grafana-lens

Imported from awsome-o/grafana-lens (skills/SKILL.md). Install upstream with npx skills add awsome-o/grafana-lens --skill skills. Copyright stays with the author.

Grafana Lens

You have full native Grafana access — query data, create dashboards, set alerts, receive alert notifications, annotate events, explore datasources, push custom data, and deliver visualizations inline. Works with ANY data in Grafana, not just agent metrics.

Musts

• Always call grafana_explore_datasources first when you need a datasource UID — never guess UIDs
• Always call grafana_search before creating a dashboard — avoid duplicates
• Always call grafana_get_dashboard before grafana_share_dashboard — you need exact panel IDs
• Always call grafana_get_dashboard before grafana_update_dashboard — you need panel IDs and current structure
• Prefer grafana_query for direct answers over creating dashboards — "what's my cost?" needs a number, not a URL
• Prefer grafana_query over grafana_create_dashboard + grafana_share_dashboard for simple data questions — a number is faster than a chart
• Use grafana_query_logs for log searches — LogQL for logs, PromQL for metrics, TraceQL for traces. Never use grafana_query for Loki datasources
• Use grafana_query_traces for trace searches — TraceQL for traces, PromQL for metrics, LogQL for logs. Never use grafana_query or grafana_query_logs for Tempo datasources
• All tools work with ANY Prometheus datasource — not just openclaw_lens_* metrics
• When you see "GRAFANA ALERTS" in prompt context, investigate immediately with grafana_check_alerts — use the suggestedInvestigation field to go directly to querying (it provides the tool, query, and datasource)
• Run grafana_check_alerts with action setup once before alert notifications can reach the agent — this creates the webhook contact point
• Push data before querying or dashboarding it — data is pushed via OTLP and available immediately
• Prefer grafana_explain_metric for "what is this metric?" questions over manual grafana_query — it returns current value, trend, stats, and metadata in one call
• Use queryNames from push response for PromQL queries — don't guess metric names (counters get _total suffix)
• Use openclaw_ext_ prefix for custom metrics — grafana_push_metrics auto-prepends it if missing
• Follow statistics-first discipline for log investigation — always run count/rate LogQL before reading individual entries. Use grafana_query_logs with metric-over-logs queries (count_over_time, rate, topk) before switching to raw log entries
• Silence alerts during investigation — use grafana_check_alerts with action silence to prevent repeat notifications while investigating
• Use list_rules for complete alert health — grafana_check_alerts with action list_rules returns all rules with live eval state (normal/firing/pending/nodata/error), health, and lastEvaluation — no need to cross-reference with list action
• Use dashboardUid + panelId to re-run panel queries — don't manually extract PromQL/LogQL from get_dashboard output. Both grafana_query and grafana_query_logs accept these params to auto-resolve the panel's query expression and datasource. The tool handles template variable replacement and datasource routing automatically
• Confirm with user before deleting dashboards or alert rules — grafana_update_dashboard with operation delete and grafana_check_alerts with action delete_rule are permanent and cannot be undone
• Always use alloy_pipeline action recipes first when unsure which pipeline recipe fits the user's request — because recipes provide validation, credential handling, and sample queries that raw config does not
• Always call alloy_pipeline action status after creating a pipeline — because data takes 15-20s to flow through the pipeline, and components may fail silently after reload
• Never guess Alloy component names — use recipes for known patterns, or raw config only when the user explicitly provides Alloy syntax
• Prefer recipes over raw config when a recipe exists — recipes provide validation, sample queries, credential handling, dashboard templates, and automatic export target wiring
• Never write credentials into raw config — when the user provides a connection string, DSN, password, or API key, ALWAYS use the matching recipe (which routes credentials through sys.env(), keeping secrets off disk). If you must use raw config, wrap sensitive values in sys.env("MY_VAR_NAME") and tell the user to set that env var where Alloy runs
• Read envVarsRequired from every pipeline create response — credential recipes may return pending_credentials status when env vars aren't set yet. Tell the user the exact var names and that they must set them where Alloy runs, then verify with action status
• Warn users before creating credential-required pipelines — Alloy config reload is atomic: if a credential recipe's env vars aren't set, the reload failure blocks ALL managed pipelines (not just the new one) until the env vars are set or the pipeline is deleted. Always ask: "Do you have the credentials ready to set as env vars on the Alloy host?"
• Chain pipeline creation into existing tools — after pipeline is active: grafana_list_metrics or grafana_query_logs to discover data, grafana_create_dashboard to visualize, grafana_create_alert to monitor
• Use alloy_pipeline action diagnose as first step when user reports pipeline issues — because it checks Alloy connectivity, all pipeline health, config file drift, and limits in one call
• Confirm with user before deleting pipelines — alloy_pipeline with action delete removes the config and data stops flowing
• All log recipes accept processing params — don't create separate "processing" pipelines. Add jsonExpressions, labelFields, structuredMetadata, tenantValue, matchRoutes, etc. directly to any log recipe (docker-logs, file-logs, syslog, etc.)
• Use samplingPolicies for multi-policy tail sampling — don't create raw config when application-traces can handle it. sampleRate is for simple probabilistic, samplingPolicies is for intelligent multi-policy (keep errors, keep slow, sample rest)
• Use log processing params for multi-tenant routing — tenantValue/tenantSource/matchRoutes work on ALL log recipes. Don't create separate "routing" pipelines
• Read references/alloy-components.md before composing raw config — it has copy-pasteable snippets for all common Alloy components

