dt-app-notebooks

Construct and update Dynatrace notebooks with DQL queries and visualizations.

Updated Aug 23, 2026

Search Tags:#documentation #investigation #workflows #notebooks #dql #dynatrace

Ready to run

npx skills add https://github.com/virtualrussel/dynatrace-ai-workspace --skill dt-app-notebooks-virtualrussel

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dt-app-notebooks
Source: https://github.com/virtualrussel/dynatrace-ai-workspace/tree/main/.agents/skills/dt-app-notebooks
Command: npx skills add https://github.com/virtualrussel/dynatrace-ai-workspace --skill dt-app-notebooks-virtualrussel

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Dynatrace notebooks are complex to create, modify, and reason about; this skill provides a structured approach to authoring, querying, and analyzing notebook JSON, sections, DQL queries, and visualizations to accelerate investigations and knowledge capture.

Core Features & Use Cases

Create, update, and query Dynatrace notebooks stored in the Document Store, including sections, timeframes, and visualizations.
Analyze notebook structure, extract metadata, and support progressive loading of references for task-focused learning.
Collaborate on investigation workflows and documentation with reusable patterns and templates.

Quick Start

Create a new notebook with a markdown context, then add DQL sections to start an investigation and run queries to gain insights.

Frequently Asked Questions about dt-app-notebooks

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create Dynatrace notebooks with DQL queries for incident investigation?▼

Create Dynatrace notebooks by composing markdown context sections and DQL queries stored in the Document Store, adding unique IDs, default timeframes, and visualizations to structure incident investigations and capture insights.

What is the best way to structure Dynatrace notebooks for performance analysis?▼

The best way to structure Dynatrace notebooks is by composing sections with unique IDs, default timeframes, and validation, applying progressive reference loading to ensure production-ready performance analysis and query libraries.

Can I use DQL queries in Dynatrace notebooks stored in the Document Store?▼

Yes, you can construct, update, and query Dynatrace notebooks stored in the Document Store by composing DQL sections, timeframes, and visualizations to accelerate investigations and knowledge capture workflows.

How do I collaborate on investigation workflows using Dynatrace notebooks?▼

Collaborate on investigation workflows by creating reusable documentation templates and query libraries within Dynatrace notebooks, extracting metadata and applying structured sections to support end-to-end knowledge capture.

Does this approach support on-demand references and scripts for Dynatrace notebooks?▼

Yes, this approach supports on-demand references, scripts, and assets integration within Dynatrace notebooks, applying progressive reference loading and validation to ensure production-ready investigation and documentation workflows.

What are the limitations of using Dynatrace notebooks for query libraries?▼

Dynatrace notebooks require proper structure including unique IDs, default timeframes, and validation to function effectively as query libraries; without these, complex notebook JSON becomes difficult to create, modify, and reason about.