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colocation-pipeline

colocation-pipeline is a plugin for Claude Code from aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow. Its manifest loads nothing; the 2 skills, 15 commands, 2 hooks it bundles cost 571 tokens per session together, scanned A, original, Apache-2.0.

A workflow plugin for measuring how several machine-learning models share GPUs. It runs the ordered steps from preparing the image and planning through profiling, searching, reading results, exporting them, and deploying them on KAI GPU fractions.

In plain words
What is it for?
Use it to plan and measure model co-location, review the results, export a placement, and serve it on KAI.
Why use it?
It keeps a multi-step measurement and deployment process in the required order and includes safeguards against commands that could destroy a live run.

Plugin for Claude Code ✓ vendor

Written for Claude Code: a Claude Code plugin manifest.

not rated 132repo 4d ago A scan tokens not measured original Apache-2.0

Good fit Use it to plan and measure model co-location, review the results, export a placement, and serve it on KAI.

Compare 6 plugins from other repositories ↓
Install in Claude Code
/plugin marketplace add aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow
/plugin install colocation-pipeline
View source ↗ aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow
About the project

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.

aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow · 132 stars · on GitHub

Install

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.

Clone the repo
git clone --depth 1 https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow

Made for: Claude Code.

Wrote 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.

agentmods badge for colocation-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline/github.svg)](https://agentmods.dev/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline)
Your own site
<a href="https://agentmods.dev/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline"><img src="https://agentmods.dev/badge/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline/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.

agentmods 80×15 button for colocation-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline"><img src="https://agentmods.dev/badge/plugins/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/colocation-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session not measured What this adds to a session before it is invoked.
When invoked not measured The manifest loads nothing itself; its 2 skills, 15 commands, 2 hooks cost 571 tokens a session between them.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Security

Grade A, and why

colocation-pipeline 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.

tutorials/multi-model-gpu-sharing/plugins/colocation-pipeline/.claude-plugin/plugin.json · 9 lines

What it actually says

{
  "name": "colocation-pipeline",
  "version": "1.0.0",
  "description": "Runs the GPU co-location pipeline on a model fleet file: harness image, plan, demand sweep, profile, plan again, search, read, export. Carries the sequence as one skill and serving an export on KAI GPU fractions as a second, a command for each step and for each KAI action, and hooks that refuse the commands which destroy a live run.",
  "author": {
    "name": "amazon-eks-machine-learning-with-terraform-and-kubeflow"
  }
}
Contents

What it installs

The manifest is a name and a version. 2 skills, 15 commands, 2 hooks travel with it, and installing the plugin installs all of them — 571 tokens a session between them. Each is measured on its own page, and each can be installed alone.

Changes

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.

  1. 4d ago First seen · 9 lines scan A 4db447cfb798

Subscribe to this mod's changes

colocation-pipeline is a plugin 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. Its token cost is not measured: this kind of file is read by the harness, not the model. 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.

CloudDevOps and cloud
Related

colocation-pipeline compared with plugins from other repositories

colocation-pipeline next to 6 plugins from other repositories
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colocation-pipelineaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflowPlugin1324d agoApache-2.0A—Claude Code
elastic-observabilityelastic/agent-skillsPlugin605todayApache-2.0A—Claude Code
frontend-architectcuriositech/some_claude_skillsPlugin2709d agoMITA—Claude Code
harness-platformharness/harness-skillsPlugin1342d agoApache-2.0A—Claude Code
porkbunoborseth/Porkbun-MCPPlugin36todayMITA—Claude Code
spaceliftspacelift-io/claude-pluginsPlugin311d agoMITA—Claude Code
cloud-sql-sqlservergemini-cli-extensions/cloud-sql-sqlserverPlugin101mo agoApache-2.0A—Gemini CLI