Quick Decision Tree

• "What is [metric]?" / "Why did it spike?" → grafana_explain_metric
• "What's the current value of X?" / complex PromQL → grafana_query
• "Find error logs" / "Search logs for..." → grafana_query_logs
• "Find slow traces" / "Show trace for session X" / "Debug distributed spans" → grafana_query_traces
• "Debug this session" / "Why did it fail?" / "What went wrong?" → grafana_query_traces (search error/slow) → grafana_query_traces (get → follow correlationHint) → grafana_query_logs → grafana_query → grafana_annotate
• "Show me a chart" / "Visualize..." → grafana_search → grafana_get_dashboard → grafana_share_dashboard
• "Create a dashboard for..." → grafana_search (check duplicates) → grafana_create_dashboard
• "Add a panel to my dashboard" → grafana_get_dashboard → grafana_update_dashboard
• "Delete this dashboard" → grafana_update_dashboard with operation delete (confirm with user first)
• "Alert me when..." → grafana_check_alerts (setup) → grafana_create_alert
• "List my alert rules" / "What alerts do I have?" → grafana_check_alerts with action list_rules
• "Delete alert rule X" → grafana_check_alerts with action list_rules → delete_rule with ruleUid
• "Track my [custom data]" / "Record my [past data]" → grafana_push_metrics (with optional timestamp for historical data, auto-registers, returns queryNames) → grafana_query with queryNames
• "What data sources do I have?" → grafana_explore_datasources
• "What metrics are available?" → grafana_list_metrics
• "Set up monitoring" / "Monitor my agent" / "What dashboards should I have?" → grafana_search (check existing) → grafana_create_dashboard with llm-command-center → follow suggestedNext chain through remaining templates
• "GenAI observability" / "OTel gen_ai metrics" / "Standard AI monitoring" → grafana_create_dashboard with genai-observability template
• "What happened in session X?" / "Debug this session" → grafana_create_dashboard with session-explorer template → paste session ID
• "Show me LLM traces" / "Show agent logs" → grafana_create_dashboard with llm-command-center template (Loki + Tempo)
• "How much am I spending?" / "Cost analysis" → grafana_create_dashboard with cost-intelligence template
• "Which tools are slow?" / "Tool errors" → grafana_create_dashboard with tool-performance template
• "Queue health" / "Webhook issues" / "Stuck sessions" → grafana_create_dashboard with sre-operations template
• "System health check" / "Status report" / "Review all dashboards" → grafana_explore_datasources → grafana_check_alerts (list + list_rules) → grafana_search → grafana_get_dashboard (audit=true for each) → summarize
• "Audit my dashboard" / "Which panels are broken?" → grafana_get_dashboard (audit=true) → review auditSummary + per-panel health
• "Am I being attacked?" / "Security check" / "Security status" → grafana_security_check
• "Set up security monitoring" → grafana_check_alerts (setup) → grafana_create_dashboard (security-overview) → grafana_create_alert (webhook error burst, cost spike, tool loops, injection signals)
• "Investigate security alert" → grafana_security_check → grafana_query_logs (correlate) → grafana_annotate (mark investigation) → grafana_check_alerts (silence)
• "Investigate this alert" / "Why is X broken?" / "Debug this issue" / "Triage" / "Root cause" → grafana_investigate (multi-signal triage) → follow suggestedHypotheses.testWith for deep-dives
• "Is this metric normal?" / "Is there an anomaly?" → grafana_explain_metric (returns anomaly z-score + seasonality vs 1d/7d ago for 24h period)
• "RED analysis" / "What's the error rate?" / "Service health" → RED Method queries (see sre-investigation.md §2)
• "Alert fatigue" / "Which alerts are noisy?" / "Alert health" → grafana_check_alerts with action analyze — fatigue report
• "Postmortem" / "Incident summary" / "What happened?" → grafana_investigate → 5-Phase methodology → postmortem template (see sre-investigation.md §9)
• "Compare before/after deployment" → grafana_annotate (list, tags: ["deploy"]) → grafana_explain_metric (compareWith: "previous")

Data Collection Pipelines (Alloy)

• "Monitor a service/database/app" → alloy_pipeline action recipes (filter by category) → select recipe → create → status → query → dashboard → alert
• "Scrape metrics from [endpoint]" / "My app exposes /metrics" → alloy_pipeline with recipe scrape-endpoint + params { url }
• "Monitor PostgreSQL/MySQL/Redis/MongoDB/Memcached" → alloy_pipeline with recipe [db]-exporter + params { connectionString }
• "Collect and parse logs with JSON extraction" → alloy_pipeline (log recipe + processing params: jsonExpressions, labelFields, structuredMetadata)
• "Collect Docker logs" / "See container logs in Grafana" → alloy_pipeline with recipe docker-logs
• "Tail log files" / "Collect app logs from /var/log" → alloy_pipeline with recipe file-logs + params { paths }
• "Accept logs via HTTP push API" / "Centralized log gateway" → alloy_pipeline with recipe loki-push-api
• "Consume logs from Kafka" → alloy_pipeline with recipe kafka-logs + params { brokers, topics }
• "Set up syslog collection" → alloy_pipeline with recipe syslog
• "Monitor endpoint availability" / "Synthetic probing" / "HTTP health checks" → alloy_pipeline with recipe blackbox-exporter + params { targets }
• "Kubernetes monitoring" / "Monitor my K8s cluster" → alloy_pipeline with recipe kubernetes-pods + kubernetes-services + kubernetes-logs (3 pipelines)
• "Receive OTLP data" / "Set up trace collection" → alloy_pipeline with recipe otlp-receiver
• "Generate RED metrics from traces" / "Span metrics" → alloy_pipeline with recipe span-metrics
• "Service dependency graph from traces" → alloy_pipeline with recipe service-graph
• "Monitor Alloy itself" / "Self-monitoring" → alloy_pipeline with recipe self-monitoring
• "Redact secrets from logs" / "Compliance logging" → alloy_pipeline with recipe secret-filter-logs + params { paths }
• "Monitor Elasticsearch/Kafka" → alloy_pipeline with recipe elasticsearch-exporter / kafka-exporter
• "System metrics" / "Node monitoring" / "CPU/memory/disk" → alloy_pipeline with recipe node-exporter
• "Docker container metrics" / "Container resource usage" → alloy_pipeline with recipe docker-metrics
• "Reduce trace costs" / "Keep only error traces" / "Smart trace sampling" / "Tail sampling" → alloy_pipeline with recipe application-traces + samplingPolicies array (keep errors, keep slow, filter health checks, sample rest)
• "Multi-tenant Loki" / "Route logs by tenant" / "Different tenants for different apps" → any log recipe + tenantValue or matchRoutes processing param
• "Profile my app" / "CPU profiling" / "Memory profiling" / "Continuous profiling" / "Go pprof" → alloy_pipeline with recipe continuous-profiling + targets
• "Frontend observability" / "Browser RUM" / "Web vitals" / "Faro SDK" → alloy_pipeline with recipe faro-frontend
• "GELF logs" / "Graylog" / "Docker GELF driver" → alloy_pipeline with recipe gelf-logs
• "Custom Alloy pattern" / "Advanced pipeline" → Read references/alloy-components.md → alloy_pipeline with raw config + optional sampleQueries
• "What data collection recipes are available?" → alloy_pipeline with action recipes
• "What pipelines do I have?" / "Pipeline list" → alloy_pipeline with action list
• "Is my pipeline working?" / "Pipeline health" → alloy_pipeline with action status + name
• "Pipeline problems" / "Why isn't data showing up?" → alloy_pipeline with action diagnose → follow remediation
• "Delete pipeline" / "Remove monitoring for..." → alloy_pipeline with action delete + name (confirm with user first)

