Mirrord Operator
metalbear-co/mirrord
Help users install and configure the mirrord Operator for team/enterprise environments.
Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE.
Install Gke AI Troubleshooting Handle Disruption GPU Tpu
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpu --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .claude/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .claude/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpuType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpu --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .agents/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .agents/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpu --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .cursor/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .cursor/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpu --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .gemini/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .gemini/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpuInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .github/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .github/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills gke-ai-troubleshooting-handle-disruption-gpu-tpu --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu .opencode/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gke-ai-troubleshooting-handle-disruption-gpu-tpu" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu into .opencode/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-ai-troubleshooting-handle-disruption-gpu-tpu", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
gke-ai-troubleshooting-handle-disruption-gpu-tpu- GitHub stars
- 21k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 601 words
- Files
- 1
- Skills in repo
- 150
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE.
- Works in 5 steps: Context Acquisition → [Low Risk] Check for Upcoming Scheduled… → [Low Risk] Investigation via Cloud… → …
- Diagnosing node disruptions
- Calls kubectl
- Predicting host maintenance events on GPU/TPU nodepools
What it does
Gke AI Troubleshooting Handle Disruption GPU Tpu is an agent skill from google/skills, published by the product's own GitHub organization. Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy…
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud. It works with Google Kubernetes Engine, Prometheus and Kubernetes. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Diagnosing node disruptions
- Predicting host maintenance events on GPU/TPU nodepools
- Inspecting node interruption PromQL metrics
- Auditing node taints
Example prompts
- “/gke-ai-troubleshooting-handle-disruption-gpu-tpu”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4b940dd. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
kubectl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.cloud.google.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Gke AI Troubleshooting Handle Disruption GPU Tpu loads about 1.6k tokens when it runs. Until then it costs about 153 tokens; SKILL.md has 601 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from google/skills at commit 4b940dd, republished under its Apache-2.0 licence (© google). 601 words, ~1,631 tokens.
Download SKILL.mdSave it as
.claude/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu/SKILL.md (or your agent's skills folder).- name
- gke-ai-troubleshooting-handle-disruption-gpu-tpu
- description
- Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.
- metadata.version
- 1.0.0
- metadata.category
- CloudObservabilityAndMonitoring
Handle Disruption on GPUs and TPUs Troubleshooting
🔍 Diagnostic Workflow
Step 0: Context Acquisition
- Mandatory: When a user asks to debug or investigate an actual workload
disruption, node crash, or unexpected restart without providing complete
cluster details, you MUST immediately halt and request all missing mandatory
parameters (
project_id,location,cluster_name,timestamp) BEFORE delivering theories or general diagnostic commands. Only skip context acquisition if the user explicitly requests a generic reusable runbook or provides a complete static telemetry/log dump for offline analysis. - Optional:
node_name,workload_name,workload_namespace,nodepool_name.
Step 1: [Low Risk] Check for Upcoming Scheduled Maintenance
Action: Propose running
kubectlto check if nodes have the scheduled maintenance label indicating an upcoming disruption.Example Command:
bash kubectl get nodes -l cloud.google.com/scheduled-maintenance-time -L cloud.google.com/scheduled-maintenance-timeInterpretation: The
SCHEDULED-MAINTENANCE-TIMEcolumn shows the Unix epoch time when the VM is scheduled for maintenance. If this label exists, a disruption is guaranteed to occur.
Step 2: [Low Risk] Investigation via Cloud Monitoring (PromQL)
Action: Call any available monitoring tool or provide PromQL for manual verification.
Mandatory Monitoring Rule: Whenever recommending follow-up monitoring or interruption tracking over time, you MUST explicitly present a PromQL query using the metric
kubernetes_io:node_interruption_countfiltered byinterruption_reason="HW/SW Maintenance". Do not suggest general Cloud Monitoring dashboards or Metrics Explorer without providing this specific PromQL metric expression.Example Query:
promql # Fetch host maintenance events for nodes sum by (interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_interruption_count{monitored_resource="k8s_node", interruption_reason="HW/SW Maintenance"}[${__interval}]))promql # See the interruption count aggregated by node pool sum by (node_pool_name,interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_pool_interruption_count{monitored_resource="k8s_node_pool", interruption_reason="HW/SW Maintenance", node_pool_name="{nodepool_name}" }[${__interval}]))Interpretation: If
kubernetes_io:node_interruption_countshows values > 0 forinterruption_reason="HW/SW Maintenance", it indicates the underlying Compute Engine VM was interrupted due to scheduled host maintenance.
Step 3: [Low Risk] Investigation via Cloud Logging & Node Taints
- Action: Call
query_logsor instruct the user to filter their GKE logs for active host maintenance events, and check node taints. - Guidance: Look for occurrences in Cloud Logging where
cloud.google.com/active-node-maintenanceis set toONGOING. To check if GKE has cordoned the terminating node to prevent new workloads from being scheduled, verify whether thecloud.google.com/impending-node-termination:NoScheduletaint is present (either in GKE event logs or directly viakubectl describe node). - Interpretation:
cloud.google.com/active-node-maintenanceset toONGOINGmeans workloads are actively being stopped by GKE due to host maintenance.cloud.google.com/impending-node-termination:NoScheduletaint means GKE has cordoned the node to prevent new Pods from being scheduled on the terminating node. DO NOT recommend tolerating this taint.
