dt-app-notebooks
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.
Resumen
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
}
}
}
]
}
}- Sections render in array order.
- Section types:
markdown,dql. (functionexists but is rare.) - Use string-int IDs (
"1","2", …); UUIDs are also accepted. content.defaultTimeframesets the default timeframe; each section can override viasection.state.input.timeframe. Hardcoded time filters in DQL are allowed.
Optional content properties: defaultSegments.
Reading & Analyzing
Fetch full content with dtctl get notebook <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 section queries before adding to the notebook.
- Set
namebefore deploying. - Prefer
autoSelectVisualization: trueinvisualizationSettingsunless the user requested a specific visualization type — whenfalse,state.visualizationmust be set explicitly. - Updating — ALWAYS read the current state first:
dtctl get notebook <id> -o json, save it asnotebook.json, modify that file, then deploy it. Never reconstruct JSON from scratch or inject anidmanually — both silently overwrite 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
Notebooks support a subset of Dynatrace visualizations:
- Time-series (require
timeseries/makeTimeseries):lineChart,areaChart,barChart,bandChart - Categorical (
summarize ... by:{field}):categoricalBarChart,pieChart,donutChart - Single value / gauge / meter:
singleValue,meterBar,gauge - Tabular (any data shape):
table,raw,recordView - Distribution/status:
histogram,honeycomb - Geographic maps:
choropleth,dotMap,connectionMap,bubbleMap - Matrix/correlation:
heatmap,scatterplot
Required field types per visualization: references/sections.md.
References
| File | When to Load |
|---|---|
| create-update.md | Creating/updating notebooks |
| sections.md | Section types, visualization field requirements, settings |
| analyzing.md | Reading notebooks, extracting queries, purpose identification |
Archivos 6 archivos
Instálala a mano
Deska ejecuta esto por ti, para cada agente que elijas. Para ejecutarlo tú en una terminal:
npx skills add dynatrace/dynatrace-for-ai --skill dt-app-notebooksMás de dynatrace
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