agentleFS
nodnarbnitramnodnarbnitram/claude-code-extensions/.claude/skills/kubernetes-health/SKILL.md

kubernetes-health

Skill16 starsChanged 7 months agoMITView on GitHub

Comprehensive Kubernetes cluster health diagnostics using dynamic API discovery. Use when checking cluster health, troubleshooting K8s issues, or running health assessments.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 7 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈7

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Verify cluster access: Ensure kubectl is configured and can reach your cluster

    line 29
  2. Distinctive:

    Dispatch agents: Use the orchestrator to run health checks based on discovery

    line 31
  3. Distinctive:

    Verify kubectl context before running health checks

    line 48
  4. Distinctive:

    Use read-only kubectl commands (get, describe, logs)

    line 49
  5. Distinctive:

    Run core health checks before operator-specific checks

    line 50
  6. Distinctive:

    Ensure cluster is reachable

    line 144
  7. Distinctive:

    Check RBAC permissions for API discovery

    line 145
  8. Distinctive:

    Verify kubectl version compatibility

    line 146
  9. Very specific:

    Check agent file exists in .claude/agents/specialized/kubernetes/

    line 151

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Kubernetes Health Diagnostics

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

BEFORE YOU START

ImpactValue
Token Savings~70% vs manual kubectl exploration
Setup Time0 min (uses existing kubectl config)
CoverageAdapts to installed operators automatically

Known Issues Prevented

ProblemRoot CauseHow This Skill Helps
Missing operator healthStatic checklists miss CRDsDynamic API discovery detects all installed operators
Stale diagnosticsManual checks become outdatedReal-time cluster API interrogation
Incomplete coverageUnknown cluster configurationAutomatically 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

MistakeWhy It's WrongCorrect Approach
Hardcoding operator checksMisses installed operators, checks missing onesUse API discovery to detect what's installed
Sequential agent dispatchSlow for multi-operator clustersRun operator agents in parallel (same priority)
Raw kubectl outputToken inefficient, hard to parseUse scripts for condensed JSON output

Bundled Resources

Scripts

ScriptPurpose
scripts/discover_apis.pyDiscovers all API groups and detects installed operators
scripts/health_orchestrator.pyMaps discovered APIs to specialized health agents
scripts/aggregate_report.pyAggregates multi-agent results into unified report

References

FileContents
references/operator-checks.mdDetailed health checks for each supported operator
references/health-scoring.mdScoring methodology and weight assignments

Templates

FilePurpose
templates/health-report.jsonJSON schema for health report output

Dependencies

Required

PackageVersionPurpose
kubectlLatestCluster interaction
Python>= 3.11Script execution
uvLatestPython script runner

Optional

PackageVersionPurpose
kubernetes>= 28.1.0Python client (for advanced discovery)

Supported Operators

The skill automatically detects and dispatches specialized agents for:

OperatorAPI GroupAgent
Core K8s(always)k8s-core-health-agent
Crossplanecrossplane.iok8s-crossplane-health-agent
ArgoCDargoproj.iok8s-argocd-health-agent
Cert-Managercert-manager.iok8s-certmanager-health-agent
Prometheusmonitoring.coreos.comk8s-prometheus-health-agent

Health Scoring

StatusScore RangeCriteria
HEALTHY90-100All checks pass, no warnings
DEGRADED60-89Some warnings, no critical issues
CRITICAL0-59Critical 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/

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.