Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Smirnov-Labs/claude-skills --skill debugging-kubernetes-rolloutsgit clone --depth 1 https://github.com/Smirnov-Labs/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/smirnov-labs/claude-skills/debugging-kubernetes-rollouts)<a href="https://agentmods.dev/skills/smirnov-labs/claude-skills/debugging-kubernetes-rollouts"><img src="https://agentmods.dev/badge/skills/smirnov-labs/claude-skills/debugging-kubernetes-rollouts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/smirnov-labs/claude-skills/debugging-kubernetes-rollouts"><img src="https://agentmods.dev/badge/skills/smirnov-labs/claude-skills/debugging-kubernetes-rollouts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.04471 |
| Opus 5.5 | $0.00032 | $0.01788 |
| Sonnet 5.5 | $0.00016 | $0.00894 |
| Haiku 4.5 | $0.00008 | $0.00447 |
Grade A, and why
debugging-kubernetes-rollouts scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
# Then: wget -O- http://<service-name>:<port> How it starts
The opening of the file, as written. The whole thing — 664 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kubernetes Rollout Debugging Expert
Systematically debug Kubernetes deployments using kubectl, ArgoCD, GitOps workflows, and the kubernetes MCP server.
Core Philosophy: GitOps-First Debugging
When debugging K8s rollouts in a GitOps environment:
- Understand the flow: Git → ArgoCD → Kubernetes cluster
- Check each layer: Git manifest → ArgoCD sync status → K8s resources → Running pods
- Use the right tool: MCP for queries, kubectl for inspection, git for history
- Fix in Git: Manual kubectl changes are temporary; GitOps will revert them
Tool Selection Strategy
Use Kubernetes MCP Server When:
- Querying resource status across namespaces
- Getting structured data about deployments
- Listing resources with filtering
- Reading pod logs systematically
- Checking resource health
Use kubectl CLI When:
- Detailed resource inspection (
kubectl describe) - Executing commands in pods (
kubectl exec) - Port forwarding for debugging
- Applying temporary test manifests
- Getting raw YAML/JSON output
Use ArgoCD CLI When:
- Checking application sync status
- Forcing syncs or refreshes
- Viewing sync history
- Debugging sync errors
- Managing application health
Use Git When:
- Reviewing manifest changes
- Comparing versions
- Checking commit history
- Understanding deployment progression
Systematic Debugging Workflow
Phase 1: Deployment Status Assessment
Start by understanding the current state:
Check ArgoCD Application Status
# List all applications
argocd app list
# Get specific application status
argocd app get <app-name>
# Check sync status
argocd app get <app-name> --show-operation
# View recent sync history
argocd app history <app-name>
Check Kubernetes Deployment Status
# Get deployment status
kubectl get deployments -n <namespace>
# Check rollout status
kubectl rollout status deployment/<name> -n <namespace>
# View rollout history
kubectl rollout history deployment/<name> -n <namespace>
# Get detailed deployment info
kubectl describe deployment/<name> -n <namespace>
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 664 lines · 79 tokens per session scan A 1c86e16c699c
debugging-kubernetes-rollouts is a skill published in the GitHub repository Smirnov-Labs/claude-skills (6 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 4,471 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-17.
Other skills, from other repositories
gke-workload-troubleshooting
Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.
gke-workload-scaling-troubleshooting
Diagnoses GKE HorizontalPodAutoscaler (HPA) failures — metrics showing as , FailedGetResourceMetric / FailedGetScale / FailedComputeMetricsReplicas events, missing Pod resource requests, custom/external metrics-pipeline breakage (FailedGetExternalMetric / FailedGetCustomMetric, unavailable metrics adapter…
gke-ai-troubleshooting-tpu-mxla-hang
Diagnose GKE Cloud TPU multi-slice training hangs (Megascale HANGDETECTED logs and hang monitored events) using the ML Diagnostics Megascale XLA (MXLA) Hang Analyzer (gcloud alpha mldiagnostics monitored-events) and 1-minute Cloud Monitoring multi-slice latency metrics (kubernetes.io/container/multislice/).…
gke-ai-troubleshooting-tpu-performance-degradation
Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (gcloud alpha mldiagnostics monitored-events / hypercomputecluster.googleapis.com/v1alpha) and 1-minute Cloud Monitoring system metrics (kubernetes.io/node/accelerator/).…
gke-ai-troubleshooting-jobset-interruption
Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or…
gke-node-notready
Diagnoses GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't…