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dt-obs-logs

Query, filter, and analyze Dynatrace log data for incident investigations.

Updated Aug 27, 2026
Search Tags:#time-series#log-analysis#pattern-detection#dql#dynatrace#log-search#incident-troubleshooting
One-click install
npx skills add https://github.com/virtualrussel/dynatrace-ai-workspace --skill dt-obs-logs-virtualrussel
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dt-obs-logs
Source: https://github.com/virtualrussel/dynatrace-ai-workspace/tree/main/.agents/skills/dt-obs-logs
Command: npx skills add https://github.com/virtualrussel/dynatrace-ai-workspace --skill dt-obs-logs-virtualrussel

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Dynatrace log data is vast and noisy; analysts struggle to isolate incidents, identify patterns, and measure error rates across services. This skill provides a unified interface to query, filter, and summarize logs to speed triage and root-cause analysis.

Core Features & Use Cases

  • Query and filter logs by time, severity, and entity to surface relevant entries.
  • Analyze patterns, trends, and error rates across dimensions (service, host, process).
  • Aggregate results and generate summaries for incident reviews and post-mortems.
  • Use case example: Identify spikes of ERROR-level logs in a service over a defined window and correlate with deployments or outages.

Quick Start

Ask the AI to fetch logs for the last 30 minutes from a target entity and return top error messages with counts.

Frequently Asked Questions about dt-obs-logs

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

FAQPage Schema
How do I query and filter Dynatrace logs to troubleshoot incidents?▼

To troubleshoot incidents with Dynatrace logs, query and filter data by time, severity, and entity. This isolates relevant entries across applications and infrastructure to speed up triage and root-cause analysis.

Can I analyze log patterns and calculate error rates across services?▼

Yes, you can analyze log patterns and calculate error rates across dimensions like service, host, and process. This enables trend analysis and identifies spikes in ERROR-level logs during outages.

What is the best way to investigate spikes in ERROR-level logs during a deployment?▼

The best way to investigate ERROR-level log spikes is to query a specific time window around the deployment, filter by severity, and aggregate results to correlate error trends with the event.

Does this log search approach support time-bounded investigations across infrastructure?▼

Yes, this log search supports time-bounded investigations across applications and infrastructure. It enables field extraction, aggregation, and time-series analysis to deliver structured insights for incident reviews.

How do I extract specific fields and aggregate Dynatrace log data for post-mortems?▼

To generate summaries for post-mortems, query logs within a defined window, extract specific fields, and aggregate results. This surfaces top error messages with counts for incident reviews.

Why is it difficult to isolate incidents in Dynatrace logs and how does filtering help?▼

Dynatrace log data is vast and noisy, making incident isolation difficult. Filtering by time, severity, and entity surfaces relevant entries, enabling pattern detection and faster root-cause analysis.

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