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Official agent skill

Gke AI Troubleshooting Tpu Mxla Hang

by google in google/skills

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…

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Gke AI Troubleshooting Tpu Mxla Hang

skills CLI
$ npx skills add google/skills --skill gke-ai-troubleshooting-tpu-mxla-hang -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills gke-ai-troubleshooting-tpu-mxla-hang --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-tpu-mxla-hang .claude/skills/gke-ai-troubleshooting-tpu-mxla-hang && rm -rf skills-src

Use ~/.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/

Facts

Skill name
gke-ai-troubleshooting-tpu-mxla-hang
GitHub stars
21k
Token cost
~3.7k tokens
SKILL.md length
1,313 words
Files
6 (incl. references)
Skills in repo
150
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 5 steps: Collect context and set the… → Query HANG_DETECTED logs and hang events… → Correlate with 1-minute multi-slice and… → …
  • Multi-slice TPU training jobs freeze without progressing steps
  • SKILL.md covers Prerequisites, MXLA Hang Analyzer routing…, Diagnostic workflow and Guardrails
  • Calls gcloud and kubectl

What it does

Gke AI Troubleshooting Tpu Mxla Hang is an agent skill from google/skills, published by the product's own GitHub organization. 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/). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps…

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/path-a-compiler-hlo-divergence.md`, `references/path-b-program-queueing-input-stall.md` and `references/path-c-hardware-network-faults.md`).

It sits in DevOps & Cloud, covering Container orchestration. It works with Google Kubernetes Engine, Google Cloud 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

  • Multi-slice TPU training jobs freeze without progressing steps
  • Emit HANGDETECTED
  • Stall in collective operations
  • Gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation)

Example prompts

  • “/gke-ai-troubleshooting-tpu-mxla-hang”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Collect context and set the investigation window [Low Risk]
  2. Query HANG_DETECTED logs and hang events [Low Risk]
  3. Correlate with 1-minute multi-slice and TPU metrics [Low Risk]
  4. Map culprit Compute Engine instance IDs to GKE nodes [Low Risk]
  5. Remediation by root-cause category

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:

    • gcloud
    • 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
    • cloud.google.com
    • openxla.org

    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 Tpu Mxla Hang loads about 3.7k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 206 tokens; SKILL.md has 1,313 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~206
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.4k

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). 1,313 words, ~3,706 tokens.

Download SKILL.mdSave it as .claude/skills/gke-ai-troubleshooting-tpu-mxla-hang/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
gke-ai-troubleshooting-tpu-mxla-hang
description
Diagnose GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` 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/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction restarts (use gke-ai-troubleshooting-jobset-interruption).
metadata.version
1.0.0
metadata.category
AiAndMachineLearning

Troubleshoot GKE TPU multi-slice hangs with the MXLA Hang Analyzer

Diagnose Cloud TPU multi-slice training hangs on Google Kubernetes Engine (GKE) by correlating Megascale HANG_DETECTED logs and ML Diagnostics Workload Monitoring Megascale XLA (MXLA) Hang Analyzer reports with 1-minute multi-slice latency metrics (kubernetes.io/container/multislice/*) and GKE node topology labels.

