Fleet Management logo

Fleet Management

Organization
grafana
fleet-management

Manage a fleet of Grafana Alloy collectors with Fleet Management — author Alloy pipelines once, target them via attribute matchers (`env="production"`, regex `region=~"us-.*"`), push remotely via OpAMP without restarting collectors. Covers pipeline create / update / matcher RPCs, collector attribute API, `remotecfg` bootstrap block (standalone + Helm), pre-deploy `alloy fmt` validation, the local Alloy UI at port 12345 for component health, and post-deploy `REMOTE_CONFIG_STATUS_APPLIED` verification. Use when standing up a Cloud Alloy fleet, pushing a config change to 200 collectors, hunting why one collector shows `REMOTE_CONFIG_STATUS_FAILED`, validating River syntax before saving, or wiring `discovery.kubernetes` → `prometheus.remote_write` — even when the user says "configure Alloy", "remote config the collectors", "push pipeline", "OpAMP", "collector is unhealthy", or "manage agent config centrally" without naming Fleet Management.

Overview

Publishergrafana
Repositoryskills
Skill namefleet-management
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 Fleet Management 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/fleet-management
  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/fleet-management .claude/skills/fleet-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fleet Management 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 Fleet Management 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 Fleet Management 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 Fleet Management + Alloy Configuration

Docs: https://grafana.com/docs/grafana-cloud/send-data/fleet-management/

Remote pipeline distribution to Alloy collectors via OpAMP — author once, target with matchers, hot-apply (no restart).

Prerequisites

  • Grafana Cloud stack with Fleet Management enabled
  • API token with Fleet Management access (Authorization: Bearer <STACK_ID>:<TOKEN>)
  • Alloy ≥ 1.0 installed on the targets (standalone or via grafana/alloy Helm chart)
  • alloy CLI locally for alloy fmt syntax validation

Concepts

  • Collector — Alloy instance with unique ID + attributes
  • Pipeline — named Alloy River config stored in Fleet Management
  • Matcher — selector mapping a pipeline to collectors by attribute
  • Attributes — key/value labels on a collector (env, team, region)

Common Workflows

1. Author + validate + deploy a pipeline

bash
# 1. Save the pipeline to a local file (lint catches typos before remote)
cat > pipeline.alloy <<'EOF'
prometheus.scrape "default" {
  targets    = []
  forward_to = [prometheus.remote_write.grafana_cloud.receiver]
  scrape_interval = "60s"
}

prometheus.remote_write "grafana_cloud" {
  endpoint {
    url = "https://prometheus-prod-01-eu-west-0.grafana.net/api/prom/push"
    basic_auth {
      username = "<METRICS_USERNAME>"
      password = env("GRAFANA_CLOUD_API_KEY")
    }
  }
}
EOF

# 2. Validate syntax LOCALLY before sending to Fleet Management
alloy fmt pipeline.alloy            # rewrites in place or errors with line number
alloy validate pipeline.alloy       # full semantic check (newer Alloy releases)

# 3. Create the pipeline via API (see references/api.md for the payload schema)
BASE=https://fleet-management-prod-us-east-0.grafana.net
TOKEN=<STACK_ID>:<API_TOKEN>
PAYLOAD=$(jq -n --rawfile c pipeline.alloy '{
  name:"k8s-metrics", contents:$c,
  matchers:[{name:"env",value:"production",type:"EQUAL"}]
}')
curl -s -X POST "$BASE/pipeline.v1.PipelineService/CreatePipeline" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d "$PAYLOAD" | jq

# 4. Verify it rolled out — every targeted collector should report APPLIED within 1-2 polls
curl -s -X POST "$BASE/collector.v1.CollectorService/ListCollectors" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -d '{}' \
  | jq '.collectors[] | select(.attributes[]?.value=="production")
        | {name, remoteConfigStatus}'
# Expect every row: remoteConfigStatus == "REMOTE_CONFIG_STATUS_APPLIED"

2. Troubleshoot a REMOTE_CONFIG_STATUS_FAILED collector

bash
# 1. Find failed collectors and surface the error message
curl -s -X POST "$BASE/collector.v1.CollectorService/ListCollectors" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -d '{}' \
  | jq '.collectors[] | select(.remoteConfigStatus=="REMOTE_CONFIG_STATUS_FAILED")
        | {name, msg:.remoteConfigStatusMessage}'

# 2. Re-validate the offending pipeline locally
alloy fmt pipeline.alloy

# 3. Inspect Alloy directly — UI at port 12345 shows per-component health
#    http://<COLLECTOR_HOST>:12345 → Graph / Components / Clustering tabs
kubectl -n monitoring logs -l app.kubernetes.io/name=alloy --tail=100 | grep -iE 'remote|error'

# 4. After fixing + re-pushing, re-list collectors and confirm the row flips to APPLIED.

Failure-message decoder table: references/api.md.

3. Onboard a new Alloy with the bootstrap block

The bootstrap remotecfg block is the only local config required:

alloy
remotecfg {
  url = "https://<FLEET_MANAGEMENT_HOST>"
  basic_auth { username = "<STACK_ID>"; password = env("GRAFANA_CLOUD_API_KEY") }
  poll_frequency = "1m"
  attributes = { "env" = env("ENVIRONMENT"), "team" = "platform" }
}
bash
# Verify after start
curl -s http://localhost:12345/api/v0/web/components \
  | jq '.[] | select(.id=="remotecfg") | {id, health:.health.state}'
# health.state == "healthy"

Full bootstrap (standalone + Helm) + Assistant tool list: references/bootstrap.md.

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 Fleet Management AI skill do?

Manage a fleet of Grafana Alloy collectors with Fleet Management — author Alloy pipelines once, target them via attribute matchers (`env="production"`, regex `region=~"us-.*"`), push remotely via OpAMP without restarting collectors. Covers pipeline create / update / matcher RPCs, collector attribute API, `remotecfg` bootstrap block (standalone + Helm), pre-deploy `alloy fmt` validation, the local Alloy UI at port 12345 for component health, and post-deploy `REMOTE_CONFIG_STATUS_APPLIED` verification. Use when standing up a Cloud Alloy fleet, pushing a config change to 200 collectors, huntin...

Why use Fleet Management on TypingMind?

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

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

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 Fleet Management?

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

Is the Fleet Management 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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