Working with Multiple Grafana Instances

When several Grafana environments are configured (dev, staging, prod), every tool accepts an optional instance parameter. grafana_explore_datasources returns availableInstances — use the name values from that list.

Why this matters: Users often need to query production metrics, create dashboards in dev, or compare environments side by side. Each tool call targets one instance.

Smart defaults: Omitting instance always targets the configured default — safe and invisible for single-environment setups. Only specify instance when the user explicitly names a non-default environment.

Cross-environment workflows: Each call is independent. Query prod, create dashboard in dev — just set instance differently on each call. No context switching needed.

Tool Inventory

Tool Details

grafana_explore_datasources

When: First step when user mentions data, metrics, or monitoring. Gets datasource UIDs needed by grafana_query, grafana_query_logs, grafana_query_traces, grafana_list_metrics, grafana_create_alert, and grafana_explain_metric. Params: instance (optional — target Grafana instance, omit for default). Example: {} Example (multi-instance): { "instance": "prod" } Returns: List of datasources with uid, name, type, isDefault, plus routing hints: queryTool (which agent tool to use, e.g. "grafana_query", "grafana_query_logs", or "grafana_query_traces"), queryLanguage (e.g. "PromQL", "LogQL", "TraceQL"), and supported (boolean — whether an agent tool can query this datasource). Use queryTool to pick the right tool for each datasource. When multiple Grafana instances are configured, also returns instance (which instance was queried) and availableInstances (list of { name, url, isDefault } for all configured instances).

grafana_list_metrics

When: User asks "what metrics are available?" or you need to discover metrics before querying or composing dashboards. Also when grouping metrics by function — metadata mode adds category to each openclaw_* metric. Use purpose when user asks about a specific concern (e.g., "performance metrics", "cost metrics"). Params: datasourceUid (required), prefix (filter by prefix), search (targeted discovery — server-side regex, only matching metrics returned), purpose ("performance" | "cost" | "reliability" | "capacity" — pre-filter by intent, composable with prefix and search), label (list label values instead), metadata (boolean — enriched results with type/help/category), compact (boolean — with metadata, returns only name/type/category, ~60% smaller). Example names: { "datasourceUid": "prom1", "prefix": "openclaw_lens_" } Example search: { "datasourceUid": "prom1", "search": "steps" } Example purpose: { "datasourceUid": "prom1", "purpose": "performance", "metadata": true } Example combined: { "datasourceUid": "prom1", "prefix": "openclaw_ext_", "search": "fitness" } Example metadata: { "datasourceUid": "prom1", "metadata": true, "prefix": "openclaw_" } Example compact: { "datasourceUid": "prom1", "metadata": true, "compact": true } Returns names: { metrics: ["metric1", "metric2", ...] }. Truncated at 200. Returns metadata: { metadataSource, categorySummary: { cost: 3, usage: 4, session: 5, ... }, metrics: [{ name, type, help, category?, source? }, ...] }. Use this before composing custom dashboards — type tells you counter vs gauge vs histogram, category groups openclaw_* metrics by function. Search also matches help text. Categories: cost, usage, session, queue, messaging, webhook, tools, agent, custom. categorySummary gives counts per category for quick overview (omitted when no openclaw_* metrics). Purpose maps: performance → session + tools, cost → cost + usage, reliability → webhook + messaging + agent, capacity → queue + session. metadataSource: "prometheus" when Prometheus metadata endpoint has data, "synthetic" when OTLP-only (metadata synthesized from known metric registry — histogram sub-metrics deduplicated, type/help from Grafana Lens definitions). On OTLP stacks, includes hint explaining why metadata is synthetic. source: "synthetic" on individual entries from the registry; source: "custom" on entries from the custom metrics store. Returns compact: { metadataSource, categorySummary: {...}, metrics: [{ name, type, category? }, ...] }. Same as metadata but drops help, source, labelNames — use in multi-tool chains where you need metric names and types but not full descriptions. Example label: { "datasourceUid": "prom1", "label": "job" } Returns label: { label, count, totalCount, values: ["value1", "value2", ...] }. Truncated at 200.

