datadog

name	datadog
description	Full-stack observability with Datadog APM, logs, metrics, synthetics, and RUM. Use when implementing monitoring, tracing, alerting, or cost optimization for production systems.
user-invocable	false
disable-model-invocation	true
version	1.0.0
category	platform
author	Claude MPM Team
license	MIT
progressive_disclosure	{"entry_point":{"summary":"Unified observability platform for APM, logs, metrics, synthetics, and RUM with 1000+ integrations.","when_to_use":"When implementing production monitoring, distributed tracing, log aggregation, custom metrics, or cost optimization.","quick_start":"1. Install Datadog Agent. 2. Enable APM with automatic instrumentation. 3. Configure log collection. 4. Set up alerts."},"references":["agent-installation.md","apm-instrumentation.md","log-management.md","custom-metrics.md","alerting.md","cost-optimization.md","kubernetes.md"]}
context_limit	800
tags	["observability","monitoring","apm","logging","metrics","tracing","datadog","alerting"]
requires_tools	[]

Datadog Observability

Overview

Datadog is a SaaS observability platform providing unified monitoring across infrastructure, applications, logs, and user experience. It offers AI-powered anomaly detection, 1000+ integrations, and OpenTelemetry compatibility.

Core Capabilities:

• APM: Distributed tracing with automatic instrumentation for 8+ languages
• Infrastructure: Host, container, and cloud service monitoring
• Logs: Centralized collection with processing pipelines and 15-month retention
• Metrics: Custom metrics via DogStatsD with cardinality management
• Synthetics: Proactive API and browser testing from 29+ global locations
• RUM: Frontend performance with Core Web Vitals and session replay

When to Use This Skill

Activate when:

• Setting up production monitoring and observability
• Implementing distributed tracing across microservices
• Configuring log aggregation and analysis pipelines
• Creating custom metrics and dashboards
• Setting up alerting and anomaly detection
• Optimizing Datadog costs

Do not use when:

• Building with open-source stack (use Prometheus/Grafana instead)
• Cost is primary concern and budget is limited
• Need maximum customization over managed solution

Quick Start

1. Install Datadog Agent

Docker (simplest):

docker run -d --name dd-agent \
  -e DD_API_KEY=<YOUR_API_KEY> \
  -e DD_SITE="datadoghq.com" \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -v /proc/:/host/proc/:ro \
  -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
  gcr.io/datadoghq/agent:7

Kubernetes (Helm):

helm repo add datadog https://helm.datadoghq.com
helm install datadog-agent datadog/datadog \
  --set datadog.apiKey=<YOUR_API_KEY> \
  --set datadog.apm.enabled=true \
  --set datadog.logs.enabled=true

2. Instrument Your Application

Python:

 ddtrace  tracer, patch_all


patch_all()


 tracer.trace(, service=)  span:
    span.set_tag(, user_id)