Agent skill · DevOps & Cloud

hyperpod-version-checker

Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, component checks, or upgrade planning for HyperPod clusters.

Amazon Web Services - Labs12,649★ · +18/wk · 2 repos on radarProfile →
claude-codeships scriptsApache-2.0
Install
npx skills add awslabs/agent-plugins --skill hyperpod-version-checker --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 4 KB
Bundled scripts: yes
Version: 1.0.0
Path: plugins/sagemaker-ai/skills/hyperpod-version-checker/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 850
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# HyperPod Version Checker Upload to cluster nodes via `hyperpod-ssm` skill, then execute. ## Usage ```bash # Text report to console + file bash hyperpod_check_versions.sh # JSON only to stdout (text report still saved to file) — best for piping/parsing bash hyperpod_check_versions.sh --json # Custom output file bash hyperpod_check_versions.sh --output /tmp/versions.txt # No color (for logging) bash hyperpod_check_versions.sh --no-color ``` Output file: `component_versions_<hostname>_<timestamp>.txt` (default) ## What It Checks | Component | Detection Method | Applicable When | | ----------------- | ----------------------------------------------- | --------------------------------------------- | | NVIDIA Driver | `nvidia-smi` | GPU instances (p3/p4/p5/g5) | | CUDA Toolkit | `nvcc`, `/usr/local/cuda` symlink | GPU instances | | cuDNN | Header file, packages | GPU instances doing deep learning | | NCCL | Library filename, header, packages | Distributed GPU training | | EFA | `/opt/amazon/efa_installed_packages`, `fi_info` | EFA-capable instances (p4d/p4de/p5/trn1/trn2) | | AWS OFI NCCL | `efa_installed_packages`, library search | EFA + NCCL workloads | | GDRCopy | rpm/dpkg, kernel mo

What's inside
Steps it walks through
  1. Usage
  2. What It Checks
  3. Multi-Node Comparison
  4. Compatibility Reference
Ships with 1 file
  • scripts/hyperpod_check_versions.sh
Commands it runs
Text report to console + file
bash hyperpod_check_versions.sh
JSON only to stdout (text report still saved to file) — best for piping/parsing
bash hyperpod_check_versions.sh --json
Custom output file
bash hyperpod_check_versions.sh --output /tmp/versions.txt
No color (for logging)
bash hyperpod_check_versions.sh --no-color
More from agent-plugins
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About this skill
What does the hyperpod-version-checker skill do?

Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, component checks, or upgrade planning for HyperPod clusters.

How do I install it?

Run `npx skills add awslabs/agent-plugins --skill hyperpod-version-checker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going