What problem does it solve? Diagnosing Kubernetes incidents from support bundles, must-gather archives, and raw kubectl output is slow and error-prone, often leading to premature conclusions. This Skill applies a disciplined, evidence-driven workflow that separates observed facts from inferences and produces a defensible root-cause analysis. ## Core Features & Use Cases - Evidence Bundle Intake: Inventories tarballs, must-gather data, pod logs, events, manifests, and node artifacts while treating customer data as read-only. - Hypothesis-Driven Diagnosis: Maintains competing hypotheses with confidence tracking across CrashLoopBackOff, ImagePullBackOff, Pending pods, DNS, CNI, ingress, storage, and kubelet issues. - Timeline and Change Correlation: Normalizes timestamps across logs and events to correlate rollouts, Helm releases, and node maintenance with observed symptoms. - Use Case: Given a must-gather archive from an OpenShift cluster where pods are stuck in CrashLoopBackOff, extract the bundle, correlate previous container logs with events and Service/Endpoint state, and receive a cited root-cause report with next actions. ## Quick Start Use the kubernetes-troubleshooting skill to analyze the uploaded support bundle and produce a hypothesis-driven root-cause analysis with cited evidence.