Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.
Set up the Datadog AWS integration with Terraform - creates the cross-account IAM role Datadog assumes (external ID, no stored credentials), attaches the permission policies Datadog publishes, and registers the account through datadog_integration_aws_account…
Set up the Datadog Azure integration with Terraform - creates an Entra ID app registration and service principal, assigns Monitoring Reader across the chosen subscriptions and management groups, grants the Microsoft Graph permissions Datadog needs for…
Set up the Datadog Google Cloud integration with Terraform - creates a service account in the host project, lets Datadog's delegate principal impersonate it via roles/iam.serviceAccountTokenCreator (no service-account keys), enables the required APIs, grants…
Set up the Datadog Oracle Cloud Infrastructure (OCI) integration with Terraform - verifies ~/.oci/config, then applies Datadog's official oracle-cloud-integration module to create the Datadog service user, group, IAM policies, and API key in the tenancy and…
Instrument browser-based web applications with Datadog Browser RUM. Detect the application framework, router, package manager, bundler, entrypoint, credentials, and existing RUM setup; add or safely complete classic Browser RUM instrumentation for React,…
Bootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use…
End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases…