GKE Workload Troubleshooting Skill

Use this skill to systematically diagnose and resolve failures in application workloads deployed in GKE clusters. This skill operates non-interactively and enforces a read-only diagnostics boundary before proposing manifest or config corrections.

🔍 Diagnostic Workflow

Step 0: Non-Interactive Context Discovery & Time Window Definition

1. Parameter Extraction: Extract required context (project_id, cluster_name, cluster_location, workload_name, workload_namespace) non-interactively from the user prompt, active SETTINGS.md, or active environment defaults:

   • Default workload_namespace to default if omitted.
   • Infer missing cluster parameters from active environment (kubectl config current-context or gcloud config get-value project).
   • Prioritize non-interactive context discovery from prompts and environment defaults to ensure autonomous execution flow.

2. Cluster Credentials & Fallback Mode:

   • Attempt credential fetch: gcloud container clusters get-credentials {cluster_name} --region/--zone {cluster_location}
   • Fallback / Dry-Run Mode: If the cluster is unreachable, non-existent, or live command execution fails (such as in sandboxed evaluations, dry-run mode, or offline analysis):
     • Limit retry attempts to avoid resource exhaustion and context overflow in unreachable cluster scenarios.
     • Immediately present the exact sequence of kubectl diagnostic commands for the human operator to run.
     • Synthesize the root cause analysis and output the proposed GitOps manifest fix based on the reported symptoms.

3. Time Handling & Fallbacks:

   • Determine Issue Timestamp ({issue_time}):
     • Specific Time Provided: If the user provides a specific timestamp, use it as {issue_time}.
     • Relative Time Provided (e.g., "5 minutes ago"): Dynamically calculate the corresponding UTC timestamp based on current system time, and use it as {issue_time}.
     • No Time Provided (Default): Use current system time as {issue_time}.
   • Window Calculation: Center a 1-hour query window around {issue_time} (start_time = {issue_time} - 30m, end_time = {issue_time} + 30m).

---

Step 1: Analyze Pod Status and Conditions

Inspect the workload's active pod states and controller status.

Diagnostic Commands:

# 1. Inspect the deployment's actual selector labels:
kubectl get deployment {workload_name} -n {workload_namespace} -o jsonpath='{.spec.selector.matchLabels}'
# 2. Query the pods using the returned labels, for example:
kubectl get pods -l {selector_labels} -n {workload_namespace}
kubectl get deploy/{workload_name} -n {workload_namespace} -o yaml

Diagnostic Decision Tree:

• Phase: Pending:

  • The Pod cannot schedule on any node. Proceed directly to Step 2 (Query Namespace Events).

• State: CrashLoopBackOff / Error:

  • Container is booting but exiting repeatedly. Check the terminated status using:

  kubectl get pod {pod_name} -n {workload_namespace} -o jsonpath='{.status.containerStatuses[*].lastState.terminated}'
  

  • ExitCode: 137 (OOMKilled): Memory limit reached. Proceed to Step 3 (Inspect Logs) and inspect container startup command to differentiate between an application-level memory leak/loop vs an infrastructure capacity limit mismatch, then proceed to Step 5 to propose fixes.
  • ExitCode: 1 or other non-zero codes: The application code crashed. Proceed directly to Step 3 (Inspect Logs).

• State: ContainerCreating:

  • The container is blocked during volume mount, networking setup, or image pulling. Proceed directly to Step 2 (Query Namespace Events).

---

Step 2: Query Namespace Events

Look for infrastructure, volume, image, or scheduling alerts in GKE.

Diagnostic Command:

kubectl get events -n {workload_namespace} --sort-by='.metadata.creationTimestamp'
# Or query Cloud Logging for historical GKE events within the time window:
gcloud logging read "resource.type=\"k8s_cluster\" AND logName=\"projects/{project_id}/logs/events\" AND jsonPayload.involvedObject.namespace=\"{workload_namespace}\"" --start-time="{start_time}" --end-time="{end_time}" --project="{project_id}"

Note: Retrieve the sorted events list and manually inspect the event timestamps (CreationTimestamp/LastSeen) to identify failures occurring within the {start_time} and {end_time} window.

