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Infrastructure

Organization
grafana
infrastructure

Ship Kubernetes, host, container, and cloud-provider telemetry into Grafana Cloud — `k8s-monitoring` Helm chart for K8s clusters (metrics + logs + traces + events + cost), Alloy `prometheus.exporter.unix` for Linux hosts, cAdvisor + Docker discovery for containers, and CloudWatch / Azure Monitor / Google Cloud Monitoring datasource setup. Use when onboarding a new cluster or VM fleet to Grafana Cloud, picking the right Helm values for K8s scraping, wiring kube-state-metrics + node-exporter + cAdvisor, alerting on `PodCrashLooping` / node memory / PVC capacity, or pulling AWS / Azure / GCP cloud metrics — even when the user says "monitor my cluster", "send K8s metrics to Grafana", "scrape EC2 metrics", "cluster pod logs", or "install the monitoring helm chart" without naming `k8s-monitoring` or Alloy.

Overview

Publishergrafana
Repositoryskills
Skill nameinfrastructure
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 Infrastructure 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-cloud/infrastructure
  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-cloud/infrastructure .claude/skills/infrastructure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Infrastructure 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 Infrastructure 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 Infrastructure 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 Cloud Infrastructure Monitoring

Docs: https://grafana.com/docs/grafana-cloud/monitor-infrastructure/

K8s + host + container + cloud-provider telemetry, mostly via the grafana/k8s-monitoring Helm chart or Alloy.

Prerequisites

  • Grafana Cloud stack with Prometheus / Loki / Tempo endpoints + API key (metrics:write, logs:write, traces:write)
  • For Kubernetes: a cluster + helm 3.x + kubectl context pointing at it
  • For hosts / Docker: Alloy installed on the node

Common Workflows

1. Onboard a Kubernetes cluster (k8s-monitoring chart)

bash
# 1. Create the namespace + secret
kubectl create namespace monitoring
kubectl create secret generic grafana-cloud-secret \
  -n monitoring --from-literal=api-key=<your-api-key>

# 2. Install — values.yaml in references/k8s-monitoring-values.md
helm repo add grafana https://grafana.github.io/helm-charts && helm repo update
helm install k8s-monitoring grafana/k8s-monitoring \
  --version 4.1.4 -n monitoring -f values.yaml

# 3. Verify every pod is Running
kubectl get pods -n monitoring
# Expect alloy-*, kube-state-metrics-*, node-exporter-*, etc. all Ready.

# 4. Verify no error logs in the metrics/logs/traces Alloys
kubectl -n monitoring logs deploy/k8s-monitoring-alloy-metrics --tail=50 | grep -iE 'error|level=err' || echo "clean"

# 5. Verify telemetry landed in Grafana Cloud
#    PromQL on the metrics datasource (should be > 0):
#      sum(up{cluster="production-us-east"})
#    LogQL on Loki:
#      sum(count_over_time({cluster="production-us-east"}[5m]))

Full values.yaml, key PromQL, dashboard IDs (15520, 1860, 14282…), and alert rules: references/k8s-monitoring-values.md.

2. Monitor a Linux host

alloy
# 1. /etc/alloy/config.alloy — see references/clouds-and-hosts.md for the full block
prometheus.exporter.unix "host"  { rootfs_path = "/" }
prometheus.scrape         "node" { targets = prometheus.exporter.unix.host.targets
                                   forward_to = [prometheus.remote_write.cloud.receiver] }
bash
# 2. Reload Alloy and verify the unix exporter is up
systemctl reload alloy
curl -s http://localhost:12345/api/v0/web/components | jq '.[] | select(.id|contains("prometheus.exporter.unix"))'

# 3. Verify in Grafana Cloud — open the "Node Exporter Full" dashboard (ID 1860)
#    and pick your host from the `instance` dropdown.

3. Pull AWS / Azure / GCP metrics

Provision the datasource (full YAML in references/clouds-and-hosts.md), then:

bash
# 1. After provisioning, restart Grafana to pick up the file
# 2. Verify the datasource — Grafana → Connections → Data sources → "Test"
#    Expect "Successfully queried the CloudWatch metrics API" (or equivalent).
# 3. Confirm a query — Explore → datasource → metric e.g.
#    CloudWatch namespace AWS/EC2 metric CPUUtilization, last 1h.

Troubleshooting

  • chart installed but no metrics in Cloud → check the grafana-cloud-secret api-key value; check Alloy logs for 401
  • kube-state-metrics pod Pending → likely RBAC; reapply the chart's CRDs/CRBs
  • Node-exporter pod CrashLoopBackOff → typically hostNetwork: true collision with the host's :9100; change the port
  • CloudWatch "Access denied" → IAM role missing cloudwatch:GetMetricData, cloudwatch:ListMetrics

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 Infrastructure AI skill do?

Ship Kubernetes, host, container, and cloud-provider telemetry into Grafana Cloud — `k8s-monitoring` Helm chart for K8s clusters (metrics + logs + traces + events + cost), Alloy `prometheus.exporter.unix` for Linux hosts, cAdvisor + Docker discovery for containers, and CloudWatch / Azure Monitor / Google Cloud Monitoring datasource setup. Use when onboarding a new cluster or VM fleet to Grafana Cloud, picking the right Helm values for K8s scraping, wiring kube-state-metrics + node-exporter + cAdvisor, alerting on `PodCrashLooping` / node memory / PVC capacity, or pulling AWS / Azure / GCP c...

Why use Infrastructure on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grafana/skills/tree/main/skills/grafana-cloud/infrastructure. 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 Infrastructure?

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 Infrastructure?

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

Is the Infrastructure 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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