gke-workload-troubleshooting
Диагностика сбоев приложений в GKE
Что получите
Разбор причины сбоя и YAML-исправление в новом или существующем запросе на включение изменений в GitHub; без доступа к кластеру — также команды для ручной диагностики.
Что передать
Проект, кластер и его расположение, приложение, пространство имён, симптомы и время сбоя.
Проверен состав файлов. Запуск не тестировался. Что проверено
Оценки пользователей
Как использовать
Помогает инженерам разобраться, почему приложение в GKE не запускается или постоянно падает. Сопоставляет состояние контейнеров, события и журналы, чтобы отличить ошибки приложения от нехватки ресурсов, проблем образа и сетевых ограничений.
- Назовите проблемное приложение и время ошибки. Недостающие параметры агент попробует взять из активного окружения; пространство имён по умолчанию — default.
- Передайте симптомы: повторные падения, нехватка памяти, ожидание запуска, ошибка загрузки образа или тайм-аут соединения.
- Укажите репозиторий с манифестами, чтобы предложенное исправление можно было оформить через GitOps.
Условия работы
- Для живой диагностики нужны доступ к кластеру и настроенные gcloud и kubectl. При недоступности кластера работа продолжается по симптомам, исходному коду и манифестам без повторяющихся попыток подключения.
- Диагностика выполняется только на чтение. Исправления нельзя применять напрямую к кластеру: они передаются через GitOps, без ожидания слияния пользователем.
- Скилл предназначен для приложений в GKE, а не для создания инфраструктуры кластера, пулов узлов или других сервисов Google Cloud.
Пример запроса после установки
Разбери повторные падения payment-api в нашем GKE-кластере после обновления образа: проверь события и журналы, предложи исправление манифеста через запрос в GitHub.
Пример составлен по инструкции автора; результат запуска не проверялся.
Основание: инструкция закреплённой версииДля установки
Node.js, npm и Git для команды выше. Подключения и инструменты для работы скилла настраиваются отдельно.
Совместимые агенты
Совместимость указана для инструкций. Запуск в каждом приложении не тестировался; для веб-чата способ подключения может отличаться.
Файлы скилла
GitHubЧитать инструкцию SKILL.md
Исходный текст закреплённой версии.
---
name: gke-workload-troubleshooting
metadata:
category: Containers
description: >-
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 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:**
```bash
# 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:
```bash
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:**
```bash
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:**
```bash
# 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:**
```bash
# 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).
Проверка и особенности
Автоматическая проверка источника и файлов
Автоматически сверены публичный репозиторий, лицензия, YAML-заголовок SKILL.md, состав папки и Git-хеш каждого файла. Исполняемые файлы и срабатывания статических правил исключены из этой партии. Ручной аудит всех инструкций и проверка работы не проводились; эта проверка не гарантирует безопасность. Команда закреплена на проверенном коммите.
Интерес к репозиторию оцениваем по звёздам и форкам GitHub. Это показатели всего репозитория, а не числа установок отдельного скилла.
- Проверка состава
- 16.09.2026
- Проверенная версия
569cb1d55ac3- Файлы с кодом
- Не указаны в данных проверки
- Изменения на GitHub
- Нужна повторная проверкаПоследняя проверка: 11.10.2026
Репозиторий и лицензия
Это показатели всего репозитория, а не установки скилла. Снимок требует обновления.
- Последняя активность GitHub
- 09.10.2026
- Лицензия
- Apache-2.0
- Добавлен в каталог
- 16.09.2026
Полный текст лицензии
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