resources before they are persisted in a grafana-app-sdk app. Provides guidance on implementing validation and mutation admission handlers for grafana-app-sdk apps.

Use when: user asks to create a Grafana app, initialize a grafana-app-sdk project, set up a Grafana App Platform app, scaffold a new app, or asks about deployment modes (standalone operator, grafana/apps, frontend-only), how grafana-app-sdk works, or the overall development workflow. Provides foundational knowledge of the grafana-app-sdk CLI, project structure, deployment modes, and overall workflow.

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. Pro...

Use when the user asks to "write a reconciler", "implement a reconciler", "add business logic", "handle resource changes", "process resource events", "implement the reconcile loop", "add async processing", "write a controller", "handle create/update/delete events", "use TypedReconciler", "use a Watcher", or asks how to respond to resource state changes in a grafana-app-sdk app. Provides guidance on implementing reconciler and watcher business logic for grafana-app-sdk apps.

Adaptive Metrics analyses your Prometheus metrics usage and suggests aggregation rules that reduce series count without breaking any queries. Rules pre-aggregate high-cardinality metrics into lower-cardinality forms before storage. **How it works:** 1. Adaptive Metrics scans your metric usage (dashboards, alerts, recording rules) over a lookback window 2. It identifies labels that are never queried for a given metric 3. It generates aggregation rules that drop those labels, reducing series co...

Grafana Cloud account management — organizations, stacks, RBAC, SSO/SAML/OAuth, service accounts, API keys, team management, billing, and cloud-level provisioning. Use when managing Grafana Cloud access, configuring SSO, setting up service accounts for CI/CD, assigning roles, managing multiple stacks or organizations, or provisioning cloud resources via API.

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.

The Grafana MCP server exposes Grafana Cloud capabilities as tools that AI agents can call via the Model Context Protocol. Once connected, agents can query metrics, search dashboards, manage alerts, investigate incidents, and interact with Fleet Management - all without leaving their coding environment. **Available transports:** - `stdio` - agent spawns the server as a subprocess (simplest, works everywhere) - `SSE` - server runs independently, agent connects via HTTP ---

Grafana Cloud Integrations connect cloud provider monitoring APIs to your Grafana stack without running your own exporters. Hosted exporters scrape cloud APIs on your behalf and push metrics to your Grafana Cloud stack. **Supported hosted exporters:** - **AWS CloudWatch** - all CloudWatch namespaces via YACE (Yet Another CloudWatch Exporter) - **Azure Monitor** - Azure resource metrics via the Azure Monitor API - **Confluent Cloud** - Kafka cluster metrics via the Confluent Metrics API - **Ge...

Grafana Cloud cost management — usage monitoring, cost attribution by label, usage alerts, invoice management, and optimization strategies. Covers Adaptive Metrics (cardinality reduction), Adaptive Logs (log filtering), cost attribution labels, and the FOCUS-compliant billing application. Use when analyzing Grafana Cloud spending, setting up cost alerts, attributing costs to teams, reducing metric/log cardinality, or forecasting observability budgets.

Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL. Covers setup with Grafana Alloy, query samples, visual explain plans, RED metrics, pg_stat_statements and Performance Schema integration, and correlation with application traces. Use when monitoring database performance, diagnosing slow queries, setting up database observability for MySQL or PostgreSQL (self-managed, RDS, Aurora, Azure, Cloud SQL), or correlating DB metrics with APM data.

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.

Fleet Management lets you author pipeline configurations once and distribute them to many Alloy collectors remotely via OpAMP. Collectors poll for updates and apply new configurations without a restart. **Key concepts:** - **Collector** - an Alloy agent instance, identified by a unique ID and set of attributes - **Pipeline** - a named Alloy configuration (YAML) stored in Fleet Management - **Matcher** - a label selector that maps a pipeline to matching collectors - **Attributes** - key/value ...

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 Grafana Loki label strategy. Audits, designs, and improves label schemas using cardinality scoring, access-pattern alignment, static vs. dynamic label rules, and consistency checks. Use when the user asks to evaluate, audit, design, or improve a Loki label strategy — or asks why their Loki queries are slow.

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...

Grafana OnCall and Incident Response Management (IRM) — alert routing, escalation chains, on-call schedules, Jinja2 routing templates, Slack/mobile notifications, integrations (Alertmanager, Grafana Alerting, webhooks, PagerDuty), and incident lifecycle management. Use when setting up on-call rotations, configuring escalation policies, routing alerts to the right team, declaring and managing incidents, integrating with Alertmanager or Grafana Alerting, or configuring Slack-based alert workflows.

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.

Diagnostic guide for active Prometheus cardinality problems — slow queries, OOMing Prometheus, high Grafana Cloud Active Series or DPM bills, "too many samples" ingest errors, series churn, or rapid memory growth. Walks through tsdb status endpoints, per-metric and per-label drill-downs, common-culprit galleries, and remediation paths. Use when the user is *currently experiencing* a cardinality fire. For preventing cardinality issues at the source, route to prometheus-label-strategy. For post...

