cluster-agent-lifecycle

cluster-agent-lifecycle is a skill for Claude Code, Codex from gke-labs/kube-agents. It costs 54 tokens per session (2,070 once invoked), scanned A, original, Apache-2.0.

A lifecycle manager for isolated helper agents assigned to individual Google Kubernetes Engine clusters. GKE is Google Cloud’s managed service for running Kubernetes clusters.

In plain words
What is it for?
Creating, delegating read-only runtime investigations to, and deleting one Cluster Agent profile per managed GKE cluster.
Why use it?
It keeps each cluster’s debugging work separated and ensures an agent is created when a cluster is onboarded and removed when the cluster is torn down.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 /opt/data/scripts/cluster_agent_profile.py create \.

Good fit Creating, delegating read-only runtime investigations to, and deleting one Cluster Agent profile per managed GKE cluster.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/gke-labs/kube-agents
agentmods
npx agentmods add skills/gke-labs/kube-agents/cluster-agent-lifecycle

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cluster-agent-lifecycle

README.md
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Your own site
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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.

agentmods 80×15 button for cluster-agent-lifecycle

Your own site · 80×15
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Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,070 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 23
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 87
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 102
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.02070
Opus 5 $0.00027 $0.01035
Sonnet 5 $0.00011 $0.00414
Haiku 4.5 $0.00005 $0.00207

Measured 5d ago against content hash 66d753ec1c7d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

cluster-agent-lifecycle 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 5d 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.

agents/platform/skills/cluster-agent-lifecycle/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cluster Agent Lifecycle Skill

As the Platform Agent you own the lifecycle of Cluster Agents. A Cluster Agent is a Hermes profile — an isolated agent instance with its own persona (SOUL.md), scoped toolset, and home directory — that you create dynamically inside your own pod, one per managed GKE cluster. It handles read-only runtime operations and deep workload diagnostics on that single cluster, and returns its findings to you.

You never debug tenant workloads directly. You delegate that to the cluster's Cluster Agent and act on what it returns.

The engine for all of this is the helper script scripts/cluster_agent_profile.py (resolved at /opt/data/scripts/cluster_agent_profile.py at runtime).

When to create a profile

Create the Cluster Agent profile as part of cluster onboarding — immediately after a cluster is successfully provisioned (see gke-cluster-creation) or when an existing cluster is first brought under management (see manage-cluster). A managed cluster and its Cluster Agent profile are created together: when you onboard or tear down a cluster, keep the profile in step with it. That rule is scoped to onboarding and teardown, where the profile is yours to manage. It is not a roster invariant to enforce from other work — the reconcile job below owns the roster, and an empty roster is a supported state, not damage to repair. In particular, the first-run discovery sweep must never create or repair profiles.

python3 /opt/data/scripts/cluster_agent_profile.py create \
  --project "<project>" --cluster "<cluster>" --location "<location>"

This scaffolds the profile home on the persistent data PVC, pins a kubeconfig scoped to that cluster, writes the cluster identity into the profile's USER.md, and registers the profile. It is idempotent — safe to re-run. It prints the profile name.

How to delegate a debugging / runtime-ops task (kanban board)

For any request that concerns runtime behavior of workloads on a single, specific cluster (crash loops, OOMs, scheduling failures, mount errors, connectivity, autoscaling, storage, observability gaps), delegate to that cluster's Cluster Agent instead of investigating yourself.

Read the full file on GitHub · 133 lines

Changes

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.

  1. 5d ago Changed 66d753ec1c7d
  2. 6d ago Changed · +20 lines 793f36814857
  3. 10d ago First seen · 113 lines · 54 tokens per session scan A 1604fd10ff8a

Subscribe to this mod's changes

cluster-agent-lifecycle is a skill published in the GitHub repository gke-labs/kube-agents (53 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 2,070 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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