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-inspect)<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-inspect"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-inspect/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-inspect"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-inspect.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 | $0.00011 | $0.00389 |
| Opus 5.5 | $0.00004 | $0.00156 |
| Sonnet 5.5 | $0.00002 | $0.00078 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
fleet-inspect 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.inspect record $ARGUMENTS
The id is per candidate (search-rqolmi-001-chat-solo-4gpu), not the run id. A run id gets you
no record ... locally or on EFS. A path to a record's .json needs no --key. --json dumps the
raw record; --ordinals labels GPUs by host ordinal instead of by UUID prefix, which for an older
record without provenance.gpu_ordinal_by_uuid needs the cluster and the node to still exist.
This prints one placement's record. It is for checking a measurement, not for reading an outcome: the
BASELINE / STRICT / RELAXED block belongs to search's own output and its --summary JSON, and
inspect does not reproduce it.
inspect has no view of the hardware store, so filed shapes and device totals are only readable as raw
JSON under ~/.colocation/store/hwfacts/ (and /efs/colocation/hwfacts/, which is readable only from
inside a pod). You should not need to: plan's header prints the total it planned against (N GPUs, X.XX GiB each), and on an uncaptured node the load log names where that figure came from. Quote those
rather than a number dug out of the store.
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 · 27 lines · 11 tokens per session scan A 909511033fc6
fleet-inspect 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 11 tokens to every session and 389 once invoked, about $0.0000 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-inspect compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fleet-inspectaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow | Command | 132 | 4d ago | Apache-2.0 | A | 11 | 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 |