grafana_query

When: User asks a data question that needs a direct answer, not a dashboard. Also for re-running an existing dashboard panel's query with different time ranges. Params: datasourceUid, expr (PromQL), queryType (instant/range), start (range only, required), end (range only, default "now"), step (range only, optional — auto-calculated from time range if omitted, targeting ~300 datapoints), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example instant: { "datasourceUid": "prom1", "expr": "sum(increase(openclaw_lens_cost_by_model_total[1d])) or vector(0)" } Example range (auto-step): { "datasourceUid": "prom1", "expr": "rate(openclaw_tokens_total[5m])", "queryType": "range", "start": "now-30d" } Example range (explicit step): { "datasourceUid": "prom1", "expr": "rate(openclaw_tokens_total[5m])", "queryType": "range", "start": "now-1h", "end": "now", "step": "60" } Example panel re-run: { "dashboardUid": "openclaw-command-center", "panelId": 10, "queryType": "range", "start": "now-7d" } Tip: start/end accept Unix seconds or relative expressions like "now-1h", "now-7d". For range queries, just set start — end defaults to "now" and step is auto-calculated. Override step only when you need specific resolution. Tip (panel re-run): Set dashboardUid + panelId to re-run a panel's query without manually extracting PromQL. The tool auto-resolves expr and datasourceUid from the panel definition. Template variables are replaced with wildcards. You can still override expr or datasourceUid explicitly if needed. Get panel IDs from grafana_get_dashboard. Returns instant: { metrics: [{ metric: {...}, value: "1.23", timestamp: "...", healthContext?: { status, thresholds, description, direction } }], datasourceUid, resultCount, warnings?, hint? } — healthContext is included for well-known openclaw_lens_* gauge metrics, providing SRE-grade health assessment: status ("healthy"/"warning"/"critical"), thresholds (warning/critical values), description (what the metric means), direction ("higher_is_worse"/"lower_is_worse"). Omitted for unknown metrics. Capped at 50 results; when exceeded includes truncated: true, totalResults, and truncationHint advising to narrow the query. Returns range: { series: [{ metric: {...}, values: [{ time, value }...] }], datasourceUid, resultCount, warnings?, hint? } — truncated to 20 points per series and 50 series max. When series are truncated includes truncated: true, totalSeries, and truncationHint. When step is auto-calculated, includes step: { value: "288s", display: "5m", auto: true }. Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, templateVarsReplaced alongside normal query results. If the panel uses a Loki datasource, returns an error directing you to use grafana_query_logs instead. Returns (warnings): When Prometheus flags a non-fatal issue (e.g., rate() on a gauge), warnings: [{ cause, suggestion, example? }] is included. Example: rate() on a gauge → cause says "rate() applied to 'metric' which appears to be a gauge", suggestion says "use delta() or deriv() instead", example shows the corrected query. Returns (hint): When the query returns zero results, hint: { cause, suggestion } explains why (metric may not exist, label filters may not match) and suggests using grafana_list_metrics to verify. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } alongside the raw error. Pattern-matched for common PromQL mistakes: unclosed parenthesis, missing range selector, timeout, auth failure, rate on gauge, etc. Omitted when the error is unrecognized. Tip (chaining): Both instant and range responses include datasourceUid — pass it directly to grafana_create_alert or other tools without re-calling grafana_explore_datasources. This enables zero-friction query→alert chains.

grafana_query_logs

When: User asks about logs, errors, or needs to investigate issues by searching log data. Also for session debugging, OTel log investigation, and re-running existing log panel queries. Params: datasourceUid, expr (LogQL), queryType (instant/range, default range), start/end (default now-1h/now), step (metric queries only), limit (default 100), direction (backward/forward), lineLimit (max chars per log line, default 500, max 2000), extractFields (boolean, default false — extract structured OTel attributes into a clean fields object), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example log search: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |= \"error\"" } Example with filters: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |~ \"timeout|refused\"", "limit": 50, "direction": "forward" } Example full stack traces: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |= \"Exception\"", "lineLimit": 2000 } Example session debugging: { "datasourceUid": "loki1", "expr": "{service_name=\"openclaw\"} | json | component=\"lifecycle\"", "extractFields": true } Example metric query: { "datasourceUid": "loki1", "expr": "rate({job=\"api\"}[5m])", "queryType": "range", "start": "now-6h", "end": "now", "step": "60" } Example panel re-run: { "dashboardUid": "openclaw-command-center", "panelId": 18, "start": "now-24h", "extractFields": true } Returns streams: { entries: [{ labels: {...}, timestamp: "...", line: "..." }], datasourceUid, totalEntries, truncated } — capped at 100 entries, lines at 500 chars (set lineLimit: 2000 for full stack traces). Returns streams (extractFields): { entries: [{ labels: {...cleaned...}, timestamp: "...", line: "...", fields: { component, event_name, session_id, trace_id, model, duration_s, ... } }], datasourceUid } — infrastructure noise labels removed, openclaw_ prefix stripped from field keys, numeric values auto-converted. Also parses JSON log bodies if present. Returns streams (traceCorrelation): When extractFields: true and entries contain trace_id, includes traceCorrelation: { traceIds: [...], tool: "grafana_query_traces", tip } — up to 5 unique trace IDs ready for grafana_query_traces with queryType: "get". Returns metric: Same shape as grafana_query range/instant results (matrix capped at 50 series, vector capped at 50 results — includes datasourceUid, truncated, totalSeries/totalResults, and truncationHint when exceeded). Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, templateVarsReplaced alongside normal results. If the panel uses a Prometheus datasource, returns an error directing you to use grafana_query instead. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } alongside the raw error. Pattern-matched for common LogQL mistakes: bare text without stream selector, empty {}, unclosed braces, missing label matchers, auth failure, timeout. Omitted when the error is unrecognized. Tip: LogQL: {label="value"} selects streams, |= substring filter, |~ regex, != exclude. Metric wrappers: rate(), count_over_time(), bytes_rate(). Use extractFields: true when investigating OTel/lifecycle logs — it surfaces trace_id, session_id, event_name, model, and other attributes as first-class fields instead of buried in raw labels. Tip (panel re-run): Same as grafana_query — set dashboardUid + panelId to auto-resolve LogQL and datasource. The tool routes Prometheus panels to grafana_query with a helpful error.

