SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM).
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install awslabs/agent-plugins hyperpod-ssm --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .claude/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .claude/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssmType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install awslabs/agent-plugins hyperpod-ssm --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .agents/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .agents/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install awslabs/agent-plugins hyperpod-ssm --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .cursor/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .cursor/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/awslabs/agent-plugins.git --path plugins/sagemaker-ai/skills/hyperpod-ssm--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install awslabs/agent-plugins hyperpod-ssm --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .gemini/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .gemini/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install awslabs/agent-plugins hyperpod-ssmInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .github/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .github/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install awslabs/agent-plugins hyperpod-ssm --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/sagemaker-ai/skills/hyperpod-ssm .opencode/skills/hyperpod-ssm && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hyperpod-ssm" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-ssm into .opencode/skills/hyperpod-ssm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hyperpod-ssm", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hyperpod-ssmRemote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM).
Hyperpod Ssm is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level…
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/troubleshooting.md`, `scripts/get-cluster-info.sh` and `scripts/list-nodes.sh`).
It sits in AI & LLM Engineering. It works with Amazon Web Services and Amazon SageMaker. The repository describes itself as: Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit da51970. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 3 files in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
awsyumaptbrewbashFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use aws, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Hyperpod Ssm loads about 1.3k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 134 tokens; SKILL.md has 353 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
even when the command ran. Install with `sudo yum install expect`, `sudo apt install expect`, or `brew install expect`.| CloudWatch agent | `sudo systemctl status amazon-cloudwatch-agent` |Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from awslabs/agent-plugins at commit da51970, republished under its Apache-2.0 licence (© awslabs). 353 words, ~1,293 tokens.
.claude/skills/hyperpod-ssm/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.aws CLI v2, authenticated for the target account/Region.session-manager-plugin — installed alongside the AWS CLI.jq — the scripts build JSON payloads with it.unbuffer (from the expect package) — wraps aws ssm start-session with a PTY so the session-manager-plugin flushes stdout instead of racing to close. Without it, calls intermittently return empty output with Cannot perform start session: EOF even when the command ran. Install with sudo yum install expect, sudo apt install expect, or brew install expect. ssm-exec.sh detects and uses it automatically; falls back with a warning if missing.Target: sagemaker-cluster:<CLUSTER_ID>_<GROUP_NAME>-<INSTANCE_ID>
CLUSTER_ID: Last segment of cluster ARN (NOT the cluster name). Extract via get-cluster-info.sh.GROUP_NAME: Instance group name — retrieve via list-nodes.sh.INSTANCE_ID: EC2 instance ID (e.g., i-0123456789abcdef0)Three scripts under scripts/. Resolve cluster info and nodes once, then execute per node.
scripts/get-cluster-info.sh CLUSTER_NAME [--region REGION]
# Output: {"cluster_id":"...","cluster_arn":"...","cluster_name":"...","region":"..."}scripts/list-nodes.sh CLUSTER_NAME [--region REGION] [--instance-group GROUP] [--instance-id ID]
# Output: JSON array of ClusterNodeSummaries (InstanceId, InstanceGroupName, InstanceStatus, etc.)list-cluster-nodes paginates at 100 nodes. This script handles pagination automatically.
# Execute — with pre-built target
scripts/ssm-exec.sh --target "sagemaker-cluster:CLUSTERID_GROUP-INSTANCEID" 'command' [--region REGION]
# Execute — with parts
scripts/ssm-exec.sh --cluster-id ID --group GROUP --instance-id INSTANCE_ID 'command' [--region REGION]
# Upload
scripts/ssm-exec.sh --target TARGET --upload LOCAL_PATH REMOTE_PATH [--region REGION]
# Read remote file
scripts/ssm-exec.sh --target TARGET --read REMOTE_PATH [--region REGION]SSM start-session rate limit: 3 TPS per account. Plan batch size and delay accordingly.
aws ssm send-command does NOT support sagemaker-cluster: targets — only start-session works.
