Dt Obs Services

This is your DQL companion for service health monitoring across the full stack. It handles RED metrics (rate, errors, duration) with built-in timeseries queries, then goes deeper with runtime telemetry for six languages: JVM heap and GC pauses, Node.js event loop lag, .NET CLR stats, Python thread counts, PHP OPcache hits, and Go goroutine leaks. The skill knows when to use metric queries versus span-based analysis, covers service mesh overhead calculations, and includes messaging queue metrics. What I like here is the opinionated defaults: ask for threshold violations and it assumes 1000ms without making you specify, then shows you what it picked. Good for SLA tracking, performance degradation hunts, and runtime troubleshooting when response times spike.

skills.sh installs: 2.9K
GitHub stars: 177
last updated: Aug 21, 2026

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Install

$ npx -y skills add dynatrace/dynatrace-for-ai --skill dt-obs-services --agent claude-code

Installs into .claude/skills of the current project.

Files

Application Services Skill

Monitor application service performance, health, and runtime-specific metrics using DQL.

Core Capabilities

1. Service Performance (RED Metrics)

Monitor service Rate, Errors, Duration using metrics-based timeseries queries.

Key Metrics:

dt.service.request.response_time - Response time (microseconds)
dt.service.request.count - Request count
dt.service.request.failure_count - Failed request count

Common Use Cases:

Response time monitoring (avg, p50, p95, p99)
Error rate tracking and spike detection
Traffic analysis (throughput, peaks, growth)
Performance degradation detection
Multi-cluster comparison

Quick Example:

timeseries {
  p95 = percentile(dt.service.request.response_time, 95),
  total_requests = sum(dt.service.request.count),
  failures = sum(dt.service.request.failure_count)
}, by: {dt.service.name}
| fieldsAdd p95_ms = p95[] / 1000, error_rate_pct = (failures[] * 100.0) / total_requests[]

→ For detailed queries: See references/service-metrics.md