Dt app notebooks
Skills, prompts, and instructions for building AI agents on top of Dynatrace production context
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Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.
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SKILL.md
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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 --plain (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 download first:
dtctl get notebook <id> -o json --plain > notebook.json, modify, then deploy the downloaded file. 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, and the local file is deleted on success.
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 |