Prerequisites

  • Tools: Install the Google Cloud SDK (gcloud with alpha component for gcloud alpha mldiagnostics) and kubectl.
  • Cloud Billing & Project Configuration: Verify an active billing account is linked (gcloud billing projects describe {project_id}), authenticate (gcloud auth login), set the target project (gcloud config set project {project_id}), and ensure container.googleapis.com, logging.googleapis.com, monitoring.googleapis.com, and hypercomputecluster.googleapis.com are enabled.
  • Supported workloads and versions: Google Cloud ML Diagnostics only supports JAX on TPUs (see ML Diagnostics platform). Workload Monitoring is enabled by default, supports the jobset and job GKE job types, and is compatible with GKE versions 1.36.0-gke.4681000 and later (Configure GKE for ML Diagnostics, which also covers the cluster setup needed for on-demand profiling in Step 4 Path B). If a workload uses another framework (such as PyTorch) or another custom resource type, gcloud alpha mldiagnostics won't list ML runs or monitored events for it. The Megascale XLA hang analyzer and Megascale XLA metrics require LibTPU 0.40.0 or later (see "Get started" in Workload monitoring with ML Diagnostics).
  • Required IAM Roles:
    • Cluster Director Editor (roles/hypercomputecluster.editor), the role listed in the "IAM permissions" section of ML Diagnostics platform, for the ML Diagnostics CLI and API calls in this skill (ML runs, monitored events, and on-demand profiler sessions)
    • Monitoring Viewer (roles/monitoring.viewer) for the PromQL queries in Step 2
    • Logs Viewer (roles/logging.viewer) for the Cloud Logging query in Step 1
    • Kubernetes Engine Viewer (roles/container.viewer) for the kubectl get nodes query in Step 3
    • For remediation ([High Risk] steps): Kubernetes Engine Cluster Admin (roles/container.clusterAdmin)
  • Reference Documentation:

Read-only rule: Run read-only diagnostic commands only. Never drain, delete, or re-create nodes, or run any other command that changes the cluster. Give the user any fix to apply themselves.

When you recommend a fix, link the doc section that describes it.

MXLA Hang Analyzer routing overview

A Megascale hang occurs when a multi-slice worker has waited on a Megascale communication operation for a set timeout period. The TPU logs then show a Megascale HANG_DETECTED message. HANG_DETECTED is a catch-all signal that the workload isn't progressing, and the cause can be in software or in hardware. When a hang occurs, ML Diagnostics runs the Megascale XLA (MXLA) Hang Analyzer, which reports the likely cause as a code. Consult the Megascale XLA (MXLA) Hang Analyzer section for the definition and recommended action of each code, and route by category:

  • Category A: compiler or HLO divergence (never cordon or replace nodes):
    • When the analyzer reports different HLO modules, inconsistent HLO compilation, or an inconsistent launch order across VMs (such as FINGERPRINT_MISMATCH), route to Path A: Compiler or HLO divergence.
  • Category B: host program queueing or data input stall (never cordon or replace nodes):
  • Category C: hardware or network faults on specific instances:
    • When the analyzer attributes the hang to a TPU chip, SparseCore, ICI, or DCN networking issue, or to an unrecoverable error on specific instances, route to Path C: Hardware or network faults.
  • Category D: hang signal or event exists, but the analyzer is NOT_DETECTED or reports UNKNOWN:
    • When HANG_DETECTED logs or a hang monitored event fired, but the analyzer report has detectionState: "NOT_DETECTED" or reports UNKNOWN ("The MXLA hang was detected but a potential cause is not determined"), route to Path D: No DETECTED analyzer or UNKNOWN cause.

Diagnostic workflow

Step 0: Collect context and set the investigation window [Low Risk]

