prometheus-mimir-grafana

Solid

Query Prometheus and Grafana Mimir, write and debug PromQL, and build or fix Grafana dashboards — for agents solving problems from metrics. Covers the Prometheus HTTP API (`/api/v1/query`, `query_range`, `series`, `labels`, `metadata`), Mimir multi-tenancy (`X-Scope-OrgID`, federation `a|b|c`, per-tenant 422/429 limits), the PromQL surface (selectors, rate family, classic + native histograms, `histogram_quantile`, vector matching `on()`/`group_left`, recording rules), Grafana dashboard JSON (panels, targets, variables + interpolation specifiers, legacy `/api/dashboards/db` vs Grafana-12 `/apis/dashboard.grafana.app/v1beta1/…`), KPI frameworks (RED, USE, Golden Signals, SLO burn-rate), connection recipes, MCP servers vs curl, and the PromQL trap list.

AI & Automation 3 stars 1 forks Updated 2 days ago MIT

Install

View on GitHub

Quality Score: 79/100

Stars 20%
20
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Prometheus, Mimir, and Grafana — for agents Target audience: an AI agent (or a human working through one) that has to *do things with metrics* — query, triage, alert, build and fix dashboards, and pick the right KPIs — against a stack that runs Prometheus, Grafana Mimir, and/or Grafana. Works whether the agent is given curl access to a Mimir gateway, an MCP server wrapper, or just a Grafana URL and a service-account token. **This skill uses the stack; it does not change it.** Siblings in the `observability` plugin own the lifecycle: version-laddering a Mimir install is **`mimir-upgrade`**, getting SNMP devices to produce metrics in the first place is **`snmp-exporter`**, and logs — a different pipeline entirely — are **`logging-operator`** (with **`rancher-logging-exit`** for migrating off the Rancher-bundled chart). If a query returns nothing, check here first; if the component itself is broken or stale, you are in the wrong skill. ## Why this matters Metrics lie in three directions: (1) the agent queries the wrong metric or wrong label, (2) the query is syntactically fine but semantically broken (`rate` after aggregation, `histogram_quantile` of the mean, default histogram buckets sized for the wrong service), (3) the dashboard *looks* correct but the datasource variable is empty or the unit is off by 1000×. Each failure mode has an easy check. This skill is those checks, organized so the agent reaches for them before issuing the first query. ## The one-paragraph rub...

Details

Author
air-gapped
Repository
air-gapped/skills
Created
3 months ago
Last Updated
2 days ago
Language
Python
License
MIT

Integrates with

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category