dt-app-notebooks

Automate Dynatrace notebook creation by structuring markdown context and DQL sections, then validating the output against the notebook schema version 7 to ensure proper formatting and metadata.

Yes, you can analyze existing notebook JSON to extract queries, sections, and visualizations. This allows rapid insights and review of investigation documentation without manually parsing the file structure.

Build a query library by generating documentation notebooks that capture reusable patterns and explanations. Structure the notebook with markdown context and DQL sections to organize the queries effectively.

Notebook validation requires adherence to the notebook schema version 7. This ensures the structured sections, timeframes, visualizations, and metadata conform to expected Dynatrace standards for automated processing.

Modify Dynatrace notebooks by structuring timeframes and visualizations within the notebook JSON. Add markdown sections for context and DQL sections for data queries, then validate the complete structure against the schema.

A local skill repository is required to support optional scripts, references, and assets used during notebook analysis and validation. It provides the necessary environment for automating creation and modification workflows.