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Dt App Dashboards

Organization
Dynatrace
dt-app-dashboards

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

Overview

PublisherDynatrace
Repositorydynatrace-for-ai
Skill namedt-app-dashboards
Stars
156
Forks
30
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Dynatrace on GitHub. Read the source before you install it.

Installation

Install the Dt App Dashboards AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git /tmp/dynatrace-for-ai
mkdir -p .claude/skills
cp -r /tmp/dynatrace-for-ai/skills/dt-app-dashboards .claude/skills/dt-app-dashboards
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dt App Dashboards in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dt App Dashboards on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dt App Dashboards is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

json
{
  "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 (describe returns metadata only), then inspect the JSON to discover its available properties. Carefully read references/analyzing.md before analyzing.

Create/Update Workflow (Mandatory Order)

Carefully follow the workflow described in 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 read the current state first: dtctl get dashboard <id> -o json, save it as dashboard.json, modify that file, then deploy it. 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. If it fails, fix all reported errors before re-applying.

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

Variables Quick Reference

json
{ "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

FileWhen to Load
create-update.mdCreating/updating dashboards
tiles.mdTile types, visualization field requirements, settings
variables.mdVariable types, replacement strategies, patterns
analyzing.mdReading dashboards, extracting queries, health assessment

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dt App Dashboards AI skill do?

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

Why use Dt App Dashboards on TypingMind?

Because you install it once and use it with any model. Dt App Dashboards is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dt App Dashboards in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-app-dashboards. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dt App Dashboards?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dt App Dashboards?

As many as you like. As long as a model supports skills, you can use Dt App Dashboards with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dt App Dashboards AI skill free?

Yes. It is published on GitHub by Dynatrace under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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