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dt-obs-services

Monitor service performance and runtime metrics for multiple languages using DQL.

2|Updated Apr 23, 2026
Search Tags:#java#service mesh#performance monitoring#distributed tracing#service performance#runtime metrics
One-click install
npx skills add https://github.com/pvellido1/traces-flow --skill dt-obs-services-pvellido1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dt-obs-services
Source: https://github.com/pvellido1/traces-flow/tree/main/.github/skills/dt-obs-services
Command: npx skills add https://github.com/pvellido1/traces-flow --skill dt-obs-services-pvellido1

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive monitoring and analysis of service performance and runtime metrics for various programming languages, enabling users to track and optimize their application performance.

Core Features & Use Cases

  • Service Performance Monitoring: Track RED metrics (Rate, Errors, Duration) for .NET, Java, Node.js, Python, PHP, and Go applications.
  • Advanced Service Analysis: Perform detailed analysis with custom filtering, aggregations, and SLA tracking.
  • Service Messaging Metrics: Monitor message-based service communication and processing.
  • Service Mesh Monitoring: Evaluate service mesh performance and overhead.
  • Runtime-Specific Monitoring: Monitor technology-specific runtime performance and resource usage metrics.
  • Use Case: Imagine you are a DevOps engineer responsible for a Java application running on a Kubernetes cluster. Use this Skill to monitor the application's performance, detect anomalies, and optimize resource usage.

Quick Start

Use the dt-obs-services skill to get a summary of service performance metrics for your Java application running on Kubernetes.

Frequently Asked Questions about dt-obs-services

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

FAQPage Schema
How do I monitor service performance and runtime metrics for a Java application?▼

Service performance monitoring tracks RED metrics (Rate, Errors, Duration) to measure application health and throughput. It captures request rates, failure counts, and response durations to evaluate overall service behavior.

Can I track service mesh performance and message-based communication metrics?▼

Yes, you can monitor service mesh performance and overhead alongside message-based service communication. This evaluates messaging processing metrics to track inter-service communication efficiency.

Does this service performance monitoring work with Node.js, Python, and Go applications?▼

Yes, runtime-specific monitoring supports .NET, Java, Node.js, Python, PHP, and Go applications. It captures technology-specific runtime performance and resource usage metrics across these environments.

What do I need to query distributed tracing and runtime metrics for my applications?▼

You need a Dynatrace environment with appropriate permissions to query distributed tracing and runtime metrics. The Skill uses DQL to analyze service performance and resource usage.

How do I analyze service performance with custom filtering and SLA tracking?▼

Advanced service analysis performs detailed evaluation using custom filtering, aggregations, and SLA tracking. This enables targeted investigation of service performance metrics and anomaly detection.

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