datadog:dashboard

What problem does it solve?

Building reliable, production-grade Datadog dashboards is time-consuming and error-prone because it requires comprehensive metric discovery, consistent widget grouping and layout, template variable design, team tagging, and generation of correctly formatted Terraform HCL that integrates with project conventions.

Core Features & Use Cases

• Metric discovery using Datadog MCP, official documentation, and integrations-core CSVs to find metrics, tags, and units.
• Design and layout of grouped widgets with recommended widget types, sizes, and conditional formats to surface KPIs, trends, and breakdowns.
• Terraform HCL generation and validation with template variables (always including env), team: tagging, outputs for dashboard_id/url, and terraform fmt checks for repo-ready code.
• Use Case: Create a Redis or Sidekiq dashboard that includes overview KPIs, latency timeseries, and error breakdowns with env and service filters and write the HCL into the project's monitoring module.

Quick Start

Ask the skill to generate a Terraform Datadog dashboard for the puma integration focusing on throughput, errors, and latency and return a formatted HCL file ready to save to the repo.