Kubernetes Health Diagnostics

Dynamic, discovery-driven health checks for any Kubernetes cluster configuration

BEFORE YOU START

Impact	Value
Token Savings	~70% vs manual kubectl exploration
Setup Time	0 min (uses existing kubectl config)
Coverage	Adapts to installed operators automatically

Known Issues Prevented

Problem	Root Cause	How This Skill Helps
Missing operator health	Static checklists miss CRDs	Dynamic API discovery detects all installed operators
Stale diagnostics	Manual checks become outdated	Real-time cluster API interrogation
Incomplete coverage	Unknown cluster configuration	Automatically activates relevant sub-agents

Quick Start

1. Verify cluster access: Ensure kubectl is configured and can reach your cluster
2. Run discovery: Execute discover_apis.py to detect installed operators
3. Dispatch agents: Use the orchestrator to run health checks based on discovery

# Step 1: Verify kubectl context
kubectl config current-context
kubectl cluster-info

# Step 2: Run API discovery
uv run .claude/skills/kubernetes-health/scripts/discover_apis.py

# Step 3: Review detected operators and dispatch health agents

Critical Rules

Always

• Verify kubectl context before running health checks
• Use read-only kubectl commands (get, describe, logs)
• Run core health checks before operator-specific checks
• Aggregate results using the provided scoring methodology

Never

• Modify cluster resources during health checks
• Expose secret values in health reports (metadata only)
• Skip context verification for production clusters
• Assume operator presence without API discovery

Common Mistakes

Mistake	Why It's Wrong	Correct Approach
Hardcoding operator checks	Misses installed operators, checks missing ones	Use API discovery to detect what's installed
Sequential agent dispatch	Slow for multi-operator clusters	Run operator agents in parallel (same priority)
Raw kubectl output	Token inefficient, hard to parse	Use scripts for condensed JSON output

Bundled Resources

Scripts

Script	Purpose
scripts/discover_apis.py	Discovers all API groups and detects installed operators
scripts/health_orchestrator.py	Maps discovered APIs to specialized health agents
scripts/aggregate_report.py	Aggregates multi-agent results into unified report

References

File	Contents
references/operator-checks.md	Detailed health checks for each supported operator
references/health-scoring.md	Scoring methodology and weight assignments

Templates

File	Purpose
templates/health-report.json	JSON schema for health report output

Dependencies

Required

Package	Version	Purpose
kubectl	Latest	Cluster interaction
Python	>= 3.11	Script execution
uv	Latest	Python script runner

Optional

Package	Version	Purpose
kubernetes	>= 28.1.0	Python client (for advanced discovery)

Supported Operators

The skill automatically detects and dispatches specialized agents for:

Operator	API Group	Agent
Core K8s	(always)	k8s-core-health-agent
Crossplane	crossplane.io	k8s-crossplane-health-agent
ArgoCD	argoproj.io	k8s-argocd-health-agent
Cert-Manager	cert-manager.io	k8s-certmanager-health-agent
Prometheus	monitoring.coreos.com	k8s-prometheus-health-agent

Health Scoring

Status	Score Range	Criteria
HEALTHY	90-100	All checks pass, no warnings
DEGRADED	60-89	Some warnings, no critical issues
CRITICAL	0-59	Critical issues affecting availability

Troubleshooting

kubectl connection issues

# Verify context
kubectl config current-context

# Test connectivity
kubectl cluster-info

# Check permissions
kubectl auth can-i get pods --all-namespaces

Discovery returns empty results

• Ensure cluster is reachable
• Check RBAC permissions for API discovery
• Verify kubectl version compatibility

Agent dispatch failures

• Confirm discovered API group matches agent trigger
• Check agent file exists in .claude/agents/specialized/kubernetes/
• Review agent tool restrictions

Setup Checklist

• kubectl configured and connected to cluster
• Python 3.11+ installed
• uv installed for script execution
• Read permissions on cluster resources
• Agent files present in .claude/agents/specialized/kubernetes/