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dt-obs-services

Analyze application service health using RED metrics and runtime telemetry.

2|1|Updated Apr 15, 2026
Search Tags:#multilang#dql#service-health#service-monitoring#red-metrics#runtime-metrics
One-click install
npx skills add https://github.com/israel-salgado/dt-mcp-server --skill dt-obs-services-israel-salgado
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dt-obs-services
Source: https://github.com/israel-salgado/dt-mcp-server/tree/main/.agents/skills/dt-obs-services
Command: npx skills add https://github.com/israel-salgado/dt-mcp-server --skill dt-obs-services-israel-salgado

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Helps operators monitor application services by collecting and analyzing RED metrics, runtime telemetry, and service health across languages.

Core Features & Use Cases

  • Service Performance (RED Metrics): Monitor rate, errors, and duration to gauge service health and throughput.
  • Advanced Service Analysis: Span-based queries for SLA-like tracking, health scoring, and operation-level analysis.
  • Service Mesh Monitoring: Ingress performance, overhead comparison, and mesh failures.
  • Runtime-Specific Monitoring: JVM, Node.js, .NET, Python, PHP, and Go metrics by runtime for targeted troubleshooting.
  • Service Messaging Metrics: Throughput and processing metrics for queues and topics.

Quick Start

Run a quick health check by querying the RED metrics for your services using the DQL prompts provided in this Skill.

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 health using RED metrics across multiple programming languages?▼

Service health monitoring with RED metrics tracks rate, errors, and duration across Java, .NET, Node.js, Python, PHP, and Go services. It analyzes runtime telemetry and provides structured DQL prompts to guide multi-cluster performance comparisons.

What is the best way to track SLA-like performance for application services?▼

Tracking SLA-like performance uses span-based queries to measure service duration and health scoring. This approach enables operation-level analysis and structured health checks without requiring complex custom instrumentation.

Can I use RED metrics to monitor service mesh ingress and overhead?▼

RED metrics support service mesh monitoring by analyzing ingress performance, comparing overhead, and identifying mesh failures. This provides visibility into how the mesh impacts overall service throughput and latency.

How do I check runtime-specific metrics for JVM and Node.js applications?▼

Runtime-specific monitoring analyzes targeted telemetry for JVM, Node.js, .NET, Python, PHP, and Go environments. It surfaces runtime metrics to help troubleshoot language-specific performance bottlenecks.

Does this approach support multi-cluster service performance comparisons?▼

Multi-cluster service comparisons are supported by analyzing RED metrics and runtime telemetry across different environments. This enables side-by-side health scoring and throughput analysis for distributed deployments.

How do I measure messaging queue throughput and processing metrics?▼

Messaging metrics measure throughput and processing performance for queues and topics. By analyzing service messaging telemetry, you can identify bottlenecks in asynchronous communication and gauge overall processing health.

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