grafana_query_traces

When: User asks about traces, distributed tracing, slow spans, session trace hierarchies, or needs to debug request flows across services. Params: datasourceUid, query (TraceQL expression or trace ID), queryType (search/get, default search), start/end (default now-1h/now), limit (default 20, max 50), minDuration/maxDuration (e.g., "1s", "10s"), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example search: { "datasourceUid": "tempo1", "query": "{ resource.service.name = \"openclaw\" }" } Example search slow: { "datasourceUid": "tempo1", "query": "{ resource.service.name = \"openclaw\" }", "minDuration": "5s" } Example search with time: { "datasourceUid": "tempo1", "query": "{ span.gen_ai.system = \"anthropic\" }", "start": "now-24h", "limit": 50 } Example get: { "datasourceUid": "tempo1", "query": "abc123def456789...", "queryType": "get" } Example panel re-run: { "dashboardUid": "openclaw-session-explorer", "panelId": 12, "start": "now-24h" } Returns search: { traces: [{ traceId, rootServiceName, rootTraceName, startTime, durationMs, spanCount? }], datasourceUid, totalTraces, truncated?, correlationHint? } — capped at 50 traces. When exceeded includes truncated: true and truncationHint. When traces are found, includes correlationHint: { logQuery, tool, tip } with a ready-to-use LogQL expression for grafana_query_logs. Returns get: { traceId, spans: [{ traceId, spanId, parentSpanId?, operationName, serviceName, startTime, durationMs, status, kind?, attributes: {...} }], datasourceUid, totalSpans, truncated? } — flattened OTLP spans with resolved attributes (string/number/boolean). Capped at 200 spans. Sorted by start time (earliest first). Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, templateVarsReplaced alongside normal results. If the panel uses a Prometheus or Loki datasource, returns an error directing you to use the correct tool. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } alongside the raw error. Pattern-matched for common TraceQL mistakes: syntax errors, invalid attributes, auth failure, timeout, not-found, invalid trace ID. Omitted when the error is unrecognized. Returns (no results): When search returns zero traces, includes hint: { cause, suggestion } suggesting to broaden the query or check the datasource. Tip: TraceQL: { } matches all traces, resource.service.name for service filter, span.http.status_code for HTTP spans, name for operation name, duration for span duration, status for error/ok filtering. Use minDuration/maxDuration to find performance outliers. Trace-to-Log: search and get results include correlationHint.logQuery — pass it directly to grafana_query_logs to find correlated logs. Log-to-Trace: grafana_query_logs results (with extractFields: true) include traceCorrelation.traceIds — pass any ID to grafana_query_traces with queryType: "get". Tip (panel re-run): Same as grafana_query — set dashboardUid + panelId to auto-resolve TraceQL and datasource. The tool routes Prometheus/Loki panels to the correct tool with a helpful error.

grafana_create_dashboard

When: User wants a persistent dashboard for ongoing monitoring. Params: template or dashboard (custom JSON) — one required. Optional: title (overrides template default), folderUid (target folder), overwrite (default true). Returns: { uid, url, status, message, suggestedNext?: [{ template, reason }], validation?: DashboardValidation }. For template-based dashboards, suggestedNext lists complementary templates to deploy next. For custom JSON dashboards, validation dry-runs each panel's PromQL and reports per-panel health — check validation.panelsError for broken queries.

Choose the right template (3-tier SRE drill-down hierarchy):

Tier 1 → System: Start here for overall health. Tier 2 → Session: Click a session from Tier 1 to investigate. Tier 3 → Deep Dive: Cost, tool, or SRE details.

All AI templates have Loki log-to-trace correlation via Tempo + stable UIDs for cross-dashboard navigation.

Example AI health: { "template": "llm-command-center", "title": "My AI Dashboard" } Example session debug: { "template": "session-explorer", "title": "Session Debug" } Example cost analysis: { "template": "cost-intelligence", "title": "My AI Costs" } Example tool analytics: { "template": "tool-performance", "title": "Tool Health" } Example SRE ops: { "template": "sre-operations", "title": "SRE Health" } Example GenAI observability: { "template": "genai-observability", "title": "GenAI Observability" } Example system: { "template": "node-exporter", "title": "Server Health" } Example generic: { "template": "metric-explorer", "title": "Explore My Data" } Example multi-KPI: { "template": "multi-kpi", "title": "Business KPIs" } Example weekly review: { "template": "weekly-review", "title": "My Weekly Review" } Example custom with validation: { "dashboard": { "title": "Model Comparison", "panels": [{ "id": 1, "title": "Cost by Model", "type": "timeseries", "targets": [{ "refId": "A", "expr": "sum by (model) (rate(openclaw_lens_cost_by_token_type[1h]))", "datasource": { "uid": "prometheus" } }] }] } }

Custom dashboard validation (returned only for dashboard param, not templates): validation: { panelsTotal: 3, panelsValid: 1, panelsNoData: 1, panelsError: 1, panelsSkipped: 0, details: [{ panelId: 1, title: "Cost by Model", status: "ok", queries: [{ refId: "A", expr: "...", valid: true, sampleValue: 0.42 }] }, { panelId: 2, title: "Latency", status: "nodata" }, { panelId: 3, title: "Bad Query", status: "error", error: "parse error at char 5" }] } Panel statuses: ok (query returned data), nodata (valid query, no results — metric may not exist yet), error (PromQL syntax error or datasource issue), skipped (no datasource UID found). Dashboard is always created regardless — validation is informational.

grafana_update_dashboard

When: User wants to add a panel, remove a panel, change a query, update dashboard settings, or delete a dashboard. Params: uid (required), operation (required: add_panel, remove_panel, update_panel, update_metadata, delete). add_panel params: panel (object with title, type, targets). Auto-layouts below existing panels. remove_panel / update_panel params: panelId (preferred) or panelTitle (case-insensitive substring fallback). updates (object) for update_panel. update_metadata params: title, description, tags, time (e.g., { "from": "now-7d", "to": "now" }), refresh (e.g., "1m"). delete params: None besides uid — permanently removes the dashboard. Always confirm with user first. Example add: { "uid": "abc123", "operation": "add_panel", "panel": { "title": "Error Rate", "type": "timeseries", "targets": [{ "refId": "A", "expr": "rate(errors_total[5m])", "datasource": { "uid": "prom1" } }] } } Example add (no datasource): { "uid": "abc123", "operation": "add_panel", "panel": { "title": "Latency", "type": "timeseries", "targets": [{ "refId": "A", "expr": "histogram_quantile(0.99, rate(http_duration_bucket[5m]))" }] } } — validation skipped if no datasource UID found, panel still saved. Example remove: { "uid": "abc123", "operation": "remove_panel", "panelId": 3 } Example update panel: { "uid": "abc123", "operation": "update_panel", "panelId": 1, "updates": { "title": "New Title", "targets": [{ "refId": "A", "expr": "new_query" }] } } Example update metadata: { "uid": "abc123", "operation": "update_metadata", "title": "My Dashboard v2", "time": { "from": "now-7d", "to": "now" }, "refresh": "5m" } Example delete: { "uid": "abc123", "operation": "delete" } Returns update: { status: "updated", uid, url, version, operation, panelCount, affectedPanel?: { id, title }, changedFields?: [...], queryValidation?: { validated, results, datasourceUid?, skippedReason? } }. Returns queryValidation: For add_panel and update_panel (when targets change), PromQL queries are dry-run against Grafana. Each result: { refId, expr, valid: boolean, error?: string, sampleValue?: number }. Panel is always saved — validation is informational. If valid: false, check the error field for PromQL syntax issues. If skippedReason is set, no datasource UID was found — include datasource: { uid: "..." } on targets to enable validation. Returns delete: { status: "deleted", uid, title, message }. Tip: targets in update_panel replaces entirely — include all targets, not just changed ones. Include datasource.uid on targets for query validation feedback.