Collect the target parameters. By default, query a 60-minute window `[T - 30m, T

  • 30m]around{issue_time}`:
  • {project_id}: Google Cloud project ID
  • {location}: Google Cloud region where the ML run and GKE cluster reside (for example, us-central1)
  • {cluster_name}: GKE cluster name
  • {namespace} / {workload_name}: Kubernetes namespace and JobSet/Pod prefix
  • {ml_run_id}: ML Diagnostics run ID
  • {issue_time}: Timestamp when the hang occurred (T, ISO-8601 UTC)
  • {start_time}: T - 30m
  • {end_time}: T + 30m
Show full SKILL.md (495 more words)Show less
Step 1: Query HANG_DETECTED logs and hang events [Low Risk]
  1. Check GKE container logs for HANG_DETECTED (read-only Cloud Logging LQL): Query k8s_container logs over [{start_time}, {end_time}] to confirm HANG_DETECTED and identify the first stalled pods:
sql
resource.type="k8s_container"
resource.labels.project_id="{project_id}"
resource.labels.cluster_name="{cluster_name}"
"HANG_DETECTED"
timestamp >= "{start_time}" AND timestamp <= "{end_time}"
  1. List active or recent ML runs: Follow the List machine learning runs section of the ML Diagnostics CLI reference (gcloud alpha mldiagnostics machine-learning-run list) to identify {ml_run_id}.
  2. List and describe hang monitored events: Follow the Monitored-events commands section (gcloud alpha mldiagnostics monitored-events list and gcloud alpha mldiagnostics monitored-events describe) or Access Workload Monitoring information through the API to inspect the Megascale XLA (MXLA) Hang Analyzer report (detectionState, details, and recommendedActions).
    • If HANG_DETECTED logs or a hang monitored event fired, run Step 2 to correlate with the 1-minute metrics; if no analyzer reports detectionState: "DETECTED" or the analyzer reports UNKNOWN, follow Path D: No DETECTED analyzer or UNKNOWN cause.
    • If no HANG_DETECTED log or hang monitored event exists and multi-slice latencies are normal in Step 2, rule out an MXLA hang by following Path E: Healthy telemetry.
Step 2: Correlate with 1-minute multi-slice and TPU metrics [Low Risk]

Consult the System Metrics section of the Workload Monitoring guide for the 1-minute multi-slice network (kubernetes.io/container/multislice/network/*), multi-slice accelerator (kubernetes.io/container/multislice/accelerator/*), and node duty-cycle (kubernetes.io/node/accelerator/duty_cycle) metrics, and run read-only PromQL queries over [{start_time}, {end_time}]:

promql
# 1. P95 multi-slice collective end-to-end latency by pod
histogram_quantile(
  0.95,
  sum by (pod_name, le) (
    rate(kubernetes_io:container_multislice_network_collective_end_to_end_latencies_bucket{
      monitored_resource="k8s_container",
      project_id="{project_id}",
      cluster_name="{cluster_name}"
    }[5m])
  )
)

# 2. P95 multi-slice DCN transfer latency by pod
histogram_quantile(
  0.95,
  sum by (pod_name, le) (
    rate(kubernetes_io:container_multislice_network_dcn_transfer_latencies_bucket{
      monitored_resource="k8s_container",
      project_id="{project_id}",
      cluster_name="{cluster_name}"
    }[5m])
  )
)

# 3. P95 host-to-device transfer latency by pod
histogram_quantile(
  0.95,
  sum by (pod_name, le) (
    rate(kubernetes_io:container_multislice_accelerator_host_to_device_transfer_latencies_bucket{
      monitored_resource="k8s_container",
      project_id="{project_id}",
      cluster_name="{cluster_name}"
    }[5m])
  )
)

# 4. Node TPU duty cycle (Workload Monitoring detects a hang as a prolonged
#    period of minimal to no TPU activity)
kubernetes_io:node_accelerator_duty_cycle{
  monitored_resource="k8s_node",
  project_id="{project_id}",
  cluster_name="{cluster_name}"
}
Step 3: Map culprit Compute Engine instance IDs to GKE nodes [Low Risk]

When the Megascale XLA (MXLA) Hang Analyzer reports culprit numeric Compute Engine instance IDs in details or recommendedActions, map those numeric IDs to GKE Node names and physical topology blocks using this read-only kubectl query inspecting container.googleapis.com/instance_id:

bash
kubectl get nodes -l cloud.google.com/gke-tpu-accelerator \
  -o jsonpath='{range .items[*]}{.metadata.name}{"\tinstance_id="}{.metadata.annotations.container\.googleapis\.com/instance_id}{"\tblock="}{.metadata.labels.cloud\.google\.com/gce-topology-block}{"\tsubblock="}{.metadata.labels.cloud\.google\.com/gce-topology-subblock}{"\thost="}{.metadata.labels.cloud\.google\.com/gce-topology-host}{"\n"}{end}'
Step 4: Remediation by root-cause category

Load and follow only the reference file that matches the root-cause category from Step 1:

Guardrails

  1. Never change GKE-managed instance groups or VMs through Compute Engine: Don't run gcloud compute instance-groups managed commands, such as delete, on a node pool's managed instance group. Handle nodes through GKE, as described in Path C: Hardware or network faults.
  2. Never cordon or replace nodes for compiler divergence or input stalls: If the analyzer reports a compiler or HLO divergence, a program queueing issue, or a data input stall, don't cordon or replace TPU nodes. Follow Path A: Compiler or HLO divergence or Path B: Host program queueing or data input stall instead.

