skills.rest
openshift-eng
openshift-engOFFICIAL
Explore all skills by this author →
WebsiteGitHub

Analyze Disruption

Aggregate interval data, audit logs, and pod logs to identify disruption root causes.

1|Updated Apr 29, 2026
Search Tags:#root-cause#log-analysis#disruption#prow-ci#timeline-json#cluster-telemetry
One-click install
npx skills add https://github.com/openshift-eng/ga-regression-reports --skill analyze-disruption
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Analyze Disruption
Source: https://github.com/openshift-eng/ga-regression-reports/tree/main/.claude/skills/analyze-disruption
Command: npx skills add https://github.com/openshift-eng/ga-regression-reports --skill analyze-disruption

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill identifies and interprets disruption events across OpenShift Prow CI runs by ingesting interval data, audit logs, pod logs, and CPU metrics to surface actionable root causes.

Core Features & Use Cases

  • Collects and correlates disruption signals across multiple Prow job runs and backends
  • Clasifies disruptions by backend type and detects single-source fan-out versus multi-source patterns
  • Summarizes concurrent signals (OVS stalls, CPU pressure, etcd delays, cloud metrics) to guide remediation

Quick Start

Feed one or more disruption timeline JSON files to this tool to generate a structured disruption analysis report.

Frequently Asked Questions about Analyze Disruption

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

FAQPage Schema
How do I find the root cause of disruption events in OpenShift Prow CI runs?▼

You diagnose disruption events by feeding timeline JSON files to an analysis tool that aggregates interval data, audit logs, and pod logs. This categorizes backend types and reveals resource pressure patterns across Prow CI runs.

What is the best way to analyze cluster telemetry and pod logs for CI disruption investigations?▼

The best way is correlating concurrent signals like CPU pressure and etcd delays across multiple job runs. This detects single-source fan-out versus multi-source patterns to guide remediation.

Can I use timeline JSON files to diagnose OVS stalls and CPU pressure in OpenShift GA disruptions?▼

Yes, timeline JSON extraction enables the diagnosis of OVS stalls and CPU pressure in OpenShift GA disruptions. It summarizes concurrent signals across backends and runs to identify underlying root causes.

How do I detect single-source fan-out patterns across multiple Prow job backends?▼

You detect single-source fan-out patterns by classifying disruption events by backend type and correlating telemetry across Prow job runs. This reveals whether disruptions originate from one source or multiple sources.

Do I need to provide audit logs and CPU metrics to analyze disruption events in Prow CI?▼

Yes, analyzing disruption events in Prow CI requires ingesting interval data, audit logs, pod logs, and CPU metrics. These data sources are correlated to surface actionable root causes for remediation.

Related Skills

Explore More Data & Analytics Skills→
jesseotremblayjesseotremblay
1

processing-data

Transform, clean, and extract structured information from CSV and JSON data.

Community
Basic
edwardmonteiroedwardmonteiro

optimization.postmortem

Generate standardized postmortem documents with root-cause analysis and corrective actions.

Community
Basic
camoneartcamoneart
5

grafana-dashboards

Design and deploy Grafana dashboards for real-time system and application metrics.

Community
Intermediate
drndrn
23

ci-investigate

Analyze flaky CI failures across CircleCI and GitHub Actions runs.

Community
Advanced