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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflownpx agentmods add commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-imageWrote 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-image)<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-image"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-image/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-image"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-image.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 | $0.00028 | $0.00339 |
| Opus 5.5 | $0.00011 | $0.00136 |
| Sonnet 5.5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
fleet-image 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:
bash plugins/colocation-pipeline/scripts/harness-image.sh --ensure
The image reference is the only thing on stdout; docker and aws output goes to stderr.
The tag is a digest of the build context, never a number chosen by hand. That is what makes
"pullable but stale" impossible: colocation-harness:v5 sat in ECR pulling perfectly while
predating the per-tenant tokenizer, so it could not parse what render.py writes. A changed
harness hashes to a different tag automatically, and an unchanged one costs nothing — the script
checks ECR first and exits.
If the build's in-image selftest fails, the script stops before the push. Report that and stop. A failing harness image measures wrongly rather than not at all, and a measuring defect in this repo has voided complete search results before. Do not fall back to an older tag that happens to pull.
Then say in your report which account, region and tag the run will use. harness_image is not in
the observation's provenance, so your report is the only place the instrument is written down.
/fleet-profile and /fleet-search invoke this script themselves, so running it separately is
only for seeing what they will use.
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 · 31 lines · 28 tokens per session scan A a7316466c176
fleet-image 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 28 tokens to every session and 339 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-image compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fleet-imageaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow | Command | 132 | 4d ago | Apache-2.0 | A | 28 | Claude Code |
| create-docker-mcp-tunnelanthropics/claude-plugins-official | Command | 47k | today | Apache-2.0 | B | 26 | Claude Code |
| cisco-isovalent-platform-setupchambear2809/splunk-cisco-skills | Command | 40 | yesterday | Apache-2.0 | A | 0 | Claude Code |
| build-agentTheLobbi/claude | Command | 22 | 15d ago | MIT | A | 0 | Claude Code |
| application-crudtranfu-labs/tranfu-skills | Command | 2 | 1mo ago | MIT | A | 0 | Claude Code |
| review-helminfraspecdev/tesseract | Command | 6 | 4mo ago | MIT | A | 25 | Claude Code |
| create-operatorSagart-cactus/claude-k8s-plugin | Command | 34 | 8mo ago | MIT | A | 12 | Claude Code |