© 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

SKILL.md and 5 other files (references) in skills/cloud/gke-ai-troubleshooting-tpu-mxla-hang of google/skills.

  • SKILL.md
  • references/path-a-compiler-hlo-divergence.md
  • references/path-b-program-queueing-input-stall.md
  • references/path-c-hardware-network-faults.md
  • references/path-d-no-detected-or-unknown.md
  • references/path-e-healthy-telemetry.md

Open the folder on GitHubat commit 4b940dd

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Works with

Categories

Questions about Gke AI Troubleshooting Tpu Mxla Hang

What does Gke AI Troubleshooting Tpu Mxla Hang do?

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…. Gke AI Troubleshooting Tpu Mxla Hang is an agent skill from google/skills, published by the product's own GitHub organization.io/container/multislice/).

When should I use Gke AI Troubleshooting Tpu Mxla Hang?

Gke AI Troubleshooting Tpu Mxla Hang fits situations like: multi-slice TPU training jobs freeze without progressing steps; emit HANGDETECTED; stall in collective operations; gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation).

How do I install Gke AI Troubleshooting Tpu Mxla Hang in Claude Code?

Run `npx skills add google/skills --skill gke-ai-troubleshooting-tpu-mxla-hang -a claude-code`. Or copy the skill folder (skills/cloud/gke-ai-troubleshooting-tpu-mxla-hang in google/skills) into .claude/skills/gke-ai-troubleshooting-tpu-mxla-hang in your project. Claude Code loads it when a task matches its description.

How do I install Gke AI Troubleshooting Tpu Mxla Hang in Codex?

Run `npx skills add google/skills --skill gke-ai-troubleshooting-tpu-mxla-hang -a codex`. Or copy the skill folder (skills/cloud/gke-ai-troubleshooting-tpu-mxla-hang in google/skills) into .agents/skills/gke-ai-troubleshooting-tpu-mxla-hang in your project. Codex loads it when a task matches its description.

Can I use Gke AI Troubleshooting Tpu Mxla Hang 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-tpu-mxla-hang -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-tpu-mxla-hang, .gemini/skills/gke-ai-troubleshooting-tpu-mxla-hang, .github/skills/gke-ai-troubleshooting-tpu-mxla-hang and .opencode/skills/gke-ai-troubleshooting-tpu-mxla-hang in your project.

What does Gke AI Troubleshooting Tpu Mxla Hang need to run?

Going by SKILL.md and its folder, Gke AI Troubleshooting Tpu Mxla Hang needs the command-line tools its instructions call (gcloud and kubectl).

Does Gke AI Troubleshooting Tpu Mxla Hang access the network?

SKILL.md names 3 domains. As links in the text: docs.cloud.google.com, cloud.google.com and openxla.org. This is read from the text; nothing was executed.

Is Gke AI Troubleshooting Tpu Mxla Hang 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 Tpu Mxla Hang use?

Gke AI Troubleshooting Tpu Mxla Hang 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 Tpu Mxla Hang use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Gke AI Troubleshooting Tpu Mxla Hang?

Skills that share tags, products or a category with Gke AI Troubleshooting Tpu Mxla Hang: Devops (nicepkg/auto-company, 195 stars), Kcli Cluster Deployment (karmab/kcli, 653 stars), GCP Gke (sickn33/agentic-awesome-skills, 47k stars) and Apex Azure Cloud Migrate (jonathan-vella/apex, 217 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 Tpu Mxla Hang?

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.