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.