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 gke-labs/kube-agents --skill gke-ai-troubleshooting-jobset-interruptiongit clone --depth 1 https://github.com/gke-labs/kube-agentsWrote 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/gke-labs/kube-agents/gke-ai-troubleshooting-jobset-interruption)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-jobset-interruption"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-jobset-interruption/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/gke-labs/kube-agents/gke-ai-troubleshooting-jobset-interruption"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-jobset-interruption.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 | $0.00083 | $0.02579 |
| Opus 5.5 | $0.00033 | $0.01032 |
| Sonnet 5.5 | $0.00017 | $0.00516 |
| Haiku 4.5 | $0.00008 | $0.00258 |
Grade A, and why
gke-ai-troubleshooting-jobset-interruption scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to gke-ai-troubleshooting-jobset-interruption — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE JobSet Interruption Troubleshooting
Use this skill to systematically diagnose and resolve JobSet interruptions, restarts, and preemptions on GKE clusters hosting large-scale AI/ML workloads.
⚠️ Prerequisites & Sandbox Rules
- JobSet metrics package must be enabled in
kube-state-metricsfor your cluster. - Cloud Logging and Cloud Monitoring enabled for the Google Cloud Project.
- Autonomous & Sandbox Execution Rule: If API queries, tools, or commands
encounter
403 Permission Denied, authentication errors, or network isolation, do NOT enter authentication or credential troubleshooting loops. Populate the query templates with the acquired variables ({project_id},{cluster_name},{workload_name},{start_time},{end_time}), inspect any locally staged telemetry or mock data files if available, and complete the diagnostic workflow and resolution recommendations autonomously.
🔍 Diagnostic Workflow
Step 0: Context Acquisition & Time Window Definition
Independently gather context using tools, workspace files, environment details, or user prompt context:
- Project ID (
{project_id}) - Cluster Name (
{cluster_name}) - Workload Name (JobSet Name) (
{workload_name}) - Workload Namespace (
{namespace}) - Issue Time (
{issue_time})
If specific variables are not explicitly provided by the user, inspect cluster
resources or logs to determine them, or use the {variable} placeholders
provided.
Time Handling Rules
- Autonomous Time Window: If a relative time (e.g., "X minutes ago") or no exact timestamp is provided, calculate the query window based on current time or available log timestamps.
- Window Calculation: If a timestamp
{issue_time}is available (or calculated asT), set{start_time}=T - 30mand{end_time}=T + 30m.
Step 1: Identify JobSet Restarts and Attempts [Low Risk]
What ships with it
2 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.
- 1mo ago First seen · 315 lines · 83 tokens per session scan A 597115bc05f1
gke-ai-troubleshooting-jobset-interruption is a skill published in the GitHub repository gke-labs/kube-agents (116 stars, last pushed today), licensed Apache-2.0. It adds 83 tokens to every session and 2,579 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to gke-ai-troubleshooting-jobset-interruption, differing in 10 lines, and is treated as a copy.
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