Alerting Irm logo

Alerting Irm

Organization
grafana
alerting-irm

Configure Grafana Alerting, Incident Response Management (IRM), and SLOs end-to-end — provisions Grafana-managed and data-source-managed alert rules, contact points (Slack/PagerDuty/email/webhook), notification policies with hierarchical matchers, silences, mute timings, on-call schedules and escalation chains, incident-management integrations, and SLOs with multi-window burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, declaring or managing incidents, defining SLOs, provisioning alerting via YAML or API, picking matchers for a notification policy, building a PagerDuty/Slack webhook receiver, or troubleshooting why an alert isn't firing — even when the user says "page me on errors", "alert me when X happens", "route this to the platform team", or "set up an SLO" without naming Alerting or IRM.

Overview

Publishergrafana
Repositoryskills
Skill namealerting-irm
Stars
289
Forks
27
Bundled files
3
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.

  • 3 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 Alerting Irm 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-core/alerting-irm
  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-core/alerting-irm .claude/skills/alerting-irm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alerting Irm 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 Alerting Irm 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 Alerting Irm 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 Alerting & IRM

Docs: https://grafana.com/docs/grafana/latest/alerting.md

Common Workflows

Provisioning a new alert end-to-end

  1. Create contact points (where notifications go):

    bash
    curl -X POST https://grafana.example.com/api/v1/provisioning/contact-points \
      -H 'Authorization: Bearer <token>' -H 'Content-Type: application/json' \
      -d @contact-points.json

    Verify:

    bash
    curl https://grafana.example.com/api/v1/provisioning/contact-points \
      -H 'Authorization: Bearer <token>' | jq '.[].name'
  2. Add notification policies (which alerts go where) — see § Notification policies below for the matchers pattern.

  3. Write the alert rule — pick the type:

  4. Verify routing before going live:

    bash
    # Force-fire a test alert from the rule's UI, then check Alertmanager's view
    curl https://grafana.example.com/api/alertmanager/grafana/api/v2/alerts \
      -H 'Authorization: Bearer <token>' | jq '.[] | {alertname: .labels.alertname, receiver: .receivers}'

    The expected receiver should appear. If the wrong receiver appears, re-check the policy's matchers.

Routing alerts to IRM / on-call

  1. In IRM, create an Integration of type "Grafana Alerting webhook" → copy the integration URL
  2. Add a webhook contact point in Grafana Alerting pointing at that URL (full YAML in references/irm.md § Routing)
  3. Add a notification policy matcher routing the right severity to the new contact point
  4. Verify: trigger a test alert; it should appear in IRM within ~30s. Full debug procedure in references/irm.md § Verifying the IRM integration.

Defining an SLO

  1. Create the SLO via UI or API → Grafana auto-generates recording rules, dashboards, and burn-rate alerts (the generated YAML is in references/slo.md)
  2. Use multi-window burn-rate alerts, not single-window — see references/slo.md § Multi-window burn-rate alerts for why single-window fires on noise
  3. Verify with the 4-step pattern in references/slo.md § Validating SLO config

Contact Points (YAML provisioning)

yaml
# provisioning/alerting/contact_points.yaml
apiVersion: 1
contactPoints:
  - orgId: 1
    name: pagerduty-critical
    receivers:
      - uid: pd-receiver
        type: pagerduty
        settings:
          integrationKey: YOUR_PAGERDUTY_KEY
          severity: critical

  - orgId: 1
    name: slack-alerts
    receivers:
      - uid: slack-receiver
        type: slack
        settings:
          url: https://hooks.slack.com/services/YOUR/WEBHOOK/URL
          channel: '#alerts'

For email, webhook, Teams, Telegram, OnCall, and other receiver types, see references/alerting.md § Contact point receiver types.

Notification policies

Hierarchical routing tree with label matchers:

yaml
# provisioning/alerting/notification_policies.yaml
apiVersion: 1
policies:
  - orgId: 1
    receiver: default-receiver
    group_by: ['alertname', 'cluster', 'service']
    group_wait: 30s
    group_interval: 5m
    repeat_interval: 12h
    routes:
      # Critical alerts → PagerDuty
      - receiver: pagerduty-critical
        matchers:
          - severity = critical
        group_wait: 10s
        repeat_interval: 4h

      # Platform team → Slack, but page on critical
      - receiver: slack-alerts
        matchers:
          - team = platform
        routes:
          - receiver: pagerduty-critical
            matchers:
              - severity = critical

      # Everything else → email
      - receiver: email-alerts
        matchers:
          - severity =~ "warning|info"

Silences

Suppress notifications for matching alerts without stopping evaluation:

bash
curl -X POST https://grafana.example.com/api/alertmanager/grafana/api/v2/silences \
  -H 'Authorization: Bearer <token>' \
  -H 'Content-Type: application/json' \
  -d '{
    "matchers": [
      {"name": "alertname", "value": "HighErrorRate", "isRegex": false},
      {"name": "env", "value": "staging", "isRegex": false}
    ],
    "startsAt": "2024-01-01T00:00:00Z",
    "endsAt": "2024-01-01T02:00:00Z",
    "comment": "Maintenance window",
    "createdBy": "admin"
  }'

# Verify it was created
curl https://grafana.example.com/api/alertmanager/grafana/api/v2/silences \
  -H 'Authorization: Bearer <token>' | jq '.[] | select(.status.state == "active")'

Alert rule states

StateDescription
NormalCondition not met
PendingCondition met, waiting for for duration
FiringCondition met for full for duration
NoDataQuery returned no data
ErrorQuery/evaluation error
RecoveringWas firing, condition no longer met

Provisioning directory layout

provisioning/alerting/
├── alert_rules.yaml          # Alert and recording rules
├── contact_points.yaml       # Notification destinations
├── notification_policies.yaml  # Routing tree
├── templates.yaml            # Message templates
└── mute_timings.yaml         # Recurring mute windows

API provisioning (keeps UI editable)

Add X-Disable-Provenance: true to keep resources editable in the UI after API provisioning:

bash
curl -X PUT https://grafana.example.com/api/v1/provisioning/policies \
  -H 'Authorization: Bearer <token>' \
  -H 'X-Disable-Provenance: true' \
  -H 'Content-Type: application/json' \
  -d @policy.json

curl -X POST https://grafana.example.com/api/v1/provisioning/alert-rules \
  -H 'Authorization: Bearer <token>' \
  -H 'X-Disable-Provenance: true' \
  -H 'Content-Type: application/json' \
  -d @rule.json

References

  • references/alerting.md — full alert rule YAML (Grafana-managed / Prometheus / Loki) + notification templates
  • references/slo.md — generated SLO recording rules + multi-window burn-rate alert pattern + validation steps
  • references/irm.md — IRM capabilities, integration sources, Alerting → IRM routing + verification + common failure modes

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 Alerting Irm AI skill do?

Configure Grafana Alerting, Incident Response Management (IRM), and SLOs end-to-end — provisions Grafana-managed and data-source-managed alert rules, contact points (Slack/PagerDuty/email/webhook), notification policies with hierarchical matchers, silences, mute timings, on-call schedules and escalation chains, incident-management integrations, and SLOs with multi-window burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, declaring or managing incidents, defining SLOs, provisioning alerting via YAML or API, picking matchers for a noti...

Why use Alerting Irm on TypingMind?

Because you install it once and use it with any model. Alerting Irm 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 Alerting Irm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grafana/skills/tree/main/skills/grafana-core/alerting-irm. 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 Alerting Irm?

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 Alerting Irm?

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

Is the Alerting Irm 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.

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