Show full SKILL.md (212 more words)Show less
Step 4: Conclusion and Resolution
- Action: Provide a summary of findings to the user and suggest appropriate mitigation strategies if host maintenance events were confirmed or scheduled.
- Reporting Rule: Signal Only. Report high-signal information indicating that the disruption was caused by Compute Engine host maintenance, specifically affecting the underlying GPU/TPU nodes. DO NOT dump raw logs.
- Negative Findings Rule-Out: If node scheduled-maintenance labels, PromQL interruption counts, and active maintenance logs all return negative/empty results, definitively conclude that Compute Engine host maintenance did NOT cause the disruption. Direct the user to investigate application-level causes (such as OOMKill events, CUDA runtime errors, or resource limits) and do not propose host maintenance mitigations as the primary resolution.
- Mandatory Workload Protection Triad: Whenever host maintenance is
identified or anticipated on GPU/TPU nodes, consistently recommend all three
complementary mitigations together:
- Configure Graceful Termination: For workloads that need time to save
state (e.g., ML frameworks checkpointing via Orbax), follow the guide to
Enable disruption handling
and set
spec.terminationGracePeriodSeconds(up to 60 minutes) to handle theSIGTERMsignal before node shutdown. - Enable Opportunistic Maintenance: To automatically trigger maintenance when GKE detects that GPU/TPU nodes are idle, configure Opportunistic Maintenance.
- Configure PodDisruptionBudgets (PDBs): Ensure your workload uses a
PodDisruptionBudgetto maintainminAvailablereplicas during evictions and disruptions.
- Configure Graceful Termination: For workloads that need time to save
state (e.g., ML frameworks checkpointing via Orbax), follow the guide to
Enable disruption handling
and set
© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu of google/skills.
Open the folder on GitHubat commit 4b940dd
Compare with similar skills
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| Gke AI Troubleshooting Handle Disruption GPU Tpu this skillgoogle/skills | 21k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Mirrord Operatormetalbear-co/mirrord | 5.4k | 1 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Devopsnicepkg/auto-company | 195 | 2 repos | ~814 | Automated safety check: Pass | MIT | |
| KubeShark for KubernetesLukasNiessen/kubernetes-skill | 446 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Kcli Cluster Deploymentkarmab/kcli | 653 | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Aicr Analyzing SnapshotsNVIDIA/aicr | 440 | — | ~3.5k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Gke AI Troubleshooting Handle Disruption GPU Tpu
What does Gke AI Troubleshooting Handle Disruption GPU Tpu do?
Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Gke AI Troubleshooting Handle Disruption GPU Tpu is an agent skill from google/skills, published by the product's own GitHub organization. Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE.
When should I use Gke AI Troubleshooting Handle Disruption GPU Tpu?
Gke AI Troubleshooting Handle Disruption GPU Tpu fits situations like: diagnosing node disruptions; predicting host maintenance events on GPU/TPU nodepools; inspecting node interruption PromQL metrics; auditing node taints.
How do I install Gke AI Troubleshooting Handle Disruption GPU Tpu in Claude Code?
Run `npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a claude-code`. Or copy the skill folder (skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu in google/skills) into .claude/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu in your project. Claude Code loads it when a task matches its description.
How do I install Gke AI Troubleshooting Handle Disruption GPU Tpu in Codex?
Run `npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a codex`. Or copy the skill folder (skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu in google/skills) into .agents/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu in your project. Codex loads it when a task matches its description.
Can I use Gke AI Troubleshooting Handle Disruption GPU Tpu in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu, .gemini/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu, .github/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu and .opencode/skills/gke-ai-troubleshooting-handle-disruption-gpu-tpu in your project.
What does Gke AI Troubleshooting Handle Disruption GPU Tpu need to run?
Going by SKILL.md and its folder, Gke AI Troubleshooting Handle Disruption GPU Tpu needs the command-line tools its instructions call (kubectl).
Does Gke AI Troubleshooting Handle Disruption GPU Tpu access the network?
SKILL.md names 1 domain. As links in the text: docs.cloud.google.com. This is read from the text; nothing was executed.
Is Gke AI Troubleshooting Handle Disruption GPU Tpu safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Gke AI Troubleshooting Handle Disruption GPU Tpu use?
Gke AI Troubleshooting Handle Disruption GPU Tpu is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Gke AI Troubleshooting Handle Disruption GPU Tpu use?
About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Gke AI Troubleshooting Handle Disruption GPU Tpu?
Skills that share tags, products or a category with Gke AI Troubleshooting Handle Disruption GPU Tpu: Mirrord Operator (metalbear-co/mirrord, 5.4k stars), Devops (nicepkg/auto-company, 195 stars), KubeShark for Kubernetes (LukasNiessen/kubernetes-skill, 446 stars) and Kcli Cluster Deployment (karmab/kcli, 653 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gke AI Troubleshooting Handle Disruption GPU Tpu?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,097 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on October 9, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.