support-bundle-analysis

Analyze Rancher/Longhorn support bundles to identify root causes.

Updated Jan 19, 2026

Search Tags:#Kubernetes #diagnostics #analyze #Longhorn #support-bundle #Rancher

Ready to run

npx skills add https://github.com/COLDTURNIP/longhorn-workspace --skill support-bundle-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: support-bundle-analysis
Source: https://github.com/COLDTURNIP/longhorn-workspace/tree/main/.opencode/skills/support-bundle-analysis
Command: npx skills add https://github.com/COLDTURNIP/longhorn-workspace --skill support-bundle-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured, problem-driven framework to analyze Rancher/Longhorn support bundles and identify the root cause of cluster issues.

Core Features & Use Cases

Pre-Analysis: Ensure bundle integrity and define the problem context before diving in.
Phase-based Diagnosis: Navigate through Bundle Structure (Phase 0-1), quick assessments, and deep diagnosis to pinpoint failures.
Root Cause Analysis: Use pattern libraries and evidence-based methods to reconstruct timelines and deduce root causes.
Use Case: When a support bundle reveals recurring pod failures, storage errors, or network outages, systematically follow the phases to identify whether root cause is application, Kubernetes, or node hardware.

Quick Start

Start by extracting a support bundle using the included extract-bundle.sh, then open SKILL.md to begin Pre-Analysis and Phase 0-1.

Frequently Asked Questions about support-bundle-analysis

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

FAQPage Schema

How do I diagnose Rancher and Longhorn support bundles to find cluster issues?▼

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.

What is the best way to analyze Kubernetes pod failures and storage errors from a support bundle?▼

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.

How do I extract and start analyzing a Rancher support bundle for network outages?▼

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.

Does this support bundle analysis approach work for both Rancher and Longhorn Kubernetes environments?▼

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.

When do I need a structured diagnostic flow for Kubernetes support bundle analysis?▼

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.

What are the limitations of analyzing Rancher support bundles without a pattern library?▼

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.