grafana_get_dashboard

When: Need to inspect a dashboard's panels — find panel IDs for sharing, verify structure, scan multiple dashboards for an overview, or audit which panels are returning data. Params: uid (required). Optional: compact (boolean, default false) — return panel titles and types only, no queries or metadata (~70% smaller). audit (boolean, default false) — dry-run each panel's query and add health status. Example (full): { "uid": "abc123" } Example (compact overview): { "uid": "abc123", "compact": true } Example (audit): { "uid": "abc123", "audit": true } Returns (full): { uid, title, description?, url, tags, time?, refresh?, panelCount, panels: [{ id, title, type, queries: [{ refId, expr }] }], folderUid, created?, updated? }. Returns (compact): { uid, title, url, tags, panelCount, panels: [{ id, title, type }] }. Returns (audit): Same as full, plus each panel gets health: { status: "ok"|"nodata"|"error"|"skipped", error?, sampleValue? } and the response includes auditSummary: { ok, nodata, error, skipped }. Resolves template variable datasources ($prometheus, $loki) and replaces expression template vars with wildcards. Tip: Use audit: true when the user asks "which panels are broken?" or "audit my dashboard" — it replaces N separate grafana_query calls with one tool call. Use compact: true for lightweight overview scans. Omit both when you need query details (before update or share).

grafana_search

When: User mentions a dashboard by name, before creating one (check duplicates), or for reporting/audit workflows. Params: query (required). Optional: tags (array — filter by tags), starred (boolean — only starred), sort ("alpha-asc"/"alpha-desc"), limit (number, default 100), enrich (boolean — add updatedAt + panelCount per result, default false). Example: { "query": "cost" } Example with tags: { "query": "", "tags": ["production"] } Example starred: { "query": "", "starred": true, "limit": 10 } Example enriched: { "query": "", "enrich": true } Returns: { count, enriched, dashboards: [{ uid, title, url, tags, folderTitle?, folderUid?, updatedAt?, panelCount? }] }. folderTitle/folderUid always included when dashboard is in a folder. updatedAt (ISO 8601) and panelCount only present when enrich: true — enables staleness detection and reporting without per-dashboard get_dashboard calls. Tip: Use enrich: true for reporting workflows ("which dashboards are stale?", "give me a summary of all dashboards"). Skip enrichment for simple lookups. After finding a dashboard, use grafana_get_dashboard to inspect panels, grafana_share_dashboard to render a chart, or grafana_update_dashboard to modify it.

grafana_share_dashboard

When: User says "show me" or "send me" a chart/dashboard. Params: dashboardUid, panelId (required). Optional: from (default "now-6h"), to (default "now"), width (default 1000), height (default 500), theme ("light"/"dark", default "dark"). Example: { "dashboardUid": "abc123", "panelId": 2, "from": "now-6h", "to": "now" } Returns: Image rendered inline (tier 1), or snapshot URL (tier 2), or deep link (tier 3). Always delivers something. Includes deliveryTier ("image" | "snapshot" | "link"), rendererAvailable (boolean — false when Image Renderer plugin is missing), renderFailureReason (why image rendering failed), and remediation (how to fix it). Tier 3 also includes snapshotFailureReason. Tip: Use grafana_get_dashboard first to find panel IDs. If rendererAvailable is false, tell the user to install the grafana-image-renderer plugin.

grafana_create_alert

When: User wants notifications when a metric crosses a threshold. Params: title, datasourceUid, expr (PromQL), threshold (all required). Optional: evaluation ("instant"/"rate"/"increase", default "instant"), evaluationWindow (default "5m", used with rate/increase), condition (gt/lt/gte/lte, default gt), for (duration, default 5m), folderUid, labels (e.g., { "severity": "warning" }), annotations (e.g., { "summary": "Cost too high" }), noDataState (NoData/Alerting/OK, default NoData). IMPORTANT: For counter metrics (*_total), always use evaluation: "rate" (per-second rate) or evaluation: "increase" (total change over window). Raw counter values always increase and will immediately breach any threshold. Use "instant" (default) only for gauges. Example gauge alert: { "title": "High Cost Alert", "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "threshold": 5, "condition": "gt" } Example rate alert: { "title": "High Error Rate", "datasourceUid": "prom1", "expr": "openclaw_lens_webhook_error_total", "threshold": 0.1, "evaluation": "rate" } Example increase alert: { "title": "Token Burst", "datasourceUid": "prom1", "expr": "openclaw_lens_tokens_total", "threshold": 10000, "evaluation": "increase", "evaluationWindow": "1h" } Returns: { uid, title, status: "created", datasourceUid, url, evaluation?: { mode, window, evaluatedExpr }, metricValidation: { valid, error?, sampleValue? }, message }. The datasourceUid echoes back which datasource the rule targets (verify correctness). metricValidation dry-runs the expression before creation — valid: true + sampleValue confirms data exists; valid: false + error warns of typos/missing metrics. Alert is always created regardless (metric may not have data yet). When evaluation is "rate" or "increase", validation runs the wrapped expression. Note: Auto-creates a "Grafana Lens Alerts" folder if no folderUid is specified.

