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Docker Compose Monitoring Stack

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andersonamaral2
docker-compose-monitoring-stack

Deploys a complete monitoring stack with Prometheus, Grafana, and Node Exporter using Docker Compose.

Overview

Publisherandersonamaral2
RepositoryClaude-Code-to-Deep-Agents-Skills-Converter
Skill namedocker-compose-monitoring-stack
Stars
58
Forks
8
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by andersonamaral2 on GitHub. Read the source before you install it.

Installation

Install the Docker Compose Monitoring Stack 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/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter/tree/main/examples/deep-agents-output-2
  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/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter.git /tmp/Claude-Code-to-Deep-Agents-Skills-Converter
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-to-Deep-Agents-Skills-Converter/examples/deep-agents-output-2 .claude/skills/docker-compose-monitoring-stack
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docker Compose Monitoring Stack 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 Docker Compose Monitoring Stack 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 Docker Compose Monitoring Stack 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.

Docker Compose Monitoring Stack

Deploys a complete monitoring stack with Prometheus, Grafana, and Node Exporter using Docker Compose.

Execution Context

This skill runs inside Deep Agents CLI (v0.0.34+). Available tools:

ToolUsage in this skill
write_fileCreate docker-compose.yml, prometheus.yml, Grafana provisioning files, .env, .gitignore
executeRun Docker Compose commands, verify services, run health checks
read_fileRead configuration files to verify content
write_todosTrack execution plan progress
http_requestTest service health endpoints

Critical execution rules:

  1. Always start by creating the plan via write_todos.
  2. Create files one by one via write_file — never try to generate everything at once.
  3. Test each service via execute immediately after starting it.
  4. Use task to delegate long or parallel subtasks to sub-agents.

Execution Plan (use with write_todos)

When receiving the request, run write_todos with:

  • 1. Verify Docker and Docker Compose are installed
  • 2. Check and set required environment variables
  • 3. Create project directory structure
  • 4. Create docker-compose.yml
  • 5. Create Prometheus configuration
  • 6. Create Grafana datasource provisioning
  • 7. Create Grafana dashboard provisioning and dashboard JSON
  • 8. Create .env and .gitignore files
  • 9. Create AGENTS.md with stack documentation
  • 10. Start the stack with Docker Compose
  • 11. Run health checks on all services

When to Use

Use this skill when the user asks to:

  • Set up monitoring or observability
  • Deploy Prometheus and Grafana
  • Create a monitoring stack
  • Set up metrics collection
  • Create a Grafana dashboard for infrastructure

Prerequisites Check

Before creating any files, use execute to verify:

bash
# Check Docker
docker --version || { echo "ERROR: Docker not found. Install from https://docs.docker.com/get-docker/"; exit 1; }

# Check Docker Compose
docker compose version || { echo "ERROR: Docker Compose not found"; exit 1; }

# Check if Docker daemon is running
docker info >/dev/null 2>&1 || { echo "ERROR: Docker daemon is not running"; exit 1; }

echo "All prerequisites OK"

If Docker is not installed, advise the user to follow the official installation guide at https://docs.docker.com/get-docker/.

Environment Setup

Before execution, verify required environment variables via execute:

bash
# Check required variables
for var in GRAFANA_ADMIN_PASSWORD; do
  if [ -z "${!var}" ]; then
    echo "ERROR: $var is not set"
    exit 1
  fi
done

# Check optional variables (with defaults)
echo "PROMETHEUS_RETENTION=${PROMETHEUS_RETENTION:-15d} (default: 15d)"

echo "All environment variables OK"

If using a .env file, load it via execute:

bash
set -a && source .env && set +a

Security note: Never hardcode secrets in the SKILL.md. Use environment variables or a .env file (added to .gitignore).

Steps

1. Create Project Structure

Use execute to create the directory structure:

bash
mkdir -p monitoring/{prometheus,grafana/provisioning/datasources,grafana/provisioning/dashboards,grafana/dashboards}

Test via execute:

bash
ls -R monitoring/ && echo "Directory structure OK"

2. Create Docker Compose File

Use write_file to create monitoring/docker-compose.yml:

yaml
version: "3.8"
services:
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.retention.time=${PROMETHEUS_RETENTION:-15d}'

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_ADMIN_PASSWORD}
    volumes:
      - ./grafana/provisioning:/etc/grafana/provisioning
      - ./grafana/dashboards:/var/lib/grafana/dashboards
      - grafana_data:/var/lib/grafana

  node-exporter:
    image: prom/node-exporter:latest
    ports:
      - "9100:9100"
    pid: host
    volumes:
      - /proc:/host/proc:ro
      - /sys:/host/sys:ro

volumes:
  prometheus_data:
  grafana_data:

Test via execute:

bash
cd monitoring && docker compose config --quiet && echo "docker-compose.yml syntax OK"

3. Create Prometheus Configuration

Use write_file to create monitoring/prometheus/prometheus.yml:

yaml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

4. Create Grafana Datasource Provisioning

Use write_file to create monitoring/grafana/provisioning/datasources/prometheus.yml:

yaml
apiVersion: 1
datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true

5. Create Grafana Dashboard Provisioning

Use write_file to create monitoring/grafana/provisioning/dashboards/dashboards.yml:

yaml
apiVersion: 1
providers:
  - name: 'default'
    folder: ''
    type: file
    options:
      path: /var/lib/grafana/dashboards

Use write_file to create monitoring/grafana/dashboards/node-exporter.json:

Create a basic Node Exporter dashboard JSON. Use the official Grafana dashboard ID 1860 as reference.