Expert evaluator for Prometheus label strategy on Grafana Cloud. Audits, designs, and improves label schemas using cardinality scoring, access-pattern alignment, static vs. dynamic label rules, histogram bucket discipline, and instrumentation hygiene. Prevents high cardinality at the source — in application code and scrape target labels — without dropping labels that make series unique (which breaks the data). For reducing the cost of series already in Grafana Cloud, routes to the adaptive-me...

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 Cloud testing capabilities — Synthetic Monitoring (probing URLs, DNS, TCP, ping from multiple regions), k6 Cloud (managed load testing with distributed execution), and Frontend Observability (Faro, real user monitoring). Use when setting up uptime checks, external probes, configuring k6 cloud runs, monitoring frontend performance, or testing APIs from multiple locations.

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 Alloy OpenTelemetry collector and telemetry pipeline configuration. Covers the Alloy configuration language (blocks, attributes, expressions), components for collecting metrics/logs/traces/profiles, sending data to Grafana Cloud/Prometheus/Loki/Tempo, clustering, Fleet Management remote config, and building telemetry pipelines. Use when configuring Alloy, writing Alloy config files (.alloy), building data collection pipelines, setting up scraping, or troubleshooting Alloy deployments.

Grafana Beyla eBPF auto-instrumentation for application observability without code changes. Covers supported languages/runtimes, requirements, installation, configuration (discovery, eBPF settings, OTLP traces export, Prometheus metrics export), Kubernetes deployment, and integration with Grafana Cloud. Use when setting up zero-code instrumentation, configuring eBPF probes, deploying Beyla to Kubernetes, connecting to Tempo/Prometheus, or troubleshooting instrumentation issues.

Dashboards are JSON documents stored in Grafana. Every dashboard has panels, variables, time range, and refresh settings. Understanding the JSON schema lets you programmatically create and modify dashboards via the API or Grafana Assistant tools. ---

Grafana OSS core features — dashboards, panels, visualization types, data sources, template variables, alerting, annotations, provisioning, RBAC, service accounts, and configuration. Use when building dashboards, configuring data sources, setting up provisioning YAML, managing users and permissions, writing PromQL/LogQL/TraceQL in panels, or configuring Grafana server settings.

OpenTelemetry with Grafana stack. Covers OTel SDK instrumentation for Go/Java/Python/Node.js/.NET, OTLP protocol and endpoint configuration, sending telemetry to Grafana Cloud via OTLP endpoint, Grafana Alloy as OTel collector, sampling strategies, Kubernetes OTel Operator, and migration from other observability tools. Use when instrumenting apps with OTel, configuring OTLP endpoints, setting up collectors, or migrating to OpenTelemetry.

PromQL is a functional query language for time series data. Every query returns either an **instant vector** (one value per label set at a point in time), a **range vector** (a sliding window of samples), or a **scalar**. **Golden rule:** `rate()` and `increase()` always require a range vector. The range must be at least 4x the scrape interval to avoid gaps. For a 60s scrape interval, use `[5m]` minimum. ---

Author, audit, and improve Grafana SKILL.md files against Anthropic's published Agent Skills guidance and the four-dimension rubric the grafana/skills CI gate uses (conciseness, actionability, workflow clarity, progressive disclosure). Applies the canonical SKILL.md structure (YAML frontmatter + body + references/ + scripts/ + assets/), the "pushy description" trigger pattern, the three-level progressive-disclosure model, and the validate-fix-rerun feedback loop. Use when creating a new skill...

Write or review k6 documentation across the three k6 repositories - k6-DefinitelyTyped (TypeScript types), k6-docs (user documentation), and k6 (release notes / changelog). Applies k6 doc style conventions, generates TypeScript type definitions, drafts release notes, and validates examples by running them against k6@master. Use when working on k6 documentation, the k6 changelog, the k6 release notes, k6 API reference, k6 TypeScript types, load testing docs, or when the user asks to write, edi...

k6 performance and load testing. Covers writing test scripts in JavaScript/TypeScript, all test types (load/stress/spike/soak/smoke/breakpoint), thresholds, checks, scenarios, executors, extensions, result analysis, k6 Cloud execution, and CI/CD integration. Use when writing k6 tests, debugging test failures, setting up load testing pipelines, choosing executors/scenarios, or interpreting k6 results.

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 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.

Prometheus and Grafana Cloud Metrics overview including PromQL query language, Metrics Drilldown, alerting, recording rules, and integration patterns. Use when working with Prometheus, writing PromQL queries, configuring alerting, or discussing metrics architecture and best practices.

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 Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, cac...

Build Grafana plugin pages using the @grafana/scenes framework. Use 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 Gra...

`module.js` is the render-blocking entry point for every Grafana app plugin. The smaller it is, the less impact the plugin has on Grafana's overall startup time. A well-split plugin should have a `module.js` under ~200 KB that contains nothing but lazy-loaded wrappers — all feature code loads on demand. **Target:** ~15–25 JS chunks total. Fewer means too little splitting; far more (50+) means over-engineering.

Grafana 13 (April 2026) moves from React 18 to React 19. Incompatible plugins will break. **Do not upgrade React to 19** — only make forward-compatible changes. All changes go in **one PR**. Execute steps in order. Never manually edit `yarn.lock`. ---

Clear description of what this skill does and when to use it. Use when the user asks about X or wants to work with Y. Include specific trigger phrases so agents auto-load it correctly. Max 1024 characters.