Mimir logo

Mimir

Organization
grafana
mimir

Stand up Grafana Mimir for horizontally scalable, multi-tenant, long-term Prometheus + OTLP metrics storage. Covers monolithic / read-write / microservices deployment, S3 / GCS / Azure / filesystem block storage, Prometheus `remote_write` and OTLP ingestion, multi-tenancy with `X-Scope-OrgID`, ingester replication factor, compactor retention, and per-tenant limits. Use when running Mimir locally or on Kubernetes (Helm `mimir-distributed`), scaling Prometheus past a single node, picking ingest / query / backend split, configuring tenants and ingestion rate, debugging `/ready` 503s or `429 Too Many Requests`, or pointing Grafana at a Mimir datasource — even when the user says "I need long-term Prometheus storage", "scale Prometheus", "multi-tenant metrics backend", "Cortex replacement", "remote_write target", or "store 10M active series" without naming Mimir.

Overview

Publishergrafana
Repositoryskills
Skill namemimir
Stars
289
Forks
27
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by grafana on GitHub. Read the source before you install it.

Installation

Install the Mimir AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/grafana/skills/tree/main/skills/grafana-lgtm/mimir
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/grafana/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/grafana-lgtm/mimir .claude/skills/mimir
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mimir in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mimir on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mimir is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Grafana Mimir

Docs: https://grafana.com/docs/mimir/latest/

Horizontally scalable, multi-tenant, long-term storage for Prometheus + OpenTelemetry metrics.

Prerequisites

  • Docker (for quick start) or a Kubernetes cluster (for Helm)
  • An object-storage bucket for production (S3/GCS/Azure) — filesystem only for dev
  • Prometheus or Alloy able to remote_write to the Mimir push endpoint

Common Workflows

1. Stand up monolithic Mimir locally

yaml
# demo.yaml
target: all
multitenancy_enabled: false

blocks_storage:
  backend: filesystem
  bucket_store:
    sync_dir: /tmp/mimir/tsdb-sync
  filesystem:
    dir: /tmp/mimir/data/tsdb
  tsdb:
    dir: /tmp/mimir/tsdb

compactor:
  data_dir: /tmp/mimir/compactor
  sharding_ring:
    kvstore: { store: memberlist }

distributor:
  ring:
    instance_addr: 127.0.0.1
    kvstore: { store: memberlist }

ingester:
  ring:
    instance_addr: 127.0.0.1
    kvstore: { store: memberlist }
    replication_factor: 1

server:
  http_listen_port: 9009
  grpc_listen_port: 9095
  log_level: error
bash
# 1. Run it
docker run --rm -p 9009:9009 -v $(pwd)/demo.yaml:/etc/mimir/demo.yaml \
  grafana/mimir:latest --config.file=/etc/mimir/demo.yaml

# 2. Verify readiness (expect HTTP 200, body: "ready")
curl -sf http://localhost:9009/ready

# 3. Verify it's serving the API
curl -s http://localhost:9009/api/v1/labels | jq '.status'   # → "success"

# 4. Verify self-metrics scrape
curl -s http://localhost:9009/metrics | grep -E '^mimir_(distributor|ingester)_' | head

2. Send metrics — Prometheus remote_write

yaml
remote_write:
  - url: http://localhost:9009/api/v1/push
    headers:
      X-Scope-OrgID: tenant1   # required when multitenancy_enabled: true
bash
# Verify samples are landing
curl -s -H 'X-Scope-OrgID: tenant1' \
  'http://localhost:9009/api/v1/query?query=up' | jq '.data.result | length'
# Expect > 0 within ~30s of Prometheus scraping

3. Send metrics — Grafana Alloy

alloy
prometheus.remote_write "mimir" {
  endpoint {
    url = "http://mimir:9009/api/v1/push"
    headers = { "X-Scope-OrgID" = "tenant1" }
  }
}

4. Deploy on Kubernetes (Helm, microservices)

bash
# 1. Install
helm repo add grafana https://grafana.github.io/helm-charts
helm install mimir grafana/mimir-distributed --version 6.0.6 -f values.yaml

# 2. Verify every pod is Running / Ready
kubectl get pods -n mimir
#   distributor, ingester, querier, query-frontend, store-gateway, compactor, ruler

# 3. Verify the gateway is ready
kubectl port-forward -n mimir svc/mimir-nginx 9009:80 &
curl -sf http://localhost:9009/ready

Multi-tenancy

yaml
multitenancy_enabled: true
# Every request must include header:  X-Scope-OrgID: <tenant-id>

For storage backends (S3 / GCS / Azure / filesystem) see references/storage.md. For component roles, ring options, limits, and API endpoint dumps see references/architecture.md.

Troubleshooting

  • /ready returns 503 → ingester still joining ring; check mimir_ring_members and ingester logs
  • 429 Too Many Requests on push → bump limits.ingestion_rate / ingestion_burst_size
  • Samples written but query returns empty → confirm X-Scope-OrgID matches between write and read
  • Query for old data returns nothing → check compactor logs and that store-gateway has synced blocks

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mimir AI skill do?

Stand up Grafana Mimir for horizontally scalable, multi-tenant, long-term Prometheus + OTLP metrics storage. Covers monolithic / read-write / microservices deployment, S3 / GCS / Azure / filesystem block storage, Prometheus `remote_write` and OTLP ingestion, multi-tenancy with `X-Scope-OrgID`, ingester replication factor, compactor retention, and per-tenant limits. Use when running Mimir locally or on Kubernetes (Helm `mimir-distributed`), scaling Prometheus past a single node, picking ingest / query / backend split, configuring tenants and ingestion rate, debugging `/ready` 503s or `429 To...

Why use Mimir on TypingMind?

Because you install it once and use it with any model. Mimir is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mimir in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grafana/skills/tree/main/skills/grafana-lgtm/mimir. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mimir?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mimir?

As many as you like. As long as a model supports skills, you can use Mimir with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mimir AI skill free?

Yes. It is published on GitHub by grafana under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