grafana_annotate

When: User deploys, changes config, or wants to mark an event for correlation. Params: action ("create" default, or "list"). Create params: text (required), tags, dashboardUid, panelId, time (epoch ms or relative like "now-2h", default now), timeEnd (epoch ms or relative). List params: from, to (epoch ms or relative like "now-7d", "now-24h", "now"), tags, limit (default 20). Time formats: All time params accept epoch ms (e.g., 1700000000000) OR Grafana-style relative strings ("now", "now-1h", "now-7d", "now-30m"). Prefer relative strings — they're simpler and avoid arithmetic errors. Example create: { "text": "Deployed v2.1.0", "tags": ["deploy", "production"] } Example create past: { "text": "Incident started", "time": "now-2h", "timeEnd": "now-30m", "tags": ["incident"] } Example list recent: { "action": "list", "from": "now-7d", "to": "now", "tags": ["deploy"] } Example list: { "action": "list", "tags": ["deploy"], "limit": 10 } Returns create: { status: "created", id, message, time, comparisonHint: { beforeWindow: { from, to }, afterWindow: { from, to }, suggestion } }. The comparisonHint provides ready-to-use ISO 8601 time ranges (30-min windows) for before/after comparison via grafana_query — no manual time math needed. For region annotations (with timeEnd), afterWindow starts at timeEnd. Returns list: { annotations: [{ id, text, tags, time, timeEnd?, dashboardUID?, panelId? }] }.

grafana_check_alerts

When: Prompt context shows "GRAFANA ALERTS", need to manage alert rules (list/delete), set up the alert webhook, silence alerts during investigation, or acknowledge an investigated alert. Params: action ("list" default, "acknowledge", "list_rules", "delete_rule", "silence", "unsilence", "setup"). List params: None — returns all pending (unacknowledged) alerts. Instances capped at 5 per alert. Acknowledge params: alertId (required) — marks an alert as investigated. List rules params: compact (boolean, default false — returns only uid/title/state/condition). Full mode returns all configured alert rules from Grafana with UID, title, condition (PromQL), folder, labels, annotations, AND live evaluation state (normal/firing/pending/nodata/error), health, and lastEvaluation. One call gives the complete alert health picture. Delete rule params: ruleUid (required) — permanently deletes an alert rule. Get UIDs from list_rules. Silence params: matchers (required — array of { name, value, isRegex? } from alert's commonLabels), duration (default "2h"), comment (optional). Unsilence params: silenceId (required) — removes a silence so alerts resume notifying. Setup params: webhookUrl (optional, auto-detected) — creates webhook contact point and notification policy route in Grafana. Example list: {} Example acknowledge: { "action": "acknowledge", "alertId": "alert-1" } Example list rules: { "action": "list_rules" } Example list rules compact: { "action": "list_rules", "compact": true } Example delete rule: { "action": "delete_rule", "ruleUid": "abc123-def456" } Example silence: { "action": "silence", "matchers": [{ "name": "alertname", "value": "HighCost" }], "duration": "2h", "comment": "Investigating cost spike" } Example unsilence: { "action": "unsilence", "silenceId": "silence-uuid-123" } Example setup: { "action": "setup" } Returns list: { status: "success", alertCount, alerts: [{ id, status, title, message, receivedAt, commonLabels, totalInstances, truncated?, suggestedInvestigation?: { datasourceUid, condition, tool, queryLanguage, hint }, instances: [{ status, labels, annotations, startsAt, values }] }] }. suggestedInvestigation is auto-enriched by matching the alert to its rule — provides the PromQL/LogQL expression, datasource, and tool to use for immediate investigation (eliminates the need for separate list_rules + explore_datasources calls). Returns acknowledge: { status: "acknowledged", alertId }. Returns list_rules: { status: "success", ruleCount, rules: [{ uid, title, folder, ruleGroup, state, health, lastEvaluation, for, labels, annotations, condition, updated }] }. state is the live evaluation state: "normal" (not firing), "firing", "pending" (within for duration), "nodata", or "error". Falls back to "unknown" if the eval state API is unavailable. health is "ok", "nodata", "error", or "unknown". condition is the extracted PromQL expression from the rule's data queries. Returns list_rules (compact): { status: "success", ruleCount, rules: [{ uid, title, state, condition }] }. Minimal fields for multi-tool chains — use when you need a quick overview of all rules without details. Returns delete_rule: { status: "deleted", ruleUid, message }. Returns silence: { status: "silenced", silenceId, duration, matchers, message }. Returns unsilence: { status: "unsilenced", silenceId, message }. Returns setup: { status: "created", contactPointUid, webhookUrl } or { status: "already_exists", contactPointUid }. Note: Setup is idempotent — safe to call multiple times. Only alerts with managed_by=openclaw label route to the webhook (auto-added by grafana_create_alert). Use list_rules → delete_rule for full alert lifecycle management (create via grafana_create_alert, list/delete via grafana_check_alerts).