6. Create Environment and Ignore Files

Use write_file to create monitoring/.env:

GRAFANA_ADMIN_PASSWORD=changeme
PROMETHEUS_RETENTION=15d

Use write_file to create monitoring/.gitignore:

.env
*_data/

7. Add Project Notes

Use write_file to create monitoring/AGENTS.md:

markdown
# Monitoring Stack — Project Notes

## Architecture
- **Prometheus** (port 9090): Metrics collection and storage
- **Grafana** (port 3000): Visualization and dashboards
- **Node Exporter** (port 9100): Host-level metrics

## Credentials
- Grafana login: admin / value of $GRAFANA_ADMIN_PASSWORD

## Data Persistence
- Data persists in Docker volumes even after `docker compose down`
- To fully reset: `docker compose down -v`

## Platform Notes
- On macOS, Node Exporter has limited metrics (/proc and /sys are not real Linux filesystems)
- On Linux, Node Exporter works fully

8. Start the Stack

Use execute to start all services:

bash
cd monitoring
set -a && source .env && set +a
docker compose up -d

Use execute to verify all containers are running:

bash
cd monitoring && docker compose ps

9. Run Health Checks

Platform-specific execution

Use execute to detect the platform and warn about limitations:

bash
OS=$(uname -s)
case "$OS" in
  Darwin) echo "WARNING: macOS detected — Node Exporter metrics will be limited (/proc and /sys are not real Linux filesystems)" ;;
  Linux)  echo "Linux detected — all metrics available" ;;
  *)      echo "WARNING: Unsupported OS: $OS — Node Exporter may not work correctly" ;;
esac

Use execute to test each service:

bash
echo "=== Prometheus Health ==="
curl -sf http://localhost:9090/-/healthy && echo " OK" || echo " FAILED"

echo "=== Grafana Health ==="
curl -sf http://localhost:3000/api/health && echo " OK" || echo " FAILED"

echo "=== Node Exporter Metrics ==="
curl -sf http://localhost:9100/metrics | head -5 && echo "... OK" || echo " FAILED"

echo "=== All health checks complete ==="

For each service that fails the health check, use execute to inspect logs:

bash
cd monitoring && docker compose logs <service-name>

Notes

  • Grafana default login: admin / value of $GRAFANA_ADMIN_PASSWORD
  • Prometheus UI: http://localhost:9090
  • Grafana UI: http://localhost:3000
  • Data persists in Docker volumes even after docker compose down
  • To fully reset: docker compose down -v

Usage with Deep Agents CLI

Mode 1 — Build (one-shot)

bash
deepagents -y "Deploy a monitoring stack with Prometheus and Grafana following the docker-compose-monitoring-stack skill"

Mode 2 — Interactive

bash
deepagents
> Set up a monitoring stack with Prometheus, Grafana, and Node Exporter

Mode 3 — Non-interactive (CI/CD)

bash
deepagents -n -y -S "docker,curl,mkdir" "Deploy monitoring stack with Prometheus, Grafana, and Node Exporter"

Troubleshooting

Docker not found

bash
# Check:
docker --version
# Install: follow https://docs.docker.com/get-docker/

Docker Compose not found

bash
# Check:
docker compose version
# If using older Docker, install compose plugin:
sudo apt-get install docker-compose-plugin

Docker daemon not running

bash
# Check:
docker info
# Start (Linux):
sudo systemctl start docker
# Start (macOS): open Docker Desktop app

Grafana fails health check

bash
# Check logs:
cd monitoring && docker compose logs grafana
# Verify password is set:
echo $GRAFANA_ADMIN_PASSWORD
# Restart:
docker compose restart grafana

Prometheus fails health check

bash
# Check logs:
cd monitoring && docker compose logs prometheus
# Validate config syntax:
docker run --rm -v $(pwd)/prometheus:/etc/prometheus prom/prometheus promtool check config /etc/prometheus/prometheus.yml

Node Exporter metrics incomplete on macOS

This is expected. On macOS, /proc and /sys are not real Linux filesystems,
so Node Exporter cannot collect full host metrics. For full metrics,
deploy on a Linux host or use Docker Desktop's VM metrics.

Environment variable not set

bash
# Check what's set:
env | grep -E "GRAFANA|PROMETHEUS"
# Set manually:
export GRAFANA_ADMIN_PASSWORD="your-secure-password"
export PROMETHEUS_RETENTION="15d"
# Or load from .env:
set -a && source .env && set +a

Context window overflow

Use /compact to force compaction before continuing.
Consider splitting the task with `task` sub-agents.

Frequently asked questions

What does the Docker Compose Monitoring Stack AI skill do?

Deploys a complete monitoring stack with Prometheus, Grafana, and Node Exporter using Docker Compose.

Why use Docker Compose Monitoring Stack on TypingMind?

Because you install it once and use it with any model. Docker Compose Monitoring Stack 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 Docker Compose Monitoring Stack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter/tree/main/examples/deep-agents-output-2. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Docker Compose Monitoring Stack?

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 Docker Compose Monitoring Stack?

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

Is the Docker Compose Monitoring Stack AI skill free?

Yes. It is published on GitHub by andersonamaral2 under the MIT 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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