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-calibrate)<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-calibrate"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-calibrate/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-calibrate"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-calibrate.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 | $0.00025 | $0.00236 |
| Opus 5.5 | $0.00010 | $0.00094 |
| Sonnet 5.5 | $0.00005 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
fleet-calibrate 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.agent calibrate --fleet $ARGUMENTS
It reads the store and no cluster. It writes the between-run ITL half-width per co-residency bucket.
It exits 1 and writes nothing when no placement has been measured twice, which is the normal outcome
after a first search: a repeat is a placement the reasoner chose to re-measure, and nothing forces
one. When there is no calibration, the report says "uncalibrated" in words. Never fill that gap with
a number of your own. Getting one needs another search round spent on a placement the store
already holds.
Run it after a search that produced a repeat, and before quoting any delta between two placements as real.
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 · 23 lines · 25 tokens per session scan A 0fbf38e8f746
fleet-calibrate 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 25 tokens to every session and 236 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-calibrate compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fleet-calibrateaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow | Command | 132 | 4d ago | Apache-2.0 | A | 25 | Claude Code |
| checklistgithub/spec-kit | Command | 150k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 150k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 150k | yesterday | MIT | A | 13 | Claude Code |
| convergegithub/spec-kit | Command | 150k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 150k | yesterday | MIT | A | 14 | Claude Code |
| speckit.bug.fixgithub/spec-kit | Command | 150k | yesterday | MIT | A | 12 | Claude Code |