support-bundle-analysis

Diagnose Rancher and Longhorn support bundles by following a structured, phase-based framework that navigates pre-analysis, bundle structure assessment, and deep diagnosis to pinpoint root causes. It uses evidence-based methods and pattern libraries to deduce whether failures stem from applications, Kubernetes, or node hardware.

The best way to analyze Kubernetes pod failures and storage errors is to apply a phased diagnostic flow that reconstructs timelines and references a patterns library. This approach systematically identifies whether the root cause originates from application logic, Kubernetes configuration, or underlying node hardware.

Extract a Rancher support bundle using an included shell script, then open the main skill file to begin pre-analysis and define the problem context. This initial phase ensures bundle integrity and sets the diagnostic scope before diving into quick assessments and deep diagnosis.

Yes, this support bundle analysis approach works for both Rancher and Longhorn Kubernetes environments. It applies a modular architecture that navigates through bundle structure checks and deep diagnosis phases to identify storage errors, recurring pod failures, and network outages across these platforms.

You need a structured diagnostic flow for Kubernetes support bundle analysis when a bundle reveals recurring pod failures, storage errors, or network outages. It guides you through pre-analysis, quick assessments, and deep diagnosis to systematically deduce the root cause of cluster issues.

Without a pattern library, analyzing Rancher support bundles lacks the evidence-based methods needed to efficiently reconstruct failure timelines and deduce root causes. A structured, phase-based approach is required to systematically navigate bundle structures and accurately pinpoint application, Kubernetes, or node hardware failures.