Amazon EKS Machine Learning with Terraform and Kubeflow is a modular platform for running machine-learning operations on Amazon EKS, the managed Kubernetes service, including training pipelines and model-serving systems. It is intended for teams deploying distributed training and inference with tools such as Kubeflow, MLflow, Airflow, and KServe, using configurable AWS infrastructure. The catalogue entries provide commands, skills, hooks, and a plugin for working with this deployment setup.
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/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflowWrote 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/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-kai-status)<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-kai-status"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-kai-status/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/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-kai-status"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-kai-status.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 | $0.00018 | $0.00383 |
| Opus 5.5 | $0.00007 | $0.00153 |
| Sonnet 5.5 | $0.00004 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
fleet-kai-status 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 4d 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.
What it actually says
From the repository's tutorials/multi-model-gpu-sharing directory:
PYTHONPATH=src python3 -m colocation.kai verify $ARGUMENTS
Read-only. It reads the plan from the kai-<name>-plan ConfigMap and compares it with the cards the
binder recorded on each pod. Exit 0 is placed as planned (it then lists any engine not Ready yet).
3 is placed differently: tenants spread over nodes, a tenant on the wrong number of cards or asking
for the wrong fraction, a tenant with two pods after a restart, or a card that also holds a pod this
plan did not put there. 1 is a tenant with no pod or not bound yet, or no kai-<name>-plan
ConfigMap — no deployment of that name. 2 is no name given.
A Deployment re-creates a deleted or evicted pod wherever binpack likes, so a deployment that verified
once can stop verifying later. Report the NOT AS PLANNED lines as printed; fixing it is a teardown
and a fresh /fleet-kai-deploy, and that is the user's call.
A split deploys one deployment per node, <name>-n1, <name>-n2, ...: verify each by its own name.
With no name, list what is deployed:
kubectl get configmap -n kubeflow-user-example-com -l colocation.kai/deployment
and helm list -n kubeflow-user-example-com -l colocation.kai/deployment=<name> lists one
deployment's releases.
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.
- 4d ago First seen · 34 lines · 18 tokens per session scan A df365d13bea1
fleet-kai-status is a command published in the GitHub repository aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow (132 stars, last pushed 4d ago), licensed Apache-2.0. It adds 18 tokens to every session and 383 once invoked, about $0.0001 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 2026-11-09.
fleet-kai-status compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fleet-kai-statusaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow | Command | 132 | 4d ago | Apache-2.0 | A | 18 | Claude Code |
| cost-optimizewshobson/agents | Command | 42k | 5d ago | MIT | A | 26 | Claude Code |
| deploybutterbase-ai/butterbase-skills | Command | 536 | 14d ago | MIT | A | 20 | Claude Code |
| envvercel/vercel-plugin | Command | 336 | yesterday | none | A | 35 | Claude Code |
| firebase-publishPierry/harness-kit | Command | 4 | 1mo ago | MIT | A | 20 | Claude Code |
| scanprajapatimehul/claude-aws-cost-saver | Command | 24 | 4mo ago | MIT | A | 6 | Claude Code |
| migrationdatabayt/hogwarts | Command | 18 | 3d ago | none | A | 4 | Claude Code |