dynatrace/dynatrace-for-ai

dt-app-dashboards

Work with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations.

All-time #6000 Trending #5227 Hot #4997 First seen Apr 3, 2026
8-week activity · all time api

Installation

$ npx skills add dynatrace/dynatrace-for-ai --skill dt-app-dashboards

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More details

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Repository health

Stars 135
License LICENSE
Default branch main
Open issues 7
Status Active

Skill metadata

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LicenseApache-2.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,657 B
  • docs SUMMARY.md 177 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2,100 installs

SKILL.md

Dynatrace Dashboard Skill

Overview

Dynatrace dashboards are JSON documents stored in the Document Store containing tiles (content/visualizations), layouts (grid positioning), and variables (dynamic query parameters).

When to use: Creating, modifying, querying, or analyzing dashboards.

Dashboard JSON Structure

{
  "name": "My Dashboard",
  "type": "dashboard",
  "content": {
    "version": 21,
    "variables": [],
    "tiles": { "<id>": { "type": "data|markdown", ... } },
    "layouts": { "<id>": { "x": 0, "y": 0, "w": 24, "h": 8 } }
  }
}
  • Tile IDs in tiles must match IDs in layouts
  • Grid is 24 units wide. Common widths: 24 (full), 12 (half), 6 (quarter)
  • Two tile types: markdown (text content) and data (DQL query + visualization)

Optional content properties: settings, refreshRate, annotations

Reading & Analyzing

Fetch full content with dtctl get dashboard <id> -o json --plain (describe returns metadata only), then inspect the JSON to discover its available properties. Carefully read [references/analyzing.md](references/analyzing.md) before analyzing.

Create/Update Workflow (Mandatory Order)

Carefully follow the workflow described in [references/create-update.md](references/create-update.md).

Key rules:

  • Load domain skills BEFORE generating queries — do not invent DQL
  • Validate ALL queries before adding to dashboard
  • No time-range filters in queries unless explicitly requested by user
  • Set name before deploying
  • Updating — ALWAYS download first: dtctl get dashboard <id> -o json --plain > dashboard.json, modify, then deploy the downloaded file. Never reconstruct JSON from scratch or inject an id manually — both silently overwrite any UI edits the user made since last deployment.
  • Deploy with dtctl apply — validation runs automatically, and the local file is deleted on success.

Visualization Types

  • Time-series (require timeseries/makeTimeseries): lineChart, areaChart, barChart, bandChart
  • Categorical (summarize ... by:{field}): categoricalBarChart, pieChart, donutChart
  • Single value/gauge (single numeric record): singleValue, meterBar, gauge
  • Tabular (any data shape): table, raw, recordList
  • Distribution/status: histogram, honeycomb
  • Maps: choroplethMap, dotMap, connectionMap, bubbleMap
  • Matrix: heatmap, scatterplot

Required field types per visualization: [references/tiles.md](references/tiles.md)

Variables Quick Reference

{ "version": 2, "key": "Service", "type": "query", "visible": true,
  "editable": true, "input": "smartscapeNodes SERVICE | fields name",
  "multiple": false }
  • Single-select: filter service.name == $Service
  • Multi-select: filter in(service.name, array($Service))
  • Types: query (DQL-populated), csv (static list), text (free-form)

Full variable reference: [references/variables.md](references/variables.md)

References

File When to Load
[create-update.md](references/create-update.md) Creating/updating dashboards
[tiles.md](references/tiles.md) Tile types, visualization field requirements, settings
[variables.md](references/variables.md) Variable types, replacement strategies, patterns
[analyzing.md](references/analyzing.md) Reading dashboards, extracting queries, health assessment