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datadog-dashboards

datadog-dashboards

github.com/pproencaCloud & DevOps21869 installsUpdated 11w ago
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About datadog-dashboards

datadog-dashboards is a rule-based skill for creating, editing, and reviewing Datadog dashboards and widget queries. It focuses on query semantics, account discovery, visualization choices, JSON details, and validation, assuming a Datadog MCP server is connected.

Key features

  1. 01Guidance on dashboard scope and audience
  2. 02Checks metrics, tags, and facets before querying
  3. 03Rules for metric, log, span, and RUM queries
  4. 04Visualization and dashboard JSON guidance
  5. 05Validation and endpoint checks before writing

Use cases

  1. 01Build a dashboard from an underspecified request
  2. 02Diagnose panels with incorrect or missing values
  3. 03Review dashboard JSON for deprecated fields
  4. 04Choose widgets to match a decision or metric

MCP support

The skill assumes a connected Datadog MCP server configured for dashboards and widgets. It relies on the server for current widget schemas and dashboard operations.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/pproenca/dot-skills --skill datadog-dashboards

After installing, type/datadog-dashboardsin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

AGENTS.md
metadata.json
assets/templates/
_template.md
references/
_sections.md
disco-absence-is-not-proof.md
disco-log-attributes-need-facets.md
disco-never-invent-metric-names.md
disco-read-type-and-unit-first.md
json-dashboard-tags-are-constrained.md
json-layout-follows-reflow-type.md
json-queries-and-formulas-not-q.md
json-template-variables-take-defaults.md
json-type-strings-differ-from-ui-names.md
layout-display-type-follows-metric-shape.md
layout-let-datadog-render-units.md
layout-put-widgets-in-groups.md
layout-size-for-the-medium.md
query-aggregate-before-dividing.md
query-append-as-count-explicitly.md
query-event-sources-take-objects.md
query-gauge-gaps-are-interpolated.md
query-percentiles-require-distributions.md
query-rollup-changes-the-number.md
query-scope-syntax-does-not-mix.md
query-top-n-subtotals-are-partial.md
scope-ask-audience-and-decision.md
scope-clone-before-building.md
scope-not-every-question-is-a-dashboard.md
scope-one-dashboard-many-variables.md
ship-endpoint-follows-the-site.md

Details

From the project README

Datadog Dashboards

The decisions Datadog forces when you build a dashboard, and how to settle them. Every rule names the wrong default it corrects; there is no rule for what the model already gets right.

Pinned to a date, not a version. Datadog ships continuously, so every claim here was verified against docs.datadoghq.com in July 2026. Re-verify before trusting anything version-shaped — deprecations here (q, default, is_read_only, week_before()) still parse today.

Assumes the Datadog MCP server is connected (https://mcp.<site>/v1/mcp, toolsets=dashboards,widgets). Widget schemas are deliberately not restated in this skill — get_widget_reference returns them current at call time, and a copy here would go stale. If the server is not connected, set it up first (ship-endpoint-follows-the-site); this skill does not cover Terraform or raw API management.

When to Apply

Use this skill when:

  • Creating or editing a Datadog dashboard, or reviewing one someone else built — especially a board assembled programmatically rather than in the graph editor, which is where the .as_count() divergence bites
  • Writing any Datadog query destined for a widget: metric queries with space aggregators and modifiers, or logs / spans / rum queries with search + compute + group_by
  • Choosing between visualizations, or being handed a request that says "graph" but wants a ranking, a single number, or an error budget
  • A dashboard shows numbers nobody trusts, panels that render "No data", or a value that changes when the time frame changes
  • Asked for "a dashboard for X" with no stated audience — the interview in scope-ask-audience-and-decision runs before anything is built

This skill is NOT for:

  • Managing dashboards through Terraform or the raw Dashboard API (this skill assumes the MCP server)
  • Authoring monitors or defining SLOs — though scope-not-every-question-is-a-dashboard covers recognising when the request is one of those
  • Datadog instrumentation: agent config, tracer setup, or deciding which metrics to emit in the first place
  • Non-Datadog observability platforms — the query semantics here do not transfer

Rule Categories

#CategoryPrefixCovers
1Purpose & Scopescope-Audience, the decision it drives, variables over duplication, when it should not be a dashboard
2Grounding in the Accountdisco-Confirming metrics, tags, and facets exist before querying them
3Query Semanticsquery-The queries that return a number and are wrong
4Choosing the Widgetwidget-Matching question to visualization; the pairs that are not interchangeable
5Dashboard JSONjson-What widget validation cannot see or cannot explain
6Layout & Readabilitylayout-Datadog's own standard for structure, display types, titles, sizing
7Shipping Itship-Validate-and-smoke-test before writing; region endpoints

Quick Reference

1. Purpose & Scope
2. Grounding in the Account
3. Query Semantics
4. Choosing the Widget
5. Dashboard JSON
6. Layout & Readability
7. Shipping It

How to Use

Read a reference file when its decision comes up. Each rule names the wrong default it corrects, then shows the canonical form — with an incorrect/correct contrast only where the wrong way is a real trap.

Three shortcuts worth taking first:

  • Building a dashboard from a request? Run scope-ask-audience-and-decision, then disco-never-invent-metric-names, before emitting anything. Skipping either produces a board that is fluent and useless.

  • Reviewing a dashboard whose numbers look wrong? Start with query-append-as-count-explicitly and query-percentiles-require-distributions. Together they account for most Datadog panels that are confidently incorrect, and neither leaves a trace in the UI.

  • Porting dashboard JSON written before 2025? json-queries-and-formulas-not-q and json-template-variables-take-defaults are the deprecations that still parse, so nothing points you at them.

  • Section definitions — category structure and ordering rationale

  • Rule template — for adding new rules

  • AGENTS.md — auto-built table of contents across all rules

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