
Dt App Notebooks
If you're working with Dynatrace notebooks, this builds directly on the dt-app-dashboards skill with the structural differences mapped out. Notebooks use a section array instead of tiles and grids, version "7" instead of 21, and drop variables entirely. The key workflow detail: always validate queries with dtctl first, set autoSelectVisualization to true unless the user wants something specific, and deploy through the included deploy_notebook.sh script instead of raw dtctl apply. It catches broken queries before they hit your environment. The documentation includes a clear field mapping table showing where query strings and visualization settings live in notebooks versus dashboards, which saves you from hunting through nested JSON.
- skills.sh installs
- 2.9K
- GitHub stars
- 177
- last updated
- Aug 21, 2026
Install
$ npx -y skills add dynatrace/dynatrace-for-ai --skill dt-app-notebooks --agent claude-codeInstalls into .claude/skills of the current project.
Files
Dynatrace Notebook Skill
Overview
Dynatrace notebooks are JSON documents stored in the Document Store containing an ordered array of sections — markdown blocks for narrative and dql blocks for DQL queries with visualizations. Sections render top-to-bottom in array order.
When to use: Creating, modifying, querying, or analyzing notebooks.
Notebook JSON Structure
{
"name": "My Notebook",
"type": "notebook",
"content": {
"version": "7",
"defaultTimeframe": { "from": "now()-2h", "to": "now()" },
"sections": [
{ "id": "1", "type": "markdown", "markdown": "# Title" },
{
"id": "2", "type": "dql", "title": "Query Section", "showInput": true,
"state": {
"input": { "value": "fetch logs | summarize count()" },
"visualization": "table",
"visualizationSettings": { "autoSelectVisualization": true, "chartSettings": {} },
"querySettings": {
"maxResultRecords": 1000, "defaultScanLimitGbytes": 500,
"maxResultMegaBytes": 1, "defaultSamplingRatio": 10, "enableSampling": false
}
}
}
]
}
}
Sponsors
Details
- Repository
- github.com/dynatrace/dynatrace-for-ai
- Category
- Data Science & ML
- First seen
- Jun 3, 2026