grafana_push_metrics

When: User wants to track custom data (calendar events, git commits, fitness stats, financial data) in Grafana. Params: action ("push" default, "register", "list", "delete"). Push params: metrics (required array) — each: { name, value, labels?, type?, help?, timestamp? }. Names auto-get openclaw_ext_ prefix. timestamp is optional ISO 8601 for historical data (gauge only). Register params: name (required), type ("gauge"/"counter", default "gauge"), help, labelNames (array), ttlDays. List params: None — returns all custom metric definitions. Delete params: name (required) — removes a custom metric. Example push: { "metrics": [{ "name": "steps_today", "value": 8000 }, { "name": "meetings", "value": 3, "labels": { "type": "standup" } }] } Example backfill: { "metrics": [{ "name": "steps", "value": 8000, "timestamp": "2025-01-15" }, { "name": "steps", "value": 10500, "timestamp": "2025-01-16" }] } Example mixed: { "metrics": [{ "name": "steps", "value": 9000, "timestamp": "2025-01-17" }, { "name": "heart_rate", "value": 72 }] } Example register: { "action": "register", "name": "weight_kg", "type": "gauge", "help": "Body weight", "labelNames": ["person"], "ttlDays": 90 } Example list: { "action": "list" } Example delete: { "action": "delete", "name": "old_metric" } Returns push: { status: "ok", accepted: 2, queryNames: { "openclaw_ext_steps": "openclaw_ext_steps", "openclaw_ext_events": "openclaw_ext_events_total" }, suggestedWorkflow: [{ tool, action, example }], message: "..." }. suggestedWorkflow contains concrete next-step examples using the actual pushed metric names — verify (grafana_query), visualize (grafana_create_dashboard with metric-explorer template), and alert (grafana_create_alert, single-metric only). Partial success supported. Timestamped and real-time points in the same batch are both accepted. Returns register: { status: "registered", metric: { name, type, help, labelNames, ttlMs }, queryName: "openclaw_ext_events_total", suggestedWorkflow: [{ tool, action, example }] }. suggestedWorkflow shows how to push data and query the registered metric (with rate() wrapping for counters). Returns list: { count, metrics: [{ name, type, queryName, help, labelNames, createdAt, updatedAt }] }. Returns delete: { status: "deleted", name }. Note: Push auto-registers unknown metrics. Response includes queryNames with exact PromQL names and suggestedWorkflow with concrete next steps. Follow suggestedWorkflow to complete the push→visualize pipeline. Timestamped pushes are gauge-only — counters with timestamps are rejected. See external-data.md for naming conventions and backfill patterns.

grafana_explain_metric

When: User asks "what does this metric mean?", "why did it spike?", "is this normal?", or "show me the trend". Params: datasourceUid (required), expr (PromQL or plain metric name, required), period (24h/7d/30d, default 24h), compareWith ("previous" — compare current period with the same-length window immediately before it). Example: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd" } Example counter: { "datasourceUid": "prom1", "expr": "openclaw_lens_tokens_total" } Example 7d: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "period": "7d" } Example comparison: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "period": "7d", "compareWith": "previous" } Example PromQL: { "datasourceUid": "prom1", "expr": "rate(http_requests_total[5m])", "period": "24h" } Returns: { metricType?, trendQuery?, current: { value, timestamp }, healthContext?: { status, thresholds, description, direction }, trend: { changePercent, direction, first, last }, stats: { min, max, avg, samples }, comparison?: { previousPeriod: { from, to, avg, min, max, samples }, change: { absolute, percentage, direction } }, metadata: { type, help, unit }, suggestedQueries?: [{ query, description }], suggestedBreakdowns?: string[] }. Sections omitted when data unavailable. changePercent is null when first value is zero. healthContext is included for well-known openclaw_lens_* gauge metrics — same as grafana_query. Counter-aware: Auto-detects counter metrics (via metadata type or _total suffix) and wraps the trend query in rate(expr[5m]). The current value stays raw (cumulative total), but trend and stats show rate of change. metricType field tells you the detected type (counter/gauge/histogram). trendQuery shows the actual PromQL used for trend (only present when different from expr). Drill-down: For multi-dimensional metrics (metrics with labels like model, token_type, provider), the response includes suggestedQueries — ready-to-use PromQL queries for grafana_query that break down the metric by each label. Counter metrics get rate() wrapping automatically. Use these to investigate cost attribution, identify top contributors, or decompose aggregates. Breakdowns: suggestedBreakdowns provides label names for decomposition — always available for known OpenClaw metrics (cost, session, queue, webhook families) even when the metric has no data yet. For unknown metrics, falls back to labels discovered from the instant query. Use these labels with grafana_query to build sum by (label) (...) queries for root-cause analysis. Period comparison: Use compareWith: "previous" for period-over-period analysis (e.g., this week vs. last week). Returns a comparison object with the previous period's stats and the change (absolute, percentage, direction). Works with counters too (compares rates). Eliminates the need for manual multi-query workflows. Tip: For simple trend context, call with just period. For "did things improve?" questions, add compareWith: "previous". Metadata only available for plain metric names (not complex PromQL). No need to manually wrap counters in rate() — the tool does it automatically.

grafana_security_check

When: User asks "am I being attacked?", "security status", "security audit", "security check", or wants a comprehensive threat assessment. Params: lookback (time window, default "1h". Use "24h" for daily review, "7d" for weekly). Example: {} Example weekly review: { "lookback": "7d" } Returns: { overallThreatLevel: "green"|"yellow"|"red", summary, checks: [{ name, status, value, detail }], securityEventLogs, limitations: [...], suggestedActions: [...], dashboardTemplate: "security-overview" }. Checks (6 parallel, via Promise.allSettled — partial failures return partial results):

1. webhook_error_ratio — webhook error rate vs received rate. Warning 20%, critical 50%.
2. cost_anomaly — daily cost. Warning $10, critical $50.
3. tool_loops — active tool loops. Warning 1, critical 3.
4. injection_signals — prompt injection patterns detected. Warning 1, critical 5.
5. session_enumeration — unique sessions in 1h. Warning 50, critical 200.
6. stuck_sessions — stuck sessions. Warning 1, critical 3. Limitations: Auth failures are NOT observable — openclaw's auth middleware emits no diagnostic events. The tool monitors observable signals (webhook errors, cost spikes, prompt injection patterns, session anomalies) but cannot detect silent auth-layer attacks (bad tokens, brute-force, rate-limiter lockouts). Always includes limitations in response. Auto-discovery: No datasource UID needed — auto-discovers first Prometheus datasource. Loki is optional (skips log check if absent). Follow-up: Use dashboardTemplate to create a persistent security dashboard. Use suggestedActions for investigation steps.

grafana_investigate

When: First step for "investigate this", "what's wrong?", "triage", "root cause analysis", "debug this issue", or any multi-signal investigation. Params: focus (required — alert UID, metric name, or symptom text), timeWindow (optional, default "1h", options: `"1h"

Truncated - read the full file at https://github.com/awsome-o/grafana-lens/blob/cee813a3e11f59e9a5637188b283e041bf841092/skills/SKILL.md.