Run the cluster discovery script (or the shell script directly):

bun run ./_skills/connections/k8s/k8s/scripts/discover.ts
# or
./get_k8s_cluster_overview.sh

This provides comprehensive cluster analysis (nodes, pods, resources, services, events) in a single parallelized call. Only run targeted kubectl commands AFTER reviewing the overview output for specific deep-dives.

Critical Requirements

🚨 MANDATORY: ALL independent kubectl operations MUST run in parallel using background jobs (&) + wait

FORBIDDEN: Sequential loops for item in $items; do kubectl get $item; done
REQUIRED: Background jobs for item in $items; do kubectl get $item & done; wait
Read-only commands only (get, describe, top, logs) - never modify resources

Parallel Execution

When to parallelize:

Multiple namespaces/nodes/pods/resources → ALWAYS parallel
Operations with data dependencies → Sequential only

Pattern:

# CORRECT: Parallel (0.5s for 30 pods)
for pod in $pods; do kubectl get pod "$pod" -n "$ns" & done; wait

# WRONG: Sequential (15s for 30 pods)
for pod in $pods; do kubectl get pod "$pod" -n "$ns"; done

Best practice - Batch:

# Fastest: Single kubectl call per namespace
kubectl get pods -n "$ns" -o json | jq '.items[] | {name, phase, ready}'

Output Format

Complex data: -o json with jq (better AI parsing) kubectl get pods -n $ns -o json | jq -r '.items[] | "\(.metadata.name)\t\(.status.phase)"'
Simple lists: -o jsonpath or --no-headers (token efficient) kubectl get pods -n $ns -o jsonpath='{.items[*].metadata.name}'
CRITICAL: Always filter JSON with jq - never dump raw output
Machine-readable only: no decorative formatting, never expose credentials

Optimization Rules

Batch operations: Single kubectl get -o json instead of multiple calls BAD: kubectl get node $n -o jsonpath='{.status.nodeInfo.machineID}'; kubectl get node $n -o jsonpath='{.status.nodeInfo.kubeletVersion}' GOOD: kubectl get node $n -o json | jq '{machineID: .status.nodeInfo.machineID, kubeletVersion: .status.nodeInfo.kubeletVersion}'
Cache namespaces: Get once and reuse
Filter at source: Use --field-selector and --selector before post-processing
Resource usage: kubectl top requires metrics-server; handle gracefully if unavailable

Common Patterns

List: kubectl get <resource> -n <ns> -o json | jq -r '.items[] | ...'
Filter by label: kubectl get pods -n <ns> -l app=myapp
Filter by field: kubectl get pods --field-selector=status.phase=Running
Events: kubectl get events -n <ns> --sort-by='.lastTimestamp'
Logs: kubectl logs <pod> -n <ns> --tail=100 (use cautiously)

Common Pitfalls

Multiple calls for same resource → Use single -o json + jq
Not caching namespace list → Cache once
Sequential namespace processing → Always parallelize
Raw JSON dumps → Always filter with jq first
kubectl describe verbose → Use kubectl get -o json | jq instead

Helper Script

./get_k8s_cluster_overview.sh (in sandbox home) - YOUR GO-TO STARTING POINT Provides: cluster info, nodes, pods, resources, namespaces, services, events Usage: ./get_k8s_cluster_overview.sh Output: Structured JSON or machine-readable text (all operations parallelized) TIP: Run this FIRST, then drill down into specific areas based on findings

Anti-Hallucination Rules

NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

k8s

Kubernetes CLI Skill

Workflow

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