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Colvin-Y
Colvin-YCOMMUNITY
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kubernetes-ontology-access

Explore Kubernetes cluster topology and diagnose Pod and workload failures.

3|Updated Apr 23, 2026
Search Tags:#devops#troubleshooting#ontology#kubernetes#ai-agent#topology
One-click install
npx skills add https://github.com/Colvin-Y/kubernetes-ontology --skill kubernetes-ontology-access
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kubernetes-ontology-access
Source: https://github.com/Colvin-Y/kubernetes-ontology/tree/main/skills/kubernetes-ontology-access
Command: npx skills add https://github.com/Colvin-Y/kubernetes-ontology --skill kubernetes-ontology-access

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the complexity of manual Kubernetes troubleshooting by providing a structured, read-only graph-based approach to exploring cluster relationships, dependencies, and failure points.

Core Features & Use Cases

  • Diagnostic Subgraph Queries: Automatically generate focused diagnostic graphs for Pods, Workloads, and storage components to identify root causes of failures.
  • Visual Topology Inspection: Seamlessly hand off complex cluster state data to a human-readable topology viewer for visual validation.
  • Use Case: When a deployment fails with an ImagePullBackOff or storage mounting error, use this skill to trace the entire dependency chain from the Pod through the PVC, PV, and CSI driver to pinpoint the exact configuration mismatch.

Quick Start

Use the kubernetes-ontology-access skill to diagnose the failing deployment in the production namespace and open the topology viewer for inspection.

Frequently Asked Questions about kubernetes-ontology-access

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

FAQPage Schema
How do I troubleshoot Kubernetes deployment failures by tracing dependencies?▼

Troubleshoot Kubernetes deployment failures by generating read-only diagnostic subgraphs that trace dependency chains from Pods through PVCs, PVs, and CSI drivers to pinpoint configuration mismatches. This graph-based approach visually maps cluster relationships to identify exact failure points.

What is the best way to explore Kubernetes cluster topology for an AI agent?▼

Exploring Kubernetes topology for AI agents is handled through daemon-backed graph queries that provide structured, read-only access to cluster state data. This mechanism allows automated workflows to inspect workloads, pods, and infrastructure dependencies without manual intervention.

Can I use a Kubernetes topology diagnostic skill in an air-gapped cluster environment?▼

Yes, Kubernetes topology diagnostics support air-gapped cluster environments through Helm-based deployment and release binary installation. This allows private clusters to facilitate automated troubleshooting of workloads and infrastructure dependencies without requiring external connectivity.

How do I identify the root cause of an ImagePullBackOff or storage mounting error in Kubernetes?▼

Identify root causes of ImagePullBackOff or storage mounting errors by querying focused diagnostic graphs for the failing Pod. Tracing the entire dependency chain visually validates the cluster state to reveal exact configuration mismatches causing the failure.

Does this Kubernetes troubleshooting approach modify cluster resources during diagnostics?▼

No, this Kubernetes troubleshooting approach is strictly read-only. It provides graph-based exploration of cluster relationships and dependencies to identify failure points without modifying any active resources or infrastructure configurations during the diagnostic workflow.

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