When the scripts aren't suitable, use aws ssm start-session directly with AWS-StartNonInteractiveCommand. Wrap every invocation in unbuffer — without it, stdout is intermittently empty (see Prerequisites).
cat > /tmp/cmd.json << 'EOF'
{"command": ["bash -c 'echo hello && whoami'"]}
EOF
unbuffer aws ssm start-session \
--target sagemaker-cluster:{CLUSTER_ID}_{GROUP_NAME}-{INSTANCE_ID} \
--region REGION \
--document-name AWS-StartNonInteractiveCommand \
--parameters file:///tmp/cmd.json--parameters — inline parameters break with special characters.command parameter is argv, not shell input. Wrap multi-statement scripts in bash -c '...' so pipes, semicolons, and redirects evaluate.| Task | Command |
|---|---|
| Lifecycle logs | cat /var/log/provision/provisioning.log |
| Memory | free -h |
| Disk/mounts | df -h && lsblk |
| GPU status | nvidia-smi |
| GPU memory | nvidia-smi --query-gpu=memory.used,memory.total --format=csv |
| EFA/network | fi_info -p efa |
| CloudWatch agent | sudo systemctl status amazon-cloudwatch-agent |
| Top processes | ps aux --sort=-%mem | head -20 |
root.--document-name to get a shell.AWS-StartNonInteractiveCommand.© awslabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (scripts, references) in plugins/sagemaker-ai/skills/hyperpod-ssm of awslabs/agent-plugins.
Open the folder on GitHubat commit da51970
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in awslabs/agent-plugins, which our catalogue first saw on October 7, 2026.
Hyperpod Ssm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hyperpod Ssm this skillawslabs/agent-plugins | 915 | 1 repos | ~1.3k | Automated safety check: Notes | Apache-2.0 | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Python Environment Setup for SageMakerhuggingface/skills | 11k | 2 repos | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Aiml GPU Training Cluster Investigationaws/tools-for-devops-agent | 100 | — | ~5.4k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Deployment Plannerhuggingface/skills | 11k | 1 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Hf Cloud Serving Image Selectionwaybarrios/opencode-power-pack | 533 | — | ~4.3k | Automated safety check: Pass | Apache-2.0 |
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
huggingface/skills
Sets up an isolated Python environment with a supported interpreter and current boto3 before any SageMaker deployment, training or AWS automation code runs.
aws/tools-for-devops-agent
A skill your agent uses for GPU training or inference clusters on SageMaker HyperPod (Slurm or EKS), ParallelCluster, or self-managed EC2/EKS GPU instances.
huggingface/skills
Entry point for hosting a model on Amazon SageMaker: asks a few questions, picks a deployment pathway and hands off to the specialist skills.
waybarrios/opencode-power-pack
Select and verify the current region-specific serving container URI for a SageMaker model deployment.
aws/agent-toolkit-for-aws
Selects, deploys, and customizes AI models on Amazon SageMaker.
awslabs/agent-plugins
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR).
awslabs/agent-plugins
Generates code that transforms datasets between ML schemas for model training or evaluation.
awslabs/agent-plugins
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes.
awslabs/agent-plugins
Generate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases.
awslabs/agent-plugins
Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput.
awslabs/agent-plugins
Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI).
Works with
Categories
Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). Hyperpod Ssm is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM).
Hyperpod Ssm fits situations like: user request needs to execute commands on cluster nodes; upload files to nodes; read/download files from nodes; run diagnostics.
Run `npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a claude-code`. Or copy the skill folder (plugins/sagemaker-ai/skills/hyperpod-ssm in awslabs/agent-plugins) into .claude/skills/hyperpod-ssm in your project. Claude Code loads it when a task matches its description.
Run `npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a codex`. Or copy the skill folder (plugins/sagemaker-ai/skills/hyperpod-ssm in awslabs/agent-plugins) into .agents/skills/hyperpod-ssm in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add awslabs/agent-plugins --skill hyperpod-ssm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hyperpod-ssm, .gemini/skills/hyperpod-ssm, .github/skills/hyperpod-ssm and .opencode/skills/hyperpod-ssm in your project.
Going by SKILL.md and its folder, Hyperpod Ssm needs a shell for the scripts in its folder and the command-line tools its instructions call (aws, yum, apt, brew and bash). Our summary lists: A Bash shell.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Hyperpod Ssm is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 524 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Hyperpod Ssm: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Python Environment Setup for SageMaker (huggingface/skills, 11k stars), Aiml GPU Training Cluster Investigation (aws/tools-for-devops-agent, 100 stars) and SageMaker Deployment Planner (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
awslabs (a GitHub organization, an official publisher) maintains it in awslabs/agent-plugins, which has 915 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 5, 2026.
Source: awslabs/agent-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.