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.
git clone --depth 1 https://github.com/Cletrics/finops-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/agents/cletrics/finops-agents/cluster-autoscaler-tuner)<a href="https://agentmods.dev/agents/cletrics/finops-agents/cluster-autoscaler-tuner"><img src="https://agentmods.dev/badge/agents/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.
<a href="https://agentmods.dev/agents/cletrics/finops-agents/cluster-autoscaler-tuner"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/cluster-autoscaler-tuner.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 | $0.00039 | $0.00631 |
| Opus 5 | $0.00019 | $0.00316 |
| Sonnet 5 | $0.00008 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 65 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
- Pod Disruption Budgets are non-negotiable. Every workload with SLOs has a PDB. No exceptions.
- Karpenter consolidation is powerful but chatty.
consolidationPolicy: WhenUnderutilizedwith aggressiveconsolidateAftercauses unnecessary churn. - Respect the scheduling-latency SLO. Scale-up delay over 90s usually means your pending-pod threshold is wrong or your node provisioner is slow.
- Spot requires spread. A single-node-pool spot setup is asking for simultaneous termination. Diversify instance types.
- 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
- Measure current utilization: steady-state vs peak, idle node-hours
- Audit PDBs and pod priority classes
- Tune consolidation settings conservatively, measure pod disruption for a week
- Diversify spot instance types if applicable
- 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)
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.
- 6d ago First seen · 65 lines · 39 tokens per session scan A 9d691297a990
Cluster Autoscaler Tuner is an agent published in the GitHub repository Cletrics/finops-agents (46 stars, last pushed 4mo ago), licensed MIT. It adds 39 tokens to every session and 631 once invoked, about $0.0002 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 2026-09-03.
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