grafana

name	grafana
description	Query Grafana dashboards and Prometheus metrics for service health, LLM latency, and infrastructure monitoring. Use this skill when the user asks about service health, monitoring, alerts, or performance metrics.

Grafana Integration

Connection

• Base URL: $GRAFANA_URL (e.g. https://your-org.grafana.net)
• Auth: Authorization: Bearer $GRAFANA_API_TOKEN (service account token)
• Credentials: GRAFANA_URL, GRAFANA_API_TOKEN from Settings → Data sources
• Caching: 10-minute cache for metadata; query results are NOT cached (time-sensitive)
• Key datasource: Prometheus UID grafanacloud-prom

Server Lib & API Routes

• File: server/lib/grafana.ts

Exported Functions

Function	Description
listDashboards(query?)	Search dashboards by query
getDashboard(uid)	Full dashboard JSON with panels
getDatasources()	List all datasources
getAlertRules()	All alert rules (flattened from groups)
getAlertInstances()	Currently firing alert instances
queryDatasource(uid, queries[], from?, to?)	Proxy to Grafana's /api/ds/query

API Routes

Route	Description
GET /api/grafana/dashboards	Search dashboards
GET /api/grafana/dashboard?uid=...	Full dashboard JSON
GET /api/grafana/datasources	List datasources
GET /api/grafana/alerts	Alert rules and firing instances
POST /api/grafana/query	Query datasource (Prometheus, Loki, etc.)

Agent Action

Use grafana for agent-facing Grafana work. Do not call /api/grafana/* directly from the agent.

Mode	Args	Description
dashboards	search	Search dashboards
dashboard	uid	Full dashboard JSON
datasources		List datasources
alerts		Alert rules and firing instances
query	datasourceUid, queries, from, to	Query a datasource

Dashboard

• /adhoc/engineering — Engineering dashboard (mirrors a Grafana dashboard by UID)

Grafana Response Frame Format

{ results: { [refId]: { frames: [{ schema: { fields }, data: { values } }] } } }

• values[0] = timestamps (epoch ms), values[1+] = metric values
• Series labels from schema.fields[i].labels (e.g. { model: "claude-3.5" })
• Transform to Recharts: { time: number, [seriesName]: number }[]

Template Variables

• $CodegenMode → default quality-v4
• $AIModel → regex pattern, default .* (all)
• $environment → cloud or cloud-v2, default cloud
• Variables interpolated via string replace before querying

Key Prometheus Metrics

• Codegen: vcpcodegen_completion_total, vcpcodegen_completion_latency_bucket, vcpcodegen_feedback_total, vcpcodegen_error_total
• LLM: llm_completion_cost_total, llm_input_tokens_total, llm_output_tokens_total, llm_latency_bucket, llm_failures_total, llm_completions_total
• Projects: projects_proposed_config_total, projects_status_total, projects_remote_machine_*, projects_start_duration_bucket
• API/Runtime: api_request_total, with_span_duration_bucket, memory_heap_usage_percent_bucket
• Fly.io: fly_endpoint_total, fly_machine_*
• GitHub: builderbot_pr_created_total, builderbot_pr_closed_total

Key Patterns & Gotchas

• queryDatasource constructs body with datasource UID in each query target; timestamps as string ms
• getAlertRules flattens nested rule groups from unified alerting API
• getAlertInstances handles both array and wrapped object response shapes
• POST helper caches by default (fine for idempotent endpoints) but queryDatasource skips cache
• Heatmap panels render as multi-series line/area (Recharts has no native heatmap; PromQL histogram_quantile() computes quantiles)

Incident Investigation Pattern

For production issues, query Grafana/Prometheus FIRST:

1. LLM latency by model (llm_latency_bucket)
2. Request rates (api_request_total)
3. Error rates (llm_failures_total)
4. Instance counts (via Cloud Monitoring)

Then check Sentry for application errors, Cloud Logging for raw logs.