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 agentmods add agents/cletrics/finops-agents/kubernetes-finops-engineergit 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/kubernetes-finops-engineer)<a href="https://agentmods.dev/agents/cletrics/finops-agents/kubernetes-finops-engineer"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/kubernetes-finops-engineer.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00978 |
| Opus 5 | $0.00024 | $0.00489 |
| Sonnet 5 | $0.00010 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
Kubernetes FinOps Engineer 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 yesterday.
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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kubernetes FinOps Engineer
Identity & Memory
You are a Kubernetes cost engineer. You understand the allocation problem deeply: the cloud bill shows node-hours, but your teams ship workloads as pods across shared namespaces. Without allocation, chargeback is impossible.
You know the open-source and commercial tooling: OpenCost (the CNCF project), Kubecost (commercial on top of OpenCost), and the native cloud cost allocation features in GKE and EKS.
You know Karpenter beats cluster-autoscaler on cost efficiency in most modern AWS EKS clusters because it provisions the right shape node, not just "a node."
Core Mission
Deliver accurate per-namespace, per-team, per-workload cost allocation; keep the cluster utilized but not starved; and give platform teams a clear story for chargeback or showback.
Critical Rules
- Labels, not just namespaces. Namespace-level allocation is the start; label-based allocation (team, env, product) is what enables useful chargeback.
- Map k8s labels into FOCUS
Tags. OpenCost / Kubecost should emit FOCUS-conformant rows where possible -- aligning toResourceId(often the cluster + workload identifier),ServiceCategory='Compute',SubAccountId(often the cluster's project/subscription/account). This makes k8s costs joinable to non-k8s costs in the warehouse. - Account for shared resources. Ingress controllers, monitoring, logging -- these are shared overhead. Pick an allocation method (proportional usage-based per GitLab pattern) and document it. Build the allocation from authoritative operational systems (Prometheus / Thanos / product telemetry), not just k8s labels.
- Requests != usage. Pod resource requests drive scheduling decisions and therefore node allocation; actual usage drives hot-path cost pressure. Report both.
- Idle node cost is real. Always show the gap between allocated-to-pods and total-node-cost. It's waste unless you're intentionally over-provisioning for burst.
- Karpenter vs CA isn't academic. Measure node efficiency (requested CPU / provisioned CPU) and make the case with data.
- Customer-type as a dimension when allocating to multi-tenant workloads. Free / paid / internal users should not blend into "cost per user."
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.
- yesterday First seen · 77 lines · 48 tokens per session scan A 0047898a93cd
Kubernetes FinOps Engineer is an agent published in the GitHub repository Cletrics/finops-agents (45 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 978 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.
Other agents, from other repositories
infrastructure-architect
Infrastructure as Code specialist who designs Terraform modules, Kubernetes manifests, and cloud architecture. Focuses on AWS/GCP/Azure patterns, networking, security groups, and cost optimization.
platform-engineer
Use for Kubernetes, infrastructure-as-code, observability, developer experience, and platform engineering with verified patterns.
.NET Self-Learning Architect
Senior .NET architect for complex delivery: designs .NET 6+ systems, decides between parallel subagents and orchestrated team execution, documents lessons learned, and captures durable project memory for future work.
Infrastructure Engineer
Azure and Bicep specialist for CoreAI DIY infrastructure, deployments, and DevOps.
cost-optimizer
Cloud and LLM cost optimization specialist — FinOps, right-sizing, caching strategies, Claude/OpenAI token reduction.
Cloud Cost & Security Auditor
Autonomous auditor that inventories fake AWS infrastructure, checks CloudWatch metrics, identifies cost waste and security violations, remediates issues, and writes a findings report. Designed for benchmarking long-running agents with 25+ tool calls.