Signature Identifiers:

• FailedScheduling: Node resource exhaustion. Look for messages like 0/3 nodes are available: 3 Insufficient memory. or missing node affinity tolerations (e.g. Spot VM taints).
• FailedMount:
  • Missing PersistentVolumeClaim (PVC).
  • Missing Secret (Secret "{secret_name}" not found).
  • Missing ConfigMap (ConfigMap "{configmap_name}" not found).
• Failed / BackOff (Image Pull):
  • Wrong image tag, missing image registry authentication (e.g., ImagePullBackOff).
  • Resolution Steps for Wrong Image Tag:
  • Identify the failing container image name and the invalid tag.
  • Check the Git repository history for the last known working image tag for this workload. Run git log -p -S "{image_name}" -- {manifest_file_path} (or use git log on the folder containing manifests) to identify the previous working tag in Git.
  • If the invalid tag is a recent change in git history, compare it to the tag from the last successful commit.
  • Propose reverting the image tag to the last working version, or correcting the tag version in the manifest patch.

---

Step 3: Inspect Application Logs

Extract exceptions and stack traces from the application runtime.

Diagnostic Commands:

# Check current active log stream (handles multi-container pods)
kubectl logs {pod_name} -n {workload_namespace} --all-containers --tail=100

# Check logs from previously terminated container instances (handles multi-container pods)
kubectl logs {pod_name} -n {workload_namespace} --all-containers -p --tail=100

Signature Identifiers:

• Out-of-Memory (OOM) Analysis: Inspect container logs and startup commands (spec.containers[*].command). Differentiate between an Application Code Leak/Loop (unbounded array appending, memory leak signatures) vs an Infrastructure Capacity Ceiling Mismatch (legitimate workload demand exceeding limits).
• Stack Trace / Unhandled Exception: Look for language-specific stack traces (e.g., panic:, NullPointerException, Traceback (most recent call)). This indicates an application bug.
• Egress Network Timeout: Look for connection timeouts (e.g., Connection timed out, dial tcp: i/o timeout). Proceed to Step 4 (Verify Connectivity).
• Permission Errors (ReadOnlyRootFilesystem): Look for write errors (e.g., Read-only file system, Permission denied when writing to /tmp or /var/log). Propose adding an emptyDir volume mount to that directory in the manifest.

---

Step 4: Verify Service Connectivity and Network Policies

Troubleshoot connection drops to other services.

Diagnostic Commands:

# Verify target endpoint is active
kubectl get endpoints {target_service_name} -n {target_namespace}

# Query network policies inside namespace
kubectl get networkpolicies -n {workload_namespace} -o yaml

Logic & Dry-Run Fallback:

1. Live Cluster Mode:

   • If kubectl get endpoints returns an empty list, the target microservice itself is failing to schedule or boot (troubleshoot target service).
   • If endpoints exist but logs show timeouts, analyze NetworkPolicy egress blocks to verify if egress traffic to the target service's IP/port is allowed.

2. Sandboxed / Dry-Run Mode:

   • If live kubectl queries fail or cluster connection is unavailable, do NOT retry live cluster access or enter repetitive connection attempts.
   • Immediately inspect the application source code (e.g. worker.py, app.go, DB connection strings) or Deployment manifests to identify the target service hostname (e.g. account-db) and destination port (e.g. 5432).
   • Present the exact kubectl get endpoints and kubectl get networkpolicies commands for the user, and synthesize the required NetworkPolicy egress patch allowing traffic to the target service and port.

---

Step 5: Propose GitOps Correction

Following the GitOps boundary, do not apply patches directly to the cluster.

1. Synthesize the root cause analysis for the human operator (e.g. "payment-api is failing with exit code 137 because its memory limit is set to 256Mi while actual usage spiked to 270Mi").
2. Generate the corrected YAML manifest patch (e.g. increase memory limits, add missing Secret mounts, or add tolerations for Spot nodes).
3. Check if a branch or Pull Request (PR) already exists for this workload/failure. If so, update the existing branch/PR or notify the user instead of creating a duplicate. Otherwise, create a branch, commit the change, open a Pull Request (PR) on GitHub, and conclude the workflow (do not wait for human merge).