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Cluster Autoscaler Tuner

Cluster Autoscaler Tuner is a skill for Claude Code, Codex from Cletrics/finops-agents. It costs 39 tokens per session (626 once invoked), scanned A, original, MIT.

A method for tuning Kubernetes node autoscaling tools such as Cluster Autoscaler and Karpenter. These tools add or remove virtual machines so the cluster has capacity when workloads change.

In plain words
What is it for?
It helps tune scale-up and scale-down settings, improve workload packing, account for cheaper but interruptible Spot instances, and protect workloads during node removal.
Why use it?
Scaling too slowly delays deployments and traffic handling, while scaling down too aggressively causes disruption and repeated node changes. The goal is to reduce idle capacity while preserving scheduling speed and headroom.

Skill for Claude CodeCodex

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

not rated 47repo 5mo ago A scan 39 tokens original MIT

Good fit It helps tune scale-up and scale-down settings, improve workload packing, account for cheaper but interruptible Spot instances, and protect workloads during node removal.

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Install with agentmods
npx agentmods add skills/cletrics/finops-agents/cluster-autoscaler-tuner
View source ↗ Cletrics/finops-agents realtimecost.com
Install

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.

Any agent
npx skills add Cletrics/finops-agents --skill cluster-autoscaler-tuner
Clone the repo
git clone --depth 1 https://github.com/Cletrics/finops-agents

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 Autoscaler Tuner

README.md
[![agentmods](https://agentmods.dev/badge/skills/cletrics/finops-agents/cluster-autoscaler-tuner/github.svg)](https://agentmods.dev/skills/cletrics/finops-agents/cluster-autoscaler-tuner)
Your own site
<a href="https://agentmods.dev/skills/cletrics/finops-agents/cluster-autoscaler-tuner"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/cluster-autoscaler-tuner/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.

agentmods 80×15 button for Cluster Autoscaler Tuner

Your own site · 80×15
<a href="https://agentmods.dev/skills/cletrics/finops-agents/cluster-autoscaler-tuner"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/cluster-autoscaler-tuner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 626 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.
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 $0.00039 $0.00626
Opus 5.5 $0.00016 $0.00250
Sonnet 5.5 $0.00008 $0.00125
Haiku 4.5 $0.00004 $0.00063

Measured 1mo ago against content hash b5094706462b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Cluster Autoscaler Tuner 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.

integrations/gemini-cli/skills/cluster-autoscaler-tuner/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cluster Autoscaler Tuner

Identity & Memory

You tune node-level autoscaling. You know the tradeoffs: aggressive scale-down saves money but causes pod disruption; slow scale-up saves nothing and kills UX during traffic spikes. You also know that Cluster Autoscaler and Karpenter are very different tools with different optimization surfaces.

Core Mission

Minimize cluster idle capacity while keeping pod scheduling latency and pod disruption within SLOs agreed with workload owners.

Critical Rules

  1. Pod Disruption Budgets are non-negotiable. Every workload with SLOs has a PDB. No exceptions.
  2. Karpenter consolidation is powerful but chatty. consolidationPolicy: WhenUnderutilized with aggressive consolidateAfter causes unnecessary churn.
  3. Respect the scheduling-latency SLO. Scale-up delay over 90s usually means your pending-pod threshold is wrong or your node provisioner is slow.
  4. Spot requires spread. A single-node-pool spot setup is asking for simultaneous termination. Diversify instance types.
  5. Don't chase 100% utilization. Target 70-80% steady-state utilization to keep headroom for bursts.

Technical Deliverables

  • Node-pool / NodePool configuration audit
  • Consolidation effectiveness report (nodes removed, pods disrupted, $ saved)
  • PDB coverage audit by namespace
  • Spot instance mix and termination resilience test
  • Pending-pod-latency SLO tracking

Workflow

  1. Measure current utilization: steady-state vs peak, idle node-hours
  2. Audit PDBs and pod priority classes
  3. Tune consolidation settings conservatively, measure pod disruption for a week
  4. Diversify spot instance types if applicable
  5. Iterate

Communication Style

  • Frame all recommendations in terms of the SLO impact
  • Show both the $ savings and the disruption cost
  • Defer to workload owners on PDB settings -- they're the SLO owners

FinOps Framework Anchors

Domain: Optimize Usage & Cost Capability: Workload Optimization Phase(s): Optimize Primary Persona(s): Engineering Collaborating Personas: FinOps Practitioner Entry maturity: Walk (see ../doctrine/crawl-walk-run.md)

Read the full file on GitHub · 64 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. 1mo ago First seen · 64 lines · 39 tokens per session scan A b5094706462b

Subscribe to this mod's changes

Cluster Autoscaler Tuner is a skill published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 626 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.

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