troubleshoot-traefik

Diagnoses Trafik service issues by querying Netdata MCP health signals and correlating anomalies with hosts.

1|Updated Apr 17, 2026
One-click install
npx skills add https://github.com/netdata/skills --skill troubleshoot-traefik
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: troubleshoot-traefik
Source: https://github.com/netdata/skills/tree/main/skills/troubleshoot-traefik
Command: npx skills add https://github.com/netdata/skills --skill troubleshoot-traefik

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you troubleshoot Traefik service problems by turning vague symptoms (errors, latency, saturation, restarts) into a structured, signal-driven diagnostic flow using Netdata health signals.

Core Features & Use Cases

  • MCP-based Traefik triage: Queries Netdata via MCP for Traefik health signals and validates hypotheses against real chart contexts.
  • Operator playbook-aligned diagnostic tree: Guides an agent through the same remediation logic the Netdata operator playbook uses, without replacing the playbook.
  • Host correlation and anomaly ranking: Correlates Traefik signals with host-level CPU, memory, and disk I/O to distinguish application issues from noisy-neighbor or infrastructure causes.

Quick Start

Tell the agent: “Troubleshoot a Traefik incident from the Netdata alerts and walk me through the verification queries and likely remediations.”

Frequently Asked Questions about troubleshoot-traefik

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

FAQPage Schema
How do I troubleshoot Traefik high error rates and latency using Netdata?▼

Troubleshoot Traefik high error rates and latency by querying Netdata via MCP for traefik.* health signals and applying an operator playbook-aligned diagnostic tree to identify root causes. The Skill correlates Traefik metrics with host-level CPU, memory, and disk I/O to distinguish application issues from infrastructure saturation.

What is the best way to diagnose Traefik unexpected restarts and saturation during incident triage?▼

Diagnose Traefik unexpected restarts and saturation during incident triage by running list_nodes discovery, query_metrics, and find_anomalous_metrics over a time window to rank anomalies. The Skill validates hypotheses against real chart contexts and correlates host-level resource exhaustion with Traefik service degradation.

Can I use Netdata MCP queries to correlate Traefik errors with host-level CPU and memory issues?▼

Yes, you can use Netdata MCP queries to correlate Traefik errors with host-level CPU and memory issues during troubleshooting. The Skill applies host correlation and anomaly ranking to distinguish Traefik application problems from noisy-neighbor or infrastructure causes, then verifies remediation by re-running the same MCP queries.

Does Traefik troubleshooting with Netdata require manual verification after remediation?▼

Traefik troubleshooting with Netdata requires verification by re-running the same MCP queries after remediation to confirm the issue is resolved. The diagnostic flow queries traefik.* health signals, applies an operator playbook-aligned diagnostic tree, and validates that elevated errors, latency, or saturation metrics return to normal.

Why does Traefik show elevated latency and how do I find the root cause with observability signals?▼

Traefik shows elevated latency due to application errors, resource exhaustion, or infrastructure saturation, which you can root cause by querying Netdata for traefik.* health signals. The Skill applies an operator playbook-aligned diagnostic tree using list_nodes discovery, query_metrics, and find_anomalous_metrics over a time window to identify the underlying cause.

What Netdata signals do I need to diagnose Traefik operational issues during on-call debugging?▼

To diagnose Traefik operational issues during on-call debugging, you need Netdata traefik.* health signals queried via MCP using list_nodes discovery, query_metrics, and find_anomalous_metrics. The Skill applies an operator playbook-aligned diagnostic tree to turn vague symptoms like errors, latency, saturation, and restarts into a structured diagnostic flow.